Methods and systems for determining proportions of distinct cell subsets

1. A method comprising:

(a) providing a biological sample derived from a subject having cancer, the biological sample comprising a plurality of cell populations;

(b) assaying said biological sample to generate a feature profile, wherein said feature profile comprises a gene expression profile, a genotype profile, a protein expression profile, a protein-protein interaction profile, a protein phosphorylation profile, a cellular electrical activity profile, a chromatin modification profile, a chromosome binding profile, an enzymatic activity profile, a metabolite profile, a nuclear magnetic resonance (NMR) spectrum, an electromagnetic radiation absorbance or emission spectrum, a circular dichroism spectrum, a Raman spectrum, a mass spectrum, a chromatogram, or a combination thereof;

(c) computer processing said feature profile to determine (i) an estimation of relative proportions of at least one of said plurality of cell populations in said biological sample, and (ii) a significance value for said estimation;

(d) predicting a clinical outcome of a cancer therapy for said cancer, based at least in part on said estimation of said relative proportions of said at least one of said plurality of cell populations in said biological sample; and

(e) administering said cancer therapy to said subject based on said predicted clinical outcome of said cancer therapy, wherein said cancer therapy comprises a member selected from the group consisting of a chemotherapy, an immunotherapy, and an immunochemotherapy.

2. The method of claim 1 , wherein (b) further comprises assaying deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) of cells in said biological sample.

3. The method of claim 1 , wherein (c) further comprises:

(i) optimizing a regression between a feature profile m and a reference matrix B of feature signatures for a second feature profile, wherein said feature profile m is modeled as a linear combination of said reference matrix B of feature signatures, wherein said optimization of said regression comprises solving for f comprising a set of regression coefficients of said regression, and wherein said optimization of said regression minimizes: a linear loss function; and an L2-norm penalty function; and

(ii) estimating, based on said optimization of said regression, said relative proportions of said at least one of said plurality of cell populations in said biological sample.

4. The method of claim 1 , wherein said feature profile m is said gene expression profile.

5. The method of claim 1 , wherein said cancer comprises a blood cancer.

6. The method of claim 1 , wherein said biological sample comprises a tumor biopsy.

7. The method of claim 1 , wherein said estimation of said relative proportions of said at least one of said plurality of cell populations in said biological sample comprises an estimation of a fractional representation of said at least one of said plurality of cell populations in said biological sample.

8. The method of claim 7 , further comprising determining a prognosis for said cancer based at least in part on a comparison between (i) said estimation of said fractional representation of said at least one of said plurality of cell populations in said biological sample and (ii) a reference fractional representation of said at least one of said plurality of cell populations in one or more reference samples.

9. The method of claim 8 , wherein said one or more reference samples are derived from a cohort of subjects having said cancer and/or a cohort of subjects without said cancer.

10. The method of claim 7 , wherein said clinical outcome of said cancer therapy for said cancer is predicted based at least in part on a comparison between (i) said estimation of said fractional representation of said at least one of said plurality of cell populations in said biological sample and (ii) a reference fractional representation of said at least one of said plurality of cell populations in one or more reference samples.

11. The method of claim 1 , wherein said immunochemotherapy comprises a combination of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP).

12. The method of claim 1 , wherein said immunotherapy comprises rituximab.

13. The method of claim 1 , wherein said subject has received said cancer therapy for said cancer.

14. The method of claim 1 , wherein (d) further comprises determining that a first cell population of said plurality of cell populations is undetectable in said biological sample.

15. The method of claim 14 , wherein said first cell population is a population of lymphoma cells.

16. The method of claim 14 , wherein said first cell population is a population of immune cells.

17. The method of claim 1 , wherein (d) further comprises determining said estimation of said fractional representation of a population of cancer cells in said biological sample.

18. The method of claim 17 , wherein said population of cancer cells comprises blood cancer cells, breast cancer cells, colon cancer cells, lung cancer cells, prostate cancer cells, hepatocellular cancer cells, gastric cancer cells, pancreatic cancer cells, cervical cancer cells, ovarian cancer cells, liver cancer cells, bladder cancer cells, urinary tract cancer cells, thyroid cancer cells, renal cancer cells, carcinoma cells, melanoma cells, or brain cancer cells.

19. The method of claim 1 , wherein (d) further comprises determining said estimation of said fractional representation of a population of tumor infiltrating leukocytes in said biological sample.

20. The method of claim 1 , wherein said biological sample comprises a blood sample.

21. The method of claim 1 , wherein said cancer therapy comprises said chemotherapy.

22. The method of claim 1 , wherein said cancer therapy comprises said immunotherapy.

23. The method of claim 1 , wherein said cancer therapy comprises said immunochemotherapy.