Pai, Reetesh K

Abstract Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic 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 morphologic features, we analyzed 402 resected stage III colon carcinomas [191 deficient (d)-MMR; 189 proficient (p)-MMR] from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1,094 p-MMR). Association of morphologic features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazards models were developed to predict TTR. Tumor morphologic 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 (TIL), 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% confidence interval (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 morphologic differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis. Significance: A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.

Abstract Purpose: To assess whether MUC1 peptide vaccine produces an immune response and prevents subsequent colon adenoma formation. Patients and Methods: Multicenter, double-blind, placebo-controlled randomized trial in individuals age 40 to 70 with diagnosis of an advanced adenoma ≤1 year from randomization. Vaccine was administered at 0, 2, and 10 weeks with a booster injection at week 53. Adenoma recurrence was assessed ≥1 year from randomization. The primary endpoint was vaccine immunogenicity at 12 weeks defined by anti-MUC1 ratio ≥2.0. Results: Fifty-three participants received the MUC1 vaccine and 50 placebo. Thirteen of 52 (25%) MUC1 vaccine recipients had a ≥2-fold increase in MUC1 IgG (range, 2.9–17.3) at week 12 versus 0/50 placebo recipients (one-sided Fisher exact P < 0.0001). Of 13 responders at week 12, 11 (84.6%) responded to a booster injection at week 52 with a ≥2-fold increase in MUC1 IgG measured at week 55. Recurrent adenoma was observed in 31 of 47 (66.0%) in the placebo group versus 27 of 48 (56.3%) in the MUC1 group [adjusted relative risk (aRR), 0.83; 95% confidence interval (CI), 0.60–1.14; P = 0.25]. Adenoma recurrence occurred in 3/11 (27.3%) immune responders at week 12 and week 55 (aRR, 0.41; 95% CI, 0.15–1.11; P = 0.08 compared with placebo). There was no difference in serious adverse events. Conclusions: An immune response was observed only in vaccine recipients. Adenoma recurrence was not different than placebo, but a 38% absolute reduction in adenoma recurrence compared with placebo was observed in participants who had an immune response at week 12 and with the booster injection.

Objective Effective medical therapy and validated trial outcomes are lacking for small bowel Crohn’s disease (CD) strictures. Histopathology of surgically resected specimens is the gold standard for correlation with imaging techniques. However, no validated histopathological scoring systems are currently available for small bowel stricturing disease. We convened an expert panel to evaluate the appropriateness of histopathology scoring systems and items generated based on panel opinion. Design Modified RAND/University of California Los Angeles methodology was used to determine the appropriateness of 313 candidate items related to assessment of CD small bowel strictures. Results In this exercise, diagnosis of naïve and anastomotic strictures required increased bowel wall thickness, decreased luminal diameter or internal circumference, and fibrosis of the submucosa. Specific definitions for stricture features and technical sampling parameters were also identified. Histopathologically, a stricture was defined as increased thickness of all layers of the bowel wall, fibrosis of the submucosa and bowel wall, and muscularisation of the submucosa. Active mucosal inflammatory disease was defined as neutrophilic inflammation in the lamina propria and any crypt or intact surface epithelium, erosion, ulcer and fistula. Chronic mucosal inflammatory disease was defined as crypt architectural distortion and loss, pyloric gland metaplasia, Paneth cell hyperplasia, basal lymphoplasmacytosis, plasmacytosis and fibrosis, or prominent lymphoid aggregates at the mucosa/submucosa interface. None of the scoring systems used to assess CD strictures were considered appropriate for clinical trials. Conclusion Standardised assessment of gross pathology and histopathology of CD small bowel strictures will improve clinical trial efficiency and aid drug development.