U.S. patent application number 14/348275 was filed with the patent office on 2014-10-23 for biodosimetry panels and methods.
This patent application is currently assigned to MESO SCALE TECHNOLOGIES, LLC. The applicant listed for this patent is THE HENRY M. JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY MEDICINE, INC., MESO SCALE TECHNOLOGIES, LLC. Invention is credited to William F Blakely, Eli N. Glezer, John Kenten, Sudeep Kumar, Anu Mathew, Natalia I. Ossetrova, George Sigal.
Application Number | 20140315742 14/348275 |
Document ID | / |
Family ID | 47996415 |
Filed Date | 2014-10-23 |
United States Patent
Application |
20140315742 |
Kind Code |
A1 |
Blakely; William F ; et
al. |
October 23, 2014 |
BIODOSIMETRY PANELS AND METHODS
Abstract
The present invention relates to methods and kits to assess an
absorbed dose of ionizing radiation and/or the severity of tissue
injury from radiation in a patient. The invention also relates to
algorithms used to calculate an absorbed dose of radiation based on
biomarker measurements of a plurality of biomarkers that are
altered relative to a normal control in the event of radiation
exposure.
Inventors: |
Blakely; William F; (Silver
Spring, MD) ; Glezer; Eli N.; (Del Mar, CA) ;
Kenten; John; (Boyds, MD) ; Kumar; Sudeep;
(Gaithersburg, MD) ; Mathew; Anu; (North Potomac,
MD) ; Ossetrova; Natalia I.; (Silver Spring, MD)
; Sigal; George; (Rockville, MD) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MESO SCALE TECHNOLOGIES, LLC
THE HENRY M. JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY
MEDICINE, INC. |
Rockville
Bethesda |
MD
MD |
US
US |
|
|
Assignee: |
MESO SCALE TECHNOLOGIES,
LLC
Rockville
MD
THE HENRY M. JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY
MEDICINE, INC.
Bethesda
MD
|
Family ID: |
47996415 |
Appl. No.: |
14/348275 |
Filed: |
September 28, 2012 |
PCT Filed: |
September 28, 2012 |
PCT NO: |
PCT/US12/57736 |
371 Date: |
March 28, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61540584 |
Sep 29, 2011 |
|
|
|
Current U.S.
Class: |
506/9 ; 506/18;
506/39; 506/8; 702/19 |
Current CPC
Class: |
G01T 1/02 20130101; G01N
2333/70535 20130101; G01N 2800/60 20130101; G16H 20/40 20180101;
G01N 2333/70578 20130101; G01N 2800/40 20130101; G01N 33/6893
20130101; G01T 1/00 20130101 |
Class at
Publication: |
506/9 ; 506/18;
506/39; 506/8; 702/19 |
International
Class: |
G01N 33/566 20060101
G01N033/566 |
Goverment Interests
STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH
[0002] This invention was made with federal support under
HHSO100201000009C awarded by the Department of Health and Human
Services. The U.S. government has certain rights in the invention.
Claims
1. A multiplexed assay kit used to assess an absorbed dose of
ionizing radiation or radiation-induced tissue injury in a patient
sample, wherein said kit is configured to measure the level of a
plurality of biomarkers in said sample and said plurality of
biomarkers comprises: (a) a DNA-damage biomarker; (b) an
inflammatory-response biomarker; (c) a tissue-damage biomarker; (d)
a tissue-damage repair biomarker; (e) a hematology-surrogate
marker; and (f) combinations thereof.
2. The kit of claim 1 wherein said plurality of biomarkers
comprises at least two of biomarkers (a)-(e).
3. The kit of claim 1 wherein said plurality of biomarkers
comprises at least four of biomarkers (a)-(e).
4. The kit of claim 1 wherein said plurality of biomarker is
selected from the group consisting of: (a) a DNA-damage biomarker;
(b) an inflammatory-response biomarker; (c) a tissue-damage
biomarker; (d) a tissue-damage repair biomarker; and (e) a
hematology-surrogate marker.
5. The kit of claim 1 further comprising instructions for
correlating the level of said plurality of biomarkers present in
the sample with the dose of radiation absorbed by the patient.
6. The kit of claim 1 further comprising a computer readable medium
having stored thereon a computer program which, when executed by a
computer system, causes the computer system to perform a method
comprising correlating the level of said plurality of biomarkers
present in the sample with the dose of radiation absorbed by the
patient.
7. A device capable of receiving the kit of claim 1 or a component
thereof to measure level of said plurality of biomarkers, said
device operatively associated with a computer system, said computer
system having stored thereon a computer program which, when
executed by said computer system, causes the computer program to
perform a method comprising correlating the level of said plurality
of biomarkers present in the sample with the dose of radiation
absorbed by the patient.
8. The kit of claim 1 wherein said inflammatory-response biomarker
is not a tissue damage repair biomarker.
9. The kit of claim 1 wherein said inflammatory-response biomarker
is a tissue-damage repair biomarker.
10. The kit of claim 1 wherein said DNA-damage biomarker comprises
p53, p21, GADD45a, ATM, phosphorylated H2AX histone, and
combinations thereof.
11. The kit of claim 1 wherein said inflammatory-response biomarker
comprises IL-6, CRP, SAA, IL-1, IL-5, IL-10, KC/GRO, IFN, IL-2,
IL-4, TNF-alpha, IL-12, IL-3, IL-7, IL-6, and combinations
thereof.
12. The kit of claim 1 wherein said tissue-damage biomarker
comprises salivary beta-amylase, citrulinated proteins, S100B,
SP-D, BPI, TSP, CA15-3, CDBB, CKMB, CKMM, FABP2, GFAP, NSE, and
combinations thereof.
13. The kit of claim 1 wherein said hematology-surrogate marker
comprises CD5, CD-16b, CD20, CD177, CD26, CD27, CD40, CD45, and
combinations thereof.
14. The kit of claim 1 wherein said tissue damage repair biomarker
comprises Flt-3L, G-CSF, KFG, EPO, TPO, GM-CSF, SDF-1a, and
combinations thereof.
15. The kit of claim 1 wherein said tissue-damage repair biomarker
is Flt-3L.
16. The kit of claim 1 further comprising an additional
tissue-damage repair biomarker selected from G-CSF, EPO, GM-CSF,
SDF-1a, and combinations thereof.
17. The kit of claim 15, wherein said inflammatory-response
biomarker is IL-12, IL-3, IL-7, IL-6 or combinations thereof.
18. The kit of claim 1, wherein said kit comprises Flt-3L, G-CSF,
GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, and TPO.
19. The kit of claim 1 wherein said kit comprises Flt-3L, GM-CSF,
SAA, TPO, CD27, CD45, CD26, and IL-12.
20. The kit of claim 1 further comprising an additional kit
component comprising (a) a bar-coded patient identification tag;
(b) a dried blood spot collection card comprising a bar code; (c) a
sample transport bag comprising desiccant; (d) a capillary with a
plunger; or (e) a lancet.
21. The kit of claim 1 wherein said kit is configured to measure
said level using an immunoassay.
22. The kit of claim 21 wherein said kit comprises a multi-well
assay plate comprising a plurality of assay wells used in an assay
conducted in said kit, said plurality of assay wells configured to
measure said level of said plurality of biomarkers in said
sample.
23. The kit of claim 22 wherein a well of said assay plate
comprises a plurality of assay domains, at least two of said assay
domains comprising reagents for measuring different biomarkers.
24. The kit of claim 22 wherein said kit comprises an assay
cartridge for conducting a plurality of assays, said cartridge
comprising a flow cell having an inlet, an outlet or a detection
chamber, said inlet, detecting chamber, or outlet defining a flow
path through said flow cell, said detection chamber configured to
measure said level of said plurality of biomarkers in said
sample.
25. The kit of claim 1 wherein said kit further comprises one or
more additional assay reagents used in said assay, said one or more
additional assay reagents provided in one or more vials,
containers, or compartments of said kit.
26. The kit of claim 21 wherein said kit further comprises an
electrochemiluminescence (ECL) labeling reagent and said levels are
measured in an assay conducted with said kit by measuring ECL.
27. The device of claim 7 wherein said device is configured to
conduct an ECL measurement using said kit.
28. A multiplexed assay kit used to assess an absorbed dose of
ionizing radiation in a patient sample, said kit is configured to
measure the level of a plurality of biomarkers in said sample, said
plurality of biomarkers comprising (i) one or more biomarkers
including Flt-3L, G--CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26,
IL-12, TPO, and combinations thereof; and (ii) an additional
biomarker comprising: (a) a DNA-damage biomarker; (b) an
inflammatory-response biomarker; (c) a tissue-damage biomarker; (d)
a tissue-damage repair biomarker; (e) a hematology-surrogate
marker; and (f) combinations thereof.
29. The kit of claim 28 wherein said plurality of biomarkers
comprises at least two of biomarkers (a)-(e).
30. The kit of claim 28 wherein said plurality of biomarkers
comprises at least four of biomarkers (a)-(e).
31. The kit of claim 28 wherein said plurality of biomarker is
selected from the group consisting of: (a) a DNA-damage biomarker;
(b) an inflammatory-response biomarker; (c) a tissue-damage
biomarker; (d) a tissue-damage repair biomarker; and (e) a
hematology-surrogate marker.
32. The kit of claim 28 further comprising instructions for
correlating the level of said plurality of biomarkers present in
the sample with the dose of radiation absorbed by the patient.
33. The kit of claim 28 further comprising a computer readable
medium having stored thereon a computer program which, when
executed by a computer system, causes the computer system to
perform a method comprising correlating the level of said plurality
of biomarkers present in the sample with the dose of radiation
absorbed by the patient.
34. A device capable of receiving the kit of claim 28 or a
component thereof to measure level of said plurality of biomarkers,
said device operatively associated with a computer system, said
computer system having stored thereon a computer program which,
when executed by said computer system, causes the computer program
to perform a method comprising correlating the level of said
plurality of biomarkers present in the sample with the dose of
radiation absorbed by the patient.
35. The kit of claim 28 wherein said inflammatory-response
biomarker is not a tissue damage repair biomarker.
36. The kit of claim 28 wherein said inflammatory response
biomarker is a tissue-damage repair biomarker.
37. The kit of claim 28 wherein said DNA-damage biomarker comprises
p53, p21, GADD45a, ATM, phosphorylated H2AX histone, and
-combinations thereof.
38. The kit of claim 28 wherein said inflammatory-response
biomarker comprises IL-6, CRP, SAA, IL-1, IL-5, IL-10, KC/GRO, IFN,
IL-2, IL-4, TNF-alpha, IL-12, IL-3, IL-7, IL-6 and combinations
thereof.
39. The kit of claim 28 wherein said tissue-damage biomarker
comprises salivary beta-amylase, citrulinated proteins, S100B,
SP-D, BPI, TSP, CA15-3, CKBB, CKMB, CKMM, FABP2, GFAP, NSE, and
combinations thereof.
40. The kit of claim 28 wherein said hematology-surrogate marker
comprises CD5, CD-16b, CD20, CD177, CD26, CD27, CD40, CD45, and
combinations thereof.
41. The kit of claim 28 wherein said one or more biomarkers
comprise Flt-3L.
42. The kit of claim 41 wherein said one or more biomarkers further
comprise G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, and
TPO.
43. The kit of claim 42, wherein said kit comprises Flt-3L, GM-CSF,
SAA, TPO, CD27, CD45, CD26, and IL-12.
44. The kit of claim 28 further comprising an additional kit
component comprising (a) a bar-coded patient identification tag;
(b) a dried blood spot collection card comprising a bar code; (c) a
sample transport bag comprising desiccant; (d) a capillary with a
plunger; or (e) a lancet.
45. The kit of claim 28 wherein said kit is configured to measure
said level using an immunoassay.
46. The kit of claim 45 wherein said kit comprises a multi-well
assay plate comprising a plurality of assay wells used in an assay
conducted in said kit, said plurality of assay wells configured to
measure said level of said plurality of biomarkers in said
sample.
47. The kit of claim 46 wherein a well of said assay plate
comprises a plurality of assay domains, at least two of said assay
domains comprising reagents for measuring different biomarkers.
48. The kit of claim 45 wherein said kit comprises an assay
cartridge for conducting a plurality of assays, said cartridge
comprising a flow cell having an inlet, an outlet or a detection
chamber, said inlet, detecting chamber, and outlet defining a flow
path through said flow cell, said detection chamber configured to
measure said level of said plurality of biomarkers in said
sample.
49. The kit of claim 28 wherein said kit further comprises one or
more additional assay reagents used in said assay, said one or more
additional assay reagents provided in one or more vials,
containers, or compartments of said kit.
50. The kit of claim 28 wherein said kit further comprises an
electrochemiluminescence (ECL) labeling reagent and said levels are
measured in an assay conducted with said kit by measuring ECL.
51. A multiplexed hematology surrogate biomarker assay kit
configured to measure a level of a plurality of biomarkers in a
sample, said plurality of biomarkers comprises a lymphocyte cell
surface marker, a neutrophil cell surface marker, and combinations
thereof.
52. The kit of claim 51 wherein said lymphocyte surface marker
comprising CD5, CD20, CD26, CD27, CD40, and combinations
thereof.
53. The kit of claim 51 wherein said neutrophil cell surface marker
comprising CD16b, CD177, and combinations thereof.
54. The kit of claim 51 wherein said sample is blood cell pellets,
serum or plasma.
55. The kit of claim 1 further comprising an additional kit
component comprising (a) a bar-coded patient identification tag;
(b) a dried blood spot collection card comprising a bar code; (c) a
sample transport bag comprising desiccant; (d) a capillary with a
plunger; or (e) a lancet.
56. The kit of claim 51 wherein said kit is configured to measure
said level using an immunoassay.
57. The kit of claim 56 wherein said kit comprises a multi-well
assay plate comprising a plurality of assay wells used in an assay
conducted in said kit, said plurality of assay wells configured to
measure said level of said plurality of biomarkers in said
sample.
58. The kit of claim 57 wherein a well of said assay plate
comprises a plurality of assay domains, at least two of said assay
domains comprising reagents for measuring different biomarkers.
59. The kit of claim 56 wherein said kit comprises an assay
cartridge for conducting a plurality of assays, said cartridge
comprising a flow cell having an inlet, an outlet or a detection
chamber, said inlet, detecting chamber, or outlet defining a flow
path through said flow cell, said detection chamber configured to
measure said level of said plurality of biomarkers in said
sample.
60. A method of assaying peripheral blood leukocyte status in a
sample comprising (a) measuring a level of a plurality of
hematology surrogate biomarkers in a sample, said plurality of
biomarkers comprises a lymphocyte cell surface marker, a neutrophil
cell surface marker, and combinations thereof; (b) comparing said
level of said biomarkers in said sample to a level of said
biomarkers in a normal control sample; and (c) determining said
peripheral blood leukocyte status based on said comparison step
(b).
61. The method of claim 60 wherein said lymphocyte surface marker
comprising CD5, CD20, CD26, CD27, CD40, and combinations
thereof.
62. The method of claim 60 wherein said neutrophil cell surface
marker comprising CD16b, CD 177, and combinations thereof.
63. The method of claim 60 wherein said sample is blood cell
pellets, serum or plasma.
64. The method of claim 60 wherein said sample is plasma.
65. A method of assessing an absorbed dose of ionizing radiation in
a plasma sample from a patient, said method comprising (a)
measuring the level of a hematology surrogate marker in said
sample; (b) applying, by a processor, an algorithm to assess said
absorbed dose in said patient based on said level of said
hematology surrogate marker in said sample.
66. The method of claim 65 wherein said hematology-surrogate marker
comprises CD5, CD-16b, CD20, CD177, CD26, CD27, CD40, CD45 and
combinations thereof.
67. The method of claim 65 wherein said algorithm quantifies an
absorbed dose of ionizing radiation in the range of about 1-10
Gy.
68. A computer readable medium having stored thereon a computer
program which, when executed by a computer system operably
connected to an assay system configured to measure a level of a
plurality of biomarkers in a patient sample, causes the computer
system to perform a method of calculating radiation dose by a
method comprising: (a) fitting said measured level to a response
surface model as a function of radiation dose or time; (b)
computing a cost function for combining said plurality of
biomarkers; and (c) selecting a radiation dose that minimizes said
cost function at a known time interval.
69. The computer readable medium of claim 68 wherein said method
further comprises the step of (e) comparing said radiation dose to
a threshold value above which is indicative of radiation
exposure.
70. The computer readable medium of claim 68 wherein said computing
step (b) further comprises applying a weighting function, W.sub.i
(radiation dose, time), wherein W.sub.i is the weighting function
of biomarker i as a function of radiation dose or time.
71. The computer readable medium of claim 68 wherein said cost
function represents the total error for said plurality of
biomarkers as a function of radiation dose.
72. The computer readable medium of claim 68 wherein said cost
function is F ( dose ) = i = 1 n Log ( m i + LOD i M i ( dose ) +
LOD i ) , ##EQU00008## wherein m.sub.i is the measured value for
biomarker i, M.sub.i is the predicted biomarker value as a function
of dose at a known time post-exposure, LOD.sub.i is the assay Limit
of Detection for biomarker i, or n is the total number of
biomarkers being used.
73. The computer readable medium of claim 68 wherein said cost
function is: F ( dose ) = i = 1 n Log ( m i M i ( dose ) ) .
##EQU00009## wherein m.sub.i is the measured value for biomarker i,
M.sub.i is the predicted biomarker value as a function of dose at a
known time post-exposure, or n is the total number of biomarkers
being used.
74. The method of claim 65 wherein the sample comprises plasma.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims benefit of U.S. Provisional
Application No. 61/540,584 filed on Sep. 29, 2011, the contents of
all of which are incorporated herein by reference.
FIELD OF THE INVENTION
[0003] This application relates to assay methods, modules and kits
for conducting diagnostic assays useful in the detection of
radiation exposure and the severity of tissue injury to
radiation.
BACKGROUND OF THE INVENTION
[0004] In the aftermath of an incident in which a significant
number of civilians are exposed to radiation or radioactive
materials, health authorities will need to be able to rapidly
identify individuals who have been exposed to life-threatening
significant doses of radiation. The deadly effects of ionizing
radiation (IR) are wide-ranging and include systemic and
organ-specific damage. Acute effects of high-dose ionizing
radiation (>2 Gy) include depletion of specific types of
peripheral blood cells, immune suppression, mucosal damage, and
potential injury to other sites such as bone and bone marrow niche
cells, gastrointestinal system, lungs, kidneys, and the central
nervous system. In addition, exposures to low or moderate doses
(1-3 Gy) of ionizing radiation can result in increased mortality if
accompanied by physical injuries, opportunistic infections, and/or
hemorrhage. Long-term effects include dysfunction or fibrosis in a
wide range of organs and tissues, cataracts, and, ultimately, a
higher risk of cancer. In many cases, the effects of radiation
exposure can be mitigated by early triage and treatment.
[0005] Although radioactive material can be detected with
instruments, assessment of the radiation dose or injury that a
person has already received is more difficult. Because current and
foreseeable medical countermeasures for radiation injuries are
often expensive, labor-intensive and time-consuming to administer
(and monitor), have limited availability, and are occasionally
associated with serious toxicities, they should only be
administered to persons who will likely benefit from their use.
Fast, accurate radiation dose and tissue injury assessment could
greatly facilitate identification of exposed people who could
benefit from early medical intervention.
[0006] No rapid diagnostic exists that can reliably discriminate
levels of IR exposure based on samples collected at a single time
point. The complete blood count, particularly the lymphocyte count,
is useful, but optimally requires at least two samples spaced hours
to days apart to estimate dose. The diagnostic "gold standard" in
the field of radiation biodosimetry, the dicentric chromosome
assay, is labor-intensive and slow, and its use in mass-casualty
situations would be problematic.
[0007] Therefore, there is a need for sensitive and specific
biodosimetry dose assessment tools that can be used to identify
patients requiring urgent medical attention, improve risk
assessment for the delayed or late effects of radiation exposure,
improve patient tracking efficiency for repeated observation or
therapeutic administration, and play a role in monitoring therapy
and long-term follow-up. Such tools will also fill an important
need for monitoring radiation received during medical care, for
example, radiation received from medical imaging devices, radiation
received as a medical therapy (for example to treat cancer), or
radiation received in preparation for stem-cell transplants. The
tools would provide the capability to detect individuals
accidentally overexposed, to select individuals and optimize the
schedule of countermeasure doses used in treatment as well as to
monitor their efficacy for specific individuals.
SUMMARY OF THE INVENTION
[0008] The present invention provides a biodosimetry assay panel
and methods to measure multiple radiation-sensitive protein
biomarkers to assess radiation dose and tissue injury. The methods
of the present invention can be used to triage and guide the
treatment of individuals exposed to ionizing radiation after a
major radiological or nuclear event. The tools can also be used to
guide treatment of individuals exposed to ionizing radiation during
medical treatment or as a result of an accidental exposure.
[0009] Accordingly, the present invention provides a multiplexed
assay kit used to assess an absorbed dose of ionizing radiation or
radiation-induced tissue injury in a patient sample, wherein the
kit is configured to measure the level of a plurality of biomarkers
in the sample and the plurality of biomarkers includes: (a) a
DNA-damage biomarker; (b) an inflammatory-response biomarker; (c) a
tissue-damage biomarker; (d) a tissue-damage repair biomarker; (e)
a hematology-surrogate marker; and (f) combinations thereof. Also
contemplated is a device capable of receiving a kit such as this or
a component thereof to measure level of said plurality of
biomarkers, said device operatively associated with a computer
system, said computer system having stored thereon a computer
program which, when executed by said computer system, causes the
computer program to perform a method comprising correlating the
level of said plurality of biomarkers present in the sample with
the dose of radiation absorbed by the patient.
[0010] Also provided is a multiplexed assay kit used to assess an
absorbed dose of ionizing radiation in a patient sample, said kit
is configured to measure the level of a plurality of biomarkers in
said sample, said plurality of biomarkers comprising (i) one or
more biomarkers including Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45,
SAA, CD26, IL-12, TPO, and combinations thereof; and (ii) an
additional biomarker including: (a) a DNA-damage biomarker; (b) an
inflammatory-response biomarker; (c) a tissue-damage biomarker; (d)
a tissue-damage repair biomarker; (e) a hematology-surrogate
marker; and (f) combinations thereof. In a preferred embodiment,
the plurality of biomarkers comprises Flt-3L, GM-CSF, SAA, TPO,
CD27, CD45, CD26, and IL-12.
[0011] The invention also includes a method of assessing an
absorbed dose of ionizing radiation in a patient sample, said
method comprising (a) measuring levels of a plurality of biomarkers
in said sample; (b) applying, by a processor, an algorithm to
assess said absorbed dose in said patient based on said levels of
said plurality of biomarkers in said sample; wherein said plurality
of biomarkers comprises: (i) a DNA-damage biomarker; (ii) an
inflammatory-response biomarker; (iii) a tissue-damage biomarker;
(iv) a tissue-damage repair biomarker; (v) a hematology-surrogate
marker; and (vi) combinations thereof.
[0012] In addition, the invention contemplates a method of
assessing an absorbed dose of ionizing radiation in a patient
sample, said method including (a) measuring levels of a plurality
of biomarkers in said sample; (b) applying, by a processor, an
algorithm to assess said absorbed dose in said patient based on
said levels of said plurality of biomarkers in said sample; said
plurality of biomarkers comprising (i) one or more biomarkers
comprises Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12,
TPO, and combinations thereof; and (ii) an additional biomarker
comprising: (a) a DNA-damage biomarker; (b) an
inflammatory-response biomarker; (c) a tissue-damage biomarker; (d)
a tissue-damage repair biomarker; (e) a hematology-surrogate
marker; and (f) combinations thereof. In a preferred embodiment,
the plurality of biomarkers comprises Flt-3L, GM-CSF, SAA, TPO,
CD27, CD45, CD26, and IL-12.
[0013] The invention provides a number of multiplexed biodosimetry
assay kit(s) used to assess an absorbed dose of ionizing radiation
in a patient sample, said kit is configured to measure a level of a
plurality of biomarkers in said sample, wherein the plurality of
biomarkers comprises (a) Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45,
SAA, CD26, IL-12, and/or TPO; and/or (b) Flt-3L, GM-CSF, SAA, TPO,
CD27, CD45, CD26, and/or IL-12.
[0014] The invention also provides a variety of biodosimetry assay
kits used to assess an absorbed dose of ionizing radiation in a
patient sample, the kit(s) is (are) configured to measure a level
of Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45, SAA, CD26, IL-12, TPO,
and to compare said level(s) to a level of a normal control.
[0015] Another embodiment of the invention is a method of assessing
an absorbed dose of ionizing radiation in a patient sample, said
method comprising
[0016] (a) measuring levels of a plurality of biomarkers in said
sample;
[0017] (b) applying, by a processor, an algorithm to assess said
absorbed dose in said patient based on said levels of said
plurality of biomarkers in said sample; said plurality of
biomarkers comprising (a) Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45,
SAA, CD26, IL-12, TPO; or (b) Flt-3L, GM-CSF, SAA, TPO, CD27, CD45,
CD26, IL-12.
[0018] Additionally, the invention includes a method of assessing
an absorbed dose of ionizing radiation in a patient sample, said
method comprising
[0019] (a) measuring a level of Flt-3L, G-CSF, GM-CSF, EPO, CD27,
CD45, SAA, CD26, IL-12, TPO in said sample; and
[0020] (b) applying, by a processor, an algorithm to assess said
absorbed dose in said patient based on said biomarker level in said
sample.
[0021] Another embodiment of the invention is a method of
determining an injury severity value comprising
[0022] (a) measuring a level of a plurality of biomarkers in a
patient sample, wherein one or more biomarkers of said plurality of
biomarkers are altered relative to a normal control in the event of
an injury in a patient;
[0023] (b) fitting, by a processor, said measured level to a
response surface model as a function of an injury severity index
and/or time;
[0024] (c) computing a cost function for combining said plurality
of biomarkers; and
[0025] (d) identifying an injury severity value that minimizes said
cost function at a known time interval.
[0026] In addition, the invention includes a method of determining
a radiation dose comprising
[0027] (a) measuring a level of a plurality of biomarkers in a
patient sample, wherein one or more biomarkers of said plurality of
biomarkers are altered relative to a normal control in the event of
radiation exposure;
[0028] (b) fitting, by a processor, said measured level to a
response surface model as a function of radiation dose or time;
[0029] (c) computing a cost function for combining said plurality
of biomarkers; and
[0030] (d) selecting a radiation dose that minimizes said cost
function at a known time interval.
[0031] In addition, the invention contemplates a computer readable
medium having stored thereon a computer program which, when
executed by a computer system operably connected to an assay system
configured to measure a level of a plurality of biomarkers in a
patient sample, causes the computer system to perform a method of
calculating an injury severity value by a method comprising:
[0032] (a) fitting said measured level to a response surface model
as a function of a injury severity index or time;
[0033] (b) computing a cost function for combining said plurality
of biomarkers; and
[0034] (c) identifying an injury severity value that minimizes said
cost function at a known time interval.
[0035] In a further embodiment, the invention includes a computer
readable medium having stored thereon a computer program which,
when executed by a computer system operably connected to an assay
system configured to measure a level of a plurality of biomarkers
in a patient sample, causes the computer system to perform a method
of calculating radiation dose by a method comprising:
[0036] (a) fitting said measured level to a response surface model
as a function of radiation dose or time;
[0037] (b) computing a cost function for combining said plurality
of biomarkers; and
[0038] (c) selecting a radiation dose that minimizes said cost
function at a known time interval.
[0039] An additional embodiment includes a multiplexed hematology
surrogate biomarker assay kit configured to measure a level of a
plurality of biomarkers in a sample, said plurality of biomarkers
comprises a lymphocyte cell surface marker, a neutrophil cell
surface marker, and combinations thereof.
[0040] And a final embodiment of the invention is a method of
assaying peripheral blood leukocyte status in a sample
comprising
[0041] (a) measuring a level of a plurality of hematology surrogate
biomarkers in a sample, said plurality of biomarkers comprises a
lymphocyte cell surface marker, a neutrophil cell surface marker,
and combinations thereof;
[0042] (b) comparing said level of said biomarkers in said sample
to a level of said biomarkers in a normal control sample; and
[0043] (c) determining said peripheral blood leukocyte status based
on said comparison step (b).
BRIEF DESCRIPTION OF THE FIGURES
[0044] FIG. 1(a)-(b) shows the effect of radiation on plasma Flt-3L
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-ray), with each line
representing a different dose. Table 8 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0045] FIG. 2(a)-(b) shows the effect of radiation on plasma SAA
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-ray), with each line
representing a different dose. Table 9 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0046] FIG. 3(a)-(b) shows the effect of radiation on plasma G-CSF
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 10 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0047] FIG. 4(a)-(b) shows the effect of radiation on plasma GM-CSF
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 11 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0048] FIG. 5(a)-(b) shows the results of a mouse radiation dose
study, and in particular, the response of plasma IL-6 showing
concentration vs time (panel (a)), concentration vs. dose (panel
(b)) and the p values for irradiated vs. controls (unpaired t test,
highlighting p values<0.01) for the response to each irradiation
condition (Table 12).
[0049] FIG. 6(a)-(b) shows the effect of radiation on plasma TPO
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 13 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0050] FIG. 7(a)-(b) shows the effect of radiation on plasma EPO
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 14 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0051] FIG. 8(a)-(b) shows the effect of radiation on plasma IL-5
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-ray), with each line
representing a different dose. Table 15 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0052] FIG. 9(a)-(b) shows the results of a mouse radiation dose
study, and in particular, the response of plasma IL-10 showing
concentration vs time (panel (a)), concentration vs. dose (panel
(b)) and the p values for irradiated vs. controls (unpaired t test,
highlighting p values<0.01) for the response to each irradiation
condition (Table 16).
[0053] FIG. 10(a)-(b) shows the results of a mouse radiation dose
study, and in particular, the response of plasma KC/GRO showing
concentration vs time (panel (a)), concentration vs. dose (panel
(b)) and the p values for irradiated vs. controls (unpaired t test,
highlighting p values<0.01) for the response to each irradiation
condition.
[0054] FIG. 11(a)-(b) shows the results of a mouse radiation dose
study, and in particular, the response of plasma TNF-a showing
concentration vs time (panel (a)), concentration vs. dose (panel
(b)) and the p values for irradiated vs. controls (unpaired t test,
highlighting p values<0.01) for the response to each irradiation
condition (Table 17).
[0055] FIG. 12(a)-(b) shows the results of a mouse radiation dose
study, and in particular, the response of .gamma.-H2AX in blood
cell pellets showing concentration vs time (panel (a)),
concentration vs. dose (panel (b)) and the p values for irradiated
vs. controls (unpaired t test, highlighting p values<0.01) for
the response to each irradiation condition (Table 19).
[0056] FIG. 13(a)-(b) shows the results of a mouse radiation dose
study, and in particular, the response of p53 in blood cell pellets
showing concentration vs time (panel (a)), concentration vs. dose
(panel (b)) and the p values for irradiated vs. controls (unpaired
t test, highlighting p values<0.01) for the response to each
irradiation condition (Table 20).
[0057] FIG. 14(a)-(b) shows the effect of radiation on plasma CD-27
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 21 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0058] FIG. 15(a)-(b) shows the effect of radiation on plasma IL-12
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 22 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0059] FIG. 16(a)-(b) shows the effect of radiation on plasma CD45
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 23 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0060] FIG. 17(a)-(b) shows the effect of radiation on plasma CD26
levels in mice. Panel (a) shows the biomarker level as a function
of time after radiation (.sup.60Co .gamma.-rays), with each line
representing a different dose. Table 24 shows the significance (p
values, unpaired t test, highlighting p values<0.01) for the
change in the biomarker level for each irradiation condition,
relative to the 0 Gy controls. Panel (b) shows the results of a
Combined Injury Study comparing biomarker levels in mice receiving
radiation alone (closed circles) vs. mice receiving radiation
combined with a 15% surface area puncture wound (open circles).
Each point on both plots represents the average level from 8
replicate animals.
[0061] FIG. 18(a)-(b) are scatter plots showing the predicted dose
as a function of actual dose for the full mouse radiation dose
study sample set using a multi-parameter dose assessment algorithm.
The different colors correspond to different sampling times. The
data are slightly dithered along the X-axis to make the points
clearly visible. Points on the solid lines exactly match the actual
dose. Points within the dashed lines are within 1.5 Gy of the
actual dose (below 6 Gy) or within 25% of the actual dose (above 6
Gy). Panel (a) includes results using the optimal 5 biomarker panel
(Flt-3L, G-CSF, GM-CSF, EPO, IL12/23). Panel (b) includes results
using a smaller LPS-insensitive panel (Flt-3L, EPO).
[0062] FIG. 19(a)-(b) are ROC curves for distinguishing 6 Gy doses
from non-irradiated controls (blue) and for distinguishing 6 Gy
doses from 3 Gy doses. The ROC curves were generated by varying the
value of the predicted dose used to classify the samples. The
histogram inset shows the distribution of the predicted doses for 6
Gy and non-irradiated sample sets and demonstrates the separation
of the two distributions. Panel (a) includes results using the
optimal 5 biomarker panel (Flt-3L, G-CSF, GM-CSF, EPO, IL12/23).
Panel (b) includes results using a smaller LPS-insensitive panel
(Flt-3L, EPO).
[0063] FIG. 20(a)-(b) show the dose classification accuracy for the
mouse Biomarker Discovery data set using a multi-parameter
algorithm. Two performance metrics are plotted as a function of the
number of biomarkers used. Each point represents the performance of
a different combination of biomarkers; all possible combinations of
the 12 most radiation sensitive biomarkers are shown. In Panel (a),
accuracy is displayed as the percent of samples correctly
classified by dose (within 1.5 Gy for doses.ltoreq.6 Gy and within
25% for doses>6 Gy). In Panel (b), prediction error is displayed
as the RMS error in the dose prediction across all samples. Two
high-performing biomarker combinations were selected for each panel
size: a panel that did not include injury sensitive markers (yellow
data points) and a panel that did include injury-sensitive markers
(red data points). The performance metrics for these selected
panels are tabulated in Table 8.
[0064] FIG. 21 show the performance of the multi-parameter
algorithm and the optimal 6-plex biomarker panel
(CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) for assessing radiation dose in
the mouse model. All samples tested in this study were blinded to
the individuals conducting the study during testing and dose
prediction analysis. The plot shows the predicted dose as a
function of actual dose for samples collected between 1 and 7 days
after irradiation. The different colors correspond to the time of
sample collection. The data are slightly dithered along the X-axis
to make the points clearly visible. Points for which the predicted
dose exactly matches the actual dose will fall on the solid line.
Points within the dashed lines meet our dose prediction accuracy
criteria and are within 1.5 Gy of the actual dose (below 6 Gy) or
within 25% of the actual dose (above 6 Gy). The inset shows the
percentage of the predicted doses that fall within our accuracy
criteria and the root mean square error in the predicted doses
across the data set.
[0065] FIG. 22 shows the performance of the multi-parameter
algorithm and the optimal 6-plex biomarker panel
(CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) for classifying mouse sample by
radiation dose. All samples tested in this study were blinded to
the individual conducting the study during testing and dose
prediction analysis. The focus of the analysis shown in this plot
is the ability of the algorithm to correctly classify samples above
or below the critical 2 Gy dose threshold in humans, which is
roughly equivalent to 5 Gy in the mouse model. The plot shows ROC
curves for distinguishing doses>6 Gy from non-irradiated
controls (blue) and for distinguishing doses>6 Gy from doses 3
Gy. The ROC curves were generated by varying the value of the
predicted dose used to classify the samples. The histogram inset
shows the distribution of the predicted doses for samples receiving
0 Gy, 3 Gy or 6 Gy doses and demonstrates the separation of these
distributions. Classification performance at the optimal predicted
dose thresholds is provided in the table below the plot.
[0066] FIG. 23 shows the effect of 15% wound injury on the
performance of the multi-parameter algorithm and the optimal 6-plex
biomarker panel (CD27+Flt-3L+GM-CSF+CD45+IL-12+TPO) for assessing
radiation dose in the mouse model. The sample set included samples
from mice receiving 0 Gy or 6 Gy radiations (.sup.60Co
.gamma.-rays) with or without skin wounding (15% surface area
puncture wound). Samples were collected at different times up to 7
days after exposure. There were an equal number of replicates for
each dose/injury/time condition. The plot shows the predicted dose
as a function of actual dose for samples collected between 1 and 7
days after irradiation. The different colors correspond to the time
of sample collection. `Injury` data points are shown as triangles,
`No injury` data points are shown as circles. The data are slightly
dithered along the X-axis to make the points clearly visible.
Points for which the predicted dose exactly matches the actual dose
will fall on the solid line. Points within the dashed lines meet
our dose prediction accuracy criteria and are within 1.5 Gy of the
actual dose (below 6 Gy) or within 25% of the actual dose (above 6
Gy). The inset shows the percentage of the predicted doses that
fall within our accuracy criteria and the root mean square error in
the predicted doses across the data set.
[0067] FIG. 24(a)-(z) show the effect of radiation on plasma
biomarkers (Flt-3L, CD20, CD27, TPO, CD177, IL-12, SAA, EPO, G-CSF,
salivary amylase (AMY), CRP, TIMP-1 and TNF-RII, respectively) in
Rhesus macaque. Plots show levels as a function of the time after
radiation. Panels shown on the left reflect the results using
samples exposed to .sup.60Co .gamma.-rays and panels shown on the
right reflect the results using samples exposed to 3 MV LINAC
photons. Each point in the plots represents 5 to 6 animals for the
.sup.60Co .gamma.-rays samples, 3 to 4 animals for the 3 MV LINAC
samples up to the 9 day time point and 2 animals for the time
points >9 days. Graphs are linear, with the exception of FIG.
25(b) and (d) which is logarithmic.
[0068] FIG. 24(aa)-(dd) show the analysis of CD20, CD177,
neutrophils, and lymphocytes in irradiated NHP samples,
demonstrating the potential of the biomarkers as surrogates
radiation-responsive biomarkers in lieu of blood cell counting.
Each graph shows the time course effects of radiation on the
biomarker or biomarker ratio in NHPs (n=6 for each dose and time
cohort).
[0069] FIG. 25(a)-(b) show the performance of the multi-parameter
algorithm for classifying NHP samples from sample set (A) by
radiation dose. The data set was used to train and test the
algorithm using a random sub-sampling approach to avoid training
bias. The focus of the analysis shown in this plot is the ability
of the algorithm to correctly classify samples above or below the
critical 2 Gy dose threshold in humans, which is roughly equivalent
to 3 Gy in the NHP model. Panel A shows ROC curves for
distinguishing doses.gtoreq.3.5 Gy from non-irradiated controls
(blue) and for distinguishing doses.gtoreq.3.5 Gy from
doses.ltoreq.1 Gy. The ROC curves were generated by varying the
value of the predicted dose used to classify the samples. The
histogram inset shows the distribution of the predicted doses for
samples receiving 0 Gy, 1 Gy or 3.5 Gy doses and demonstrates the
separation of these distributions. Classification performance at
the optimal predicted dose thresholds is provided in the table
below the plot. Panel B shows predicted doses for the NHP samples
plotted as a function of actual dose.
[0070] FIG. 26 shows a comparison of biomarker levels in plasma
from the normal human population and from individuals having
high-prevalence chronic diseases. The data for each group--normal,
asthma, high blood pressure (HBP) and rheumatoid arthritis
(RA)--are shown in box and whisker format providing the median
value (center of the box), the lower and upper quartiles (top and
bottom of the box) and 1.5 interquartile ranges (whiskers).
Outliers are shown as black points directly above or below the box
and whiskers. Concentrations are provided in pg/mL except for SAA
and CRP which are in ng/mL.
[0071] FIG. 27(a)-(b) shows the results of an effort to model the
increased baseline variation in normal humans relative to
non-irradiated mice. Random noise was added to the biomarker levels
from the non-irradiated mice in the Biomarker Discovery data set so
that the observed standard deviations of the data (in log space)
matched the observed standard deviation for the analogous markers
in the study of normal human levels for the analogous markers
measured in the study of normal human levels. Panel A is a
histogram comparing the distribution in the baseline levels of one
of the biomarkers (Flt-3L), before and after the addition of noise.
A table of the observed standard deviations in mice and humans and
the standard deviation after noise injection is shown below the
plot) Panel B is a scatter plot showing the predicted doses for the
0 Gy samples from the Blinded Study before and after addition of
noise. For comparison, the predicted doses for the 6 Gy samples are
also shown (with no added noise) as is the optimal threshold
selected with the original data for classifying samples as 0 Gy or
.gtoreq.6 Gy (see FIG. 27). The addition of noise did not result in
any additional misclassifications and the classification
specificity remained at 100%.
[0072] FIG. 28 shows the biomarker levels in plasma from melanoma
patients receiving lymphocyte depleting chemotherapy in preparation
for cell-transfer therapy. The study had two arms: one set of
patients also received TBI 3 days after receiving chemotherapy and
the other arm received chemotherapy without TBI. The plot compares
biomarker levels in samples from the non-TBI patients
(Mela-Cntrl-0Gy), sample collected from the TBI patients prior to
the radiation treatment (Mela-TBI-0Gy) and samples collected from
the TBI patients 5 to 6 hours after receiving a single 2 Gy
fraction (Mela-TBI-2Gy). Biomarker levels from 40 normal blood
donors (see FIG. 27) are also provided for comparison.
Concentrations are provided in pg/mL except for SAA and CRP which
are in ng/mL. The levels of AMY1A (salivary amylase) and p53 in the
patients receiving the 2 Gy fraction showed significant elevation
(p<0.05) relative to levels prior to exposure or levels in the
non-TBI control arm of the study.
[0073] FIG. 29(a)-(b) show the biomarker levels in plasma from lung
cancer patients (top) and GI cancer patients (bottom) receiving
localized radiation therapy (2 Gy fractions, 5 fractions per week,
6 weeks). The plot shows biomarker levels prior to radiotherapy and
after cumulative doses of 30 Gy and 60 Gy (lung) or 54 Gy (GI).
Biomarker levels from 40 normal blood donors (see FIG. 27) are also
provided for comparison. Concentrations are provided in pg/mL
except for SAA and CRP which are in ng/mL.
[0074] FIG. 30(a)-(e) show the results of Confounding Effect mouse
study for plasma levels of Flt-3L, SAA, G-CSF, GM-CSF and IL-6. The
Y-axis is scaled to make the radiation response visible. In some
case the LPS response is off-scale. The maximal responses for the
different conditions can be viewed in a log scale in the figure.
Results for the 0 and 6 Gy conductions from the Radiation Dose
Study are plotted side by side for comparison.
[0075] FIG. 31(a)-(e) show the results of Confounding Effect mouse
study for plasma levels of TPO, EPO, IL-12/23, IL-5 and IL-10. The
Y-axis is scaled to make the radiation response visible. In some
case the LPS response is off-scale. The maximal responses for the
different conditions can be viewed in a log scale in figure.
Results for the 0 and 6 Gy conductions from the Radiation Dose
Study are plotted side by side for comparison.
[0076] FIG. 32(a)-(d) show the results of Confounding Effect mouse
study for plasma levels of KC/GRO and TNF.alpha. and blood cell
pellet levels of p53 and .gamma.H2AX. The Y-axis is scaled to make
the radiation response visible. In some case the LPS response is
off-scale. The maximal responses for the different conditions can
be viewed in a log scale in the figure. Results for the 0 and 6 Gy
conductions from the Radiation Dose Study are plotted side by side
for comparison.
[0077] FIG. 33 provides an overview of the Confounding Effect
study. For each assay, the bar graph presents the average
concentration for the control mice and the average concentration at
the conditions that produced the maximal response to G-CSF and LPS
over all conditions tested in both the Radiation Dose and
Confounding Effect Studies. The signals for each assay are
normalized to the maximal radiation response, which is set at
100%.
[0078] FIG. 34(a)-(d) show the results of testing of archived
plasma samples from irradiated NHPs for Flt-3L, EPO, CRP and SAA.
SAA was measured using a commercial ELISA kit. Each point
represents the average value for three different animals.
[0079] FIG. 35(a)-(d) show the results of testing of archived
plasma samples from irradiated NHPs for IL-6, BPI, TPO and p53.
Each point represents the average value for three different
animals.
[0080] FIG. 36(a)-(b) show results of testing of archived plasma
samples from irradiated NHPs for the lymphocyte cell surface marker
CD20 and the neutrophil cell surface marker CD177. The biomarker
concentrations for the two tested doses (1.0 and 3.5 Gy) are shown
as closed circles with the y-axis scale provided on the left side
of the plots. For comparison, lymphocyte and neutrophil cell counts
measured on the same animals at the same times are also shown as
open circles, with the y-axis scale provided on the right side of
the plots. Each point represents the average value for three
different animals.
[0081] FIG. 37 shows the results from a panel of 6 plasma markers
(Flt-3L, EPO, p53, CD20, CD177 and SAA) which provides good
discrimination of animals receiving greater than 3.5 Gy (equivalent
to .about.2 Gy in humans) from those receiving less than 3.5 Gy and
also provided high accuracy for semi-quantitative dose
prediction.
[0082] FIGS. 38(a)-(b) show the use of the statistical methods
described herein to generate a severity value or a radiation dose,
respectively, from a patient sample by analyzing one or more
biomarkers in the same and correlating the level(s) of those
biomarkers with an injury severity value or a radiation dose,
respectively. FIG. 38(c) illustrates one non-limiting example of a
system used to analyze a sample using the statistical methods
described herein, wherein the system includes a processor and an
algorithm module.
DETAILED DESCRIPTION OF THE INVENTION
[0083] Unless otherwise defined herein, scientific and technical
terms used herein shall have the meanings that are commonly
understood by those of ordinary skill in the art. Further, unless
otherwise required by context, singular terms shall include
pluralities and plural terms shall include the singular. The
articles "a" and "an" are used herein to refer to one or to more
than one (i.e., to at least one) of the grammatical object of the
article. By way of example, "an element" means one element or more
than one element.
[0084] One embodiment of the invention is a multiplexed
biodosimetry assay kit and methods that can be used to assess an
absorbed dose of ionizing radiation in a patient sample. The
methods and kits of the invention can be used to assess the
likelihood or risk of developing acute radiation syndrome (ARS)
and/or to assess the clinical severity of ARS in a patient.
Depending on the type of radiation exposure, the methods and kits
of the present invention may be used to assess radiation dose in
any suitable unit of measurement. For example, an absorbed dose of
ionizing radiation following external exposure can be measured in a
variety of suitable units of measurement, including but not limited
to Gray (or rad), and sievert (or rem); a dose of radiation after
internal contamination is measured as Committed Effective Dose
Equivalent (CEDE); and a dose associated with external and internal
exposure is measured as Total Effective Dose Equivalent (TEDE).
[0085] Assay panel(s) employed in certain embodiments of the
instant invention include a plurality of radiation biomarkers used
to assess radiation exposure. A radiation biomarker can be any
substance that acts as an indicator of the exposure of an organism
to radiation, including but not limited to, proteins, nucleic
acids, carbohydrates and metabolites. In one embodiment, the
biomarkers included in the panel are all proteins.
[0086] Suitable assay panels comprise at least one radiation
biomarker from at least 1, 2, 3, 4 or 5 of the following biomarker
classes: DNA-damage biomarkers, inflammatory-response biomarkers,
tissue-damage biomarkers, tissue-damage repair biomarkers, and
hematology-surrogate biomarkers. As used herein, a DNA-damage
biomarker is a radiation biomarker associated with the host
response to radiation induced DNA damage. An inflammatory-response
biomarker is a radiation biomarker that is up- or down-regulated
during a systemic or localized inflammatory response caused by
radiation exposure. A tissue-damage biomarker is a radiation
biomarker that is released from a tissue as a result of local
tissue damage caused by radiation, whereas a tissue-damage repair
biomarker is a protein that is up- or down-regulated during a
repair, regeneration, or fibroblastic phase following tissue
damage. Tissue-damage repair biomarkers may also include proteins
associated with soft-tissue repair processes, including but not
limited to fibroblast formation, collagen synthesis, tissue
remodeling, and realignment. Finally, hematology-surrogate
biomarkers are cell-surface markers for blood cells, which may be
used as surrogates to traditional blood cell counts, for assessing
the effect of radiation on specific blood-cell populations. Useful
hematology surrogate markers include markers found on general
classes of cells (e.g., leukocytes) or more specific cell types
within those classes such as lymphocytes, neutrophils and platelets
or, even more specifically, T-cells or B-cells.
[0087] There may be some overlap between the biomarker categories
described above. For example, some inflammatory response biomarkers
may also be associated with tissue-damage repair. In one
embodiment, the radiation biomarker panel includes at least one
inflammatory-response biomarker and at least one tissue-damage
repair biomarker. In an alternate embodiment, the panel includes a
biomarker that is both an inflammatory response biomarker and a
tissue-damage repair biomarker.
[0088] A non-limiting list of biomarkers that can be used in the
instant invention is provided in Table 1 below.
TABLE-US-00001 TABLE 1 Biomarker Class Exemplary Biomarkers
DNA-damage p53 ATM biomarker p21 phosphorylated H2AX histone
GADD45a (.gamma.-H2AX) Inflammatory- IL-6 IFN response CRP IL-2
biomarker SAA IL-4 IL-1 TNF-alpha IL-5 IL-12 IL-10 IL-3 IL12/23
IL-7 KC/GRO Tissue-damage salivary alpha-amylase CKBB biomarker
citrullinated proteins CKMB S100B CKMM SP-D FABP2 BPI GFAP TSP NSE
CA15-3 Tissue-damage Flt-3L TPO repair biomarker G-CSF GM-CSF KFG
SDF-1a EPO Hematology- CD5 CD26 surrogate CD-16b CD27 biomarker
CD20 CD40 CD177 CD45
[0089] In a preferred embodiment, the assay panel used in the
present invention is configured to measure a level of a plurality
of biomarkers in a sample, wherein the plurality of biomarkers
includes one or more of Flt-3L, G-CSF, GM-CSF, EPO, CD27, CD45,
SAA, CD26, IL-12, TPO, and combinations thereof, and/or an
additional biomarker comprising a DNA-damage biomarker, an
inflammatory-response biomarker, a tissue-damage biomarker, a
tissue-damage repair biomarker, a hematology-surrogate biomarker,
and combinations thereof. In a specific embodiment, the panel
includes Flt-3L, GM-CSF, SAA, TPO, CD27, CD45, CD26, IL-12, and
combinations thereof. In one embodiment, the IL-12 assay is
specific for the IL-12 p40 subunit in the IL-12 p70 heterodimer and
may also cross-react with IL-23 (which also comprises the p40
subunit). In another embodiment, the IL-12 assay is specific for
the full IL-12 p70 heterodimer.
[0090] The selected biomarkers for assessing exposure to radiation
are, preferably, not significantly affected by chronic diseases
with high prevalence in the human population, such as diabetes,
asthma, high blood pressure, heart disease, arthritis and/or other
chronic inflammatory or autoimmune diseases. The selected
biomarkers for assessing exposure to radiation are, preferably,
also not affected by other types of trauma (e.g., wounding, burns
and/or mental stress) that may also be experienced by individuals
in a radiation event. In one embodiment, the biomarker response
associated with total body radiation exposure (for example, at 2,
6, 10 or 12 Gy) is less than the biomarker response associated with
the biomarker response associated with wound, burn and/or mental
trauma. Such comparison, may be determined through the use of a
combined injury animal model. We note that biomarkers that have a
significant confounding effect from confounding diseases or trauma
may still be selected and have value in a dose assessment
algorithm. In one embodiment, biomarkers that may be affected by
such confounding effects are included, and information about the
presence or absence of such confounding conditions is included in
the algorithm for dose assessment. For example, if a potentially
confounding condition is identified in a patient (such as a
confounding disease or trauma) approaches that may be taken to
minimize the effect of the confounding condition on the accuracy of
a dose assessment algorithm include: i) excluding the patient from
analysis using the algorithm; ii) applying the algorithm, but using
a redacted biomarker panel that excludes or applies less weight to
biomarkers that are likely to be affected by the confounding
condition or iii) applying a different algorithm employing a
biomarker panel that has been selected to be robust to the
confounding condition.
[0091] The kits of the present invention can further include
devices, reagents, and/or consumables for measuring hematological
parameters, such as peripheral blood cell counts, or for measuring
"Acute Phase Reaction" (APR) biomarkers. Such assay components can
be modifications of commercially available products for assessing
blood cell counts and APR biomarkers, such as the Quikread CRP
finger-prick device (Orion Diagnostica, Finland) which measures the
level of C-reactive protein.
[0092] In a preferred embodiment, the invention includes assays for
cell-surface markers for lymphocytes and neutrophils which are
useful as surrogates for lymphocyte and neutrophil counts. The
invention provides a method of conducting a multiplexed
hematology-surrogate biomarker assay and kits therefor, including a
kit configured to measure a level of a plurality of biomarkers in a
sample, including lymphocyte cell-surface markers and/or neutrophil
cell-surface markers. In one embodiment, the lymphocyte-surface
marker comprises CD5, CD20, CD26, CD27, CD40, or combinations
thereof. Additionally, the neutrophil cell-surface marker includes
CD16b, CD177, or combinations thereof. The hematology-surrogate
biomarker assay methods of the present invention can be conducted
on a sample comprising whole blood, blood cell pellets, serum,
and/or plasma. In one embodiment, the measurements are carried out
in samples prepared by reconstituting dried blood spots. In another
embodiment, such measurements are carried out using serum and/or
plasma samples. In a preferred embodiment, measurements are carried
out using plasma samples. Surprisingly, we have discovered that the
free (i.e., non-cell bound) forms of neutrophil and lymphocyte
surface markers can be measured in plasma and the levels of these
markers in plasma after radiation exposure provide useful
diagnostic information for assessment of the effects of radiation
on neutrophils and lymphocytes.
[0093] One of average skill in the art of biological assays will be
aware of numerous suitable approaches and instrumentation for
measuring the biomarkers and biomarker panels of the invention. In
one embodiment, the kit is configured to measure biomarker levels
using an immunoassay. In a preferred embodiment, the kit includes a
multi-well assay plate comprising a plurality of assay wells
configured to measure the level of said plurality of biomarkers in
one or more samples. Preferably, the wells are configured to enable
the use of individual wells to conduct multiplexed measurements of
a plurality of different biomarkers. In one such assay plate, a
well of the assay plate includes a plurality of assay domains, at
least two of the assay domains comprising reagents for measuring
different biomarkers. In an alternative preferred embodiment, the
kit includes an assay cartridge to measure biomarkers in a sample.
Preferably, the cartridge comprises a flow cell having an inlet, an
outlet and a detection chamber, said inlet, detecting chamber, and
outlet defining a flow path through said flow cell, said detection
chamber configured to measure said level of said plurality of
biomarkers in said sample. Kits used in the present method can
further include one or more additional assay reagents used in an
assay and those additional reagents can be provided in one or more
vials, containers, or compartments of a kit. Moreover, a kit for
assessing exposure to radiation can also include (a) a bar-coded
patient identification tag; (b) a dried blood spot collection card
comprising a bar code that for example, can be used to facilitate
sample identification; (c) a sample transport bag comprising
desiccant; (d) a capillary with a plunger; and/or (e) a lancet.
[0094] The samples that can be analyzed in the kits and methods of
the invention include but are not limited to, any biological fluid,
cell, tissue, organ and combinations or portions thereof, which
includes or potentially includes a biomarker of a disease,
disorder, or abnormality of interest. For example, a sample can be
a histologic section of a specimen obtained by biopsy, or cells
that are placed in or adapted to tissue culture. A sample further
can be a subcellular fraction or extract, or a crude or
substantially pure nucleic acid molecule or protein preparation. In
one embodiment, the samples that are analyzed in the assays of the
present invention are blood or blood fractions such as, blood
pellet, serum, or plasma. Other suitable samples include biopsy
tissue, intestinal mucosa, urine, parotid gland, hematological
tissues, intestine, liver, pancreas, or nervous system. The sample
can be taken from any patient, including but not limited to
animals, mammals, primates, non-human primates, humans, and the
like. In one embodiment, the level is measured using an
immunoassay. The radiation biomarker panels disclosed herein may be
used at the onset and throughout the course of the acute radiation
syndrome to assess and monitor patient health. In a preferred
embodiment, a sample is collected from a patient within about 1 to
7 days of radiation exposure.
[0095] As used herein, a "biomarker" is a substance that is
associated with a particular biological state, which can be a
disease or abnormal condition. A change in the levels of a
biomarker can correlate with the risk or progression of a disease
or abnormality or with the susceptibility of the disease or
abnormality to a given treatment. A biomarker can be useful in the
diagnosis of disease risk or the presence of disease in an
individual, or to tailor treatments for the disease in an
individual (choices of drug treatment or administration regimes).
In evaluating potential drug therapies, a biomarker can be used as
a surrogate for a natural endpoint such as survival or irreversible
morbidity. If a treatment alters a biomarker that has a direct
connection to improved health, the biomarker serves as a "surrogate
endpoint" for evaluating clinical benefit.
[0096] As used herein, the term "level" refers to the amount,
concentration, or activity of a biomarker. The term "level" can
also refer to the rate of change of the amount, concentration or
activity of a biomarker. A level can be represented, for example,
by the amount or synthesis rate of messenger RNA (mRNA) encoded by
a gene, the amount or synthesis rate of polypeptide corresponding
to a given amino acid sequence encoded by a gene, or the amount or
synthesis rate of a biochemical form of a biomarker accumulated in
a cell, including, for example, the amount of particular
post-synthetic modifications of a biomarker such as a polypeptide,
nucleic acid or small molecule. The term can be used to refer to an
absolute amount of a biomarker in a sample or to a relative amount
of the biomarker, including amount or concentration determined
under steady-state or non-steady-state conditions. Level can also
refer to an assay signal that correlates with the amount,
concentration, activity or rate of change of a biomarker. The level
of a biomarker can be determined relative to a control marker in a
sample.
[0097] Specific biomarkers valuable in distinguishing between
normal and diseased/exposed patients can be identified by visual
inspection of the data, for example, by visual classification of
data plotted on a one-dimensional or multidimensional graph, or by
using statistical methods such as characterizing the statistically
weighted difference between control individuals and diseased
patients and/or by using Receiver Operating Characteristic (ROC)
curve analysis. A variety of suitable methods for identifying
useful biomarkers and setting detection thresholds/algorithms are
known in the art and will be apparent to the skilled artisan.
[0098] For example and without limitation, diagnostically valuable
biomarkers can be first identified using a statistically weighted
difference between control individuals and exposed or abnormal
patients, calculated as
D - N .sigma. D * .sigma. N ##EQU00001##
wherein D is the median level of a biomarker in patients diagnosed
as having been exposed to radiation, N is the median (or average)
of the control individuals, .delta..sub.D is the standard deviation
of D and .sigma..sub.N is the standard deviation of N. The larger
the magnitude, the greater the statistical difference between the
diseased and normal populations.
[0099] According to one embodiment of the invention, biomarkers
resulting in a statistically weighted difference between control
individuals and diseased/exposed patients of greater than, e.g., 1,
1.5, 2, 2.5 or 3 could be identified as diagnostically valuable
markers.
[0100] Another method of statistical analysis for identifying
biomarkers is the use of z-scores, e.g., as described in Skates et
al. (2007) Cancer Epidemiol. Biomarkers Prev. 16(2):334-341.
[0101] Another method of statistical analysis that can be useful in
the inventive methods of the invention for determining the efficacy
of particular candidate analytes, such as particular biomarkers,
for acting as diagnostic marker(s) is ROC curve analysis. An ROC
curve is a graphical approach to looking at the effect of a cut-off
criterion, e.g., a cut-off value for a diagnostic indicator such as
an assay signal or the level of an analyte in a sample, on the
ability of a diagnostic to correctly identify positive or negative
samples or subjects. One axis of the ROC curve is the true positive
rate (TPR, i.e., the probability that a true positive
sample/subject will be correctly identified as positive, or
alternatively, the false negative rate (FNR=1-TPR, the probability
that a true positive sample/subject will be incorrectly identified
as a negative). The other axis is the true negative rate, i.e.,
TNR, the probability that a true negative sample will be correctly
identified as a negative, or alternatively, the false positive rate
(FPR=1-TNR, the probability that a true negative sample will be
incorrectly identified as positive). The ROC curve is generated
using assay results for a population of samples/subjects by varying
the diagnostic cut-off value used to identify samples/subjects as
positive or negative and plotting calculated values of TPR or FNR
and TNR or FPR for each cut-off value. The area under the ROC curve
(referred to herein as the AUC) is one indication of the ability of
the diagnostic to separate positive and negative samples/subjects.
In one embodiment, a biomarker provides an AUC.gtoreq.0.7. In
another embodiment, a biomarker provides an AUC.gtoreq.0.8. In
another embodiment, a biomarker provides an AUC.gtoreq.0.9.
[0102] Diagnostic indicators analyzed by ROC curve analysis can be
a level of an analyte, e.g., a biomarker, or an assay signal.
Alternatively, the diagnostic indicator can be a function of
multiple measured values, for example, a function of the
level/assay signal of a plurality of analytes, e.g., a plurality of
biomarkers, or a function that combines the level or assay signal
of one or more analytes with a patient's scoring value that is
determined based on visual, radiological and/or histological
evaluation of a patient. The multi-parameter analysis can provide
more accurate diagnosis relative to analysis of a single
marker.
[0103] Candidates for a multi-analyte panel could be selected by
using criteria such as individual analyte ROC areas, median
difference between groups normalized by geometric interquartile
range (IQR) etc. The objective is to partition the analyte space to
improve separation between groups (for example, normal and disease
populations) or to minimize the misclassification rate.
[0104] One approach is to define a panel response as a weighted
combination of the response for the individual analytes and then
compute an objective function like ROC area, product of sensitivity
and specificity, etc. See e.g., WO 2004/058055, as well as
US2006/0205012, the disclosures of which are incorporated herein by
reference in their entireties. The weighting coefficients define
the partitioning object; for linear combinations the object is a
line in 2 dimensions, a plane in 3 dimensions and a hyperplane in
higher dimensions. The optimal coefficients maximize the objective
function and can be determined using algorithms for finding
function extrema in multiple dimensions, e.g., gradient descent
methods, downhill simplex methods, simulated annealing and the
like; more details can be found in "Numerical Recipes in C, The Art
of Scientific Computing", W. Press et al., Cambridge University
Press, 1992.
[0105] Another approach is to use discriminant analysis, where a
multivariate probability distribution (normal, multinomial etc.) is
used to describe each group. Several distributions result in
partitioning hyperplanes in analyte space. One advantage of this
approach is the ability to classify measurements into multiple
groups (e.g. normal, disease 1, disease 2) simultaneously, rather
than two at a time. For further details, see "Principles of
Multivariate Analysis, A User's Perspective", W. J. Krzanowski,
Oxford University Press, 2000 and "Multivariate Observations", G.
A. F. Seber, John Wiley, 2004.
[0106] Once the partitioning hyperplanes have been determined, the
robustness of different assay panels can be compared by evaluating
a distance metric to the separating hyperplanes for each group. It
is noteworthy that the algorithms described above are designed to
find the best classification between groups; therefore these
algorithms can also be used to distinguish between different
diseases or populations or subgroups of the same disease or
population. Finally, categorical data (age, gender, race,
ethnicity, etc.) can also be coded into different levels and used
as an optimizing variable in this process.
[0107] In one embodiment, the invention provides a radiation
dose-calculation algorithm that includes (a) measuring the levels
of a plurality of radiation biomarkers in a patient sample; (b)
fitting said measured levels to response surface models for the
response of said biomarkers as a function of radiation dose and
sample time; (c) computing a cost function for combining the
plurality of biomarkers; (d) selecting a calculated radiation dose
and calculated sample time that minimizes the cost function; and
optionally, (e) comparing said calculated radiation dose to a
threshold value to classify individuals according to dose received
(for example, to distinguish exposed from non-exposed individuals
or to identify patients who would benefit from a treatment
option.
[0108] Sample time, as used herein, refers to the time between the
radiation exposure event and the time at which the sample was
collected. In applications where the sample time is known or is
expected to be known, for example when the exposure occurred in a
defined time frame, the actual sample time can be provided to the
algorithm. In these cases, only the calculated radiation dose needs
to be selected in step (d) above.
[0109] In a preferred embodiment, 2-parameter response functions
are determined, i.e., M, (dose, time), where M, is the expected
level of marker i as a function of dose and sample time.
Alternatively, M, may also represent the expected level of a
derived value i derived from the level of one or more biomarkers,
such as the reciprocal of a marker level, the log of a marker
level, the ratio or product of the levels of two markers, etc.
Preferably, the response functions are established based on
pre-existing data from human, animal or in vitro studies.
[0110] Based on these established response functions, one can
determine the radiation exposure dose (and, optionally the sample
time, if the dose is known but the sample time is not known) that
provides the best overall fit of the different markers (or derived
values) to their respective response functions. One general form of
a cost function (F) that can be minimized to find the best fit is
provided by the equation below. E.sub.i is a function that provides
a value associated with the discrepancy between the measured level
of a biomarker (or derived value) i (m.sub.i) and the expected
level of the biomarker (or derived value) predicted by the response
surface for a given dose-time condition (M.sub.i(dose,time)).
W.sub.i is a weighting function that may be dose-dependent and/or
time-dependent for each marker i. The weighting function may be
used to vary the importance given to certain markers in certain
dose and time ranges. In one embodiment, the weighting is
determined based on the statistical significance of the measurement
at that dose and time point. A number of different weighting
functions can be used, e.g., the inverse of the coefficient of
variation (CV) of the biomarker level at that dose and time.
Optionally, the weighting function may be omitted.
F ( dose , time ) = i = 1 n W i ( dose , time ) E i ( m i , M i (
dose , time ) ) ##EQU00002##
[0111] Examples of possible methods for calculating E.sub.i include
calculating the difference, the absolute value of the difference or
the square of the difference of m.sub.i and M.sub.i. In one
embodiment, the values of m.sub.i and M.sub.i are normalized to
avoid over-emphasizing the more abundant biomarkers by, for
example, dividing them by the minimum, maximum, median or average
value for normal samples or for all expected samples. One specific
example of a preferred cost function with a normalization factor is
provided below, which corresponds to a "least squares fit" with
each term normalized to the product of the measured value and fit
value, and scaled by a weighting function.
F ( dose , time ) = i = 1 n W i ( dose , time ) ( m i - M i ( dose
, time ) ) 2 m i .times. M i ( dose , time ) ##EQU00003##
[0112] The cost function may also transform the values of M.sub.i
and m.sub.i. In the preferred cost function described below, log
values are used to minimize an over-emphasis on biomarkers with the
largest fold-changes and also to minimize bias for biomarkers with
higher abundances.
F ( dose ) = i = 1 n Log ( m i M i ( dose ) ) ##EQU00004##
[0113] The measured and fit values for the biomarker levels may
also be added linearly or in quadrature to the limit of detection
(LOD) or lower limit of quantitation to minimize the effect on the
cost function of changes in levels near to the detection limit as
in the function below.
F ( dose ) = i = 1 n Log ( m i + LOD i M i ( dose ) + LOD i ) ,
##EQU00005##
[0114] wherein m.sub.i is the measured value for biomarker i,
M.sub.i is the predicted biomarker value as a function of dose at a
known time post-exposure, LOD.sub.i is the assay Limit of Detection
for biomarker i, and n is the total number of biomarkers being
used.
[0115] The algorithm described above for assessing radiation dose
can also be applied more generally to disease conditions that can
be assessed in the clinical setting using a severity index, i.e., a
classification scale used by clinicians to characterize the stage
of a disease or disorder. A variety of conditions are assessed
using severity indices, e.g., comprising traumatic brain injury,
stroke, embolism, liver disease, kidney disease, heart disease,
inflammatory bowel disease, Alzheimer's disease, dementia, thyroid
disease, rheumatoid arthritis, multiple sclerosis, psoriasis,
systemic lupus erythematosus, Hashimoto's thyroiditis, Pernicious
anemia, Addison's disease, Type I diabetes, dermatomyositis,
Sjogren syndrome, myasthenia gravis, reactive arthritis, Grave's
disease, Celiac disease, or cancer.
[0116] In this regard, the algorithm described above can be used as
an injury severity value calculation algorithm that includes (a)
measuring a level of a plurality of biomarkers in a patient sample,
wherein one or more biomarkers of the plurality of biomarkers are
altered relative to a normal control in the event of to injury in a
patient; (b) fitting the measured level to a response surface model
as a function of an injury severity index or time; (c) computing a
cost function for combining the plurality of biomarkers; (d)
identifying an injury severity value that minimizes the cost
function at a known time interval; and optionally, (e) comparing
the injury severity value to a threshold value, wherein an injury
severity value above the threshold value indicates the relative
severity of the injury. This is illustrated in FIG. 38(a), in which
a user (3801) measures a plurality of biomarkers in a patient
sample (3802) using an assay system (3803) (including a processor
(3804) and an algorithm module (3805), as illustrated in FIG.
38(c)). The processor and algorithm module of the assay system fits
the measured biomarker levels (3806) to a response surface model
(3807), computes a cost function (3808), and identifies a severity
index (3809). Likewise, the use of the statistical methods to
analyze a radiation dose in a sample is illustrated in FIG. 38(b)
in which a radiation dose (3810) is generated using the statistical
method.
[0117] In this embodiment, the cost function is
F ( SI ) = i = 1 n Log ( m i + LOD i M i ( SI ) + LOD i ) ,
##EQU00006##
[0118] wherein m.sub.i is the measured value for biomarker i,
M.sub.i is the predicted biomarker value as a function of a
severity index (SI) at a known time post-injury, LOD.sub.i is the
assay limit of detection for biomarker i, or n is the total number
of biomarkers being used. Preferably, the cost function is:
F ( SI ) = i = 1 n Log ( m i M i ( SI ) ) . ##EQU00007##
[0119] wherein m.sub.i is the measured value for biomarker i,
M.sub.i is the predicted biomarker value as a function of a
severity index (SI) at a known time post-injury, or n is the total
number of biomarkers being used.
[0120] Other statistical methods can be used to conduct
multivariate analyses of biomarker levels. For example, a neural
net approach can be used (see e.g., Musavi, et al., Neural Networks
(1992) (5): 595-603; Wang, et al., Artif. Intel'. Med. (2010) 48
(2-3): 119-127; Lancashire, L. et al., Computational Intelligence
in Bioinformatics and Computational Biology (2005): pp. 1-6,
14-15). Neural networks are a wide class of flexible models used
for simulating nonlinear systems. They consist of an often large
number of "neurons," i.e. simple linear or nonlinear computing
elements, interconnected in often complex ways and often organized
into layers. Other modeling approaches include but are not limited
to linear models, support vector machines and discriminant analysis
(Lancashire et al., "Utilizing Artificial Neural Networks to
Elucidate Serum Biomarker Patterns Which Discriminate Between
Clinical Stages in Melanoma," Proceedings of the 2005 IEEE
Symposium on Computational Intelligence in Bioinformatics and
Computational Biology, Nov. 14-15, 2005, pp. 1-6; Wang et al.,
"Method of regulatory network that can explore protein regulations
for disease classification," Artif Intell Med., 48 (2-3) (2010),
pp. 119-127).
[0121] Therefore, the methods of the present invention can be used
to assess an absorbed dose of ionizing radiation in a patient
sample by measuring levels of a plurality of biomarkers in a sample
and applying an algorithm to assess the absorbed dose in the sample
based on the levels of the plurality of biomarker in the samples,
wherein the plurality of biomarkers comprise a DNA-damage
biomarker, an inflammatory-response biomarker, a tissue-damage
biomarker, a tissue-damage repair biomarker, or a
hematology-surrogate biomarker. In a preferred embodiment, the
algorithm quantifies an absorbed dose of ionizing radiation in the
range of about 1-10 Gy, preferably between about 1-6 Gy, more
preferably between about 2-6 Gy, or between about 6-10 Gy.
[0122] All or one or more parts of the algorithm(s) and statistical
method(s) disclosed herein can be performed by or executed on a
processor, general purpose or special purpose or other such
machines, integrated circuits or by any combination thereof.
Moreover, the software instructions for performing the algorithm(s)
and statistical methods(s) disclosed herein may also be stored in
whole or in part on a computer-readable medium, i.e., a storage
device for use by a computer, processor, general or special purpose
or other such machines, integrated circuits or by any combination
thereof. A non-limiting list of suitable storage devices includes
but is not limited to a computer hard drive, compact disk,
transitory propagating signals, a network, or a portable media
device to be read by an appropriate drive or via an appropriate
connection.
[0123] In addition to biomarker measurements, biodosimetry
assessment can benefit from additional inputs, such as information
regarding clinical symptoms. For example, the Biodosimetry
Assessment Tool (BAT) is a software application that equips
healthcare providers with diagnostic information (clinical signs
and symptoms, physical dosimetry, etc.) relevant to the management
of human radiation casualties. Designed primarily for prompt use
after a radiation incident, the software application facilitates
the collection, integration, and archival of data obtained from
exposed persons. Data collected in templates are compared with
established radiation dose responses, obtained from the literature,
to provide multi-parameter dose assessments. The program archives
clinical information (extent of radioactive contamination, wounds,
infection, etc.) useful for casualty management, displays relevant
diagnostic information in a concise format, and can be used to
manage both military and civilian radiation accidents.
[0124] Biomarker levels can be measured using any of a number of
techniques available to the person of ordinary skill in the art,
e.g., direct physical measurements (e.g., mass spectrometry) or
binding assays (e.g., immunoassays, agglutination assays and
immunochromatographic assays). Biomarkers identified herein can be
measured by any suitable immunoassay method, including but not
limited to, ELISA, microsphere-based immunoassay methods, lateral
flow test strips, antibody based dot blots or western blots. The
method can also comprise measuring a signal that results from a
chemical reactions, e.g., a change in optical absorbance, a change
in fluorescence, the generation of chemiluminescence or
electrochemiluminescence, a change in reflectivity, refractive
index or light scattering, the accumulation or release of
detectable labels from the surface, the oxidation or reduction or
redox species, an electrical current or potential, changes in
magnetic fields, etc. Suitable detection techniques can detect
binding events by measuring the participation of labeled binding
reagents through the measurement of the labels via their
photoluminescence (e.g., via measurement of fluorescence,
time-resolved fluorescence, evanescent wave fluorescence,
up-converting phosphors, multi-photon fluorescence, etc.),
chemiluminescence, electrochemiluminescence, light scattering,
optical absorbance, radioactivity, magnetic fields, enzymatic
activity (e.g., by measuring enzyme activity through enzymatic
reactions that cause changes in optical absorbance or fluorescence
or cause the emission of chemiluminescence). Alternatively,
detection techniques can be used that do not require the use of
labels, e.g., techniques based on measuring mass (e.g., surface
acoustic wave measurements), refractive index (e.g., surface
plasmon resonance measurements), or the inherent luminescence of an
analyte.
[0125] Binding assays for measuring biomarker levels can use solid
phase or homogenous formats. Suitable assay methods include
sandwich or competitive binding assays. Examples of sandwich
immunoassays are described in U.S. Pat. No. 4,168,146 and U.S. Pat.
No. 4,366,241, both of which are incorporated herein by reference
in their entireties. Examples of competitive immunoassays include
those disclosed in U.S. Pat. No. 4,235,601, U.S. Pat. No. 4,442,204
and U.S. Pat. No. 5,208,535, each of which are incorporated herein
by reference in their entireties.
[0126] Multiple biomarkers can be measured using a multiplexed
assay format, e.g., multiplexing through the use of binding reagent
arrays, multiplexing using spectral discrimination of labels,
multiplexing of flow cytometric analysis of binding assays carried
out on particles, e.g., using the Luminex.RTM. system. Suitable
multiplexing methods include array based binding assays using
patterned arrays of immobilized antibodies directed against the
biomarkers of interest. Various approaches for conducting
multiplexed assays have been described (See e.g., US 20040022677;
US 20050052646; US 20030207290; US 20030113713; US 20050142033; and
US 20040189311, each of which is incorporated herein by reference
in their entireties. One approach to multiplexing binding assays
involves the use of patterned arrays of binding reagents, e.g.,
U.S. Pat. Nos. 5,807,522 and 6,110,426; Delehanty J-B., Printing
functional protein microarrays using piezoelectric capillaries,
Methods Mol. Bio. (2004) 278: 135-44; Lue R Y et al., Site-specific
immobilization of biotinylated proteins for protein microarray
analysis, Methods Mol. Biol. (2004) 278: 85-100; Lovett,
Toxicogenomics: Toxicologists Brace for Genomics Revolution,
Science (2000) 289: 536-537; Berns A, Cancer: Gene expression in
diagnosis, nature (2000), 403,491-92; Walt, Molecular Biology:
Bead-based Fiber-Optic Arrays, Science (2000) 287: 451-52 for more
details). Another approach involves the use of binding reagents
coated on beads that can be individually identified and
interrogated. See e.g., WO 9926067, which describes the use of
magnetic particles that vary in size to assay multiple analytes;
particles belonging to different distinct size ranges are used to
assay different analytes. The particles are designed to be
distinguished and individually interrogated by flow cytometry.
Vignali has described a multiplex binding assay in which 64
different bead sets of microparticles are employed, each having a
uniform and distinct proportion of two dyes (Vignali, D. A A,
"Multiplexed Particle-Based Flow Cytometric Assays" J. ImmunoL
Meth. (2000) 243: 243-55). A similar approach involving a set of 15
different beads of differing size and fluorescence has been
disclosed as useful for simultaneous typing of multiple
pneumococcal serotypes (Park, M. K et al., "A Latex Bead-Based Flow
Cytometric Immunoassay Capable of Simultaneous Typing of Multiple
Pneumococcal Serotypes (Multibead Assay)" Clin. Diag. Lab ImmunoL
(2000) 7: 4869). Bishop, J E et al. have described a multiplex
sandwich assay for simultaneous quantification of six human
cytokines (Bishop, L E. et al., "Simultaneous Quantification of Six
Human Cytokines in a Single Sample Using Microparticle-based Flow
Cytometric Technology," Clin. Chem (1999) 45:1693-1694).
[0127] A diagnostic test can be conducted in a single assay
chamber, such as a single well of an assay plate or an assay
chamber that is an assay chamber of a cartridge. The assay modules,
e.g., assay plates or cartridges or multi-well assay plates),
methods and apparatuses for conducting assay measurements suitable
for the present invention are described for example, in US
20040022677; US 20050052646; US 20050142033; US 20040189311, each
of which is incorporated herein by reference in their entireties.
Assay plates and plate readers are now commercially available
(MULTI-SPOT.RTM. and MULTI-ARRAY.RTM. plates and SECTOR.RTM.
instruments, MESO SCALE DISCOVERY,.RTM. a division of Meso Scale
Diagnostics, LLC, Gaithersburg, Md.).
[0128] Reference is made to specific examples illustrating the
constructs and methods above. It is to be understood that the
examples are provided to illustrate rather than limit the scope of
various embodiments of the invention.
EXAMPLES
Methods
[0129] Assays.
[0130] Assays were developed as a number of different singleplex or
multiplexed panels in MSD MULTI-ARRAY 96-well plates and analyzed
using ECL detection on an MSD plate reader (such as the SECTOR or
PR2 lines of plate readers available from Meso Scale Discovery, a
division of Meso Scale Diagnostics, LLC, Gaithersburg, Md.). The
biomarkers analyzed in the assay panels include the biomarkers
listed in Table 1.
[0131] Prior to conducting an assay measurement with an assay
panel, the sample was first diluted in an appropriate sample
diluent to the specified dilution for that panel. The diluted
sample (typically about 10 to 25 uL) was then combined with an
additional volume of sample diluent (typically one to three times
the diluted sample volume) in a well of a MULTI-ARRAY assay plate
containing an array of capture antibodies for the targets in the
panel. The plate was incubated for about 2 hours with shaking,
sample was removed and the wells were washed three times with
phosphate buffered saline. A 50-.mu.L volume of a mixture of
labeled (MSD SULFO-TAG.TM. label, an ECL label also available from
MESO SCALE DISCOVERY) detection antibodies against the targets in
the panel was added and the plate was incubated for about one hour
with shaking. The wells were washed three times with phosphate
buffered saline and about 125 .mu.L MSD T Read Buffer (available
from MESO SCALE DISCOVERY) was added. The plate was read using an
MSD ECL plate reader (available from MESO SCALE DISCOVERY). The
reader reports assay signals for each array element in relative ECL
units.
[0132] The same procedure was used for analyzing intracellular
markers in blood cell pellets, except that the initial sample was
prepared by extracting the blood cell pellet in a histone
extraction buffer (50 mM TRIS pH 7.5, 500-mM NaCl, 0.5%
NaDeoxycholate, 1% Triton X100, 2-mM EDTA, 1% PhIC, 1% PIC and 1-mM
PMSF; 200 .mu.L per 10.sup.6 white blood cells).
[0133] The experimental plate layout contained a negative QC
control, a positive QC control and an 8 point calibration curve,
all run in duplicate. The calibration curve was fit to a 4
parameter logistic (4-PL) fit using 1/y2 weighting and used to
calculate sample concentrations.
[0134] Radiation Dose Studies in Mice.
[0135] Female mice (strain B6D2F1/J) were subjected to total body
irradiation (TBI) at a range of doses (doses in one study included
0, 1.5, 3, 6, 10 Gy and 14 Gy), using a .sup.60Co .gamma.-ray
source, at a dose rate of .about.0.6 Gy/min. Mice from these
radiation dose levels were sampled at 6 hrs, 1, 2, 3, 5 and 7 days
post irradiation (see Table 2). Whole blood was collected at the
sampling times and processed into a platelet poor EDTA plasma
fraction and peripheral blood leukocyte pellet (PBL). A separate
aliquot of whole blood was collected and used to measure blood cell
counts. In a given study, typically 6 to 8 mice were tested per
dose/time condition. In one study (the "Blinded Study"), the
samples covered the conditions shown in Table 3, but were provided
for analysis in a blinded fashion to enable an unbiased
characterization of the dose-estimation algorithm performance.
[0136] Combined Injury Study in Mice.
[0137] This study examined the effect of a dorsal puncture wound
covering 15% of total body surface area on radiation biomarker
levels (the wound model is described in Ledney et al. 2010). Female
mice (strain B6D2F1/J) exposed to 0 (sham irradiation), 3, 6 or 10
Gy were then subject, within an hour of irradiation, to the 15%
surface area puncture wound. Plasma samples were collected at 6
hrs, 1, 2, 3, 5 or 7 days post irradiation. Irradiation conditions
and sample collection were as described above for the Biomarker
Discovery Study. As non-injury controls, an equal number of mice
were subject to the same dose/time conditions and processing as the
injured mice, but without receiving the puncture wound. Samples
were also collected from true negative control mice, not subject to
sham wounding or irradiation procedures. At least 8 replicate mice
were subjected to each dose/time/wounding condition. Prior to
running the full Combined Injury Study, a smaller Pilot Combined
Injury Study was run that was limited to the 0 and 6 Gy dose
conditions, and only 6 replicate mice for each dose/time/injury
conditions (see Table 3 and Table 4 for summaries of the test
conditions for the Pilot and Full Combined Injury Studies,
respectively).
[0138] Radiation Dose Testing with NHP Samples.
[0139] Remnant non-human primate (NHP) samples (Rhesus
monkeys--Macaca mulatta) from prior radiation studies were
evaluated as follows. Remnant Sample Set A included EDTA plasma
samples collected pre-irradiation and at various times after TBI
irradiation with 0, 1.0, 3.5, 6.5, or 8.5 Gy from a .sup.60Co
.gamma.-ray source and Remnant Sample Set B included EDTA plasma
samples collected pre-irradiation and at various times after TBI
irradiation with 7.5, 10.0 or 11.5 Gy (6 MV LINAC photon, 0.80
Gy/min). Table 5 summarizes the samples that were tested in
feasibility testing. Sample Set A was provided with blood cell
counts measured at the time of sample collection.
[0140] Human Samples.
[0141] To measure the expected normal variation in radiation
biomarkers, remnant platelet-poor EDTA plasma was collected from
individual blood samples donated at blood donation centers (through
Bioreclamation, LLC, Liverpool, N.Y.). Samples were collected from
40 normal individuals and up to 10 self-identified individuals for
each of four high prevalence chronic diseases (hypertension,
diabetes, asthma or rheumatoid arthritis). The samples, summarized
in Table 6, were selected to be diverse in sex, age and race.
[0142] To evaluate potential new human models for radiation
exposure, remnant human samples were collected from radiation
oncology patients. EDTA plasma samples were collected from patients
receiving standard radiotherapy for lung cancer (15 patients) and
GI cancer (8 patients). These treatments involve localized, but
relatively large area, irradiation of the affected organ. Typical
treatment schedules have patients receiving 1.8 or 2.0 Gy per day,
5 days per week for 6 weeks for a total dose of 54 to 60 Gy.
Samples were collected prior to irradiation and at the 3 and 6 week
time-points. EDTA plasma was also collected from melanoma patients
(13 patients) receiving TBI in preparation for cell-transfer
therapy (samples collected before and 6 hours after receiving a 2
Gy dose). All treatments also included chemotherapy. More details
on the samples and protocols can be found in Table 7.
TABLE-US-00002 TABLE 2 Summary of the test groups, and numbers of
mice in each test group, for the Biomarker Discovery Study. Test
Groups Sample Collection Time (Post Exposure) Dose (Gy) 6 h 1 d 2 d
3 d 5 d 7 d 0 (Sham) 8 8 8 8 8 8 1.5 8 8 8 8 8 8 3 8 8 8 8 8 8 6 8
8 8 8 8 8 10 8 8 8 8 10 10 14 8 8 8 12 16 20
TABLE-US-00003 TABLE 3 Summary of the test groups, and numbers of
mice in each test group, for the Combined Injury Study. Test Groups
Sample Collection Time (Post Exposure) Dose (Gy) Injury 6 h 1 d 2 d
3 d 5 d 7 d 0 (Control) No (Sham) 6 6 6 6 6 6 0 (Control) Yes 6 6 6
6 6 6 6 No (Sham) 6 6 6 6 6 6 6 Yes 6 6 6 6 6 6
TABLE-US-00004 TABLE 4 Summary of the test groups, and numbers of
mice in each test group, for the Full Combined Injury Study. Larger
numbers or replicate mice were included at higher doses and times
to account for mortality in these groups. Because of the large
number of animals the study was broken into two sub-studies that
were carried out at different times, each sub-study comprising half
the animals for each test condition. Test Groups Sample Collection
Time (Post Exposure) Dose (Gy) Injury 6 h 1 d 2 d 3 d 5 d 7 d 0
(Control) No (Control) 8 8 8 8 8 8 0 (Sham) No (Sham) 8 8 8 8 8 8 0
(Sham) Yes 8 8 8 8 8 8 3 No (Sham) 8 8 8 8 8 8 3 Yes 8 8 8 8 8 8 6
No (Sham) 8 8 8 8 8 8 6 Yes 8 8 8 8 12 12 10 No (Sham) 8 8 8 8 10
10 10 Yes 8 8 8 12 12 12
TABLE-US-00005 TABLE 5 Summary of the remnant NHP samples analyzed
in feasibility testing. Dose/Time Conditions and Number of
Replicates Per Condition for NHP Sample Sets A & B Sample Set B
(6 Sample Set A (.sup.60Co y-ray) MV LINAC Photon) Time Dose (Gy)
Dose (Gy) (Days) 0.0 1.0 3.5 6.0 8.5 Time 7.5 10.0 11.5 0 6 6 6 6 5
0 3 4 3 0.25 4 4 3 NA 6 1 3 4 3 1 5 5 6 6 6 3 3 4 3 2 6 6 6 6 6 5 3
4 3 3 6 6 6 6 6 7 3 4 3 4 6 6 6 6 6 9 4 4 4 7 4 6 NA 6 6 13 3 NA NA
8 NA NA NA NA 1 17 3 NA NA 9 6 6 6 6 6 21 2 NA NA 10 NA NA NA NA 5
25 2 NA NA 30 2 NA NA
TABLE-US-00006 TABLE 6(a-b) List of remnant human plasma sample
from blood donors used in testing. The sample set included samples
from normal individuals, as well as from individuals that
self-identified as having one of four high prevalence chronic
diseases. Individuals with High Prevalence Normal Individuals
Chronic Diseases (a) Disease Gender Age Race (b) Disease Gender Age
Race 1 N M 29 AA 1 Hy M 43 C 2 N M 39 AA 2 Hy M 73 C 3 N M 52 AA 3
Hy M 65 C 4 N F 20 AA 4 Hy F 56 C 5 N F 36 AA 5 Hy M 53 AA 6 N M 39
AA 6 Hy F 50 AA 7 N M 48 C 7 Hy F 66 H 8 N M 61 C 8 Hy F 54 C 9 N M
48 C 9 Hy F 50 C 10 N M 60 C 10 Hy M 44 H 11 N M 49 C 11 As M 75 C
12 N M 51 C 12 As M 41 C 13 N M 47 C 13 As F 40 C 14 N M 37 C 14 As
F 21 C 15 N M 29 C 15 As F 46 C 16 N M 42 C 16 As M 33 AA 17 N M 49
C 17 As F 24 AA 18 N M 56 C 18 As M 45 H 19 N M 44 C 19 As F 27 H
20 N M 29 C 20 As M 23 C 21 N F 32 C 21 Dia M 50 C 22 N F 53 C 22
Dia M 69 C 23 N F 39 C 23 Dia F 48 C 24 N F 39 C 24 Dia M 42 AA 25
N F 24 C 25 Dia F 57 AA 26 N F 48 C 26 Dia M 59 H 27 N F 50 C 27
Dia F 59 H 28 N F 48 C 28 Dia M 65 C 29 N F 39 C 29 Dia F 68 C 30 N
F 46 C 30 RA F 47 AA 31 N F 66 C 31 RA M 59 C 32 N F 22 C 32 RA M
58 AA 33 N F 52 C 33 RA M 60 AA 34 N F 23 C 34 RA M 59 C 35 N M 52
H 35 RA M 47 C 36 N M 24 H 37 N M 39 H 38 N M 46 H 39 N F 29 H 40 N
F 48 H 41 N F 34 H 42 N F 29 H N = Normal; AA = African American; C
= Caucasian; H = Hispanic; Hy = Hypertension As = Asthma; Dia =
Diabetes; RA = Rheumatoid Arthritis
TABLE-US-00007 TABLE 7(a-c) List of remnant human plasma samples
from radiation oncology studies. Brief summaries of the protocols
are provided under the tables. Melanoma Lung Cancer GI Cancer
Patients Patients Patients (a) Pre- 2 Gy (b) Pre- 30 60 (c) Pre- 30
54 Patient Rad (TBI) Patient Rad Gy Gy Patient Rad Gy Gy 1 X X 1 X
X X 1 X X X 2 X X 2 X X X 2 X X 3 X X 3 X X X 3 X X X 4 X X 4 X X X
4 X X X 5 X 5 X X X 5 X 6 X 6 X X X 6 X X X 7 X 7 X X X 7 X X X 8 X
8 X X X 8 X X X 9 X 9 X X X Total 7 7 7 10 X X 10 X X X 11 X X 11 X
X X 12 X X 12 X X X 13 X X 13 X X X 14 X X 14 X X X 15 X 15 X X X
16 X Total 15 15 15 17 X 18 X 19 X 20 X 21 X 22 X 23 X 24 X 25 X 26
X 27 X 28 X 29 X 30 X Total 29 10 (a)Melanoma patients receiving
total body irradiation (TBI) prior to cell-transfer therapy were
treated with cyclophosphamide and fludarabine 3-5 days prior to TBI
(lymphocyte depletion chemotherapy) and plasma was collected
pre-TBI and 6 hours after a single 2 Gy fraction. Melanoma patients
15-30 were in the non-TBI arm of the study. (b)Lung cancer patients
receiving radiation to the thorax received 2 Gy fractions, 5 days
per week, for 6 weeks, as well as neo-adjuvant and/or concurrent
5-FU and taxol. Samples were collected pre-radiation and after
total doses of 30 to 60 Gy. (c)GI cancer patients receiving
radiation to the GI tracts received 2 Gy fractions, 5 days per
week, for 6 weeks, as well as concurrent 5-FU or gemcitabine.
Samples were collected pre-radiation and after total doses of 30
and 54 Gy.
[0143] Data Analysis.
[0144] In evaluating the dose and time responses of individual
biomarkers, the significance of differences in observed responses
to different test conditions were determined by calculating a p
value using a two-tailed unpaired t-test. The measured dose and
time responses of the individual biomarkers, as determined in the
mouse radiation dose study, were used to develop a multi-parameter
algorithm for predicting dose. The basic approach is to model the
dose and time response for each biomarker. To predict dose, the
biomarker levels of a patient (or animal model) are measured and
the dose is calculated that provides the best compromise for
fitting each biomarker to its response surface model. In the
studies described herein, it is assumed that the time of exposure
will be known, so only the dose providing the best compromise fit
needs to be calculated.
Results
[0145] Mouse Radiation Dose and Combined Injury Studies--Individual
Biomarker Responses. The results of biomarker testing for the mouse
radiation dose study are shown in FIG. 1-17. Each figure includes a
plot of biomarker levels vs. dose for each collection time, and a
plot of biomarker levels vs. time for each dose. Each data point
for markers in Panels A-D represents the average over 7-8 mice for
the control conditions and 10-12 mice for the sham and irradiated
conditions, with the exception of the EPO, IL-5, IL10, KC/GRO and
TNF-a measurements. The data points for these assays represent the
averages are over 3-4 mice for the controls and 6-8 mice for the
sham and irradiation conditions. The figures also provide tables of
p values indicating the significance in the change in biomarker
levels for each condition, relative to the non-irradiated
control.
[0146] Figures are provided for selected biomarkers showing a
significant change (p<0.05) over a range of dose/time
conditions. The DNA damage and inflammatory markers were early
radiation markers, peaking at 6 hrs or 1 day and dropping
significantly by 2 days, although there was some evidence that IL-6
and SAA increased at late time points for the higher doses as acute
radiation syndrome progressed. The exceptions were IL-5 and IL-12
which showed strong responses at later time points, IL-12 being the
only marker that showed a strong dose-dependent decrease in
concentration. The biomarkers of tissue-damage repair tended to
rise 2 days or more after radiation, although G-CSF had a strong
early and late response and Flt-3L showed a significant response at
all but the earliest (6 h) time point.
[0147] FIG. 1-17 also include a plot of biomarker levels vs. time
for each dose and injury condition tested in the Full Combined
Injury Study. Focusing on the 0 Gy (non-irradiated) condition and
comparing the biomarker levels for non-injured mice (closed circle)
and injured mice (open circle), showed that there were a number of
biomarkers that were not changed by wounding in the absence of
radiation exposure (Flt-3L, GM-CSF, TPO, EPO, IL-5 and CD27). SAA,
G-CSF and CD-26 by contrast were significantly elevated (p<0.05)
by wounding, with the effect of wounding being larger in magnitude
than the effect of radiation exposure. IL-12 and CD-45 showed a
moderate wounding effect that was statistically significant at some
time points, but lower in magnitude than the effect of radiation
exposure. The results obtained with the combined injury model are
roughly consistent with results using an LPS injection model,
described herein, except that GM-CSF and IL-12 levels are strongly
elevated in response to LPS, but not affected (GM-CSF) or only
weakly affected (IL-12) by wounding.
[0148] If the biomarkers were insensitive to wounding in
non-irradiated animals, wounding also did not generally affect
biomarker levels after radiation exposure. The one exception was
TPO; wounding appeared to accelerate the kinetics for the
appearance of elevated TPO levels in irradiated animals. The effect
was most pronounced for animals receiving a 6 Gy dose. At day 5
post-irradiation the wounded animals had average TPO levels that
were almost 5 times higher than non-wounded animals. By day 7, the
levels for wounded and non-wounded animals were roughly comparable.
We anticipate that use of biomarkers with this type of effect
should not affect the ability to identify patients who have been
irradiated, but could potentially lead to an overestimation of
dose. There is, however, to further improve algorithm accuracy by
adjusting the dose assessment algorithm based on information about
wounding or other trauma.
[0149] Mouse Radiation Dose Study--Algorithm Development and
Testing.
[0150] A multi-parameter algorithm (as described in the Methods
section) using 5 biomarkers (Flt-3L, G-CSF, GM-CSF, EPO, IL12/23)
was applied to the full sample set from a first mouse radiation
dose and time study. For each sample, with its set of biomarker
measurements, the predicted dose was calculated using the repeated
random sub-sampling approach (see Methods). FIG. 18(a) shows a plot
of the predicted dose vs. the actual given dose for the full set of
samples. The dashed lines indicate a range of +/-1.5 Gy from 0 to 6
Gy, and indicate +/-25% above 6 Gy. Accounting for the 2.5-fold
higher sensitivity of humans to radiation dose relative to the
mouse model (LD50/30 for the B6D2F1/J female mouse model is
.about.9.5 Gy--see, Ledney et al., 2010--vs. .about.3-4 Gy for
humans), the corresponding human equivalent dose ranges would be
+/-0.6 Gy from 0 to 2.4 Gy, and +/-25% above 2.4 Gy.
[0151] The ability of the algorithm to correctly classify doses
into appropriate ranges can be calculated from FIG. 18(a). The
percentage of samples that fall within the defined boundaries in
FIG. 18(a) for all doses over the time window of 1-7 days after
exposure is 90.+-.3%, where the standard deviation is the variation
in the calculated percentage across the different combinations of
test and training sets. Note that the accuracy decreases slightly
to 88.+-.3% if the time range is expanded to include the 6 hr time
point.
[0152] The algorithm was also characterized for its overall ability
to discriminate doses of 6 Gy and above from non-irradiated control
mice (0 Gy) and to discriminate doses of 6 Gy and above from 3 Gy
and below. Given the lower relative survival sensitivity of the
mouse model to radiation dose than humans (LD50/30 is roughly
2.5-fold higher for the mouse model), this classification should
roughly correspond to the ability to classify dose above or below
about 2 to 3 Gy in humans. FIG. 19(a) shows ROC curves generated by
varying the predicted dose threshold used to classify the samples.
The ROC curves demonstrate excellent classification ability with
area under curves (ADCs) of 0.999 for distinguishing 0 Gy from
.gtoreq.6 Gy and 0.956 for distinguishing .ltoreq.3 Gy from
.gtoreq.6 Gy.
[0153] Supplemental Mouse Radiation Dose Study--Algorithm
Development and Testing.
[0154] Algorithm development was advanced using results from a
second Mouse Radiation Dose Study. To select optimal biomarker
panels for use with our multi-parameter dose-estimation algorithm
(as described above), the performance of each possible combination
of the 12 most radiation sensitive biomarkers was tested against
the Biomarker Discovery Study data set. A repeated random
sub-sampling approach was used so that the training and test
samples were always independent. Algorithm performance was
calculated using two different metrics: (i) a prediction error
metric provided as the root mean square error (RMSE) in the
predicted doses across the full sample set and (ii) an accuracy
metric, where we defined accuracy to be the percentage of samples
for which the predicted dose was within +/-1.5 Gy from 0 to 6 Gy,
and +/-25% above 6 Gy. Accounting for the 2.5-fold higher
sensitivity of humans to radiation dose relative to the mouse model
(LD50/30 for the B6D2F1/J female mouse model is +/-9.5 Gy--see,
Ledney et al., 2010--vs. .about.3-4 Gy for humans), the
corresponding human equivalent dose ranges would be +/-0.6 Gy from
0 to 2.4 Gy, and +/-25% above 2.4 Gy.
[0155] FIG. 20 shows the RMSE and Accuracy metrics for the
different possible biomarkers combinations (each point in the graph
represents a different combination of 1 to 12 biomarkers). Focusing
on the highest performing combinations for each possible panel size
(the top-most points for each panel size), the figure shows that
there is no benefit to panel sizes larger than six markers. Table 8
shows the performance metrics for two top performing panels for
each panel: the top performing panel including one of the three
highly wound-sensitive biomarkers (SAA, G-CSF and CD26) and the top
performing panel that does not include any of these three markers.
There was no evidence that the injury sensitive markers were
required for optimal performance, so the six marker panel without
injury sensitive biomarkers (CD27, Flt-3L, GM-CSF, CD45, IL12 and
TPO) was selected as the preferred panel for further performance
characterization.
TABLE-US-00008 TABLE 8 The table shows the preferred biomarker
panels identified by applying the multi-parameter dose assessment
algorithm to the mouse Biomarker Discovery data set. Two panels are
presented for each possible panel size. One panel is the highest
performing panel, for a given panel size, that does not include the
three most injury sensitive biomarkers as determined by the
Combined Injury Study Results: SAA, G-CSF and CD26. The other panel
is the highest performing panel that includes one or more of these
injury sensitive markers. The accuracy represents the % of samples
that were correctly classified by dose (i.e., predicted dose within
1.5 Gy of actual dose for doses .ltoreq.6 Gy or within 25% of the
actual dose for doses >6 Gy). The table also provides the root
mean square error (RMSE) in the predicted dose. The six biomarker
panel without injury sensitive biomarkers (CD27 + Flt3L + GM-CSF +
CD45 + IL-12 + TPO) was chosen for subsequent algorithm validation
work. # Accuracy Markers Panel (%) RMSE (Gy) 1 CD27 84.5 .+-. 3.0
1.66 .+-. 0.16 1 Flt-3L 72.7 .+-. 3.2 2.74 .+-. 0.20 2 CD27 +
Flt-3L 88.9 .+-. 2.7 1.46 .+-. 0.15 2 CD27 + G-CSF* 88.7 .+-. 2.5
1.48 .+-. 0.15 2 CD27 + GM-CSF 88.6 .+-. 2.4 1.40 .+-. 0.14 3 CD27
+ Flt-3L + GM-CSF 92.9 .+-. 2.0 1.18 .+-. 0.12 3 CD27 + IL-12 +
G-CSF* 91.9 .+-. 2.4 1.37 .+-. 0.17 4 CD27 + Flt-3L + GM-CSF +
CD26* 93.8 .+-. 2.0 1.13 .+-. 0.12 4 CD27 + Flt-3L + GM-CSF + IL-12
93.7 .+-. 2.0 1.16 .+-. 0.15 5 CD27 + Flt-3L + GM-CSF + SAA + CD26*
94.2 .+-. 2.3 1.09 .+-. 0.11 5 CD27 + Flt-3L + GM-CSF + CD45 +
IL-12 93.6 .+-. 2.1 1.15 .+-. 0.14 6 CD27 + Flt-3L + GM-CSF + CD45
+ SAA + CD26* 94.7 .+-. 2.1 1.08 .+-. 0.11 6 CD27 + Flt-3L + GM-CSF
+ CD45 + IL-12 + TPO 93.7 .+-. 2.1 1.14 .+-. 0.13 7 CD27 + Flt-3L +
GM-CSF + CD45 + IL-12 + SAA + CD26* 94.8 .+-. 1.9 1.06 .+-. 0.11 7
CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + TPO + CD40 93.1 .+-. 2.1
1.17 .+-. 0.13 8 CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + SAA + CD26
+ G-CSF* 94.7 .+-. 2.1 1.09 .+-. 0.12 8 CD27 + Flt-3L + GM-CSF +
CD45 + IL-12 + TPO + IL-5 + CD40 92.3 .+-. 2.2 1.21 .+-. 0.15 9
CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + TPO + IL-5 + SAA + CD26*
94.4 .+-. 2.0 1.08 .+-. 0.14 9 CD27 + Flt-3L + GM-CSF + CD45 +
IL-12 + TPO + IL-5 + CD40 + EPO 91.4 .+-. 2.4 1.25 .+-. 0.16 10
CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + TPO + CD40 + SAA + CD26 +
G-CSF* 94.0 .+-. 2.2 1.12 .+-. 0.12 11 CD27 + Flt-3L + GM-CSF +
CD45 + IL-12 + TPO + IL-5 + CD40 + SAA + CD26 + G-CSF* 93.6 .+-.
2.1 1.14 .+-. 0.13 12 CD27 + Flt-3L + GM-CSF + CD45 + IL-12 + TPO +
IL-5 + CD40 + EPO + SAA + CD26 + G-CSF* 93.2 .+-. 2.2 1.14 .+-.
0.14 The * and red font indicate the presence of the injury
sensitive markers G-CSF, SAA and CD26
[0156] Performance of Algorithm for Predicting Dose in Blinded
Samples.
[0157] Using the full Biomarker Discovery data set as the training
set for the multi-parameter algorithm, the optimal 6-biomarker
panel was used to calculate an estimated dose for each sample from
the Blinded Study. In this analysis, the sampling time information
was available and used in dose estimation, but the actual dose
information was blinded to the analysts until the dose estimation
was complete. FIG. 21 shows the correlation of the predicted dose
and the actual dose and includes dashed lines that represent our
Accuracy criteria as described above. Using this approach, 94.7% of
the predicted doses fell within our accuracy criteria for all doses
over the time window of 1-7 days after exposure. The RMS error in
dose estimation was 1.14 Gy (which should be roughly equivalent to
+/-0.46 Gy in humans). FIG. 21 also shows that the majority of the
points that fell outside of our accuracy criteria were samples
collected 1 day after radiation, indicating that there may be
opportunities for improving performance by identifying additional
early biomarkers or weighting them more heavily.
[0158] The algorithm was also characterized for its overall ability
to discriminate doses of 6 Gy and above from non-irradiated control
mice (0 Gy) and to discriminate doses of 6 Gy and above from 3 Gy
and below. Given the lower sensitivity of the mouse model to
radiation than humans (LD50/30 is roughly 2.5-fold higher for the
mouse model), this classification should roughly correspond to the
ability to classify doses around the critical 2 Gy threshold in
humans. FIG. 22 shows ROC curves generated by varying the predicted
dose threshold used to classify the samples. The ROC curves
demonstrate excellent classification ability with area under curves
(ADCs) of 1.000 for distinguishing 0 Gy from .gtoreq.6 Gy and 0.998
for distinguishing .ltoreq.3 Gy from .gtoreq.6 Gy. Using the
optimal classifications determined by the ROC analysis, there was
perfect separation of 0 Gy and .gtoreq.6 Gy samples (100%
sensitivity, 100% specificity) and near perfect separation of 3 Gy
and .gtoreq.6 Gy samples (99.2% sensitivity and 100%
specificity).
[0159] Effect of Combined Injury on Algorithm Performance.
[0160] To provide a preliminary view of the robustness of the
algorithm to the potential confounding effects of injury, the
algorithm (using the selected optimal 6-biomarker panel) was used
to predict dose in the samples from Pilot Combined Injury study (0
and 6 Gy doses, with and without 15% surface wound). FIG. 23
provides a plot of predicted dose vs. actual dose. The percentage
of samples that fell within our criteria for dose prediction
accuracy was 93.3%, which is roughly in line with the values that
that were observed for studies without an injury component (the %
accuracy for the blinded study was 94.7%). The RMSE error in dose
(0.85 Gy) is lower than the value observed for the blinded study
(1.14 Gy), most likely because the blinded study included higher
doses. Despite the inclusion of injured animals, it was also
possible to set a classification threshold in predicted dose that
completely distinguished (100% sensitivity, 100% specificity) the
non-irradiated and irradiated animals.
[0161] Preliminary Testing of Radiation Biomarkers in NHP
Samples.
[0162] Archived plasma samples from irradiated NHP (Rhesus
macaques) were tested with the NHP biomarker assay panels. For
almost all dose/time conditions, however, at least 5 of the 6
replicate samples were tested on all assays.
[0163] FIGS. 24(a-z) show the radiation dose responses for 13
biomarkers selected for their radiation sensitivity. FIGS. 24(a-z)
show that 5 of the 6 biomarkers that were selected for the mouse
dose-assessment algorithm were either radiation responsive in NHP
(Flt-3L, CD27, TPO, and IL-12) and/or had mechanistic analogs that
were radiation responsive in NHP (i.e., the neutrophil surface
marker CD177 as an analog for CD45 in mice and the lymphocyte
surface marker CD20 as an analog for CD27 in mice). One marker from
the mouse panel, GM-CSF, was not detected in control or irradiated
NHP; the assay may simply not have sufficient sensitivity for
native Rhesus GM-CSF. FIGS. 24(a-z) show that three markers that
demonstrated radiation sensitivity in the mouse model (SAA, EPO and
G-CSF), but that were not selected for use in the dose-assessment
algorithm also responded to radiation in the NHP model. FIGS.
24(a-z) show the response of two markers (CRP and salivary amylase)
that are not useful in mice but that have been used to assess
radiation exposure in people, and confirms that these biomarkers
respond to radiation in the NHP model. Finally, FIGS. 24(a-z) show
dose responses for two novel early responders to radiation (TIMP-1
and TNF-RII) that were identified by screening cancer biomarker
panels. TIMP-1 (Tissue Inhibitor of MetalloProteinase 1) is
regulates a variety of physiological processes through the
inhibition of metalloproteases and also has erythroid-potentiating
activity. Soluble TNF-RII (soluble TNF receptor II) is released
into plasma by proteolytic cleavage of cell bound TNF receptors,
and is elevated in a number of inflammatory conditions.
[0164] A first pass examination of using the multi-parameter
algorithm for dose assessment in the NHP model was carried using a
biomarker panel selected to roughly correspond to the preferred
mouse panel. The panel included Flt-3L, TPO, IL-12, CD20, CD27,
CD177 and salivary amylase. CD20 and CD177 are mechanistically
analogous to CD27 and CD45 in the mouse panel. GM-CSF was not
included because the NHP GM-CSF assay does not appear to be
sensitive enough to detect native GM-CSF in the plasma from normal
or irradiated mice. The one marker without an analog in the mouse
panel was salivary amylase, which is an established marker in NHP,
but is not affected by radiation in mice. The data set gathered
from the NHP Sample Set A was analyzed using a slight variation for
the algorithm used for the mouse studies, in which the contribution
of the different markers was weighted based on their dose
responsivity in a specific time range. The algorithm was trained
and tested on the data set using the random sub-sampling method to
avoid training bias. FIGS. 24(a-z) show the performance of the
algorithm for discriminating animals receiving doses of 3.5 Gy and
above from non-irradiated control animals (0 Gy) and for
discriminating animals receiving doses of 3.5 Gy and above from
those receiving 1 Gy and below. Given the lower sensitivity of the
NHP model to radiation than humans (LD50/30 is roughly 1.5-fold
higher for the mouse model), this classification should roughly
correspond to the ability to classify doses around the critical 2
Gy threshold in humans (3 Gy in NHP). FIG. 25 shows ROC curves
generated by varying the predicted dose threshold used to classify
the samples. The ROC curves demonstrate excellent classification
ability with area under curves (AOCs) of 1.000 for distinguishing 0
Gy from .gtoreq.3.5 Gy and 0.995 for distinguishing .ltoreq.1 Gy
from .gtoreq.3.5 Gy. Using the optimal classifications determined
by the ROC analysis, there was perfect separation of 0 Gy and
.gtoreq.3.5 Gy samples (100% sensitivity, 100% specificity) and
near perfect separation of .ltoreq.1 Gy and .gtoreq.3.5 Gy samples
(96.9% sensitivity and 98.5% specificity). FIG. 25 also shows the
correlation of predicted dose and actual dose and shows that the
correlation is very good near the critical 3 Gy point.
[0165] Human Normal and Diseased Samples.
[0166] The human biomarker panels were tested with a set of remnant
plasma samples from blood donors (Table 6) that included 42 normal
healthy individuals as well as samples from donors self-reporting
as suffering from one of four high-prevalence chronic diseases:
hypertension (10 samples), rheumatoid arthritis (6 samples), asthma
(10 samples) and diabetes (9 samples). The results are plotted in
bar and whisker format in FIG. 26. There was no evidence that the
disease populations were significantly different (p<0.05) than
the normal population for any of the biomarkers.
[0167] When judging the ability of the mouse model data to support
the use of a dose-assessment algorithm in humans, one consideration
is whether the increased normal range one would expect for
biomarkers in a diverse human population (relative to an inbred
mouse strain) would increase the likelihood for false positives. We
decided to study this problem by adding random noise to the
measured biomarker levels from the non-irradiated mice in the
Biomarker Discovery Study, so that the variability in the "normal"
mouse levels matched the observed variability in the normal human
population. We applied this noise to 4 of the biomarkers in the
preferred 6-biomarker panel (Flt-3L, CD27, GM-CSF, and IL-12). CD45
was not measured in the human sample set, so there was no reference
for comparison. TPO actually showed lower variation in the human
sample set than in the mouse sample set, so the mouse levels were
left unchanged.
[0168] The data was then analyzed to determine how the added noise
affected the specificity by which non-irradiated control mice could
be distinguished from mice exposed to 6 Gy. The classification as 0
Gy or >6 Gy was carried out using the optimal threshold selected
in the absence of injected noise. As shown in FIG. 27, increasing
the baseline biomarker variability did not cause any additional
misclassifications and the measured specificity for classification
remained at 100%.
[0169] Human Samples from Patients Receiving Radiation
Oncology.
[0170] Sample sets from cancer patients receiving radiation (Table
7) were evaluated as potential models for assessing biodosimetry
algorithms. One set of samples were from melanoma patients
receiving lymphocyte depleting chemotherapy prior to cell-transfer
therapy. This study included one arm that also received total body
irradiation (3 days after chemotherapy) and one arm that did not.
Samples were only available pre-irradiation and 5 to 6 hours after
the first 2 Gy fraction, so the sample set was relevant for early
onset biomarkers. The results shown in FIG. 28 indicate that the
biomarker levels in the pre-radiation draws and in patients in the
non-TBI arm could be substantially different from the normal range
due to the chemotherapy regimen. Nevertheless, two biomarkers
showed up as significantly elevated in the post-irradiation group
relative to the pre-irradiation group and the non-TBI arm: salivary
amylase (p=0.0012), a well-known early onset radiation marker and
p53 (p=0.013), a marker identified as an early (<1 day)
marker.
[0171] Samples were also tested from patients receiving localized
radiation for lung of GI cancer (2 Gy per day, 5 times per week, 6
weeks), in combination with neo-adjuvant or concurrent
chemotherapy. Biomarker levels were measured in samples taken
pre-radiation and after cumulative doses of 30 and 54 to 60 Gy
(FIG. 29). Overall, there were no significant observed changes in
biomarker levels as a result of these localized radiation
therapies. There was evidence for small increases in average levels
of hematopoietic markers (Flt-3L and TPO) and small drops in levels
of soluble lymphocyte surface markers (CD5 and CD20), but the
changes were moderate (.about.a factor of 2) and there was
considerable overlap between the distributions.
[0172] Mouse Confounding Effect Study--Individual Biomarker
Responses.
[0173] The results of the preliminary mouse confounding effect
study are provided in FIGS. 30-32 for the biomarkers showing
radiation sensitivity. The figures provide biomarker levels vs.
collection time in the absence of confounding factors or after
injection of LPS or G-CSF. Plots are provided for both
un-irradiated mice and mice exposed to 6 Gy at 2 hours after
treatment with the confounding factor. The preliminary confounding
effect study used a different radiation source (X-ray) than the
radiation dose-response study (.gamma.-ray), so the 0 and 6 Gy
conditions from the radiation dose study are overlaid to gauge
consistency of results between the studies. FIG. 33 provides bar
graphs showing the relative magnitudes of the maximal observed LPS
and G-CSF responses relative to the maximal observed radiation
response (including samples from the Radiation Response study) and
allows for a rapid assessment of which markers can be subject to
confounding effects.
[0174] FIG. 33 shows that several assays (SAA, G-CSF, GM-CSF, IL-6,
IL-12, IL-12/23 and KC/GRO) showed an LPS response on the same
scale or greater than the maximal radiation response. These assays
were primarily cytokines or inflammatory-response markers. Flt-3L,
EPO, TPO, p53 and .gamma.H2AX, in contrast, were insensitive or
showed only slight elevations in response to LPS. Interestingly,
TNF-a has little or no radiation response but responds strongly to
LPS, and can have utility for identifying samples with significant
inflammation. Most markers showed no response to G-CSF treatment.
Even when a response was observed, for example EPO, the response
was relatively small on the scale of the radiation response.
[0175] Mouse Confounding Effect Study--Algorithm with Reduced LPS
Insensitive Biomarker Set.
[0176] One approach to address the confounding effects of
non-radiation related inflammatory responses is to remove the
inflammatory biomarkers from the dose assessment algorithm for
patients with obvious trauma or infections. Algorithm performance
was characterized after removing the LPS-sensitive biomarkers from
the preferred 5 panel biomarker set to produce a 2 biomarker
LPS-insensitive panel (Flt-3L and G-CSF). FIGS. 18b and 19b provide
plots of predicted dose vs. actual dose and ROC curves for
classifying samples with dose.gtoreq.6 Gy, that were produced
analogously to the curves provide in FIGS. 18a and 19a for the full
5 marker panel. The reduced panel was still useful for predicting
dose, although there was some degradation in performance relative
to the full panel. The accuracy for classifying doses within 1.5 Gy
for doses up to 6 Gy or within 25% for doses greater than 6 Gy was
74.+-.4%, compared to 91.+-.3% for the full panel. The AUC for the
ROC curve for distinguishing doses.gtoreq.6 Gy from non-irradiated
controls was 0.989, compared to 0.999 for the full panel. The AUC
for distinguishing doses.gtoreq.6 Gy from doses.ltoreq.6 Gy was
0.882, compared to 0.956 for the full panel.
[0177] Preliminary Testing of NHP Samples.
[0178] FIGS. 34-35 show the results obtained based on preliminary
testing of a subset of the Rhesus plasma samples collected between
0 and 9 days after TBI with 1 or 3.5 Gy (3 animals per dose). The
results largely confirm those seen with the mouse model; the
differences are highlighted below. The NHP model, generally, showed
higher radiation sensitivity with stronger responses at .about.3 Gy
and many markers providing good responses at 1 Gy. EPO and SAA
responded over a broader time range (1-9 days for EPO. 1-3 days for
SAA) than was observed in mice. CRP, while being non-radiation
responsive in mice, provided a strong early response in NHP. BPI
and p53 were both observed as very early (6 h) markers. GM-CSF did
not respond to radiation in the NHP model (data not shown).
[0179] FIG. 36 shows interesting results obtained when the plasma
samples were tested with the CD20(lymphocyte) and CD177
(neutrophil) surrogate markers. Preliminary testing with a small
set of archived blood pellet samples collected 0, 1 or 2 days after
irradiation showed that the CD20 and CD177 levels showed a similar
drop over time as observed by cell counting (data not shown). More
interestingly, FIG. 36 shows, for the plasma samples from the 1 and
3.5 Gy cohort, that there are measurable levels of free (not
cell-bound) CD-20 and CD-177 in the plasma and that the dose- and
time-course changes in the plasma levels of these markers exhibit
useful diagnostic information.
[0180] Additional NHP Sample Testing and Development of Dose
Assessment Algorithm for the NHP Model.
[0181] Using data from the NHP model, the approach of assessing
radiation dose by fitting multiplexed biomarker data to time-dose
response surfaces for each biomarker was evaluated (the same
approach described above for the mouse data). The results showed
that a panel of 6 plasma markers (Flt-3L, EPO, p53, CD20, CD177 and
SAA) can provide good discrimination of animals receiving greater
than 3.5 Gy (equivalent to .about.2 Gy in humans) from those
receiving less than 3.5 Gy and also provided high accuracy for
semi-quantitative dose prediction. The results for these 6
biomarkers are shown in Table 26 below and in FIG. 37. Table 26
also illustrates the discrimination utility of additional
biomarkers measured in this study.
TABLE-US-00009 TABLE 26 # % RMSE Markers Panel Accuracy (Gy) 1 CD20
64.8 1.51 1 SAA 63.5 1.57 1 Flt-3L 69.6 1.44 1 CD177 45.5 2.85 2
p21 + CD20 69.1 0.97 2 Flt-3L + CD20 69.1 1.04 2 CD20 + CD177 66.4
1.01 3 TPO + p21 + CD20 96.5 0.73 3 Flt-3L + EPO + CD20 92.0 0.87 4
Flt-3L + EPO + CD20 + SAA 94.2 0.58 4 Flt-3L + EPO + CD20 + p53
94.2 0.69 5 Flt-3L + EPO + CD20 + CD177 + SAA 97.1 0.44 5 Flt-3L +
EPO + CD20 + CD177 + CRP 96.4 0.49 6 Flt-3L + EPO + CD20 + CD177 +
p53 + 97.5 0.35 SAA 7 Flt-3L + EPO + CD20 + CD177 + CRP + 96.5 0.41
p53 + p21 8 Flt-3L + EPO + CD20 + CD177 + CRP + 99.3 0.34 p53 + p21
+ TPO 9 Flt-3L + EPO + CD20 + CD177 + CRP + 96.6 0.42 p53 + p21 +
TPO + Amylase 10 Flt-3L + EPO + CD20 + CD177 + CRP + 97.1 0.42 p53
+ p21 + TPO + Amylase + SAA
[0182] The impact of replacing the individual values of CD20 and
CD177 in the model with the ratio of CD177/CD20 was evaluated in
order to determine if this change would improve the accuracy of the
algorithm (analogous to the use of neutrophil/lymphocyte ratio for
dose assessment based on hematology results). As shown in Table 27
(a)-(b), the use of the ratio provided roughly equivalent accuracy
to the use of the individual values.
TABLE-US-00010 TABLE 27 (a)-(b) X = CD177 + CD20 X = CD177/CD20 #
of Markers % Correct RMSE % Correct RMSE Best Panel Including X,
When X = CD177 + CD20 2 X 88 1.17 75 1.41 3 X + SAA 91 1.01 86 1.41
4 X + Flt-3L + CRP 94 0.80 89 1.16 5 X + Flt-3L + EPO + SAA 97 0.44
92 0.77 6 X + Flt-3L + EPO + p53 + SAA 98 0.35 94 0.51 7 X + Flt-3L
+ EPO + CRP + p53 + p21 99 0.41 94 0.49 8 X + Flt-3L + EPO + CRP +
p53 + p21 + TPO 99 0.34 96 0.42 9 X + Flt-3L + EPO + CRP + p53 +
p21 + TPO + Amylase 99 0.42 96 0.50 10 X + Flt-3L + EPO + CRP + p53
+ p21 + TPO + Amylase + SAA 97 0.42 97 0.41 Best Panel Including X,
When X = CD177/CD20 2 X 88 1.17 75 1.41 3 X + SAA 91 1.01 86 1.41 4
X + p21 + SAA 92 0.84 91 1.12 5 X + Flt-3L + p21 + SAA 94 0.72 93
0.77 6 X + Flt-3L + CRP + p21 + SAA 95 0.65 95 0.65 7 X + Flt-3L +
EPO + CRP + p21 + SAA 97 0.45 97 0.42 8 X + Flt-3L + EPO + CRP +
TPO + p53 + SAA 98 0.34 98 0.34 9 X + Flt-3L + EPO + CRP + TPO +
p21 + p53 + SAA 97 0.40 97 0.37 10 X + Flt-3L + EPO + CRP + TPO +
p21 + p53 + SAA + Amylase 97 0.42 97 0.41
[0183] Alternate algorithms for dose assessment were also
evaluated. A simple linear model
(Dose=A.sub.1C.sub.1+A.sub.2C.sub.2+A.sub.3C.sub.3+ . . . , where
Ci is the concentration of marker i and Ai is an empirically
determined coefficient) was equivalent to the response surface
based model for distinguishing animals exposed to 0 and 3.5 Gy
(both models were able to correctly classify all samples from
animals in these two groups). The linear model has the advantage of
not requiring knowledge of the time between exposure and sample
collection, but does not quantify dose as well as the response
surface model (root mean square error for dose assessment for the
optimal panel was .about.1 Gy vs. .about.0.3 Gy for the response
surface model).
[0184] Various publications and test methods are cited herein, the
disclosures of which are incorporated herein by reference in their
entireties, In cases where the present specification and a document
incorporated by reference and/or referred to herein include
conflicting disclosure, and/or inconsistent use of terminology,
and/or the incorporated/referenced documents use or define terms
differently than they are used or defined in the present
specification, the present specification shall control.
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