Pai, Rish K.

Abstract Background: DEPendency of association on the number of Top Hits (DEPTH) is an approach to identify candidate susceptibility regions by considering the risk signals from overlapping groups of sequential variants across the genome. Methods: We applied a DEPTH analysis using a sliding window of 200 SNPs to colorectal cancer data from the Colon Cancer Family Registry (CCFR; 5,735 cases and 3,688 controls), and Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO; 8,865 cases and 10,285 controls) studies. A DEPTH score > 1 was used to identify candidate susceptibility regions common to both analyses. We compared DEPTH results against those from conventional genome-wide association study (GWAS) analyses of these two studies as well as against 132 published susceptibility regions. Results: Initial DEPTH analysis revealed 2,622 (CCFR) and 3,686 (GECCO) candidate susceptibility regions, of which 569 were common to both studies. Bootstrapping revealed 40 and 49 candidate susceptibility regions in the CCFR and GECCO data sets, respectively. Notably, DEPTH identified at least 82 regions that would not be detected using conventional GWAS methods, nor had they been identified by previous colorectal cancer GWASs. We found four reproducible candidate susceptibility regions (2q22.2, 2q33.1, 6p21.32, 13q14.3). The highest DEPTH scores were in the human leukocyte antigen locus at 6p21 where the strongest associated SNPs were rs762216297, rs149490268, rs114741460, and rs199707618 for the CCFR data, and rs9270761 for the GECCO data. Conclusions: DEPTH can identify candidate susceptibility regions for colorectal cancer not identified using conventional analyses of larger datasets. Impact: DEPTH has potential as a powerful complementary tool to conventional GWAS analyses for discovering susceptibility regions within the genome.

e22537 Background: Germline and tumor next-generation sequencing (NGS) are complementary tests whose benefit in capturing patients (pts) potentially eligible for precision therapies is maximized when the tests are paired, as qualifying pathogenic variants (PVs) are missed if either test was done in isolation (Lincoln et al. 2020). Germline NGS has the additional benefit of informing prevention through surveillance and cascade testing. We highlight the benefits of a universal approach to concurrent testing in a subset of the previously reported cohort of pan-cancer pts who underwent universal germline NGS (Samadder et al. 2020). Methods: Unselected pts with solid tumors underwent an 80+ gene germline panel and 315+ gene tumor panel. Variants in genes unique to the germline or tumor panels, including microsatellite instability (MSI) and tumor mutation burden (TMB), were also evaluated. Results: 104 pts [81.7% White, non-Hispanic, 71.2% male] with 23 cancer types [28% colorectal, 13% lung, 11% prostate, 49% other] were included. Five (5%) pts had no actionable tumor alterations or germline variants identified (Table). 18 pts (17%) had 19 pathogenic germline variants (PGVs) identified, 11 (58%) of which were detected on tumor NGS (Table). 92 pts had >1 tumor alteration identified, of which 12% were confirmed as PGVs (Table). Notably, tumor NGS revealed 81 pts (78%) pts with biomarkers that would could not have been identified with germline testing alone, such as variants in genes not on germline panel (80) or MSI-high (4), or TMB-high (21) status of the tumor. Conclusions: Germline and tumor NGS are complementary tests that often detect different actionable alterations. There is value in confirming both the presence and absence of somatic variants in the germline in order to make appropriate clinical recommendations for the patient and their family members. [Table: see text]

3527 Background: Advances in deep learning may improve the ability to evaluate and quantify pathological features in solid tumors to improve prediction of patient disease-free survival (DFS). We used an artificial intelligence (AI) algorithm to quantify morphological features in the tumor microenvironment of stage III colon cancers. Methods: We analyzed digitized stage III colon carcinomas (N = 402; 382 met QC) from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy that included all available tumors with dMMR and a randomly selected cohort of pMMR tumors (median follow-up 60 months) [NCCTG N0147; Alliance for Clinical Trials in Oncology]. Fifteen morphological features were extracted using the QuantCRC algorithm that segments images into carcinoma (low-grade, high-grade, signet ring cell), stroma (immature, mature, and inflammatory), mucin, tumor/stroma ratio, tumor budding/poorly differentiated clusters (TB/PDC), necrosis, smooth muscle, fat, and tumor-infiltrating lymphocytes (TILs) and tumor area (mucin, epithelium and TB/PDC). Analysis of AI-derived features with clinical variables, molecular alterations ( BRAF/KRAS), and DFS were examined using Kaplan-Meier methodology and multivariable Cox regression. Results: Among the 15 AI-derived morphological features, the following differed significantly between dMMR (n = 191) and pMMR (n = 189) whereby dMMR tumors had lower mature stroma, but higher inflammatory stroma and tumor, higher tumor grade and increased mucin, TB/PDCs, signet ring cells, and TILs (all p< 0.05). Among dMMR tumors, multivariable analysis revealed that tumor area [mucin, epithelium and TB/PDC] (HR adj 0.45, 95%CI (0.24, 0.84), p:0.01, [Q2/3 vs Q1]) and N stage were the only significant and independent prognostic variables. Among pMMR tumors, multivariable analysis identified that tumor budding/poorly differentiated clusters (TB/PDCs) (HR adj 0.20, 95%CI (0.06, 0.61), p:0.01, [Q1 vs Q4]) was the strongest prognostic variable and the only morphological feature that was significantly associated with DFS along with age, N stage and T stage. Conclusions: Using AI, we can extract and quantify distinct morphological features in tumor sections that differ between dMMR and pMMR and multivariately, can significantly and robustly enhance prognostication within each MMR group. Among pMMR tumors, tumor budding/poorly differentiated clusters was the strongest predictor of DFS. Support: NIH U10CA180821, U10CA180882, U24CA196171, R01 CA210509 (to FAS). Study NCCTG N0147 received funds from Sanofi https://acknowledgments.alliancefound.org . ClinicalTrials.gov Identifier: NCT00079274 Key words Artificial Intelligence; Tumor Microenvironment; Colonic Neoplasms; Disease-free survival.

Abstract Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. Significance: This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

Abstract Background and Aims To evaluate the effect of mirikizumab, a p19-targeted anti-interleukin-23, on histological and/or endoscopic outcomes in moderately-to-severely active ulcerative colitis [UC]. Methods Endoscopic remission [ER], histological improvement [HI], histological remission [HR], histological-endoscopic mucosal improvement [HEMI], and histological-endoscopic mucosal remission [HEMR] were assessed at Week [W]12 [LUCENT-1: N = 1162, induction] and W40 [LUCENT-2: N = 544, maintenance] for patients randomised to mirikizumab or placebo. Analyses were performed to evaluate predictors of: HEMI at W12 with mirikizumab and HEMR at W40 in patients re-randomised to subcutaneous [SC] mirikizumab; associations between W12 histological/endoscopic endpoints and W40 outcomes in mirikizumab responders re-randomised to mirikizumab SC; and associations between W40 endoscopic normalisation [EN] with/without HR. Results Significantly more patients treated with mirikizumab achieved HI, HR, ER, HEMI, and HEMR vs placebo [p &lt;0.001], irrespective of prior biologic/tofacitinib failure [p &lt;0.05]. Lower clinical baseline disease activity, female sex, no baseline immunomodulator use, and no prior biologic/tofacitinib failure were predictors of HEMI at W12 [p &lt;0.05]. Corticosteroid use and longer disease duration were negative predictors of achieving HEMR at W40 [p &lt;0.05]. W12 HI, HR, or ER was associated with W40 HEMI or HEMR [p &lt;0.05]; ER at W12 was associated with clinical remission [CR] [p &lt;0.05] and corticosteroid-free remission [CSFR] at W40 [p = 0.052]. HR and HEMR at W12 were associated with CSFR, CR, and symptomatic remission at W40. Alternate HEMR [EN + HR] at W40 was associated with bowel urgency remission at W40 [p &lt;0.05]. Conclusions Early resolution of endoscopic and histological inflammation with mirikizumab is associated with better UC outcomes. Clinicaltrials.gov: LUCENT-1, NCT03518086; LUCENT-2, NCT03524092.

Abstract Artificial intelligence (AI) based digital pathology has been getting rapid adoption over the last 2 to 3 years These AI based digital pathology models can analyze samples at high resolution and scale Retaining that resolution at scale through molecular analysis is key to the discovery of novel biomarkers We have developed a technology that can integrate AI-supported digital pathology (DP) algorithms to identify informative tissue regions for selective microdissection to improve molecular analysis of FFPE material The system is capable of extracting DNA, RNA or proteins with cellular (10 micron) resolution from FFPE tissue sections. The samples are ready for use in off-the-shelf molecular analysis workflows (qPCR, Next Gen Sequencing, etc) The system decreases the tumor cellularity requirements for clinical analysis by extracting only the area enriched for informative biomarkers To demonstrate the capability of the system we used an AI based digital pathology model that was developed to identify multiple cellular phenotypes from a H&E stained colorectal cancer FFPE tissue sections We used the digital pathology output to guide the microdissection of RNA from tumor buds, carcinoma, immature stroma and inflammatory stroma in a paired designed RNA seq experiment The data showed that the system is capable of enriching the tumor-bud tissue content from&lt;1% in the tissue to ~50% purity in the crude lysate Bioinformatic analysis of the RNAseq signatures showed that the tumor buds had a distinct RNAseq signature relative to the other three cell types and that the gene expression pattern was consistent with EMT pathways. The methodology is scalable, robust, and simpler than current microdissection techniques. This technology can be used to develop novel tests by integrating with AI algorithms, targeting tissue enriched for predictive biomarkers, and enabling low tumor cellularity samples to be analyzed with high precision. Citation Format: John H. Butler, Bidhan Chaudhuri, Katie Konigsfeld, Rish Pai. A novel digitally directed tissue microdissection platform integrates digital pathology with molecular analysis: Case study in metastatic colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4320.

Abstract Background Histological remission (HR) is an achievable therapeutic target in ulcerative colitis (UC), however there is limited data on the association of HR with long-term clinical outcomes. We evaluated the association between HR and a composite outcome of colectomy, UC-related hospitalization, or intravenous (IV) corticosteroid use in patients with UC. Methods This was a multi-national retrospective study of adult patients with UC treated with a biologic or small molecule drug between 09/01/2005 and 06/30/2019 in Canada, Italy, Netherlands and United States. The study included biologic-naïve (bio-naïve) and biologic-experienced (bio-exp) patients. Biopsies taken during routine practice were procured and re-assessed for histological activity by blinded gastrointestinal pathologists in a central laboratory. HR was defined as the absence of neutrophilic inflammation (Nancy Histological Index ≤1 or Robarts Histological Index ≤3 plus Geboes subgrades 2B.0/3.0) at the most recent endoscopic evaluation within 3 to 9 months after treatment initiation. Cumulative risk of time-to-event outcome over 12 months from treatment initiation was evaluated using Kaplan-Meier (KM) methods and Cox proportional hazard analysis. Statistical significance was set as p-value &lt;0.05. Results We included 227 patients with UC (bio-naïve and bio-exp) (Table 1); of whom, 132 (58%) were bio-naïve (Table 2). Of the bio-naïve patients, 62 (47%) achieved HR and 70 (53%) had histologic activity; 9 (6.8%) underwent colectomy, 16 (12.1%) had UC-related hospitalizations, and 8 (6.1%) received IV corticosteroid. Unadjusted time-to-event for the composite outcome was significantly greater in bio-naïve patients who achieved HR compared to those who did not (p=0.0023; Figure 1A). In multivariable analysis, the association of HR on the composite outcome in bio-naïve patients was not statistically significant (adjusted hazard ratio [aHR], 0.26; 95% CI, 0.02-4.42). In the overall patients (bio-naïve and bio-exp), unadjusted (p&lt;0.001; Figure 1B) and multivariable (aHR, 0.32; 95% CI, 0.06-1.75) associations of HR and the composite outcome were similar to the bio-naïve patients. Conclusion We used central reading to evaluate the association of HR and clinical outcomes in patients with UC. While unadjusted KM curves showed that HR was associated with decreased risk of the composite outcome, the association was no longer significant after adjustment for covariates. The target sample size for this study was not achieved and the number of events was small, resulting in reduced statistical power. Future studies are warranted to explore the association further.