Use of deep learning to evaluate tumor microenvironmental features for prediction of colon cancer recurrence.

Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphological features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphological features, we analyzed 402 resected stage III colon carcinomas (191 d-MMR; 189 p-MMR) from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1094 p-MMR). Association of morphological features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazard models were developed to predict TTR. Tumor morphological features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TILs), high grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% CI,1.14-3.57; P=0.018; 3-year recurrence: 40.2% vs 20.4%; Q1 vs Q2-4]. Among d-MMR tumors, extent of inflammatory stroma [continuous HRadj 0.98; 95% CI,0.96-0.99; P=0.028; 3-year recurrence: 13.3% vs 33.4%, Q4 vs Q1] and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphological differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis.