U.S. patent application number 16/733013 was filed with the patent office on 2020-06-11 for systems and methods for comprehensive analysis of molecular profiles across multiple tumor and germline exomes.
The applicant listed for this patent is FIVE3 GENOMICS, LLC NANTOMICS, LLC NANT HOLDINGS IP, LLC. Invention is credited to Stephen Charles Benz, Shahrooz Rabizadeh, John Zachary Sanborn, Patrick Soon-Shiong, Charles Joseph Vaske.
Application Number | 20200185053 16/733013 |
Document ID | / |
Family ID | 54699962 |
Filed Date | 2020-06-11 |
United States Patent
Application |
20200185053 |
Kind Code |
A1 |
Rabizadeh; Shahrooz ; et
al. |
June 11, 2020 |
SYSTEMS AND METHODS FOR COMPREHENSIVE ANALYSIS OF MOLECULAR
PROFILES ACROSS MULTIPLE TUMOR AND GERMLINE EXOMES
Abstract
Omics patient data are analyzed using sequences or diff objects
of tumor and matched normal tissue to identify patient and disease
specific mutations, using transcriptomic data to identify
expression levels of the mutated genes, and pathway analysis based
on the so obtained omic data to identify specific pathway
characteristics for the diseased tissue. Most notably, many
different tumors have shared pathway characteristics, and
identification of a pathway characteristic of a tumor may thus
indicate effective treatment options ordinarily not considered when
tumor analysis is based on anatomical tumor type only.
Inventors: |
Rabizadeh; Shahrooz; (Los
Angeles, CA) ; Sanborn; John Zachary; (Santa Cruz,
CA) ; Vaske; Charles Joseph; (Santa Cruz, CA)
; Benz; Stephen Charles; (Santa Cruz, CA) ;
Soon-Shiong; Patrick; (Los Angeles, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
FIVE3 GENOMICS, LLC
NANTOMICS, LLC
NANT HOLDINGS IP, LLC |
Santa Cruz
Culver City
Culver City |
CA
CA
CA |
US
US
US |
|
|
Family ID: |
54699962 |
Appl. No.: |
16/733013 |
Filed: |
January 2, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14726930 |
Jun 1, 2015 |
10614910 |
|
|
16733013 |
|
|
|
|
62005766 |
May 30, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 20/00 20190201;
G16B 30/00 20190201 |
International
Class: |
G16B 20/00 20060101
G16B020/00; G16B 30/00 20060101 G16B030/00 |
Claims
1. A computer-executed method for identification of a treatment
option based on omics data of tumor cells, comprising: identifying,
by an analysis engine, shared pathway characteristics among tumor
cells of a plurality of patients using a plurality of respective
omics data sets of the tumor cells; wherein each omics data set
belongs to each of the plurality of patients, respectively, and
wherein at least two of the plurality of patients are diagnosed
with different tumors; wherein each set of omics data comprises
genomics information and transcriptomics information from tumor and
matched normal cells of each of the plurality patients; wherein the
transcriptomics information comprises expression levels or
sequences of transcripts; stratifying the tumor cells as belonging
to a class of tumors based on the shared pathway characteristics;
using the analysis engine to provide, based on the class, a
treatment option to the tumor cells, wherein the treatment option
is common to the tumor cells independently of an anatomical types
of the tumor cells; wherein the treatment option includes
identification of a drug and indication that the tumor cells are
treatable with the drug, or wherein the treatment option is
selected based on known or available treatments for the
anatomically unrelated and distinct tumors; and treating the tumor
cells using the drug or known or available treatment.
2. The method of claim 1 wherein the at least two sets of omics
data are in a BAMBAM format, a SAMBAM format, a FASTQ format, or a
FASTA format.
3. The method of claim 1 wherein the at least two sets of omics
data are BAMBAM diff objects.
4. The method of claim 1 wherein the genomics information comprise
mutation information, copy number information, insertion
information, deletion information, orientation information and/or
breakpoint information.
5. The method of claim 1 wherein the genomics information is whole
genome sequencing information.
6. The method of claim 1 wherein the genomics information is exome
sequencing information.
7. The method of claim 1 wherein the transcriptomics information
covers at least 50% of all exomes in the genomics information from
the tumor cells.
8. The method of claim 1 wherein the transcriptomics information
covers at least 80% of all exomes in the genomics information from
the tumor cells.
9. The method of claim 1 wherein the shared pathway characteristics
are selected from the group consisting of a constitutively
activated pathway, a functionally impaired pathway, and a
dysregulated pathway.
10. The method of claim 1 wherein the shared pathway
characteristics are characterized by a mutated non-functional
protein, mutated dysfunctional protein, an overexpressed protein,
or an underexpressed protein in a pathway.
11. The method of claim 1 wherein the transcriptomics information
is used in the step of identifying to infer reduced or absence of
function of a protein encoded by a mutated gene.
12. The method of claim 1 wherein the step of identifying is
performed using PARADIGM.
13. The method of claim 1, wherein the treatment option includes
identification of a drug and indication that the tumor cells are
treatable with the drug.
14. The method of claim 1, wherein the treatment option for a first
patient with a first tumor is based on shared pathway
characteristics with a second patient with a distinct second
tumor.
15. The method of claim 1, further comprising analyzing
information, by an analysis engine, on pathway usage or
compensation where a pathway function is compromised.
16. The method of claim 1, wherein the sequences of transcripts are
used to identify RNA editing or RNA splicing.
17. The method of claim 1, wherein the treatment option is selected
based on known or available treatments for the anatomically
unrelated and distinct tumors.
18. The method of claim 1, wherein the treatment option target a
mutated element common to the tumor cells.
19. The method of claim 1, wherein the treatment option targets a
non-mutated element, which compensates for a defect of a pathway
where a mutated element common to the tumor cells is disposed.
20. The method of claim 1, wherein the expression level comprises
quantity of the transcripts.
Description
[0001] This application is a continuation application of allowed
U.S. patent application with the Ser. No. 14/726,930, which was
filed Jun. 1, 2015, which claims the benefit of priority to U.S.
provisional application having Ser. No. 62/005,766, filed May 30,
2014, all of which are incorporated by reference herein.
FIELD OF THE INVENTION
[0002] The field of the invention is computational omics,
especially as it relates to analysis of molecular profiles across a
large number of tumor and germline exomes from multiple patient and
tumor samples.
BACKGROUND OF THE INVENTION
[0003] The background description includes information that may be
useful in understanding the present invention. It is not an
admission that any of the information provided herein is prior art
or relevant to the presently claimed invention, or that any
publication specifically or implicitly referenced is prior art.
[0004] While the clinical world is familiar with genomic assays
targeted to a limited number of mutations as a means to derive
molecular insight to therapies, the power to deliver more
comprehensive, non-assumptive, and stochastic molecular analysis is
sorely needed to guide treatment decisions that are unbiased to
traditional tissue-by-tissue anatomical assignment of therapeutics,
or a priori assumptions that a few hundred DNA mutations are
drivers of cancer. Indeed, most clinicians today are challenged by
a deluge of rapidly advancing science with which it becomes
increasingly difficult to keep pace. In this era of personalized
medicine, there are nearly 800 drugs in development targeted
against specific protein targets driving the growth of the tumor.
This cognitive overload may have significant consequences in
decision making in life-threatening diseases as complex as
cancer.
[0005] Today the approach most widely used by oncologists to guide
treatment selection of drugs that are targeted against altered
proteins is to identify gene DNA mutations in tumor samples
deploying panels of fewer than 500 "actionable" genes. Such
actionable genes are typically identified from large-scale studies
of various cancers (see e.g., Nature Genetics 45, 1127-1133
(2013)). All publications and applications herein are incorporated
by reference to the same extent as if each individual publication
or patent application were specifically and individually indicated
to be incorporated by reference. Where a definition or use of a
term in an incorporated reference is inconsistent or contrary to
the definition of that term provided herein, the definition of that
term provided herein applies and the definition of that term in the
reference does not apply.
[0006] Unfortunately, the current reliance on genotyping of tumor
samples to drive treatment decisions is largely based on the
assumption that identification of mutated DNA routinely translates
downstream (from "DNA to protein expression") to an alteration in
the underlying protein pathways that are targeted by the therapy to
be selected, and these identified DNA mutations are thus nominated
as clinically actionable. However, exclusive analysis of genetic
mutations in tumor genomes fails to take into account whether or
not the mutated genes are transcribed at all, whether changes in
the genome are variants or disease-drivers, and/or what the
functional context of such mutations are, and whether or not
compensatory mechanisms exists in a cell affected by such
mutation.
[0007] Therefore, analysis of selected mutations with disregard of
the above drawbacks will likely lead to various false-positive,
false negative, and non-relevant results that in turn may misdirect
treatment of a patient. Therefore, there remains a need for
improved systems and methods for comprehensive analysis of
molecular profiles.
SUMMARY OF THE INVENTION
[0008] The inventive subject matter is drawn to systems and methods
of omics analysis in which shared pathway characteristics are
obtained from various distinct tumor samples. Most preferably,
omics analysis includes analysis of tumor and matched normal tissue
to identify patient and tumor specific changes, which is further
refined using transcriptomics data. Based on such analysis, a
treatment recommendation is then prepared that is typically
independent of the anatomical tumor type but that takes into
account a molecular signature characteristic of shared pathway
characteristics.
[0009] In one aspect of the inventive subject matter, the inventors
contemplate a method of identifying a molecular signature for a
tumor cell that includes a step of using an analysis engine to
receive a plurality of data sets from a respective plurality of
patients, wherein at least two (or at least three, or at least
five) of the plurality of patients are diagnosed with different
tumors, and wherein each data set is representative of genomics
information from tumor and matched normal cells. In another step,
the analysis engine receives transcriptomics information for the at
least two patients, and in yet another step, the analysis engine
identifies shared pathway characteristics among the tumor cells of
the at least two patients using the genomics information and the
transcriptomics information. In a still further step, the analysis
engine is then used to assign, on the basis of the shared pathway
characteristics, a molecular signature to the tumor cells, wherein
the molecular signature is assigned independently of an anatomical
tumor type, and a patient record is then generated or updated using
the molecular signature.
[0010] While not limiting to the inventive subject matter, it is
generally contemplated that the data sets are in a BAMBAM format, a
SAMBAM format, a FASTQ format, or a FASTA format, and it is
typically preferred that the data sets are BAMBAM diff objects.
Therefore, in further contemplated aspects, the data sets will
preferably comprise mutation information, copy number information,
insertion information, deletion information, orientation
information and/or breakpoint information.
[0011] With respect to the genomics information it is contemplated
that such information may be whole genome sequencing information or
exome sequencing information, and that the transcriptomics
information comprises information on transcription level and/or
sequence information. Most typically, the transcriptomics
information will cover at least 50% (or at least 80%) of all exomes
in the genomics information from the tumor cells. Furthermore, it
is contemplated that the transcriptomics information is used in the
step of identifying to infer reduced or absence of function of a
protein encoded by a mutated gene.
[0012] Therefore, the inventors contemplate that the shared pathway
characteristics will include a constitutively activated pathway, a
functionally impaired pathway, and a dysregulated pathway, and/or
that the shared pathway characteristics may be characterized by a
mutated non-functional protein, mutated dysfunctional protein, an
overexpressed protein, or an under-expressed protein. In still
further preferred aspects, the step of identifying is performed
using PARADIGM or other pathway-centric method of analysis.
[0013] Additionally, it is contemplated that the molecular
signature comprises information about one or more pathway elements,
and especially drug identification and type of interaction with the
one or more pathway elements. Therefore, it should be appreciated
that the patient record may also include a treatment recommendation
based on the molecular signature of the tumor cells (e.g.,
treatment recommendation for a first patient with a first tumor may
be based on shared pathway characteristics with a second patient
with a distinct second tumor).
[0014] Various objects, features, aspects and advantages of the
inventive subject matter will become more apparent from the
following detailed description of preferred embodiments, along with
the accompanying drawing figures in which like numerals represent
like components.
BRIEF DESCRIPTION OF THE DRAWING
[0015] FIG. 1 is a graph illustrating frequency distribution of
`actionable genes` for selected tumors.
[0016] FIG. 2 is a graph correlating RNA expression levels of
mutated genomic DNA for selected tumors.
[0017] FIG. 3 is an exemplary graph depicting principal component
analysis for selected oncogenes in selected tumors.
[0018] FIG. 4 is a an exemplary graph depicting survival times as a
function of genomic rearrangements.
[0019] FIG. 5 is a chart depicting an exemplary breakpoint analysis
for selected tumors.
[0020] FIG. 6 is a graph depicting pathway activity clusters based
on core pathways that are over- or under-activated.
[0021] FIG. 7 is a graph depicting pathway activities clustered
across various tumor types.
[0022] FIG. 8 is an exemplary graph depicting mutation distribution
for various tumor types.
DETAILED DESCRIPTION
[0023] The following description includes information that may be
useful in understanding the present invention. It is not an
admission that any of the information provided herein is prior art
or relevant to the presently claimed invention, or that any
publication specifically or implicitly referenced is prior art.
[0024] The inventive subject matter provides apparatus, systems,
and methods for improved omics analysis of various tumors. More
specifically, the inventors discovered that omics data analysis can
be significantly improved by first identifying patient and tumor
relevant changes in the genome, typically via comparison of tumor
and matched normal samples. Once such differences are ascertained,
further transcriptomic data of the same patient are used to
identify whether the changed sequences are expressed in the tumor.
The so obtained patient data are then subjected to pathway analysis
to identify pathway characteristics of the tumor, and particularly
shared pathway characteristics of the tumor with various other
types of tumors. As should be readily appreciated, shared pathway
characteristics may be employed to inform treatment using one or
more treatment modalities from anatomically unrelated tumors that
would otherwise not have been identified. Viewed from a different
perspective, different tumor types share pathway characteristics
irrespective of the anatomical tumor type, and the knowledge of
shared pathway characteristics with respective molecular signatures
may identify drug treatment strategies that had not been
appreciated for a particular tumor type.
[0025] Consequently, in one aspect of the inventive subject matter,
the inventors contemplate a method of identifying a molecular
signature for a tumor cell, and especially a molecular signature of
a cell signaling pathway. Most typically, identification and
analysis is performed using a fully integrated, cloud-based,
supercomputer-driven, genomic, and transcriptomic analytic engine.
It should be noted that any language directed to a computer should
be read to include any suitable combination of computing devices,
including servers, interfaces, systems, databases, agents, peers,
engines, controllers, or other types of computing devices operating
individually or collectively. One should appreciate the computing
devices comprise a processor configured to execute software
instructions stored on a tangible, non-transitory computer readable
storage medium (e.g., hard drive, solid state drive, RAM, flash,
ROM, etc.). The software instructions preferably configure the
computing device to provide the roles, responsibilities, or other
functionality as discussed below with respect to the disclosed
apparatus. In especially preferred embodiments, the various
servers, systems, databases, or interfaces exchange data using
standardized protocols or algorithms, possibly based on HTTP,
HTTPS, AES, public-private key exchanges, web service APIs, known
financial transaction protocols, or other electronic information
exchanging methods. Data exchanges preferably are conducted over a
packet-switched network, the Internet, LAN, WAN, VPN, or other type
of packet switched network.
[0026] In especially preferred methods, an analysis engine receives
a plurality of data sets from a respective plurality of patients,
wherein at least two of the plurality of patients are diagnosed
with different tumors, and wherein each data set is representative
of genomics information from tumor and matched normal cells. In a
further step, the analysis engine receives transcriptomics
information for the at least two patients and identifies shared
pathway characteristics among the tumor cells of the at least two
patients using the genomics information and the transcriptomics
information (of course, it should be noted that shared pathway
characteristics may also be identified only for a single patient
sample while pathway characteristics of other tumors may be
obtained from a pathway database). In yet another step, the
analysis engine is then used to assign, on the basis of the shared
pathway characteristics, a molecular signature to the tumor cells,
wherein the molecular signature is assigned independently (i.e., in
an agnostic manner) of an anatomical tumor type. In a still further
step, a patient record may be generated or updated using the
molecular signature.
[0027] With respect to the data sets from the plurality of patients
it is contemplated that the type of data sets may vary considerably
and that numerous types of data sets are deemed suitable for use
herein. Therefore, data sets may include unprocessed or processed
data sets, and exemplary data sets include those having BAMBAM
format, SAMBAM format, FASTQ format, or FASTA format. However, it
is especially preferred that the data sets are provided in BAMBAM
format or as BAMBAM diff objects (see e.g., US2012/0059670A1 and
US2012/0066001A1). Therefore, and viewed from another perspective,
it should be noted that the data sets are reflective of a tumor and
a matched normal sample of the same patient to so obtain patient
and tumor specific information. Thus, genetic germ line alterations
not giving rise to the tumor (e.g., silent mutation, SNP, etc.) can
be excluded. Of course, it should be recognized that the tumor
sample may be from an initial tumor, from the tumor upon start of
treatment, from a recurrent tumor or metastatic site, etc. In most
cases, the matched normal sample of the patient may be blood, or
non-diseased tissue from the same tissue type as the tumor.
[0028] It should also be noted that the data sets may be streamed
from a data set generating device (e.g., sequencer, qPCR machine,
etc.) or provided from a data base storing the data sets. For
example, suitable data sets may be derived from a BAM server (e.g.,
as described in US2012/0059670A1 and US2012/0066001A1) and/or a
pathway analysis engine (e.g., as described in WO2011/139345A2 and
WO2013/062505A1). Such is particularly true where the data sets
from a tumor and matched normal sample are not derived from the
patient. Thus, at least some of the data sets may be independently
stored and provided, and analysis may be performed on a newly
obtained patient sample (e.g., within one week of obtaining patient
tissue samples) using data sets from the patient's tumor and
matched normal sample and previously stored tumor and matched
normal sample not derived from the patient.
[0029] With further respect to the data sets it is noted that the
data sets from all tumors are in a format that allows ready
comparison without further conversion and/or processing. Thus, the
data sets will preferably comprise mutation information,
methylation status information, copy number information,
insertion/deletion information, orientation information, and/or
breakpoint information specific to the tumor and the patient. It is
still further contemplated that the data set is representative of
at least a portion of the entire genome, and most typically the
whole genome. Therefore, the data sets are preferably prepared form
whole genome sequencing covering the entire genome (or at least
50%, or at least 70%, or at least 90% of the entire genome).
Alternatively, exome sequencing is also contemplated, and in most
cases it is contemplated that at least 50%, more typically at least
70, and most typically at least 90% of the entire exome is
sequenced.
[0030] Moreover, and with respect to the origin of the data sets it
should be appreciated that numerous non-patient tumor data are
used. Therefore, it is contemplated that for data sets other than a
patient data set will be derived from at least two different
tumors, and more preferably from at least three, or at least five
different tumor types to identify shared pathway characteristics.
Data sets from different tumor types can be obtained from different
patient samples as such samples are available (e.g., from a
hospital, clinical trial, epidemiological study, etc.) and/or can
be provided from previously acquired analyses or data. For example,
the TCGA provides a good sample of well-characterized omic
information useful to prepare data sets suitable for use herein and
Table 1 below exemplarily illustrates data used in the present
analysis.
TABLE-US-00001 Median Sex Tumor Grade Survival Tissue Subtype N Age
M F G1 G2 G3 G4 GX GB ? (months) Breast ER- 16 59.3 (58.9) 0 16 0 0
0 0 0 0 16 26.04 lobular ER+ 148 62.0 (61.9) 0 148 0 0 0 0 0 0 148
140.48 Lung Squamous 386 68.5 (67.8) 287 99 0 0 0 0 0 0 386 46.78
Rectal 96 67.7 (66.0) 53 43 0 0 0 0 0 0 96 51.98 Breast ER- 225
55.3 (56.7) 0 225 0 0 0 0 0 0 225 100.7 Ductal ER+ 516 59.5 (59.2)
9 507 0 0 0 0 0 0 516 113.82 Glioblastoma 354 60.8 (60.1) 222 132 0
0 0 0 0 0 354 13.94 Stomach MSI 88 69.0 (68.6) 46 42 3 30 55 0 0 0
0 26.47 MSS 160 67.0 (65.9) 105 55 3 54 99 0 4 0 0 72.23 AML 4 50.1
(50.2) 3 1 0 0 0 0 0 0 4 47.05 Low Grade Glioma 138 41.1 (42.8) 72
66 0 68 70 0 0 0 0 78.21 Head & HPV- 246 62.4 (62.7) 168 78 26
159 55 0 6 0 0 47.97 Neck HPV+ 56 59.4 (59.6) 48 8 1 31 19 2 3 0 0
52.31 Bladder 118 68.8 (67.1) 86 32 0 0 0 0 0 0 118 19.5 Uterine
299 63.8 (64.0) 0 299 0 0 0 0 0 0 299 NA Prostate 178 61.3 (60.8)
178 0 0 0 0 0 0 0 178 NA Lung Adeno. 369 66.9 (65.7) 171 198 0 0 0
0 0 0 369 40.6 Colon MSS 144 68.2 (66.8) 82 62 0 0 0 0 0 0 144 NA
MSI 76 73.1 (69.0) 33 43 0 0 0 0 0 0 76 NA Thyroid 419 46.9 (47.6)
104 315 0 0 0 0 0 0 419 NA Kidney Clear Cell 325 60.4 (60.4) 209
116 6 142 128 47 2 0 0 90.48 Kidney Chromophobe 50 48.3 (50.5) 29
21 0 0 0 0 0 0 50 NA Ovarian 336 59.5 (60.4) 0 336 3 37 285 0 8 1 2
43.86 Melanoma 301 56.8 (57.3) 192 109 0 0 0 0 0 0 301 98.4
Pancreatic 4 ? ? 0 0 0 0 0 0 4 NA Total 5052
[0031] With reference to the TCGA data it was further observed that
different tumor types had multiple mutations in multiple genes. As
such, it is apparent that simple targeting of an individual
druggable target is in most circumstances not a viable option.
Indeed, FIG. 1 exemplarily illustrates the predicament for
conventional singular molecular diagnostics where various tumor
types are shown with their respective numerical distribution of
potentially actionable genes. As is readily apparent from FIG. 1,
there was a multitude of actionable genes, not just single
mutation, in almost all tumors. Thus, it should be appreciated that
the analysis and treatment of a tumor requires consideration of
more than one changed gene. In addition, it has previously not yet
been appreciated that not all of the mutated genes are indeed
expressed, and with that may or may not lead to actionable or
druggable protein targets as is exemplarily depicted in FIG. 2.
[0032] As can be taken from FIG. 2, selected mutations in certain
tumors were not or only weakly expressed (i.e., transcribed into
RNA, see lower box). Consequently, pharmaceutical intervention
targeting such mutant proteins (e.g., targeting BRAF V600 in
glioblastoma) are not expected to impact the tumor in a significant
manner. Conversely, certain other mutated proteins will provide an
attractive target due to their very high rate of expression (e.g.,
by targeting BRAF V600 in melanoma). Thus, it should be appreciated
that the same mutated protein may be a suitable target in some
cancers or patients and an entirely unsuitable target in others.
Viewed from a different perspective, genomics information without
consideration of transcriptomics data will lack detail needed to
guide treatment decisions.
[0033] In particularly preferred aspects, transcription information
is obtained to cover at least 50%, or at least 70, or at least 80,
or at least 90% of all exomes in the genomics information from the
tumor cells. Thus, it is contemplated that transcripts of a tumor
cell or tissue may also be analyzed for their quantity (and
optionally also for sequence information to identify RNA editing
and/or RNA splicing). Such analysis may include threshold values
that are typically user defined as further described in copending
US provisional application with the Ser. No. 62/162,530, filed
15-May-15.
[0034] In addition to the lack of consideration of transcriptomics
data, the functional impact of a mutation within a cell signaling
network has not been appreciated in most of heretofore known
systems and methods, especially where multiple mutations are
present in multiple genes associated with a tumor. To overcome such
shortcoming, the inventors used the patient and tumor specific
mutation information and associated expression levels in an
analysis of cell signaling pathways to thereby obtain information
on pathway usage and compensation where a pathway function was
compromised. Therefore, it is noted that the transcriptomics
information is preferably used to infer reduced or absence of
function of a protein encoded by a mutated gene, and with that
influence on a particular pathway.
[0035] While various pathway analytical tools are know in the art,
the inventors especially contemplate use of dynamic pathway maps in
which pathways are expressed as probabilistic pathway model. For
example, pathway analyses may be performed using PARADIGM, as
described in WO2011/139345, WO2013/062505, WO2014/059036, or
WO2014/193982, using the data sets and transcriptomics information
to so arrive at the particular pathways usage of a specific tumor.
As will be readily appreciated, where multiple data sets from
multiple patients having distinct tumors as employed, the analysis
engine will be able to identify for each tumor particular pathway
characteristics with a molecular signature of the tumor cells. For
example, the analysis engine may identify shared pathway
characteristics among multiple tumor types where such shared
characteristics may include a constitutively activated pathway, a
functionally impaired pathway, and a dysregulated pathway. Such
shared pathway may be characterized or due to a variety of factors
and exemplary factors leading to a particular pathway
characteristic include a mutated non-functional protein, mutated
dysfunctional protein, an overexpressed protein, or an
underexpressed protein in a pathway, etc. Of course, it should be
noted that at least some of the pathway characteristics may be
previously determined and stored in a data base or that at least
some of the pathway characteristics may also be determined de novo.
Therefore, it should be recognized that new patient data may be
compared against already obtained data from a database.
[0036] Among other benefits of integrated genomics,
transcriptomics, and pathway analysis for multiple tumor types of
multiple patients, it should be appreciated various subsequent
analyses are now possible to group or classify certain molecular
events into otherwise not observable categories. For example, as is
illustrated in FIG. 3, a principal component analysis of various
expressed mutated oncogenes from different tumors can be performed
to so associate a plurality of specific mutations with a plurality
of different tumors. Likewise, breakpoint analysis over different
tumors can be associated with prognostic outcome as exemplarily
shown in FIG. 4, or breakpoint frequency and distribution can be
associated with different tumors as exemplarily shown in FIG.
5.
[0037] Most notably, and as exemplarily shown in FIG. 6, pathway
analysis on the basis of genomics and transcriptomics information
may serve to identify certain shared molecular signatures common to
a variety of different tumors. Thus, it should be recognized that a
tumor may be classified as belonging to a class of tumors that are
characterized by specific shared pathway characteristics. With
further reference to FIG. 6, it is noted that the tumors of Table 1
together with genomics and transcriptomics information were
stratified into six distinct classes independent of anatomical
location. Here, common classes for different tumors were defined by
activation or inhibition of selected signaling pathways (e.g.,
over-activation of myc transcription and inhibition of NOTCH
signaling), which is entirely independent from a classification
based on anatomical tumor type (classified as pancreatic tumor,
breast ductal tumor, etc.).
[0038] FIG. 7 exemplarily illustrates a different perspective of
the findings of FIG. 6 where the tumor classification is expressed
as clusters per FIG. 6. Here, it is readily apparent that entirely
unrelated tumors (e.g., uterine, rectal, lung adeno.) can be
classified according to specific signaling pathways characteristics
having specific molecular signatures. For example, the molecular
signature may comprise information about one or more pathway
elements within a pathway (e.g., Ras, Raf, MEK, Myc). As such,
where a tumor shares a common pathway characteristic with one or
more common molecular signatures with another unrelated tumor, the
tumor may in fact be treatable using treatment modalities know for
the unrelated tumor. Most typically, the molecular signature
information may include a drug identification (e.g., where Ras is
mutated and overexpressed, drug information may include suitable
Ras inhibitors) and/or a type of interaction with the one or more
pathway elements (e.g., where Hec1 is mutated and overactive, drug
information may include suitable Hec1/Nek inhibitors). Therefore,
and viewed from another perspective, a patient tumor may be
characterized as belonging to a specific class where that class is
defined as having unrelated and distinct members (tumors) sharing
common pathway characteristics/molecular signatures within a
pathway. Based on the so established classification, treatment
options may be selected based on treatment options known or
available for the unrelated and distinct members. It should be
appreciated that the treatment option may target a mutated element
of a particular pathway, but also that the treatment option may
target a non-mutated element of another pathway that compensates
for a defect in a pathway in which a mutated element is
disposed.
[0039] In another manner of classification, the inventors
contemplate that selected pathways and/or pathway elements may be
analyzed from a multiple different tumors as is exemplarily shown
in FIG. 8. Here, selected pathway elements (e.g., tumor suppressors
and oncogenes) are plotted against different tumors, which provides
a rapid identification of shared pathway characteristics and
molecular signatures common to multiple tumors. For example, the
KRAS G12 mutant is associated with uterine, rectal, and colon
cancers, while mutated APC is associated with colon adenocarcinoma
and rectal cancers.
[0040] Therefore, the inventors contemplate that a patient record
will typically include one or more treatment recommendations based
on the molecular signature of the tumor cells (and with that based
on the shared pathway characteristics with other unrelated tumors).
In other words, a treatment recommendation for a first patient with
a first tumor may be based on a shared pathway characteristics with
a second patient with a distinct second tumor.
[0041] As used in the description herein and throughout the claims
that follow, the meaning of "a," "an," and "the" includes plural
reference unless the context clearly dictates otherwise. Also, as
used in the description herein, the meaning of "in" includes "in"
and "on" unless the context clearly dictates otherwise. Moreover,
as used herein, and unless the context dictates otherwise, the term
"coupled to" is intended to include both direct coupling (in which
two elements that are coupled to each other contact each other) and
indirect coupling (in which at least one additional element is
located between the two elements). Therefore, the terms "coupled
to" and "coupled with" are used synonymously. Moreover, all methods
described herein can be performed in any suitable order unless
otherwise indicated herein or otherwise clearly contradicted by
context. The use of any and all examples, or exemplary language
(e.g. "such as") provided with respect to certain embodiments
herein is intended merely to better illuminate the invention and
does not pose a limitation on the scope of the invention otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element essential to the practice of the
invention.
[0042] It should be apparent to those skilled in the art that many
more modifications besides those already described are possible
without departing from the inventive concepts herein. The inventive
subject matter, therefore, is not to be restricted except in the
scope of the appended claims. Moreover, in interpreting both the
specification and the claims, all terms should be interpreted in
the broadest possible manner consistent with the context. In
particular, the terms "comprises" and "comprising" should be
interpreted as referring to elements, components, or steps in a
non-exclusive manner, indicating that the referenced elements,
components, or steps may be present, or utilized, or combined with
other elements, components, or steps that are not expressly
referenced. Where the specification claims refers to at least one
of something selected from the group consisting of A, B, C . . .
and N, the text should be interpreted as requiring only one element
from the group, not A plus N, or B plus N, etc.
* * * * *