Gene expression analysis techniques using gene ranking and statistical models for identifying biological sample characteristics

Abstract

Techniques for determining one or more characteristics of a biological sample using rankings of gene expression levels in expression data obtained using one or more sequencing platforms is described. The techniques may include obtaining expression data for a biological sample of a subject. The techniques further include ranking genes in a set of genes based on their expression levels in the expression data to obtain a gene ranking and determining using the gene ranking and a statistical model, one or more characteristics of the biological sample.

What is claimed is: 
     
         1 - 30 . (canceled) 
     
     
         31 . A system, comprising:
 at least one computer hardware processor; and   at least one non-transitory computer-readable storage medium storing processor-executable instructions configured to perform a method comprising:
 accessing a trained statistical model comprising parameters estimated using training data, the training data indicating a plurality of gene rankings of at least some genes in a set of genes, the parameters being estimated after the at least some genes in the set of genes were selected; 
 obtaining expression data for a biological sample of a subject, the expression data previously obtained at least in part by sequencing the biological sample and comprising first expression levels for the set of genes; 
 ranking the at least some genes in the set of genes, based on their first expression levels in the expression data to obtain a gene ranking; and 
 determining at least one characteristic of the biological sample by providing the gene ranking as an input to the trained statistical model and processing the input with the trained statistical model, using the parameters, to obtain an output indicating the at least one characteristic. 
   
     
     
         32 . The system of  claim 31 , wherein the at least one characteristic is selected from the group consisting of cancer grade for cells in the biological sample, tissue of origin for cells in the biological sample, tissue type for cells in the biological sample, and cancer subtype for cells in the biological sample. 
     
     
         33 . The system of  claim 31 , wherein the at least one characteristic includes cancer grade for cells in the biological sample, and the cancer grade is selected from the group consisting of Grade 1, Grade 2, Grade 3, Grade 4, and Grade 5. 
     
     
         34 . The system of  claim 31 , wherein the at least one characteristic includes tissue of origin for cells in the biological sample, and the tissue of origin is selected from the group consisting of lung tissue, pancreas tissue, stomach tissue, colon tissue, liver tissue, bladder tissue, kidney tissue, thyroid tissue, lymph node tissue, adrenal gland tissue, skin tissue, breast tissue, ovary tissue, prostate tissue, urothelial tissue, cervical tissue, esophagus tissue, brain tissue, soft tissue, connective tissue, head tissue, and neck tissue. 
     
     
         35 . The system of  claim 31 , wherein the at least one characteristic includes tissue type for cells in the biological sample, and the tissue type is selected from the group consisting of adenocarcinoma, squamous cell carcinoma, carcinoma, cystadenocarcinoma, sarcoma, and glioma. 
     
     
         36 . The system of  claim 31 , wherein the at least one characteristic includes human papillomavirus (HPV) status for cells in the biological sample, and wherein the set of genes includes at least 5 genes selected from the group of genes listed in Table 8. 
     
     
         37 . The system of  claim 31 , wherein the at least one characteristic includes a subtype of peripheral T-cell lymphoma (PTCL) for cells in the biological sample, and wherein the set of genes includes at least 5 genes selected from the group of genes listed in Table 10. 
     
     
         38 . The system of  claim 37 , wherein the subtype of PTCL is selected from the group consisting of: anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), natural killer/T-cell lymphoma (NKTCL), and adult T-cell leukemia/lymphoma (ATLL). 
     
     
         39 . The system of  claim 31 , wherein the method further comprises: presenting, to a user, an indication of the at least one characteristic via a graphical user interface (GUI). 
     
     
         40 . A method, comprising:
 using at least one computer hardware processor to perform:
 accessing a trained statistical model comprising parameters estimated using training data, the training data indicating a plurality of gene rankings of some genes in a set of genes, the parameters being estimated after the some genes in the set of genes were selected; 
 obtaining expression data for a biological sample of a subject, the expression data previously obtained at least in part by sequencing the biological sample and comprising first expression levels for the set of genes; 
 ranking the some genes in the set of genes, based on their first expression levels in the expression data to obtain a gene ranking; and 
 determining at least one characteristic of the biological sample by providing the gene ranking as an input to the trained statistical model and processing the input with the trained statistical model, using the parameters, to obtain an output indicating the at least one characteristic. 
   
     
     
         41 . The method of  claim 40 , wherein the at least one characteristic includes cancer grade for cells in the biological sample. 
     
     
         42 . The method of  claim 41 , wherein the subject has, is suspected of having, or is at risk of having breast cancer, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 1. 
     
     
         43 . The method of  claim 42 , wherein the set of genes comprises at least 10 genes selected from the group of genes listed in Table 1. 
     
     
         44 . The method of  claim 41 , wherein the subject has, is suspected of having, or is at risk of having kidney cancer, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 2. 
     
     
         45 . The method of  claim 41 , wherein the subject has, is suspected of having, or is at risk of having lymphoma, and wherein the set of genes comprises at least 5 genes selected from the group of genes listed in Table 3. 
     
     
         46 . The method of  claim 45 , wherein the subject has, is suspected of having, or is at risk of having Diffuse Large B-Cell Lymphoma (DLBCL), the set of genes comprises at least 10 genes selected from the group of genes listed in Table 3, and the at least one characteristic is a cell of origin selected from the group consisting of germinal center B-cell (GCB) and activated B-cell (ABC). 
     
     
         47 . The method of  claim 40 , wherein the at least one characteristic is selected from the group consisting of cancer grade for cells in the biological sample, tissue of origin for cells in the biological sample, tissue type for cells in the biological sample, and cancer subtype for cells in the biological sample. 
     
     
         48 . At least one non-transitory computer-readable storage medium storing processor-executable instructions that are configured to cause, when executed by at least one computer hardware processor, the at least one computer hardware processor to perform:
 accessing a trained statistical model previously trained and comprising parameters estimated using training data after at least some genes in a set of genes were selected, the training data indicating a plurality of gene rankings of the at least some genes in the set of genes;   obtaining expression data for a biological sample of a subject, the expression data previously obtained at least in part by sequencing the biological sample and comprising first expression levels for the set of genes;   ranking the at least some genes in the set of genes, based on their first expression levels in the expression data to obtain a gene ranking; and   determining at least one characteristic of the biological sample by providing the gene ranking as an input to the trained statistical model and processing the input with the trained statistical model, using the parameters, to obtain an output indicating the at least one characteristic.   
     
     
         49 . The at least one non-transitory computer-readable storage medium of  claim 48 , wherein the gene ranking includes a value identifying a relative rank for each gene of the at least some genes in the set of genes. 
     
     
         50 . The at least one non-transitory computer-readable storage medium of  claim 48 , wherein the gene ranking includes values identifying relative ranks of the at least some genes in the gene ranking, wherein the values are different from the first expression levels.