PAN-CANCER MODEL TO PREDICT THE PD-L1 STATUS OF A CANCER CELL SAMPLE USING RNA EXPRESSION DATA AND OTHER PATIENT DATA

1 . A computer-implemented method of identifying programmed-death ligand 1 (PD-L1) expression status of a subject's sample comprising a cancer cell, the method comprising:

receiving an unlabeled expression data set for the subject's sample; and

aligning the unlabeled expression data set to labeled expression data according to a trained PD-L1 predictive model, wherein the trained PD-L1 predictive model has been trained with a plurality of labeled expression data sets, each labeled expression data set comprising expression data for a sample of a labeled PD-L1 expression status;

wherein aligning the unlabeled gene expression data set to labeled expression data according to the trained PD-L1 predictive model identifies PD-L1 expression status for the subject's sample.

2 . The computer-implemented method of claim 1 , wherein the trained PD-L1 predictive model has been trained by (i) inputting a plurality of labeled expression data sets, wherein each labeled expression data set comprises a labeled cancer type and a labeled PD-L1 expression status, and, optionally, one or more labeled features.

3 . The computer-implemented method of claim 2 , wherein the plurality of labeled expression data sets comprises expression data for samples of a single cancer type.

4 . The computer-implemented method of claim 2 , wherein the plurality of labeled expression data sets comprises expression data for samples of 2, 3, 4, 5, 6, 7, 8, 9, 10 or more different cancer types.

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7 . The computer-implemented method of claim 2 , wherein the plurality of labeled expression data sets comprises expression data for breast cancer, prostate cancer, colorectal cancer, lung cancer, skin cancer, kidney cancer, pancreatic cancer, stomach cancer, or a combination thereof.

8 . The computer-implemented method of claim 2 , wherein the plurality of labeled expression data sets comprises expression data for a subtype of one or more of labeled cancer type(s), optionally, a subtype of breast cancer.

9 . The computer-implemented method of claim 8 , wherein the subtype for breast cancer is luminal breast cancer, triple negative breast, or a combination thereof.

10 . The computer-implemented method of claim 2 , wherein the plurality of labeled expression data sets comprises expression data for lung adenocarcinoma, melanoma, renal cell carcinoma, bladder cancer, mesothelioma, and lung small cell cancer.

11 . The computer-implemented method of claim 2 , wherein the labeled expression data comprises RNA expression data, optionally, mRNA expression data.

12 . The computer-implemented method of claim 11 , wherein the mRNA expression data is RNA-seq data.

13 . The computer-implemented method of claim 12 , wherein the RNA-seq data is normalized RNA-seq data.

14 . The computer-implemented method of claim 1 , wherein the unlabeled expression data set for the sample comprises RNA expression data, optionally, mRNA expression data.

15 . The computer-implemented method of claim 14 , wherein the mRNA expression data is RNA-seq data.

16 . The computer-implemented method of claim 15 , wherein the RNA seq data is normalized RNA-seq data.

17 . The computer-implemented method of claim 1 , further comprising one or more of: obtaining the sample from a subject, isolating mRNA from cells of the sample, fragmenting the mRNA, producing double-stranded cDNA based on the mRNA fragments, carrying out high throughput, short-read sequencing on the cDNA, aligning the sequences to a reference genome, and normalizing raw RNA-seq data.

18 . The computer-implemented method of claim 17 , wherein the high throughput, short-read sequencing is next generation sequencing (NGS), optionally, wherein the NGS comprises hybrid capture.

19 . The computer-implemented method of claim 18 , wherein the hybrid capture comprises use of biotinylated probes which bind to specific target nucleotide sequences.

20 . The computer-implemented method of claim 19 , wherein at least one of the target nucleotide sequences encodes PD-L1, PD-1, or a combination thereof.

21 . The computer-implemented method of claim 19 , wherein at least one of the target nucleotide sequences encodes 4-1BB, TIM-3, or other immune checkpoint blockade molecules.

22 . The computer-implemented method of claim 2 , wherein each labeled expression data set further comprises data from images, image features, clinical data, epigenetic data, pharmacogenetic data, metabolomics data, or a combination thereof.

23 . The computer-implemented method of claim 1 , wherein the unlabeled expression data set further comprises data from images, image features, clinical data, epigenetic data, pharmacogenetic data, metabolomics data, or a combination thereof, of the subject.

24 . The computer-implemented method of claim 1 , wherein the trained PD-L1 predictive model has been trained with a plurality of labeled expression data sets, each labeled expression data set comprises one or more labeled features, and the trained PD-L1 predictive model has been trained according to select labeled features pre-determined to have an association with a phenotype of biological relevance.

25 . The computer-implemented method of claim 24 , wherein the trained PD-L1 predictive model was trained using a clustering algorithm to determine which labeled features associate with the phenotype of biological relevance.

26 . The computer-implemented method of claim 25 , wherein the phenotype of biological relevance is PD-L1 expression status.

27 . The computer-implemented method of claim 24 , wherein the at least one or more of the select labeled features comprises expression data for at least one gene selected from the group consisting of CD274, TIGIT, CXCL13, IL21, FASLG, TFPI2, GAGE12C, POMC, PAX6, NPHS1, HLA-DPB1, PDCD1, PDCD1LG2, IFNG, GZMB, CXCL9, TGFB1, VIM, STX2, and ZEB2.

28 . The computer-implemented method of claim 1 , wherein the trained PD-L1 predictive model is a logistic regression model, a random forest model, or a support vector machine (SVM) model, optionally, wherein the logistic regression model is a single-gene or multi-gene logistic regression model.

29 . The computer-implemented method of claim 1 , wherein the labeled PD-L1 expression status is based on an reverse phase protein array (RPPA) data, fluorescence in situ hybridization (FISH) data, immunohistochemistry (IHC) data, or a combination thereof, optionally, wherein the trained PD-L1 predictive model correlates the labeled PD-L1 expression status with select labeled expression data and/or labeled features.

30 . The computer-implemented method of claim 1 , the method further comprising:

generating a clinical decision support information (CDSI) report including at least the subject's identity and the identified PD-L1 expression status, and, optionally, providing the CDSI report to a healthcare provider for use in selecting a candidate therapy based on the identified PD-L1 expression status for the subject's sample.

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