| CPC G16B 40/20 (2019.02) [C12Q 1/6886 (2013.01); G06F 17/153 (2013.01); G06F 17/16 (2013.01); G06F 17/18 (2013.01); G16B 45/00 (2019.02); G16B 50/20 (2019.02); G16B 50/30 (2019.02); G16H 50/20 (2018.01); G16H 50/50 (2018.01)] | 16 Claims |
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1. A method for administering a cancer treatment, comprising:
a) obtaining a responsiveness score for a non-training subject having cancer, said obtaining comprising:
i) inputting total mutational burden, tumor infiltration, and factor expression of the non-training subject to a machine learning classifier;
wherein total mutational burden comprises one or both of all mutations and nonsynonymous mutations, tumor infiltration comprises any one or more of tumor infiltration by cells expressing cluster of differentiation 8 (CD8), tumor infiltration by cells expressing cluster of differentiation 4 (CD4), and tumor infiltration by cells expressing cluster of differentiation 19 (CD19), and factor expression comprises any one or more of beta 2 microglobulin (B2M) expression, proteasome subunit beta 10 (PSMB10) expression, antigen peptide transmitter 1 (TAP1) expression, antigen peptide transporter 2 (TAP2) expression, human leukocyte antigen A (HLA-A) expression, major histocompatibility complex class I B (HLA-B) expression, major histocompatibility complex class I C (HLA-C) expression, major histocompatibility complex class II DQ alpha 1 (HLA-DQA1) expression, HLA class II histocompatibility antigen DRB1 beta chain (HLA-DRB1) expression, HLA class I histocompatibility antigen alpha chain E (HLA-E) expression, natural killer cell granule protein 7 (NKG7) expression, chemokine like receptor 1 (CMKLR1) expression, granzyme A (GZMA) expression, perforin-1 (PRF1) expression, cytotoxic T-lymphocyte-associated protein 4 (CTLA4) expression, programmed cell death protein 1 (PD1) expression, programmed death-ligand 1 (PDL1) expression, programmed cell death 1 ligand 2 (PDL2) expression, lymphocyte-activation gene 3 (LAG3) expression, T cell immunoreceptor with Ig and ITIM domains (TIGIT) expression, cluster of differentiation 276 (CD276) expression, chemokine (C-C motif) ligand 5 (CCL5) expression, cluster of differentiation 27 (CD27) expression, chemokine (C-X-C motif) ligand 9 (CXCL9) expression, C-X-C motif chemokine receptor 6 (CXCR6) expression, indoleamine 2,3-dioxygenase (IDO) expression, signal transducer and activator of transcription 1 (STAT1) expression, 3-fucosyl-N-acetyl-lactosamine (CD15) expression, interleukin-2 receptor alpha chain (CD25) expression, siglec-3 (CD33) expression, cluster of differentiation 39 (CD39) expression, cluster of differentiation (CD118) expression, and forkhead box P3 (FOXP3) expression;
wherein the machine learning classifier is selected from the group consisting of a neural network classifier, a support vector machine, a max entropy classifier, an extreme gradient boosting classifier, a random fern classifier, and a random forest classifier,
and wherein the machine learning classifier was trained on said total mutational burden, tumor infiltration, and factor expression of a plurality of training subjects having cancer and a responsiveness of each of said plurality of training subjects to cancer treatment to predict responsiveness of said non-training subject to said cancer treatment; and
ii) generating, using the machine-learning classifier, a responsiveness score for the non-training subject; and
b) administering, based on said obtaining, the cancer treatment to the non-training subject for responsiveness scores that equal or exceed a predetermined threshold of 5;
wherein the cancer treatment is a checkpoint inhibitor selected from an anti-CTLA4 drug, an anti-PD1 drug, an anti-PDL1 drug, and any combination of two or more of said drugs.
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