COMPREHENSIVE IMMUNOPROFILING OF PERIPHERAL BLOOD
Aspects of the disclosure relate to methods, systems, and computer-readable storage media, that are useful for characterizing subjects having cancer. The disclosure is based, in part, on methods for immunoprofiling a cancer subject and the subject's prognosis and/or likelihood of responding to an immunotherapy based upon analysis of leukocyte populations in the peripheral blood of the subject.
1 . A method for determining a leukocyte immunoprofile type of a subject having, suspected of having, or at risk of having cancer, the method comprising:
using at least one computer hardware processor to perform:
obtaining cytometry data or RNA expression data from a biological sample obtained from the subject;
processing the cytometry data or the RNA expression data to determine cell composition percentages for at least 20 cell types listed in Table 4;
generating a leukocyte signature for the subject using the determined cell composition percentages for the at least 20 cell types, the leukocyte signature comprising the cell composition percentages for the at least 20 cell types; and
identifying, using the leukocyte signature and from among a plurality of leukocyte immunoprofile types, a leukocyte immunoprofile type for the subject.
2 . The method of claim 1 ,
wherein the cytometry data comprises flow cytometry data, and
wherein processing the cytometry data or the RNA expression data comprises processing the flow cytometry data to determine the cell composition percentages for at least the 20 cell types listed in Table 4.
3 .- 5 . (canceled)
6 . The method of claim 2 , wherein processing the flow cytometry data comprises determining cell composition percentages for each cell type listed in Table 2.
7 . (canceled)
8 . The method of claim 1 ,
wherein obtaining RNA expression data comprises obtaining sequencing data previously obtained by sequencing the biological sample obtained from the subject, and
wherein processing the cytometry data or the RNA expression data comprises processing the RNA expression data to determine the cell composition percentages for at least the 20 cell types listed in Table 4.
9 .- 11 . (canceled)
12 . The method of claim 8 , wherein processing the RNA expression data comprises applying a cell deconvolution algorithm to the RNA expression data to obtain cell composition percentages for the at least 20 cell types listed in Table 3.
13 . The method of claim 1 ,
wherein the plurality of leukocyte immunoprofile types is associated with a respective plurality of leukocyte immunoprofile types, and
wherein identifying, using the leukocyte signature and from among a plurality of leukocyte immunoprofile types, the leukocyte immunoprofile type for the subject comprises:
associating the leukocyte signature of the subject with a particular one of the plurality of leukocyte immunoprofile types, and
identifying the leukocyte immunoprofile type for the subject as the leukocyte immunoprofile type corresponding to the particular one of the plurality of leukocyte immunoprofile types to which the leukocyte signature of the subject is associated.
14 .- 16 . (canceled)
17 . The method of claim 1 , further comprising generating the plurality of leukocyte immunoprofile types, the generating comprising:
obtaining multiple sets of cytometry data or RNA expression data from biological samples obtained from multiple respective subjects, each of the multiple sets of cytometry data or RNA expression data indicating cell composition percentages for at least 20 cell types listed in Table 4;
generating multiple leukocyte signatures from the multiple sets of cytometry data or RNA expression data, each of the multiple leukocyte signatures comprising cell composition percentages for the at least 20 cell types listed in Table 4, the generating comprising, for each particular one of the multiple leukocyte signatures:
determining the leukocyte signature by determining the cell composition percentages using the cytometry data or RNA expression data in the particular set of cytometry data or RNA expression data for which the particular one leukocyte signature is being generated; and
clustering the multiple leukocyte signatures to obtain the plurality of leukocyte immunoprofile types.
18 . The method of claim 17 , further comprising:
updating the plurality of leukocyte immunoprofile types using the leukocyte signature of the subject, wherein the leukocyte signature of the subject is one of a threshold number of leukocyte signatures for a threshold number of subjects, wherein when the threshold number of leukocyte signatures is generated the leukocyte immunoprofile types are updated,
wherein the threshold number of leukocyte signatures is at least 50, at least 75, at least 100, at least 200, at least 500, at least 1000, or at least 5000 leukocyte signatures.
19 .- 22 . (canceled)
23 . The method of claim 1 , wherein the plurality of leukocyte immunoprofile types comprises: a Naïve type, a Primed type, a Progressive type, a Chronic type, and a Suppressive type.
24 . The method of claim 1 , further comprising identifying the subject as a candidate for treatment with an immunotherapy based upon the identifying the leukocyte immunoprofile type for the subject.
25 . The method of claim 1 , further comprising identifying the subject as a candidate for treatment with an immunotherapy when the subject is identified as having a Primed type.
26 . The method of claim 1 , further comprising administering a therapeutic agent to the subject based upon identification of the subject's leukocyte immunoprofile type.
27 . The method of claim 1 , further comprising administering an immunotherapy to the subject when the subject is identified as having a Primed type.
28 . A method for determining a leukocyte immunoprofile type of a subject having, suspected of having, or at risk of having cancer, the method comprising:
using at least one computer hardware processor to perform:
obtaining flow cytometry data for white blood cells (WBC) isolated from a biological sample obtained from the subject;
processing the flow cytometry data to determine cell composition percentages for at least 20 cell types listed in Table 1;
generating a leukocyte signature for the subject using the determined cell composition percentages for the at least 20 cell types, the leukocyte signature comprising the cell composition percentages for the at least 20 cell types; and
identifying, using the leukocyte signature and from among a plurality of leukocyte immunoprofile types, a leukocyte immunoprofile type for the subject.
29 .- 30 . (canceled)
31 . The method claim 28 , wherein processing the flow cytometry data comprises determining cell composition percentages for: Naive CD4+ Tregs, Naive CD4+ T cells, Naive CD8+ T cells, Naive B cells, Effector memory CD4+ T cells, Effector memory CD8+ T cells, Classical monocytes, and Non-classical monocytes.
32 .- 34 . (canceled)
35 . The method of claim 28 ,
wherein the plurality of leukocyte immunoprofile types is associated with a respective plurality of leukocyte immunoprofile types,
wherein identifying, using the leukocyte signature and from among a plurality of leukocyte immunoprofile types, the leukocyte immunoprofile type for the subject comprises:
associating the leukocyte signature of the subject with a particular one of the plurality of leukocyte immunoprofile types, and
identifying the leukocyte immunoprofile type for the subject as the leukocyte immunoprofile type corresponding to the particular one of the plurality of leukocyte immunoprofile types to which the leukocyte signature of the subject is associated.
36 .- 44 . (canceled)
45 . The method of claim 28 , wherein the plurality of leukocyte immunoprofile types comprises: a Naïve type, a Primed type, a Progressive type, a Chronic type, and a Suppressive type.
46 .- 49 . (canceled)
50 . The method of claim 1 , wherein the subject has head and neck squamous cell carcinoma (HNSCC).
51 .- 73 . (canceled)
74 . A system, comprising:
at least one computer hardware processor; and
at least one computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method of claim 1 .
75 . At least one computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method of claim 1 .