U.S. patent application number 16/819684 was filed with the patent office on 2021-02-04 for methods of using non-rare cells to detect rare cells.
The applicant listed for this patent is Epic Sciences, Inc., The Scripps Research Institute. Invention is credited to Anand Kolatkar, Peter Kuhn, Joshua Kunken, Dena Marrinucci, John R. Stuelpnagel, Xing Yang.
Application Number | 20210033612 16/819684 |
Document ID | / |
Family ID | 1000005162155 |
Filed Date | 2021-02-04 |
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United States Patent
Application |
20210033612 |
Kind Code |
A1 |
Kuhn; Peter ; et
al. |
February 4, 2021 |
METHODS OF USING NON-RARE CELLS TO DETECT RARE CELLS
Abstract
The invention provides seminal computational approaches
utilizing data from non-rare cells to detect rare cells, such as
circulating tumor cells (CTCs). The invention is applicable at two
distinct stages of CTC detection; the first being to make decisions
about data collection parameters and the second being to make
decisions during data reduction and analysis. Additionally, the
invention utilizes both one and multi-dimensional parameterized
data in a decision making process.
Inventors: |
Kuhn; Peter; (Solana Beach,
CA) ; Kolatkar; Anand; (San Diego, CA) ;
Kunken; Joshua; (San Diego, CA) ; Marrinucci;
Dena; (San Diego, CA) ; Yang; Xing; (San
Diego, CA) ; Stuelpnagel; John R.; (Santa Barbara,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Scripps Research Institute
Epic Sciences, Inc. |
La Jolla
San Diego |
CA
CA |
US
US |
|
|
Family ID: |
1000005162155 |
Appl. No.: |
16/819684 |
Filed: |
March 16, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16252472 |
Jan 18, 2019 |
10613089 |
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16819684 |
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15601696 |
May 22, 2017 |
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16252472 |
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13503014 |
Jul 9, 2012 |
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PCT/US10/53431 |
Oct 20, 2010 |
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15601696 |
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15710102 |
Sep 20, 2017 |
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16252472 |
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12223351 |
Nov 25, 2008 |
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PCT/US07/02798 |
Jan 30, 2007 |
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15710102 |
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61253787 |
Oct 21, 2009 |
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60763625 |
Jan 30, 2006 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2800/52 20130101;
G01N 33/6875 20130101; G01N 2800/56 20130101; G01N 33/5076
20130101; G01N 33/5091 20130101; G01N 33/574 20130101 |
International
Class: |
G01N 33/574 20060101
G01N033/574; G01N 33/50 20060101 G01N033/50; G01N 33/68 20060101
G01N033/68 |
Claims
1-53. (canceled).
54. A method for detecting a circulating tumor cell (CTC) in a
sample from a subject comprising: a) plating a non-enriched sample
comprising nucleated blood cells onto a slide, said non-enriched
sample suspected of comprising circulating tumor cell (CTCs) and
non-CTCs wherein said non-CTCs are present at a concentration that
is at least 10 times that of the CTCs; b) contacting the sample
with a first immunuofluorescently detectable agent targeting a
positive marker and a second immunuofluorescently detectable agent
targeting a negative marker; c) scanning the slide with fluorescent
scanning microscopy to generate a cell image of the nucleated cells
in the sample; and d) identifying the CTC in the sample based on
based on assessment of said detectable agents and morphology in
said cell image.
55. The method of claim 54, wherein said positive marker targets a
specific cell type or tissue.
56. The method of claim 55, wherein said positive marker targets a
specific cell type.
57. The method of claim 56, wherein said specific cell type is
epithelial.
58. The method of claim 57, wherein said positive marker is
cytokeratin.
59. The method of claim 54, wherein said negative marker targets
non-CTCs.
60. The method of claim 59, wherein said non-CTCs are white blood
cells.
61. The method of claim 60, wherein said negative marker is
CD45.
62. The method of claim 54, further comprising a third
immunofluorescently detectable agent.
63. The method of claim 62, wherein said third immunofluorescently
detectable agent targets a content marker.
64. The method of claim 63, wherein said content marker is androgen
receptor (AR).
Description
BACKGROUND OF THE INVENTION
Field of the Invention
[0001] The invention relates generally to medical diagnostics and
more specifically to detection and categorization of rare cells,
such as circulating tumor cells (CTCs).
Background Information
[0002] Significant unmet medical need exists for the longitudinal
disease monitoring in patients with epithelial cancers at the
cellular level. Predicting and monitoring therapy response and
disease progression are particularly important in epithelial cancer
patients due to the natural history of the disease and the
selective selection process in response to the therapeutic
pressure. While progress has been made in understanding the primary
and metastatic tumors in their respective microenvironments, a
substantial barrier exists in understanding carcinoma behavior
during the fluid phase, as it spreads within and occupies the
bloodstream. The circulating component of cancer contains within it
the cells giving rise to future metastases, and as such, represents
a compelling target for investigation.
[0003] Research to fully characterize the clinical significance of
this fluid phase of solid tumors has been hindered by the lack of
easily accessible and reliable experimental tools for the
identification of CTCs. The unknown character and low and unknown
frequency of CTCs in the blood, combined with the difficulty of
distinguishing between cancerous versus normal epithelial cells,
has significantly impeded research into how the fluid phase might
be clinically important. The ideal fluid phase biopsy should find
significant numbers of a specific CTC population in most epithelial
cancer patients and preserve and present CTCs to a pathologist
and/or researcher in a format that enables not only enumeration but
further molecular, morphologic and/or phenotypic analysis. In
addition, it should preserve the remaining rare populations for
further analysis.
[0004] CTCs are generally, although not exclusively, epithelial
cells that originate from a solid tumor in very low concentration
and enter into the blood stream of patients with various types of
cancer. CTCs are also thought to be capable of originating in the
blood, forming small colonies throughout the body. The shedding of
CTCs by an existing tumor or metastasis often results in formation
of secondary tumors. Secondary tumors typically go undetected and
lead to 90% of all cancer deaths. Circulating tumor cells provide
the link between the primary and metastatic tumors. This leads to
the promise of using the identification and characterization of
circulating tumor cells for the early detection and treatment
management of metastatic epithelial malignancies. Detection of CTCs
in cancer patients offers an effective tool in early diagnosis of
primary or secondary cancer growth and determining the prognosis of
cancer patients undergoing cancer treatment because number and
characterization of CTCs present in the blood of such patients has
been correlated with overall prognosis and response to therapy.
Accordingly, CTCs serve as an early indicator of tumor expansion or
metastasis before the appearance of clinical symptoms.
[0005] While the detection of CTCs has important prognostic and
potential therapeutic implications in the management and treatment
of cancer, because of their occult nature in the bloodstream, these
rare cells are not easily detected. CTCs were first described in
the 1800s, however only recent technological advances have allowed
their reliable detection. The challenge in the detection of
circulating tumor cells is that they are present in relatively low
frequency compared to other nucleated cells, commonly less than
1:100,000. To compensate for this challenge, most conventional
approaches for detecting circulating tumor cells rely on
experimental enrichment methods, whereby the CTCs are
preferentially separated from the other cellular components (e.g.,
non-CTCs), most importantly other nucleated cells that are the most
similar to CTCs.
[0006] Currently, the most utilized methods of positive enrichment
for enumeration/characterization of CTCs are immunomagnetic
enrichment methods targeting the surface protein EpCAM and the "CTC
chip". The most widely used methodology to detect CTCs, J&J's
Veridex technology, utilizes immunomagnetic enrichment. The
technology relies upon immunomagnetic enrichment of tumor cell
populations using magnetic ferrofluids linked to an antibody which
binds epithelial cell adhesion molecule (EpCAM), expressed only on
epithelial derived cells. This methodology requires 7.5 mL of blood
for analysis and finds greater than 2 CTCs in only some metastatic
cancer patients.
[0007] Microfluidic or "CTC-Chip" technology, is another positive
enrichment method for enumeration/characterization of CTCs. The
methods utilizes 1-3 mL of blood in which whole blood flows past
78,000 EpCAM-coated microposts. EpCAM+cells stick to the posts and
are subsequently stained with cytokeratin, CD45, and DAPI. With
this methodology, CTCs are found in virtually all metastatic cancer
patients at a relatively high purity and not in healthy controls.
Additionally, CTC-chip technology identifies CTCs in all patients
and in higher numbers than other technologies by a factor of
approximately 10 to 100 fold as reported in two recent
publications.
[0008] The only routinely used technology for CTC detection is
based on immunomagnetic enrichment. This current "gold standard"
and FDA approved test is called CellSearch.RTM. and employs an
immunomagnetic enrichment step to isolate cells that express the
epithelial cells adhesion molecule (EpCAM). Additionally, to be
identified as a CTC, the cell must contain a nucleus, express
cytoplasmic cytokeratin, and have a diameter larger than five
microns. This system has uncovered the prognostic utility of
enumerating and monitoring CTC counts in patients with metastatic
breast, prostate, and colorectal cancers; however, the sensitivity
of this system is low, finding no or few CTCs in most patients.
Most follow-on CTC technologies have reported higher sensitivity
and are pursuing variations of the enrichment strategy, however
this directly biases the detectable events towards those that have
sufficient expression of the protein selected for the initial
enrichment step.
[0009] A standardized microscope based approach has also been
previously utilized to identify and morphologically characterize
and credential CTCs in case studies of breast, colorectal, and lung
cancer patients.
[0010] Although many CTC detection approaches are currently in use,
significant limitations have been identified with the current
approaches. For example, one significant limitation of positive
selection methods to enumerate/characterize CTCs is that positive
physical selection invariably leads to loss of CTCs and is less
than 100% efficient. Thus the number of CTCs detected per sample
using current methods is often too low to provide robust
interpretation or clinically meaningful content of a particular
sample. Additional limitations of current methods include low CTC
detection due to CTC heterogeneity. For example, differences in
individual CTC features within the CTC population of interest
further hinder the number of CTCs detected using current
methodologies. Such differences may include size variations between
individual CTCs, and variable or down regulated expression between
individual CTCs of the cell surface markers used to detect CTCs. A
further limitation of existing methodologies includes limitations
in purity levels and variable purity. Any enrichment will have a
certain number of false positives, for instance other nucleated
blood cells that stick to the enrichment. For example, the Veridex
magnet has typically 5,000 to 10,000 false positives on top of the
5 to 10 positives.
SUMMARY OF THE INVENTION
[0011] The present invention is based in part on the discovery of
innovative methods for analyzing samples to detect, enumerate and
characterize rare cells, such as CTCs. Accordingly, the present
invention provides methods for improved detection and
characterization allowing for clinically meaningful analysis of
samples for use in clinical, research and development settings.
[0012] Accordingly, the present invention provides methods for the
improved detection and characterization of rare cells in a sample
by utilizing data from non-rare cells (cells present at a
concentration of 10, 50, 100, 200, 300, 400, 500, 1,000, 5,000,
10,000 times or greater as compared to the rare cell) in the
sample. Thus the method of the invention utilizes similarity
measures to assess non-similarity of cells, requiring both the
biggest distance exclusion, e.g., events that are clearly non-rare
cell related and the fine distinction of a cutoff based on
similarities of surrounding non-rare cells.
[0013] The method includes providing a sample suspected of having
at least one rare cell and at least one cell that is present at a
concentration that is at least 10 times that of the rare cell;
contacting the sample with at least one detectable agent, such as
an agent that binds a cell marker; performing cell imaging on the
sample to generate an image; and detecting the at least one rare
cell as compared with other cells in the sample by analyzing the
cell from the image, thereby detecting the rare cell in the sample.
In various aspects of the invention, the method further includes
plating of the suspected rare cell and at least one cell on a solid
support, such as a slide, to facilitate contacting the cells with
the detectable agent and cell imaging. In various aspects of the
invention, the detectable agent is any agent used to stain the
cells, such as an agent that binds a cell marker, including, but
not limited to, a positive marker, negative marker, nuclear marker,
content marker, or any combination thereof.
[0014] In various aspects of the invention, the methods described
herein are performed on an apparatus for efficiently imaging a
slide containing a detectable signal, such as a fluorescent signal.
The apparatus may typically include a computer having at least one
system processor with image processing capability, a computer
monitor, an input device, a power supply and a microscope
subsystem. Thus the apparatus includes a computer having executable
code for performing the various analysis required to practice the
invention. The microscope subsystem includes an optical sensing
array for acquiring images. A two-dimensional motion stage for
sample movement and for focus adjustment, and input and output
mechanisms for multiple sample analysis and storage. The apparatus
may also include a transmitted light source as well as an
illuminating/fluorescent excitation light source for fluorescing
samples.
[0015] In one embodiment of the invention, the method includes
establishing optimal exposure limits for performing the cell
imaging that facilitate detection of rare cells present. In one
aspect, the exposure limit for the detectable agent is determined
using a signal from at least one cell. In various aspects, the
detectable marker may be a positive marker, negative marker,
nuclear marker or content marker. In a related aspect, the exposure
limits may be set using data relating to the cells and/or suspected
rare cells gathered from a first image, to re-image the slide.
[0016] In another embodiment, the method includes minimizing
exposure settings to minimize data collection time and maximize
throughput to facilitate detection of rare cells.
[0017] In another embodiment, the method includes utilizing data
associated with non-rare cells to generate a quality control
parameter that facilitates detection of rare cells. In various
aspects, the quality control parameter is distribution of at least
one non-rare cell on the slide, alignment of multiple cell images
via alignment of non-rare cell markers, quality of cell staining,
distribution of a positive marker throughout the non-rare cells, or
cell loss from repeated processing.
[0018] In another embodiment, the method includes determining
intensity cut-off limits to minimize false negatives, as well as
false positives and to facilitate rare cell detection. In one
aspect, the detectable agent is a positive marker and the intensity
limits are determined using mean, standard deviation, coefficient
of variation, other statistical parameters or any combination
thereof, for a background signal of the positive marker. In another
aspect, the detectable agent is a positive marker and the intensity
limits are determined within a single image, or portions of that
image, by identifying the highest signal event from a positive
marker and comparing the highest signal to the mean and standard
deviation calculated from signals of all, or a subset of events. In
yet another aspect, the detectable agent is a negative marker and
the intensity limit for the negative marker is determined using
mean and standard deviation of signals from the negative markers
from non-rare cells (either all non-rare cells or a specific
subset).
[0019] In another embodiment, cytological features of non-rare
cells, such as cellular and nuclear size (absolute and relative;
overall and apparent) and distribution, are utilized to facilitate
detection of non-rare cells.
[0020] In another embodiment, the method includes utilizing data
associated with non-rare cells to enumerate rare cells, thus
facilitating their detection. In various embodiments, data may
include, but is not limited to, total intensity, mean intensity,
segmented intensity, fixed circle, variable circle, or any
combination thereof.
[0021] In another embodiment, the method includes determination of
the expression level of a content marker in rare cells and non-rare
cells to facilitate detection of rare cells.
[0022] In various aspects of the invention, a rare cell is a CTC or
subpopulation thereof
[0023] As such, in another embodiment, the invention provides a
method for diagnosing or prognosing cancer in a subject. The method
includes performing the method of improved detection and
characterization of CTCs as described herein and analyzing detected
CTCs and provide a diagnosis or prognosis based on analysis of the
CTCs, thereby diagnosing or prognosing cancer in a subject.
[0024] In another embodiment, the invention provides a method for
determining responsiveness of a subject to a therapeutic regime.
The method includes performing the method of improved detection and
characterization of CTCs as described herein and analyzing the
CTCs, thereby determining the responsiveness of the subject to a
therapeutic regime.
[0025] In another embodiment, the invention provides a method for
determining a candidate subject for a clinical trial. The method
includes performing the method of improved detection and
characterization of CTCs as described herein and analyzing the
CTCs, thereby determining a candidate subject for a clinical
trial.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] FIG. 1 is a graphical representation of mean observed SKBR3s
plotted against expected SKBR3s. Four aliquots of normal control
blood were spiked with varying numbers of SKBR2 cells to produce 4
slides with approximately 10, 30, 100, and 300 cancer cells per
slide. The mean of each quadruplicate is displayed as well as error
bars noting standard deviation.
[0027] FIG. 2 is a pictorial representation of a gallery of a
representative subpopulation of CTCs found in cancer patients. Each
CTC of the subpopulation is cytokeratin positive, CD45 negative,
contains a DAPI nucleus, and is morphologically distinct from
surrounding white blood cells which are circular in shape.
[0028] FIG. 3 is a graphical representation comparing CTC counts
between two separate processors on 9 different cancer patient
samples. CTC/mL counts ranged from 0 to 203.
[0029] FIGS. 4A-4D are graphical representations including four
graphs plotting CTC and PSA levels of serial blood draws from 4
different prostate cancer patients over a three month time period.
Two patients had increasing CTC and PSA levels and two patients had
decreasing/stable CTC and PSA levels. PSA levels increased in
patients that had increasing CTC counts and decreased in patients
that had decreasing/stable CTC counts.
[0030] FIG. 5 is a graphical representation showing the incidence
rate of a putative rare cell population across patients relative to
a CTC subpopulation (HD-CTC).
DETAILED DESCRIPTION OF THE INVENTION
[0031] The present invention provides a method which omits physical
methods for positively enriching for rare cells, such as CTCs, from
a mixed population, thereby minimizing the loss of rare cells. This
methodology further allows for the capture/identification of
subsets of cell populations, such as subpopulations of CTCs orother
rare populations by detection of the same or different markers
using different parameters, such as cutoff values, that allow for
distinguishing between events and non-events. For example, as
discussed in detail herein, different cutoffs may be utilized to
characterize different cell subpopulations.
[0032] While the disclosure highlights CTCs and subpopulations
thereof, the same methodologies may be used to find any other rare
cell type in a background of non-rare cells. As used herein, a
"rare cell" is intended to include a cell that is either 1) of a
cell type that is less than about 5%, 4%, 3%, 2%, 1%, 0.1%, 0.01%
or 0.001% of the total nucleated cell population in a fluid sample,
or 2) of a cell type that is present at less than one million cells
per milliliter of fluid sample. Exemplary rare cells include, but
are not limited to CTCs, circulating endothelial cells (CECs),white
blood cells in emboli, cancer stem cells, activated or infected
cells, such as activated or infected blood cells, and fetal
cells.
[0033] Accordingly, it will be understood by one in the art that
references to CTCs throughout the specification include reference
to rare cells and vice versa.
[0034] The present method allows for identification of rare cells,
such as CTCs or subpopulations of CTCs from the background of other
blood cells using microscopy, cytometry, automation, and
computation. The present invention utilizes these components,
individually and collectively, to identify rare cells. The benefits
include the ability to find more rare cells, to present them in a
way that enables subsequent analyses for content markers, and to do
so in a time and resource efficient manner.
[0035] Further, the present disclosure is based in part on a next
generation assay capable of identifying subpopulations of CTCs in
cancer patients. One particular subpopulation identified was from a
small cohort of cancer patients. In addition to using specific
parameters defining subpopulations of CTCs, such as one referred to
herein as the High-Definition-CTC (HD-CTC) subpopulation, the assay
affords greater sensitivity with a smaller volume of blood than
previous efforts. The key innovative aspects of this assay are
driven by the need for simplicity and minimal processing of the
blood specimen as well as conforming to the need to enable
professional interpretation with diagnostic quality imagery.
[0036] The approach used to identify a rare cell population, such
as CTCs, or subpopulation thereof, is distinct in that it does not
rely on any single protein enrichment strategies. All nucleated
blood cells are imaged in multiple colors to locate and
morphologically evaluate rare events. This enrichment-free strategy
results in an assay capable of `tunable specificity/sensitivity`
allowing high sensitivity and high specificity while still enabling
the study of a rare cell population known to be heterogeneous. A
key advantage and difference to physical enrichment is that one may
`tune` the outcome, while physical enrichment is `yes` or `no`.
Another key advantage of this approach is that one or multiple
analysis parameters can be pursued to identify and characterize
specific populations of interest.
[0037] Before the present compositions and methods are described,
it is to be understood that this invention is not limited to
particular compositions, methods, and experimental conditions
described, as such compositions, methods, and conditions may vary.
It is also to be understood that the terminology used herein is for
purposes of describing particular embodiments only, and is not
intended to be limiting, since the, scope of the present invention
will be limited only in the appended claims.
[0038] As used in this specification and the appended claims, the
singular forms "a", "an", and "the" include plural references
unless the context clearly dictates otherwise. Thus, for example,
references to "the method" includes one or more methods, and/or
steps of the type described herein which will become apparent to
those persons skilled in the art upon reading this disclosure and
so forth.
[0039] Unless defined otherwise, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. Although
any methods and materials similar or equivalent to those described
herein can be used in the practice or testing of the invention, the
preferred methods and materials are now described.
[0040] In general, reference to "a circulating tumor cell" is
intended to refer to a single cell, while reference to "circulating
tumor cells" or "cluster of circulating tumor cells" is intended to
refer to more than one cell. However, one of skill in the art would
understand that reference to "circulating tumor cells" is intended
to include a population of circulating tumor cells including one or
more circulating tumor cells.
[0041] The term "circulating tumor cell" (CTC) or CTC "cluster" is
intended to mean any cancer cell or cluster of cancer cells that is
found in a subject's sample. Typically CTCs have been exfoliated
from a solid tumor. As such, CTCs are often epithelial cells shed
from solid tumors found in very low concentrations in the
circulation of patients with advanced cancers. CTCs may also be
mesothelial from sarcomas or melanocytes from melanomas. CTCs may
also be cells originating from a primary, secondary, or tertiary
tumor. CTCs may also be circulating cancer stem cells. While the
term "circulating tumor cell" (CTC) or CTC "cluster" includes
cancer cells, it also is intended to include non-tumor cells that
are not commonly found in circulation, for example, circulating
epithelial or endothelial cells. Accordingly tumor cells and
non-tumor epithelial cells are encompassed within the definition of
CTCs.
[0042] The term "cancer" as used herein, includes a variety of
cancer types which are well known in the art, including but not
limited to, dysplasias, hyperplasias, solid tumors and
hematopoietic cancers. Many types of cancers are known to
metastasize and shed circulating tumor cells or be metastatic, for
example, a secondary cancer resulting from a primary cancer that
has metastasized. Additional cancers may include, but are not
limited to, the following organs or systems: brain, cardiac, lung,
gastrointestinal, genitourinary tract, liver, bone, nervous system,
gynecological, hematologic, skin, breast, and adrenal glands.
Additional types of cancer cells include gliomas (Schwannoma,
glioblastoma, astrocytoma), neuroblastoma, pheochromocytoma,
paraganlioma, meningioma, adrenalcortical carcinoma,
medulloblastoma, rhabdomyoscarcoma, kidney cancer, vascular cancer
of various types, osteoblastic osteocarcinoma, prostate cancer,
ovarian cancer, uterine leiomyomas, salivary gland cancer, choroid
plexus carcinoma, mammary cancer, pancreatic cancer, colon cancer,
and megakaryoblastic leukemia; and skin cancers including malignant
melanoma, basal cell carcinoma, squamous cell carcinoma, Karposi's
sarcoma, moles dysplastic nevi, lipoma, angioma, dermatofibroma,
keloids, sarcomas such as fibrosarcoma or hemangiosarcoma, and
melanoma.
[0043] Using the methods described herein, rare cells, such as CTCs
may be detected and characterized from any suitable sample type. As
used herein, the term "sample" refers to any sample suitable for
the methods provided by the present invention. The sample may be
any sample that includes rare cells suitable for detection. Sources
of samples include whole blood, bone marrow, pleural fluid,
peritoneal fluid, central spinal fluid, urine, saliva and bronchial
washes. In one aspect, the sample is a blood sample, including, for
example, whole blood or any fraction or component thereof. A blood
sample, suitable for use with the present invention may be
extracted from any source known that includes blood cells or
components thereof, such as veinous, arterial, peripheral, tissue,
cord, and the like. For example, a sample may be obtained and
processed using well known and routine clinical methods (e.g.,
procedures for drawing and processing whole blood). In one aspect,
an exemplary sample may be peripheral blood drawn from a subject
with cancer.
[0044] The term "blood component" is intended to include any
component of whole blood, including red blood cells, white blood
cells, platelets, endothelial cells, mesotheial cells or epithelial
cells. Blood components also include the components of plasma, such
as proteins, lipids, nucleic acids, and carbohydrates, and any
other cells that may be present in blood, due to pregnancy, organ
transplant, infection, injury, or disease.
[0045] As used herein, a "white blood cell" is a leukocyte, or a
cell of the hematopoietic lineage that is not a reticulocyte or
platelet. Leukocytes can include nature killer cells ("AK cells")
and lymphocytes, such as B lymphocytes ("B cells") or T lymphocytes
("T cells"). Leukocytes can also include phagocytic cells, such as
monocytes, macrophages, and granulocytes, including basophils,
eosinophils and neutrophils. Leukocytes can also comprise mast
cells.
[0046] As used herein, a "red blood cell" or "RBC" is an
erythrocyte. Unless designated a "nucleated red blood cell"
("nRBC") or "fetal nucleated red blood cell", as used herein, "red
blood cell" is used to mean a non-nucleated red blood cell.
[0047] The present invention provides a method whereby a biological
sample may be assayed or examined in many different ways to detect
and characterize rare cells. A sample may be stained or labeled
with one or more detectable markers and examined by fluorescent
microscopy and/or light microscopy. Unlike conventional enrichment
schemes whose goal it is to eliminate the non-rare or non-CTCs from
evaluation, the present invention relies on the non-rare cells or
non-CTCs present in the sample to aid in the identification and
characterization of the rare cells or CTCs. In the presently
described non-enrichment method, the sample (e.g., blood or other
body fluid, including urine, peritoneal, pleural, saliva, cerebral
spinal, and the like) is minimally processed, and the rare cells,
such as CTCs are not separated from other nucleated cells (e.g.,
non-rare or non-CTCs).
[0048] As used herein, the terms "non-rare cell" and "non-rare
cells", generally refer to any cell that is not a rare cell as
defined herein. Similarly, as used herein, the terms "non-CTC" and
"non-CTCs", generally refer to any cell that is not a CTC as
defined herein. Non-rare and non-CTCs may include nucleated or
enucleated cells, such as, in the case of blood, white blood cells
(also called leukocytes) including neutrophils, eosinophils,
basophils, lymphocytes, and monocytes; red blood cells (also known
as erythrocytes); and platelets.
[0049] In the case of blood, while the CTCs may not be separated
from other nucleated cells, red blood cells, which are typically
only found nucleated in the blood of newborns, are removed from the
sample before plating. This is commonly performed by lysing the red
blood cells, although several alternative approaches are well known
in the literature and may be utilized with the present methods, for
example, removing the cells by filtration or density gradient
centrifugation. After removing the red blood cells, the remaining
cells may be processed by spinning, re-suspending, and plating the
cells onto a solid support that may be used in cell imaging.
[0050] A variety of solid supports are well known in the art and
include slides that may be treated to promote cellular attachment
to the slide surface. The slide may be constructed from a variety
of materials sufficient to provide a support for performing a
biological assay. In an exemplary aspect, the support is composed
of a material that may be coated with a compound that promotes
electrostatic interaction of biological material to the support. A
variety of substrate materials are well known in the art and
suitable for use with the present invention. Such materials may
include one or more of glass; organoplastics such as polycarbonate
and polymethylmethacrylate, polyolefins; polyamides; polyesters;
silicones; polyurethanes; epoxies; acrylics; polyacrylates;
polyesters; polysulfones; polymethacrylates; polycarbonate; PEEK;
polyimide; polystyrene; and fluoropolymers. In an exemplary aspect,
the slide is manufactured from glass or plastic and includes one or
more biologically interactive coatings.
[0051] Slides may include one or more active areas defined on the
surface thereof. An active field, as used herein, is intended to
include areas in which the slide has been chemically or
electrically treated, such as with a biologically interactive
coating, for example to promote the adhesion of cells to the slide.
For example, the slide may be treated such that the surface is
positively charged which allows for cells to be anchored to the
surface though the electrostatic adhesion of a negatively charged
cell. The slide may include from 1 to any number of active areas
depending on the size of the slide and the intended application. In
various aspects, the slide includes a single active area.
[0052] The total number of rare cells or CTCs that are adhered to a
given slide is dependent, in part, on the initial sample volume. In
various aspects, a wide range of initial sample volumes may be used
to practice the present method and provide clinically significant
results. As such, the initial sample volume may be less than about
1 .mu.l, 2 .mu.l , 2.5 .mu.l, 3 .mu.l, 4 .mu.l, 5 .mu.l, 6 .mu.l, 7
.mu.l, 7.5 .mu.l, 9 .mu.l, 10 .mu.l, 12.5 .mu.l, 15 .mu.l, 17.5
.mu.l, 20 .mu.l, 25 .mu.l, 75 .mu.l, 100 .mu.l, 125 .mu.l, 150
.mu.l, 175 .mu.l, 200 .mu.l, 225 .mu.l, 250 .mu.l, 300 .mu.l, 400
.mu.l, 500 .mu.l, 750 .mu.l, 1 ml, 2 ml, 3 ml, 4 ml, 5 ml, 6 ml, 7
ml, 8 ml, 9 ml or greater than about 10 ml. In an exemplary aspect,
the initial sample volume is between about 200 and 500 .mu.l, 200
and 1000 .mu.l, 1000 to 2000 .mu.l, 1000 to 3000 .mu.l or 1000 to
5000 .mu.l. In another exemplary aspect, a sample processed as
described herein includes greater than about 1, 2, 5, 7, 10, 15,
20, 30, 40, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, or
even 1000 rare cells or CTCs.
[0053] After adhering the minimally processed cells to a solid
support, for example by plating the cells on a slide, the cells are
contacted with one or more detectable markers to facilitate cell
imaging via examination of the cells by fluorescent microscopy
and/or light microscopy. In general, detectable markers include a
variety of agents useful in detecting and characterizing cellular
phenomenon. For example, detectable markers may include agents such
as polynucleotides, polypeptides, small molecules, and/or
antibodies that specifically bind to a marker present in a sample
and which are labeled such that the agent is detectable when bound
or hybridized to its target marker or ligand. For example,
detectable markers may include enzymatic, fluorescent, or
radionuclide labels. Additional reporter means and labels are well
known in the art.
[0054] A marker can be any cell component present in a sample that
is identifiable by known microscopic, histologic, or molecular
biology techniques. Markers can be used, for example, to detect and
characterize rare cells, including CTCs, and distinguish rare cells
from non-rare cells and non-CTCs. In general a marker can be, for
example, a molecule present on a cell surface, an overexpressed
target protein, a nucleic acid mutation or a morphological
characteristic of a cell present in a sample. Thus markers may
include any cellular component that may be detected within or on
the surface of a cell, or a macromolecule bound or aggregated to
the surface of the cell. As such, markers are not limited to
markers physically on the surface of a cell. For example, markers
may include, but are not limited to surface antigens, transmembrane
receptors or coreceptors, macromolecules bound to the surface, such
as bound or aggregated proteins or carbohydrates, internal cellular
components, such as cytoplasmic or nuclear components, and the
like. A marker may also include a blood component that binds
preferentially to specific cell types, such as platelets or
fibrin.
[0055] In one aspect, a detectable marker may be a detectably
labeled antibody. Antibodies useful in the methods of the invention
include intact polyclonal or monoclonal antibodies, as well as any
fragments thereof, such as Fab and F(ab').sub.2, as well as
combinations of such antibodies or fragments. Methods for
generating fluorescently labeled antibodies are well known in the
art, for example, fluorescent molecules may be bound to an
immunoglobulin either directly or indirectly by using an
intermediate functional group. In related aspects, a detectable
marker may be a nucleic acid molecule (e.g., an oligonucleotide or
polynucleotide). For example, in situ nucleic acid hybridization
techniques are well known in the art and can be used to identify an
RNA or DNA marker present in a sample or subsample (e.g.,
individual cell).
[0056] In various aspects of the invention, the detectable markers
used to stain the cells include one or more detectable markers that
are tissue specific and thus used as a positive marker for a
specific type of cell and/or tissue. As used herein, a "positive
marker" is a detectable marker that specifically binds to a rare
cell such as a CTC, but not a non-rare cell or non-CTC. For
instance the positive marker may be epithelial and/or tissue
specific, for example, cytokeratin and/or EpCAM marker may be used
which bind preferentially to epithelial cells. Similarly, markers
that are tissue specific may be employed. There are numerous
examples of tissue-specific markers known in the art and suitable
for use in practicing the invention, such as PSA and PSMA for
prostate tissue, CDX2 for colon tissue and TTF1 for lung tissue (of
the subpopulation of lung cancer patients that are TTF1 positive).
As used herein a "positive marker" may also be a detectable marker
that specifically binds to subpopulations of rare cells or CTCs,
but not all rare cells or CTCs of a population. For example, a
"positive marker" may specifically bind to HD-CTCs, but not all
CTCs.
[0057] In various aspects of the invention, the detectable markers
used to stain the cells include one or more detectable markers that
specifically bind to non-rare cells or non-CTCs and may be used as
a negative selector. As used herein a "negative marker" is a
detectable marker that specifically binds to non-rare cells or
non-CTCs and is a negative selector. The most commonly used
negative marker for non-CTCs is CD45, which binds preferentially to
WBCs. There are other detectable markers or combinations of
detectable markers that bind to the various subpopulations of WBCs.
These may be used in various combinations, including in combination
with or as an alternative to CD45. As used herein a "negative
marker" may also be a detectable marker that specifically binds to
subpopulations of non-rare cells or non-CTCs and is a negative
selector.
[0058] In addition to positive and negative detectable markers to
identify CTCs, additional detectable markers may be used to stain
cells that specifically bind to the nucleus of the cell allowing
differentiation of cells from non-cellular material. As used
herein, a "nuclear marker" is a detectable marker that binds to a
nuclear component of a cell and allows differentiation of cells
from non-cellular material. The most common nuclear marker for use
in the present invention is DAPI.
[0059] In various aspects of the invention, the detectable markers
used to stain the cells include one or more detectable markers
referred to herein as "content markers". Content markers typically
may include, detectably labeled oligonucleotide probes, such as
FISH probes or immunohistochemistry probes. In one embodiment,
content markers are applied to the slide at the same time as the
positive and negative markers, or are applied to the slide after
the positive and negative markers and after the identification of
the rare cells by imaging. Content markers include detectable
markers directed to EGFR, HER2, ERCC1, CXCR4, EpCAM, E-Cadherin,
Mucin-1, Cytokeratin, PSA, PSMA, RRM1, Androgen Receptor, Estrogen
Receptor, Progesterone Receptor, IGF1, cMET, EML4, or leukocyte
associated receptor (LAR). In some cases, a content marker may also
be a positive marker.
[0060] The intensity of signal from a positive marker, or any
marker, is detectable on a scale of intensities, which based on the
methodology of the disclosure, is highly quantifiable. The scale of
intensity allows for vastly improved quantification and ranking of
detectable events enabling further categorization. For example, a
CTC that emits a low intensity signal for cytokeratin may be a
cancer stem cell; or the change in the number of high/low
cytokeratin cells might be either predictive of response or a
readout of response (or resistance course). The same is true for
positive, negative and content markers.
[0061] The present invention utilizes detectable markers to
facilitate cell imaging via examination of the cells by fluorescent
microscopy and/or light microscopy. In an exemplary aspect, the
minimally processed cells are stained with several fluorescent
markers, and then imaged using a fast, automated microscope.
Typically, a prepared slide may be loaded onto the automated system
or may be placed in a slide carrier that holds any number of
additional slides. The slide carriers are loaded into an input
hopper of the automated system. An operator may then enter data
identifying the size, shape, and location of a scan area on each
slide or the system can automatically locate a scan area for each
slide during slide processing. The processing parameters of the
slide may be identified by a bar code present on the slide or slide
carrier. At system activation, a slide carrier is positioned on an
X-Y stage, the entire slide, or portion thereof, is rapidly
scanned. This may be done at low or high magnification and may be
repeated at various levels of magnification and/or for various
regions of the slide. Images may be stored on an appropriate
storage medium and analyzed using executable code as is well known
in the art for performing the various analysis discussed herein. As
discussed herein, various parameters may be adjusted throughout the
imaging process to facilitate detection of rare cells, for example,
CTCs using data regarding non-rare or non-CTCs, such as exposure
limits and intensity settings.
[0062] As used herein, the terms "image" and "sample image"
generally refer to an image, digital or otherwise, of a minimally
processed sample including various cells, such as rare cells and
CTCs. Typically, a sample image is an image of all or a portion of
a sample slide having cells adhered to its surface and optionally
stained with one or more detectable markers.
[0063] One advantage of the present invention, which allows for
tunable specificity/sensitivity and focuses on data reduction and
analysis rather than enrichment, is that minimal processing is
expected to minimize bias. In alternative techniques that require
enrichment, rare cells are invariably lost in the process.
Specifically, in the use of immunocapture or size filtration to
distinguish between WBCs and CTCs, variation in the expression of
the targeted antigen in the case of immunocapture or variation in
the size differential between the WBC and CTC causes some CTCs to
be lost during the enrichment phase. This can lead to (i)
inaccurate counts of CTCs; (ii) too few CTCs for downstream
characterization or content analysis; and (iii) the creation of a
selection bias as some types of CTCs are preferentially lost based
upon their type of variation.
[0064] The challenge with the minimal processing approach is that
it is difficult to find the low frequency rare cells or CTCs in the
background of the non-rare cells or non-CTCs. The low frequency may
be 1 rare cell or CTC : 1,000 non-rare cells or non-CTCs, 1:10,000,
1:100,000, 1:1,000,000, and even 1:10,000,000, or anywhere between
those ratios. Complicating the ability to find and characterize the
rare cells is that the positive and negative markers, while very
selective, are not perfect resulting in either false positives or
false negatives. In other words, it is common to have some
background staining of the negative markers on the rare cells
and/or some background staining of the positive markers on the
non-rare cells. While assay optimization is used to minimize this
background staining, it is challenging to completely eliminate the
phenomenon with assay optimization.
[0065] As mentioned previously, most other approaches for finding
rare cells attempt to remove the non-rare cells. The present
invention uses the non-rare cells or non-CTCs to aid in finding and
characterizing the rare cells or CTCs. The numerous ways in which
non-rare cells and non-CTCs may be analyzed are discussed
throughout the disclosure. Throughout this disclosure, non-rare
cells or non-CTCs are typically referred to as a single group and
may be analyzed using the methods described herein as such.
However, the invention also recognizes that non-rare cells may
contain various discrete subgroups. For example, in the case of
CTCs, the various discrete subgroups may include neutrophils,
macrophages, lymphocytes, eosinophils and basophils, and cells in
varying states such as various states of apoptosis or cell
division, that may be distinguished using the methods described
herein by size, shape, nuclear characteristics, and staining
pattern. In some embodiments of the invention, it may be useful to
use one of these subgroups to aid in finding rare cells or CTCs,
rather than to use the entire group. The use of non-rare or
non-CTCs in the present invention is not meant to limit the
invention to using only the entire group when it may be appropriate
in some of the embodiments to use just one or more of the
subgroups.
[0066] An enabling aspect of this invention is that the low
frequency of rare cells or CTCs to non rare cells or non-CTCs
allows one to treat the majority of cells as non-rare cells or
non-CTCs even if they have not been definitively identified as
such. The low frequency of rare cells and CTCs allows one to ignore
such cells and assume the cells are non-rare cells or non-CTCs to
derive quality control, cut-off, normalization, and calibration
metrics. Since the rare cells are in low abundance, if these
metrics are to be refined taking into consideration the population
of rare cells, outlier removal techniques may be utilized. The
outlier removal techniques mathematically ensure that the
population of rare cells does not factor into the metrics.
[0067] As discussed herein, the disclosed methodology allows
detection, enumeration and characterization of populations of rare
cells or subpopulations of rare cells. The methodology utilizes
data from non-rare cells in the sample to identify and characterize
rare cells by applying defined parameters pertaining to exposure
limits, exposure settings, quality control, intensity cut-off
limits, cell size and shape calibration, cell enumeration and
content evaluation, each of which is further discussed in turn. In
various aspects, the assay allows for simultaneous cytomorphologic
review of fluorescent images with individual channel images,
augmented with cell-by-cell annotation with ancillary
semi-quantitative data regarding size and fluorescent intensity of
objects both absolute and relative to the non-rare cells or
non-rare cell candidates, e.g., non-CTCs or non-CTC candidates,
from either the full experiment or the local environment.
[0068] Establishing Exposure Limits.
[0069] While variation should be minimized through assay
optimization and instrument standardization, variation in the
staining of the markers is common, slide-to-slide, batch-to-batch,
operator-to-operator, and day-to-day. Thus selecting the right
exposure for a particular slide is non-trivial, as setting it too
low or too high will cause one to miss information. While standard
approaches work for those markers that are common on the majority
of events on the slide, it is challenging for those that are
specific to rare cells or CTCs. Within the dynamic range of the
imaging system, the signal in rare cells or CTCs and background in
non-rare cells or non-CTCs are proportional to the exposure time.
But noise which is random variation in both signal and background
caused by electronics in the imaging system decreases when exposure
increases. Ideally, exposure should be set to maximize the signal
without saturating the imaging system. But this is impractical due
to the impact on data collection time. Because a rare cell or CTC
is present in very low frequency, it is unlikely that a rare cell
or CTC would be found in a small number of Sample Images,
preventing one from using the Sample Images to set the exposure for
the positive marker. Complicating this further, there is a natural
variation in the expression of and staining of both positive and
negative markers to their target cells. A small number of Sample
Images to set exposure may not capture this natural variation on
the target rare cells or CTCs.
[0070] In one embodiment of this invention, the signal from the
non-rare cells or non-CTCs is utilized to set the exposure limit
for the positive marker. This is somewhat counter-intuitive as the
non-rare cell or non-CTC is not the target of choice for the
positive marker. However in this embodiment, the exposure is
adjusted so that a visible but low signal is observed from the
non-rare cells or non-CTCs in the Sample Images originating from
fluorescent sources such as nonspecific staining, autofluorescence
and optical system properties. The brightfield imagery, nuclear
marker and the negative marker may be used to identify the non-rare
cells or non-CTCs in the Sample Images. The low signal is a
distinguishable cellular signal in the non-rare cells or non-CTCs
when compared to the non-cellular areas in the Sample Image. This
process provides a method to set the exposure for the positive
marker when the target of those markers are in low frequency and
also helps to maximize the Signal/Background of the positive
marker, both of which are aids to finding rare cells or CTCs while
still minimizing the total time required to collect data. This
phenomenon is especially true when the signal is low or dim. Once
cellular background is statistically significant above non-cellular
background, the exposure time for this particular marker is
optimized for speed of data collection. All subsequent optimization
can be performed in silico. Once the exposure is set, the entire
slide is ready to be imaged at that setting.
[0071] While the above embodiment facilitates the setting of
exposure for the positive marker, non-rare cells or non-CTCs may
also be used to set the exposure for the negative markers, nuclear
markers and content markers in a way that is relevant for the
clinical interpretation of rare cells or CTCs. In one embodiment,
the nuclear marker on non-rare cells or non-CTCs in the Sample
Images is set to a level that allows the evaluation of the nuclear
content of a cell, and in particular whether the cell is classified
as live or dead, facilitating the calculations of live:dead ratios
for cells by cell type. These exposure levels for the nuclear
marker for the non-rare cells or non-CTCs will be satisfactory for
the same evaluation of the nuclear marker for the rare cells or
CTCs, in particular to distinguish nuclear shape on normal
vs.,malignant cells.
[0072] In another embodiment, the exposure for the negative marker
is set from the Sample Images by looking at the distribution of the
signal from that marker on the non-rare or non-CTCs where the
exposure is chosen to maximize the signal/background ratio,
especially at the critical low end of the dynamic range where a
faint signal to a negative marker in a rare cell or CTC may
occur.
[0073] In another embodiment, the exposure is set from the non-rare
cells or non-CTCs for the content marker. The setting of the signal
for the content marker using the non-rare cells or non-CTCs in
Sample Images will depend on the specific content marker. For
instance, some content markers may have relatively high expression
in the non-rare cells or non-CTCs when compared to rare cells or
CTCs, in which case one would use the information from the non-rare
cells or non-CTCs in the Sample Images to set the upper boundary
for the content marker. Conversely, the content marker may have
relatively low expression in the non-rare cells or non-CTCs when
compared to the rare cells or CTCs, in which case one would use the
information from the non-rare cells or non-CTCs in the Sample
Images to set the lower boundary for the content marker.
[0074] While the above embodiments use non-rare cells or non-CTCs
to set the exposure limits from Sample Images for various markers
prior to imaging the slide to find rare cells or CTCs, in another
embodiment information from the non-rare cells or non-CTCs and/or
rare cells or CTCs from the images taken during the first imaging
event of the entire slide is used to set the exposure limits when
selected areas of the slide are re-imaged. Selected areas are
re-imaged for a variety of reasons, including collecting images
that are in optimal focus or that are in a higher magnification. In
this embodiment, the distribution of signals for the various
markers in the non-rare cells or non-CTCs and the rare cells or
CTCs across the entire slide may be used to calculate a better
exposure that maximizes the desired signal or the desired dynamic
range.
[0075] Minimizing Exposure Setting to Minimize Data Collection Time
and Maximize Throughput.
[0076] As described above, exposure settings can be adjusted to
optimize the signal or signal:background parameters. However, in
another embodiment, exposure settings are adjusted with a goal of
minimizing data collection time and maximizing throughput. For
example, one might determine that it takes 5 seconds of exposure
time to fully utilize the dynamic range of the CCD camera but only
500 milliseconds to get the cellular background above the
non-cellular background, hence saving 10x data collection time.
Thus exposure times can be optimized either for maximum signal (or
signal:background) or for minimum time.
[0077] Quality Control.
[0078] In another embodiment, the use of non-rare cells or non-CTCs
to aid in identifying rare cells or CTCs also includes their use as
quality control parameters. Since the non-rare cells or non-CTCs
are represented in much higher frequency and distributed throughout
the slide, they provide an available resource to evaluate the
quality of the processing and imaging of the slide, both relative
to a particular slide as well as across slides and across data
sets.
[0079] In one embodiment, the invention provides observing the
distribution of the non-rare cells or non-CTCs using the nuclear
markers to identify the non-rare cells or non-CTCs. In this
embodiment, the goal is to fmd cells, not necessarily to
distinguish between non-rare cells or non-CTCs and rare cells or
CTCs, and thus the positive or negative markers may not be utilized
to distinguish between these categories; however, since the vast
majority of the cells are non-rare cells or non-CTCs, most of the
cells that utilized to determine distribution of cells on the slide
are non-rare cells or non-CTCs. The distribution of the cells is
important from a quality control standpoint as the desired
distribution is an even distribution of cells with minimal overlap
between the cells. If there is a substantial deviation from that
ideal distribution, one may elect to reject the slide from further
processing. While a nuclear marker is used in this example, any
method for identifying the cells would suffice, including
brightfield imaging and conventional stains such as Wright
Giemsa.
[0080] In another embodiment, the co-location of the nuclear marker
and the negative marker is used as a quality control method to
evaluate whether the alignment of different images is satisfactory.
In this embodiment, the nuclear marker and the negative marker
should have significant overlap.
[0081] In another embodiment, the ratio between negative marker
events and the nuclear marker events is a measure for the
effectiveness of the negative marker staining, where the higher the
ratio without exceeding 12 is desirable. A desirable negative
marker may have a 0.8, 0.9, 1.0 or 1.1 ratio.. In another
embodiment, the distribution, including mean, standard deviation
and coefficient of variation (CV) of the negative marker over the
population of the non-rare cells or non-CTCs is used as a quality
control parameter, where the distribution of the negative marker is
consistent with expected distribution patterns of past experiments
and/or consistent with the distribution of WBC's normal expression
patterns.
[0082] In another embodiment, the distribution, including mean,
standard deviation and CV of the positive marker over the
population of the non-rare cells or non-CTCs is used as a quality
control parameter, where the distribution of the positive marker is
consistent with expected distribution patterns from past
experiments.
[0083] While the quality control methods described above describe
methods to evaluate overall slide quality, the same methods may be
used to evaluate an image or a group of images. In some instances,
the parameters derived from an image or a group of images in a
region may be compared to the same parameters calculated over the
entire slide. In another instance, the quality control parameters
described above may be compared across different slides.
[0084] In another embodiment, cell loss may be calculated from the
slide during processing by comparing the ratio of the nuclear
marker events or negative marker events to the known number of
non-rare cells or non-CTCs placed on the slide, where the known
number of non-rare cells or non-CTCs is derived from the WBC count
and the volume used in the experiment.
[0085] Setting Intensity Cut-off Limits for CTC Detection
(minimizing false negatives).
[0086] As mentioned above, the challenges in this approach to rare
cell and CTC detection are 1) that the relative frequency of rare
cells, such as CTCs, to non-rare cells or non-CTCs is low; and 2)
the imperfect staining of the positive and negative markers to CTCs
and non-CTCs respectively. However, the present method takes those
challenges and turns them into strengths. In one embodiment, the
background signal from the positive markers on the highly abundant
non-rare cells or non-CTCs is used to calculate mean, standard
deviation and CV. Those metrics are subsequently used to determine
detection cut-offs to separate rare cells, such as CTCs from
non-rare cells or non-CTCs. In one aspect, the factor of 10
multiplied by the standard deviation and added to the mean for the
non-rare cell or non-CTC positive marker signal, is used as a
cut-off to distinguish rare cells or CTCs, where putative rare
cells or CTCs are determined to have a positive marker signal
greater than that cut-off. In other aspects, the metric uses a
factor of 5, 7.5, 12.5, 15, 17.5, 20 or more, or any number between
those numbers. The calculation of the metric may be set on a global
slide basis. Alternatively, it may be set on an image basis or a
regional basis.
[0087] In another embodiment, the cut-off may be determined
dynamically within each image by locating a signal of the highest
positive marker events, then comparing that signal to the standard
deviation between additional signal events. In an exemplary aspect,
the cut-off may be determined dynamically within each image by
locating signals of the five highest positive marker events, then
comparing that signal to the standard deviation between the next
fifty positive signal events. Positive marker events can also
include multiple positive markers or inclusion of positive markers
and exclusion of negative marker events. The number `five` could be
varied from 1 to 10 per field of view assuming a 10.times.
magnification (i.e., assuming no more than 5 rare cells or CTCs per
field of view or a relative concentration of no more than 1 in
500). This approach has the advantage of being entirely numerical
and not being based on shape analysis. It is expected to robustly
and substantially reduce the number of possible events with minimal
risk of missing events.
[0088] In another embodiment, the cut-off for the negative marker
signal is set using the mean and standard deviation of the non-rare
cells or non-CTCs. In this case, the cut-off is derived from a
factor of 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 multiplied by the
standard deviation of the negative marker signal for the non-rare
cells or non-CTCs and subtracted from the mean negative marker
signal. Putative CTCs will have a negative marker signal below that
cut-off. As described above, this cut-off may be generated globally
by using the signal from all non-rare cells or non-CTCs on the
slide or at the image or regional level by using the signal only
from the non-rare cells or non-CTCs in that image or that
region.
[0089] Size and Shape Calibration.
[0090] In another embodiment of the invention, the cellular size
and distribution of cell sizes for the non-rare cells or non-CTCs
as measured on the slide is compared to the published sizes and
distributions for the WBCs in the literature. In addition, the
distribution may be corrected for individual patient differences by
using differential count of the subgroups of the WBCs obtained from
an automated cell counter. The ratio between the published size and
the calculated size of the non-rare cells or non-CTCs may then be
used as a correction factor to determine an accurate size for the
rare cells or CTCs by multiplying that ratio with by the size of
the rare cell or CTC as measured on the slide. Calibration allows
for standardization and comparisons across slides and across blood
tubes and across patients and across indications.
[0091] In another embodiment, we compare one or more of the nuclear
size, distribution of nuclear size, and contour patterns for
non-rare cells or non-CTCs as measured on an image or slide to
nuclear size and contour pattern of a putative rare cell or CTC and
apply cut-off values to qualify that putative rare cell or CTC as a
valid rare cell or CTC.
[0092] Enumeration.
[0093] In an embodiment of the invention, the non-rare cells or
non-CTC information is used to accurately calculate the
concentration of rare cells or CTCs in a bodily fluid. In one
example of this embodiment, one determines the ratio of CTCs to
total CTCs +non-CTCs (all nuclear marker events) and then divides
that by the volume of the original body fluid used for that
experiment, and filially multiply that by the original
concentration of the cells in the body fluid. The later measurement
may be obtained through the use of a standard automated cell
counter (or cytometer).
[0094] Content Evaluation.
[0095] As mentioned above, the rare cell or CTC expression level of
a content marker may be evaluated. However, because of the slide
variability caused by processing and imaging variation, it is
difficult to provide universal parameters to determine the
expression level. To mitigate this challenge, the expression level
of the content marker and its distribution in non-rare cells or
non-CTCs in a patient population is first determined. Then for the
individual cancer patient, the expression level of the content
marker in rare cells or CTCs and in non-rare cells or non-CTCs is
determined. The ratio of the expression level between rare cells or
CTCs and non-rare cells or non-CTCs becomes a relative measure that
normalizes slide-to-slide variation. In addition, multiplying that
ratio by the mean non-rare cell or non-CTC expression level from
the control population provides for an absolute value for the rare
cell or CTC expression corrected for slide-to-slide variation.
[0096] Nuclear shape and size is a potential rich source of
information. It is expected to give detailed information about the
type of cell in the case of a blood cell and about the state of the
cell in the case of a are cell or CTC. For example, nuclear shape
could give insight to the malignant nature of the cell, it could
give insight into the state of cell viability and/or cell cycle.
For example, a patient undergoing a successful chemotherapy might
see a spike in CTCs but nuclear interpretation might show that
these CTCs are non-viable/apoptotic.
[0097] Another potential opportunity for using nuclear size and
shape would be similar to FACS analyses using forward scatter and
side scatter as ways to characterize cells. It may be possible to
gather enough detailed information about the nucleus and other
cellular components to recapitulate the forward and side scatter
information from FACS.
[0098] In various embodiments of the invention, a single or
combination of parameters may be utilized in performing the assay
depending, in part on the data to be determined and the rare cell
population being investigated. Additionally, subpopulations of
specific rare cell populations may be identified using the
disclosed methodology. For example, as shown in Example 1,
subpopulations of CTCs may be identified and differentiated by
further defining specific assay parameters. The Example discloses
identification and classification of a CDC subpopulation referred
to as HD-CTC.
[0099] In one embodiment of the invention, by further defining
parameters of the disclosed assay, a strict classification the
HD-CTC was established. Without being bound to any particular
theory, it is believed that the HD-CTC subpopulation as classified
herein, includes CTCs exhibiting the highest potential of becoming
an intact tumor cell. All other CTCs partially fulfill the defined
parameters but lack one or more of the strict inclusion criteria.
Non-HD-CTCs are CTCs which may be less reliable in evaluation
performed in further downstream methodologies.
[0100] In one embodiment, an HD-CTC is a cell that comprises a) a
positive marker; b) has an intact nucleus; and c) is
morphologically distinct from normal WBCs, wherein the cell is not
positive for a negative marker. As discussed herein, the positive
marker may be a marker that preferentially binds to epithelial
cells, such as cytokeratin and/or EpCAM. Further, as discussed
herein, the negative marker may be any non-cancer specific marker,
such as CD45 which preferentially binds to WBCs. Determination of
an intact nucleus is typically determined by DAPI imaging, but
other suitable nuclear markers are well known in the art. In an
exemplary embodiment, an HD-CTC is a cell that is a) cytokeratin
positive; b) CD45 negative; c) has an intact nucleus; and d) is
morphologically distinct from normal WBCs.
[0101] In various embodiments, the positive marker may have an
intensity that is greater than 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10
times that of a nucleated white blood cells' cytokeratin intensity.
In various embodiments, the intensity of the negative marker is in
the lowest 10%, 5%, 4%, 3%, 2% or less of all cellular events.
[0102] In various embodiments, the nucleus of a HD-CTC is intact
and non-apoptotic. Mild apoptotic changes in the cytoplasm are
accepted, as long as the nucleus does not appear apoptotic.
[0103] In various embodiments HD-CTCs are morphologically distinct
from normal WBCs. For example, HD-CTCs may have a morphology that
is consistent with a malignant epithelial cell by criteria used in
standard diagnostic cytopathology, predominantly embodied as
enlarged size, but that may also include cytomorphologic features,
such as, architectural organization of nucleus and cytoplasm,
cytoplasmic shape, and nuclear shape. A gallery of representative
HD-CTCs is displayed in FIG. 2.
[0104] While the methods described in this invention are useful in
detecting rare cells using data derived from analysis of non-rare
cells, as discussed throughout, the invention also is useful in
characterization of rare cells. In particular, use of the various
combinations of detectable markers and computational methods for
performing cell imaging and analysis allow for meaningful
characterization useful in assessing cancer prognosis and in
monitoring therapeutic efficacy for early detection of treatment
failure that may lead to disease relapse. In addition, CTC analysis
according to the invention enables the detection of early relapse
in presymptomatic patients who have completed a course of therapy.
This is possible because the presence of CTCs has been associated
and/or correlated with tumor progression and spread, poor response
to therapy, relapse of disease, and/or decreased survival over a
period of time. Thus, enumeration and characterization of revealed
CTCs provides methods to stratify patients for baseline
characteristics that predict initial risk and subsequent risk based
upon response to therapy.
[0105] The term "subject" as used herein refers to any individual
or patient to which the subject methods are performed. Generally
the subject is human, although as will be appreciated by those in
the art, the subject may be an animal. Thus other animals,
including mammals such as rodents (including mice, rats, hamsters
and guinea pigs), cats, dogs, rabbits, farm animals including cows,
horses, goats, sheep, pigs, etc., and primates (including monkeys,
chimpanzees, orangutans and gorillas) are included within the
definition of subject.
[0106] Accordingly, in another embodiment, the invention provides a
method for diagnosing or prognosing cancer in a subject. The method
includes detecting CTCs as described herein. CTCs may then be
analyzed to diagnose or prognose cancer in the subject. As such,
the methods of the present invention may be used, for example, to
evaluate cancer patients and those at risk for cancer. In any of
the methods of diagnosis or prognosis described herein, either the
presence or the absence of one or more indicators of cancer, such
as, a cancer cell, or of any other disorder, may be used to
generate a diagnosis or prognosis.
[0107] In one aspect, a blood sample is drawn from the patient and
processed to detect CTCs as described herein. Using the method of
the invention, the number of CTCs in the blood sample is determined
and the CTCs are characterized by analysis of the detectable
markers and other data gathered from imaging the cells. For
example, analysis may be performed to determine the number and
characterization of CTCs in the sample, and from this measurement,
the number of CTCs present in the initial blood sample may be
determined.
[0108] In various aspects, analysis of a subject's CTC number and
characterization may be made over a particular time course in
various intervals to assess a subject's progression and pathology.
For example, analysis may be performed at regular intervals such as
one day, two days, three days, one week, two weeks, one month, two
months, three months, six months, or one year, in order to track
level and characterization of circulating epithelial cells as a
function of time. In the case of existing cancer patients, this
provides a useful indication of the progression of the disease and
assists medical practitioners in making appropriate therapeutic
choices based on the increase, decrease, or lack of change in
circulating epithelial cells, such as the presence of CTCs in the
patient's bloodstream. Any increase, be it 2-fold, 5-fold, 10-fold
or higher, in the number of CTCs over time decreases the patient's
prognosis and is an early indicator that the patient should change
therapy. Similarly, any increase, be it 2-fold, 5-fold, 10-fold or
higher, indicates that a patient should undergo further testing
such as imaging to further assess prognosis and response to
therapy. Any decrease, be it 2-fold, 5-fold, 10-fold or higher, in
the number of CTCs over time shows disease stabilization and a
patient's response to therapy, and is an indicator to not change
therapy. For those at risk of cancer, a sudden increase in the
number of CTCs detected may provide an early warning that the
patient has developed a tumor thus providing an early diagnosis. In
one embodiment, the detection of revealed CTCs increases the
staging of the cancer.
[0109] In any of the methods provided herein, additional analysis
may also be performed to characterize CTCs, to provide additional
clinical assessment. For example, in addition to image analysis,
gene expression analysis and PCR techniques may be employed, such
as gene chip analysis and multiplexing with primers specific for
particular cancer markers to obtain information such as the type of
tumor, from which the CTCs originated, metastatic state, and degree
of malignancy. Additionally, cell size, DNA or RNA analysis,
proteome analysis, or metabolome analysis may be performed as a
means of assessing additional information regarding
characterization of the patient's cancer. In various aspects,
analysis includes antibodies directed to or PCR multiplexing using
primers specific for one or more of the following markers: EGFR,
HER2, ERCC1, CXCR4, EpCAM, E-Cadherin, Mucin-1, Cytokeratin, PSA,
PSMA, RRM1, Androgen Receptor, Estrogen Receptor, Progesterone
Receptor, IGF1, cMET, EML4, or Leukocyte Associated Receptor
(LAR).
[0110] For example, the additional analysis may provide data
sufficient to make determinations of responsiveness of a subject to
a particular therapeutic regime, or for determining the
effectiveness of a candidate agent in the treatment of cancer.
Accordingly, the present invention provides a method of determining
responsiveness of a subject to a particular therapeutic regime or
determining the effectiveness of a candidate agent in the treatment
of cancer by detecting CTCs of the subject as described herein and
analyzing the detected CTCs. For example, once a drug treatment is
administered to a patient, it is possible to determine the efficacy
of the drug treatment using the methods of the invention. For
example, a sample taken from the patient before the drug treatment,
as well as one or more cellular samples taken from the patient
concurrently with or subsequent to the drug treatment, may be
processed using the methods of the invention. By comparing the
results of the analysis of each processed sample, one may determine
the efficacy of the drug treatment or the responsiveness of the
patient to the agent. In this manner, early identification may be
made of failed compounds or early validation may be made of
promising compounds.
[0111] Four important indicators that provide insight to the
clinical activity of candidate compounds include HER2, EGFR, CXCR4,
and EphB4 RTK. HER2 provides an indicator of malignancy of a cell
by determining mRNA stability and subcellular localization of HER2
transcripts. The resistance of EGFR to acquire mutations, and/or
the mutations acquired provides important indicators of the
activity of a candidate compound in addition to possible
alternative compounds that may be used in combination with the
candidate compound. An assessment of the level of DNA repair
interference induced with platinum provides insight as to the
status of the CXCR4 marker and metastatic condition. Additionally,
assessment of the status of EphB4 receptor tyrosine kinase provides
insight as to the metastatic potential of the cell. Accordingly,
using the methods of the present invention, patients taking such
candidate drugs may be monitored by taking frequent samples of
blood and determining the number of circulating epithelial cells,
for example CTCs, in each sample as a function of time. A further
analysis of the Her2, EGFR, CXCR4, and EphB4 RTK indicators
provides information as to pathology of the cancer and efficacy of
the candidate drug. Similarly, ERRC1, Cytokeratin, PSA, PSMA, RRM1,
Androgen Receptor, Estrogen Receptor, Progesterone Receptor, IGF1,
cMET, EML4 and others provide insight into the clinical activity of
candidate compounds. The analysis of these indicators of clinical
activity may be through analysis of detectable markers as discussed
herein (e.g., immunohistochemistry and fluorescent in situ
hybridization (FISH)) or further analysis via techniques such as
sequencing, genotyping, gene expression or other molecular
analytical technique.
[0112] Analysis of CTCs provide a method of determining candidate
subjects for a particular clinical trial. For example, the detected
CTCs of a candidate may be analyzed to determine whether specific
markers exist in order to determine whether the particular
therapeutic regime of the clinical trail may be potentially
successful. Accordingly in another embodiment, the invention
provides a method for determining a candidate subject for a
clinical trial. The method includes detecting CTCs of the subject
as described herein. The CTCs may then be analyzed to determine
whether the candidate subject is suitable for the particular
clinical trial.
[0113] Analysis of CTCs during a clinical trial will provide
information on whether the patient is responding or not responding
to the experimental drug, where no substantial change or a decrease
in revealed CTCs indicates response and an increase in revealed
CTCs indicates poor response. The increase or decrease may be
2-fold, 10-fold or higher. This information is an early indicator
of the drug's effectiveness and may be used by the investigators as
a secondary endpoint in the clinical trial.
[0114] The following examples are intended to illustrate but not
limit the invention.
EXAMPLE 1
CTC Assay and Identification of Ctc Subpopulation
[0115] The data presented here demonstrate the methodology of the
present invention as applied to CTCs and subpopulations of CTCs,
such as HD-CTCs as defined herein. The assay is performed via a
controlled prospective protocol to address the reliability and
robustness of the assay as well as a split sample comparison with
the Cellsearch.RTM.. After this technical validation, the assay was
used to investigate the incidence and prevalence of CTCs and
specific CTC subpopulations in patients with metastatic breast,
prostate, and pancreatic cancers as well as normal controls. The
specific subpopulation of CTCs targeted by the assay requires that
the cell(s) have an intact nucleus, express cytokeratin and not
CD45, are morphologically distinct from surrounding white blood
cells (WBCs) and have cytologic features consistent with intact
malignant epithelial cells suitable for downstream analysis.
[0116] The following methods and protocols were utilized.
[0117] Patients and Blood Sample Collection was performed as
follows. Samples were collected from metastatic cancer patients in
anti-coagulated blood tubes at Scripps Clinic. University of
California, San Diego, Billings Clinic, and University of
California, San Francisco under IRB approved protocols. Samples
from non-local sites (UCSF, Billings Clinic) were shipped overnight
so that the sample was received and processed within 24 hours.
Samples from local sites (Scripps Clinic and UCSD) were held at
room temperature for 16-24 hours to mimic samples coming from
non-local sites. Blood specimens were also drawn from normal
controls from the TSRI Normal Blood Donor Service.
[0118] Blood sample processing for HD-CTC detection was performed
as follows. Blood specimens were rocked for 5 minutes before a
white blood cell (WBC) count was measured using the Hemocue.TM.
white blood cell system (HemoCue, Sweden). Based upon the WBC
count, a volume of blood was subjected to erythrocyte lysis
(ammonium chloride solution). After centrifugation, nucleated cells
were re-suspended in PBS and attached as a monolayer on custom made
glass slides (Marienfeld, Germany). The glass slides are the same
size as standard microscopy slides but have a proprietary coating
that allows maximal retention of live cells. Each slide can hold
approximately 3 million nucleated cells, thus the number of cells
plated per slide depended on the patients WBC count. Enough blood
was lysed to produce 15 slides and the cells were subsequently
dried onto the slides after a cell preservative was added. All 15
slides were stored at -80.degree. C. for at least 24 hours.
[0119] For HD-CTC detection in cancer patients for this
investigation, 4 slides were used as a test. The remaining slides
created for each patient were stored at -80.degree. C. for future
experiments. Four slides were thawed from each patient, then cells
were fixed with 2% paraformaldehyde, permeabilized with cold
methanol, and non-specific binding sites were blocked with goat
serum. Slides were subsequently incubated with monoclonal anti-pan
cytokeratin antibody (Sigma) and CD45-Alexa 647 (Serotec). After
PBS washes, slides were incubated with Alexa Fluor 555 goat
anti-mouse antibody (Invitrogen). Cells were counterstained with
DAPI and mounted with an aqueous mounting media.
[0120] Imaging and technical analysis was performed as follows. All
four slides from each patient were scanned using a custom made
fluorescent scanning microscope which has been developed and
optimized for fast, reliable scanning. One scanning instrument was
used for all patient samples in this report to standardize results.
Additionally, the light source was calibrated weekly and an
algorithm was developed to standardize the exposures of each
fluorophore on each patient slide during the scan. Each slide was
scanned entirely at 10.times. magnification in 3 colors and
produced over 6900 images. The resulting images were fed to an
analysis algorithm that identifies likely candidate HD-CTCs based
upon numerous measures, including cytokeratin intensity, CD45
intensity, as well as nuclear and cytoplasmic shape and size. A
technical analyst then goes through algorithm generated likely
candidates and removes hits that are obviously not cells, such as
dye aggregates.
[0121] Professional analysis and interpretation was performed as
follows. All likely candidate CTCs are presented to a
hematopathologist for analysis and interpretation through a web
based report where the pathologist is able to include or exclude
each candidate cell as an HD-CTC. Cells are classified as HD-CTCs
if they are cytokeratin positive, CD45 negative, contained an
intact DAPI nucleus without identifiable apoptotic changes
(blebbing, degenerated appearance) or a disrupted appearance, and
are morphologically distinct from surrounding white blood cells
(usually a shape based feature, although occasionally purely size
based. They must have cytoplasm that is clearly circumferential and
within which the entire nucleus is contained. The cytoplasm may
show apoptotic changes such as blebbing and irregular density or
mild disruption at the peripheral cytoplasmic boundary, but must
not be so disrupted that its association with the nucleus is in
question. The images are presented as a digital image, with
individual fluorescent channel viewing capability as well as a
composite image. Each cell image is annotated with ancillary
statistical data regarding relative nuclear size, fluorescent
intensities, and comparative fluorescent intensities. Each HD-CTC
candidate is presented in a field of view with sufficient
surrounding WBCS to allow for contextual comparison between
cytomorphologic features of the cell in question versus the
background white blood cells.
[0122] Cell line experiments were performed as follows. Four
aliquots from the donor (2 ml each) were spiked with varying
numbers of SKBR-3 cells to produce 4 slides with approximately 300,
100, 30 and 10 cancer cells per a slide. The 16 slides were then
processed and analyzed by a single operator according to the HD-CTC
sample preparation protocol. A single instrument was used to image
all 16 slides.
[0123] HD-CTC Classification: HD-CTCs were defined as cells that
are a) cytokeratin positive (intensity >6 times that of
nucleated white blood cells' cytokeratin intensity); b) CD45
negative (intensity in lowest 2% of all cellular events); c)
include an intact non-apoptotic appearing nucleus by DAPI imaging;
and d) are morphologically distinct from normal WBCs.
[0124] Inclusion requirements for the morphological assessment of
HD-CTC include 1) a nuclear size 30% greater than the average
surrounding WBC nuclei, and 2) circumferential cytokeratin positive
cytoplasm with an average intensity 600% greater than surrounding
nucleated WBCs. Common, although not required, features of HD-CTCs
include quite large nuclei up to five times the average size of
surrounding WBC nuclei, nuclear contours distinct from surrounding
WBC nuclei including elongation, large cytoplasmic domain with a
frequently eccentric distribution and/or polygonal or elongated
cytoplasmic shape, and doublets and clusters of 3 or more HD-CTCs.
Other cell-like objects that are cytokeratin positive, CD45
negative, and contain a nucleus but do not meet the inclusion
criteria, for example are the same size as WBCs or have cytoplasm
that is not circumferential, are not counted as HD-CTCs but are
tracked by the assay. The purpose of this approach is to have
strict inclusion criteria for a specific phenotype of CTCs, while
retaining data about events that fulfill only some of the
requirements, but which might still be clinically meaningful, such
as apoptotic tumor cells or tumor cell fragments or cells
undergoing epithelial to mesenchymal transition.
[0125] Results.
[0126] Assay Linearity and Sensitivity using Spike-in experiments:
To test assay linearity and sensitivity, specific dilutions ranging
from 10 to 300 breast cancer cell line SKBR3s were spiked into
normal control blood. Experiments were performed in quadruplicates
and processed and analyzed according to the HD-CTC assay as
explained in the Methods Section. The mean observed SKBR3s is
plotted against expected SKBR3s and displays a correlation
coefficient (R.sup.2) of 0.9997 (FIG. 1).
[0127] Assay Robustness of IID-CTC Counts in Patients with
Carcinomas: Assay robustness of the HD-CTC assay was tested against
multiple processors and split samples. Duplicate tests were
performed by two separate processors on 9 different patient
samples. A comparison of HD-CTC/mL counts between two processors
using split samples has a correlation coefficient (R.sup.2) of
0.979 (FIG. 3). All data were analyzed by a single operator blinded
to the experiment.
[0128] Assay Specificity in samples from Normal Controls: Fifteen
healthy donors from an institutional healthy donor pool were
evaluated as a control population consisting of 8 females and 7
males with an age range of 24 to 62 years. In all but one healthy
control, the number of such events when corrected for volume was 1
HD-CTC/ml or less. The outlier was a healthy female donor with an
HD-CTC count of 4/ml. Upon explicit review of her cells, about one
third of them strongly met all inclusion criteria, while the
remaining two thirds fulfilled all criteria but were near the lower
limit for inclusion by one or more criteria. Four other healthy
donors had 1 HD-CTC/ml. Explicit review of these cells revealed a
similar pattern, in that about one third strongly met all criteria,
while the remaining two thirds of the cells fulfilled criteria, but
were near the lower limit for inclusion by one or more criteria.
Examples of included events that are near the lower limit for
inclusion are cells that measure 30% larger than surrounding WBCs
but don't appear significantly larger by morphologic evaluation,
and cells that are slightly out of focus and might have apoptotic
nuclear changes that are not detectable by eye, and fmally,
occasional cells that have objective cytokeratin intensity
measurements above the cutoff but subjectively don't appear
significantly brighter than surrounding WBCs by single channel
fluorescent review.
TABLE-US-00001 TABLE 1 Comparison of HD-CTC Assay to CellSearch
.RTM. Cancer Type HD-CTCs/mL CellSearch/mL Breast #1 49.3 0.1
Breast #2 87.0 0.0 Breast #3 33.4 0.1 Breast #4 199.3 0.1 Breast #5
5.0 3.1 Prostate #1 2.3 0.0 Prostate #2 8.4 0.4 Prostate #3 107.3
2.8 Prostate #4 1.3 0.0 Prostate #5 150.5 0.1 Prostate #6 0.0 0.0
Prostate #7 1.4 0.5 Prostate #8 1.5 0.1 Prostate #9 145.3 0.8
Prostate #10 57.6 0.0 (Extrapolated to CTCs/mL)
[0129] Comparison of HD-CTC assay to CellSearch.RTM.: A total of 15
patients, 5 metastatic breast cancer and 10 metastatic prostate
cancer, were evaluated for CTCs with both Cellsearch.RTM. and the
HD-CTC assays. Two tubes of blood were collected from each patient.
One tube of 7.5 mL of blood was collected in CellSave.TM. tubes
(Veridex, Raritan N.J.) and sent to Quest Diagnostics (San Juan
Capistrano, Calif.) for enumeration of CTCs using the
Cellsearch.RTM. assay. A second tube of blood was collected from
each patient and processed according to the HD-CTC protocol 24
hours after the blood draw, consistent with the standard HD-CTC
process in order to mimic the timing at which samples were
processed at Quest Diagnostics. The CellSearch.RTM. assay detected
2 or more CTCs per 7.5 mL of blood in 5/15 patients tested. In
contrast, the HD-CTC assay detected significantly higher numbers of
CTCs in significantly more patients (HD-CTCs were identified in
14/15 patients tested, Table 1).
[0130] Incidence of HD-CTCs in patients with metastatic cancer:
HD-CTCs were also enumerated in an additional cohort of 30
metastatic breast cancer patients, 20 metastatic prostate cancer
patients, 18 metastatic pancreatic cancer patients, and 15 normal
controls. Using this approach, .gtoreq.5 HD-CTCs/mL were found in
80% of the prostate cancer patients (mean=92.2), 70% of the breast
cancer patients (mean=56.8), 50% of the pancreatic cancer patients
(mean=15.8), and 0% of normal controls (mean=0.6) (Table 2).
TABLE-US-00002 TABLE 2 Percentage of patients with HD-CTCs/mL of
blood obtained from cohort. N .gtoreq.2 .gtoreq.5 .gtoreq.10
.gtoreq.50 Prostate 20 90% 80% 65% 40% Breast 30 80% 70% 60% 27%
Pancreatic 18 61% 44% 44% 11% Normal 15 7% 0% 0% 0%
[0131] Morphology of IID-CTCs: A heterogeneous population of CTCs
within and across patients was observed. CTCs had various shapes,
sizes, and cytokeratin intensities. In some cases, distinctive
cytologic features such as large size or polygonal cytoplasmic
shape, were quite distinctive and monotonous within the patient's
sample. In other cases, there was cytomorphologic variability
between HD-CTCs within a single sample. Cell size also varied; many
patient samples had HD-CTCs with nuclei uniformly three or four
times the size of neighboring WBC nuclei, while other patients had
cells with nuclei only 1.3 times the size of neighboring WBC
nuclei. Some patients had a range of sizes. A lower limit for
HD-CTC nuclear size of 1.3 times the average WBC nucleus was
selected based on evaluation of the largest nuclear size of cells
we identified as WBCs showing false nonspecific staining with
cytokeratin, for instance, CD45 positive and cytokeratin
positive.
[0132] Interestingly, using this platform that allows for detailed
morphologic evaluation, HD-CTC clusters were identified in the
majority of the cancer patients (88%) in this cohort, ranging from
clusters of 2 HD-CTCs to greater than 30 HD-CTCs (data not shown).
Each HD-CTC was cytokeratin positive, CD45 negative, contained a
DAPI nucleus, and was morphologically distinct from surrounding
nucleated cells.
[0133] In addition to counting HD-CTCs, a number of different
categories of cells were tracked including cells that had nuclei
displaying apoptosis, cells that didn't have circumferential
cytokeratin, other cells that were the same size or smaller than
surrounding WBC, and cells that were cytokeratin dim or negative
(images not shown). Specifically, some CTCs were excluded because
they lacked various morphologic or morphometric inclusion criteria:
including one or more of: a) cytokeratin intensity too dim; b)
nuclear size too small; c) cytokeratin insufficiently
circumferential (surrounds less than 2/3 of nucleus); d)
cytokeratin too dim, although appears to be a cluster of two very
large cells; e) nucleus shows apoptotic disintegration changes; f)
nucleus too small and cytoplasm insufficiently circumferential;
appears to be a cell in late apoptosis; g) nucleus too small (same
size as surrounding WBC nuclei); h) cytokeratin present, but not
circumferential; and i) cytoplasm insufficiently circumferential,
nucleus too small.
[0134] Although many of these events may in fact represent
circulating malignant epithelial cells in various stages of anoikis
or disruption secondary to even the minimal processing utilized in
the platform, the goal is to identify a `pure` population of cells
with a very high likelihood of representing intact circulating
functional malignant potentially metastasizing epithelial cells
that are suitable for downstream analysis by secondary
methodologies. Fragmented, disrupted, pyknotic or otherwise damaged
carcinoma cells are not considered evaluable in standard diagnostic
pathology, and thus they are excluded in this fluid phase biopsy
platform as well. They are enumerated and tracked, as it is
recognized that their presence likely correlates overall with the
tumor biology in the patient, either by reflecting overall tumor
burden or by reflecting some as yet ill-understood complex equation
involving tumor burden and tumor vascularity and efficiency of
intravascular immune surveillance; however, they are not useful for
secondary analysis, and thus they are not designated as
HD-CTCs.
[0135] Many patients, in addition to having HD-CTCs, had a
substantial number of cells that had nuclei that were
morphologically distinct from surrounding WBC, resembled the nuclei
of the HD-CTCs within that sample, and were CD45 negative, but were
also cytokeratin dim or negative (data not shown). Representative
types of CTCs found in a single prostate cancer patient included
CTCs that were negative for cytokeratin and CD45, but exhibited a
large nucleus similar to other CTCs found in this patient, typical
CTCs that were cytokeratin positive, CD45 negative, and which had a
DAPI nucleus, and CTC clusters of 4 cells.
[0136] In light of the extensive current debate about the possible
existence of carcinoma cells undergoing epithelial-to-mesenchymal
transition, the appearance and protein expression pattern of these
cells identifies them as possible candidates for such a cell
type.
[0137] Discussion.
[0138] The robustness of the HD-CTC platform was evaluated with
both cell lines and patient samples. Despite little automation, and
complete manual wetlab processing, of the current HD-CTC assay used
for this cohort, the reproducibility of the assay is impressive
with an average CV of less than 9% for 9 different samples
processed by two independent technicians.
[0139] The criteria used to define a CTC are different across
different technologies. The currently disclosed identified a
subpopulation that has the highest likelihood of being bonafide
tumor cells. Even with strict criteria, the incidence of CTCs using
our assay is much higher than many technologies and in the same
range as reported by the CTC-chip. Additionally, a head on
comparison to CellSearch.RTM. showed significantly more CTCs in a
higher proportion of patients. Whereas other technologies have
observed occasional doublets and clusters of CTCs, clusters of CTCs
were observed in most patients.
[0140] It is noteworthy that in the small cohort tested, the
frequency of detection, and relative concentration, of CTCs among
different tumor types using the methodologies of the invention
(prostate>breast>pancreatic) parallels the fmdings observed
using other methods such as CellSearch.RTM..
[0141] Observationally, CTCs track over the clinical course of a
small subset of prostate cancer patients in which serial draws were
performed (FIG. 4). Serial HD-CTC detection may be embedded into
therapeutic clinical trials. This is expected to allow study of
patients with uniform clinical characteristics who are treated
similarly and in whom long-term clinical follow-up will be
performed. In addition to correlating these cells with patient
outcomes to determine their prognostic and monitoring value, these
HD-CTCs are expected to serve as a pharmacodynamic tool for
assessing on-target effects at a molecular level of drugs of
interest.
[0142] In summary, the instant example provides data that the
HD-CTC assay (i) finds significant number of CTCs in most patients
with metastatic cancer, (ii) has improved sensitivity over the
Cellsearch.RTM. System, (iii) provides HD-CTCs in an ideal format
for downstream characterization, (iii), enables the prospective
collection of samples that can be stored frozen for long periods of
time, and then retrospectively analyzed as new assays or markers
become available.
EXAMPLE 2
CTC Assay and Identification ofRare Cell Populations
[0143] The data presented here demonstrate identification of
putative rare cell populations. Using the methodology described
herein, a putative rare cell population was identified. Sample
processing and imaging was performed as disclosed in Example 1.
Additionally, HD-CTCs were identified and defined as in Example
1.
[0144] In performing the assay, no CTCs were assumed to be
cytokeratin positive. A putative rare cell population was
identified having the following characteristics: a) cytokeratin dim
or negative; b) CD45 negative; and c) intact non-apoptotic
appearing nucleus by DAPI imaging. FIG. 5 displays the incidence
rate of the putative rare cell population across patients relative
to identified 1-1D-CTCs.
[0145] Although the invention has been described, it will be
understood that modifications and variations are encompassed within
the spirit and scope of the invention. Accordingly, the invention
is limited only by the following claims.
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