Harrison, Tabitha A.

Background: Considering the pathophysiological importance of the intestine-passing microbiota, we hypothesized that the mutational and microbial profiles of colorectal cancer might gradually change along detailed anatomical locations and that these trends might differ by the age of diagnosis and the microsatellite instability (MSI) status. Methods: We assessed somatic mutations and selected bacterial species in colorectal carcinomas along the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum, utilizing targeted sequencing data ( N = 5685) from the Genetics and Epidemiology of Colorectal Cancer Consortium and tissue microbial data ( N = 1389) from the Health Professionals Follow-up Study and Nurses’ Health Study. Results: There were decreasing trends from the cecum to rectum in the prevalence of gene mutations in the IGF/PI3K, receptor tyrosine kinase (RTK)/RAS, TGFB, and WNT signaling pathways (all P trend < 0.0001), as well as high levels of Bacteroides fragilis ( P trend < 0.0001) and high levels of Fusobacterium nucleatum ( P trend = 0.0029) (but not enterotoxigenic Bacteroides fragilis or pks+ Escherichia coli ). Conversely, TP53 pathway mutations showed an increasing cecum-to-rectum trend ( P trend < 0.0001). Similar to later-onset cases diagnosed at ages 50 years and older, in early-onset cases diagnosed under the age of 50 years, we found generally decreasing cecum-to-rectum trends of IGF/PI3K, RTK/RAS, TGFB, and WNT pathway mutations and an increasing cecum-to-rectum trend in TP53 pathway mutations. By contrast, the cecum-to-rectum prevalence-changing trends in IGF/PI3K, RTK/RAS, TGFB, and TP53 pathway mutations were present in non–MSI-high tumors but not in MSI-high tumors (all P interaction < 0.0052). Conclusion: Non–MSI-high colorectal cancers exhibited decreasing cecum-to-rectum trends of IGF/PI3K, RTK/RAS, and TGFB pathway mutations (and an increasing trend of TP53 pathway mutations), in contrast to MSI-high tumors that demonstrated no such trends. Our study highlights the importance of detailed colorectal subsite information combined with tumor molecular and microbial characteristics in colorectal cancer research.

Abstract Background Physical activity is associated with lower colorectal cancer (CRC) risk, but its association with molecular subtypes defined by genetic and epigenetic alterations of the disease is unclear. Such information may enhance the understanding of the mechanisms related to the benefits of physical activity. Methods Pooled observational (cases: n = 5386; controls: n = 6798; studies n = 5) and genome-wide association data (cases: n = 8178; controls: n = 10 472; studies n = 5) were used. We used multivariable logistic regression models and Mendelian randomization to assess the association between physical activity and the risk of CRC subtypes defined by individual tumor markers (and marker combinations), namely microsatellite instability status, CpG island methylator phenotype status, and BRAF and KRAS mutations. We used case-only analysis to test for differences between molecular subtypes. We applied Bonferroni correction to account for multiple tests. Results In the pooled observational analysis, higher levels of physical activity were associated with lower CRC risk (Obs-per 1SD, odds ratio [OR] = 0.94, 95% confidence interval [CI] = 0.90 to 0.97), with an association that was stronger in males (Obs-per 1SD, OR = 0.91, 95% CI = 0.87 to 0.96) than in females (Obs-per 1SD, OR = 0.97, 95% CI = 0.91 to 1.03; Pinteraction = .04). Higher physical activity was associated with a lower risk of CRC across all molecular subtypes, especially in males. There was no difference in the associations by subtypes by pooled observational or Mendelian randomization analyses. The findings did not differ by study design, anatomical site, and early or late age onset of CRC. Conclusions Our findings suggest that physical activity is not differentially associated with the 4 major molecular subtypes involved in colorectal carcinogenesis, indicating that its benefits extend broadly across colorectal cancer pathogenesis.

Abstract Background Physical activity (PA) is an established protective factor for colorectal cancer (CRC), but it is unclear if genetic variants modify this effect. To investigate this possibility, we conducted a genome-wide gene–PA interaction analysis. Methods Using logistic regression and two-step and joint tests, we analyzed interactions between common genetic variants across the genome and PA in relation to CRC risk. Self-reported PA levels were categorized as active (≥ 8.75 MET-h/wk) vs. inactive (< 8.75 MET-h/wk) and as study- and sex-specific quartiles of activity. Results PA had an overall protective effect on CRC (OR [active vs. inactive] = 0.85; 95%CI = 0.81–0.90). The two-step GxE method identified an interaction between rs4779584, an intergenic variant near the GREM1 and SCG5 genes, and PA for CRC risk (p-interaction = 2.6×10 − 8 ). Stratification by genotype at this locus showed a significant reduction in CRC risk by 20% in active vs. inactive participants with the CC genotype (OR = 0.80; 95%CI = 0.75–0.85), but no significant PA–CRC association among CT or TT carriers. When PA was modeled as quartiles, the 1-d.f. GxE test identified that rs56906466, an intergenic variant near the KCNG1 gene, modified the association between PA and CRC (p-interaction = 3.5×10 − 8 ). Stratification at this locus showed that increase in PA (highest vs. lowest quartile) was associated with a lower CRC risk solely among TT carriers (OR = 0.77; 95%CI = 0.72–0.82). Conclusions In summary, we identified two genetic variants that modified the association between PA and CRC risk. One of them, related to GREM1 and SCG5 , suggests that the bone morphogenetic protein (BMP)-related, inflammatory, and/or insulin signaling pathways may be associated with the protective influence of PA on colorectal carcinogenesis.

Abstract Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene–environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P &lt; 5 × 10−8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.

Abstract Background: Waist circumference (WC) and its allometric counterpart, “a body shape index” (ABSI), are risk factors for colorectal cancer; however, it is uncertain whether associations with these body measurements are limited to specific molecular subtypes of the disease. Methods: Data from 2,772 colorectal cancer cases and 3,521 controls were pooled from four cohort studies within the Genetics and Epidemiology of Colorectal Cancer Consortium. Four molecular markers (BRAF mutation, KRAS mutation, CpG island methylator phenotype, and microsatellite instability) were analyzed individually and in combination (Jass types). Multivariable logistic and multinomial logistic models were used to assess the associations of WC and ABSI with overall colorectal cancer risk and, in case-only analyses, to evaluate heterogeneity by molecular subtype, respectively. Results: Higher WC (ORper 5 cm = 1.06, 95% confidence interval, 1.04–1.09) and ABSI (ORper 1-SD = 1.07, 95% confidence interval, 1.00–1.14) were associated with elevated colorectal cancer risk. There was no evidence of heterogeneity between the molecular subtypes. No difference was observed regarding the influence of WC and ABSI on the four major molecular markers in proximal colon, distal colon, and rectal cancers, as well as in early- and late-onset colorectal cancers. Associations did not differ in the Jass-type analysis. Conclusions: Higher WC and ABSI were associated with elevated colorectal cancer risk; however, they do not differentially influence all four major molecular mutations involved in colorectal carcinogenesis but underscore the importance of maintaining a healthy body weight in colorectal cancer prevention. Impact: The proposed results have potential utility in colorectal cancer prevention.

Regular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769 ), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047 ), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.

Abstract We used deep learning (DL) to predict multiple genetic biomarkers of colorectal cancer (CRC) from routine histopathology images, aiming to identify morphological changes associated with the mutational profile of tumors. A new Transformer-based prediction model was developed which can simultaneously predict multiple biomarkers from a single histopathology image. We compared the performance of the multi-target approach to conventional single-target models. We analyzed five patient cohorts from the Genetics and Epidemiology of CRC Consortium (GECCO) with colorectal cancer (N=1,385 patients). Our model was trained on an internal data set (N=739) to predict the presence of &gt;200 genetic alterations, focusing on genes with non-silent mutations and mutational signatures, which were assessed using targeted sequencing. We used a 7-fold cross validation and validated the model on an independent, external data set (N=646). Morphological features of microsatellite instability (MSI) are detectable by DL models in histopathology images, making MSI a potential confounding factor. Thus, we also assessed whether our model can predict genetic alterations regardless of MSI status, or whether the model identifies the MSI phenotype. For the majority of biomarkers, the multi-target transformer reached a higher prediction performance as measured by the mean Area Under the Receiver Operating Characteristic (AUROC) Curve and lower standard deviation than single-target DL models. For the external validation set, the mean AUROC of the multi-target versus the single-target model was 0.78 (+/- 0.01) versus 0.72 (+/- 0.06) for BRAF mutations, 0.88 (+/- 0.01) versus 0.86 (+/- 0.03) for hypermutation status, 0.94 (+/- 0.01) versus 0.91 (+/- 0.02) for MSI, 0.86 (+/- 0.01) versus 0.80 (+/- 0.05) for RNF43 mutations and 0.72 (+/- 0.02) versus 0.69 (+/- 0.05) for TP53 mutations, respectively. However, our analysis of the individual prediction scores suggests that the model does not identify the phenotype of the alterations themselves. Instead, the model seems to determine the status of specific genetic alterations based on their correlation with MSI. Consequently, biomarkers which are associated with microsatellite stability have prediction scores negatively correlated with predicted MSI scores. Conversely, biomarkers which are associated with MSI have prediction scores positively correlated with predicted MSI scores. Our results demonstrate potential benefits of multi-target DL models in improving the predictive power and efficacy of histology-based biomarker extraction compared to single-target DL models. However, our results crucially highlight the importance of considering potential confounders, such as MSI status in CRC, and emphasize the need for extensive analysis beyond AUROC in studies focusing on DL-based biomarker detection. Citation Format: Marco Gustav, Marko van Treeck, Zunamys I. Carrero, Chiara M. Loeffler, Nic G. Reitsam, Bruno Märkl, Asier Rabasco Meneghetti, Lisa A. Boardman, Amy J. French, Ellen L. Goode, Andrea Gsur, Stefanie Brezina, Marc J. Gunter, Neil Murphy, Paul Limburg, Stephen Thibodeau, Sebastian Foersch, Robert Steinfelder, Tabitha Harrison, Ulrike Peters, Amanda Phipps, Jakob N. Kather. Assessing microsatellite instability dominance in colorectal cancer phenotype: A multi-study initiative using multi-target transformers for genomic biomarker prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4924.

Abstract Background: The genotoxin colibactin causes a tumor single-base substitution (SBS) mutational signature, SBS88. It is unknown whether epidemiologic factors’ association with colorectal cancer risk and survival differs by SBS88. Methods: Within the Genetic Epidemiology of Colorectal Cancer Consortium and Colon Cancer Family Registry, we measured SBS88 in 4,308 microsatellite stable/microsatellite instability low tumors. Associations of epidemiologic factors with colorectal cancer risk by SBS88 were assessed using multinomial regression (N = 4,308 cases, 14,192 controls; cohort-only cases N = 1,911), and with colorectal cancer–specific survival using Cox proportional hazards regression (N = 3,465 cases). Results: 392 (9%) tumors were SBS88 positive. Among all cases, the highest quartile of fruit intake was associated with lower risk of SBS88-positive colorectal cancer than SBS88-negative colorectal cancer [odds ratio (OR) = 0.53, 95% confidence interval (CI) 0.37-0.76; OR = 0.75, 95% CI 0.66–0.85, respectively, Pheterogeneity = 0.047]. Among cohort studies, associations of body mass index (BMI), alcohol, and fruit intake with colorectal cancer risk differed by SBS88. BMI ≥30 kg/m2 was associated with worse colorectal cancer–specific survival among those SBS88-positive [hazard ratio (HR) = 3.40, 95% CI 1.47–7.84], but not among those SBS88-negative (HR = 0.97, 95% CI 0.78–1.21, Pheterogeneity = 0.066). Conclusions: Most epidemiologic factors did not differ by SBS88 for colorectal cancer risk or survival. Higher BMI may be associated with worse colorectal cancer–specific survival among those SBS88-positive; however, validation is needed in samples with whole-genome or whole-exome sequencing available. Impact: This study highlights the importance of identification of tumor phenotypes related to colorectal cancer and understanding potential heterogeneity for risk and survival.

AbstractWe conducted the first large genome‐wide association study to identify novel genetic variants that predict better (or poorer) prognosis in colorectal cancer patients receiving standard first‐line oxaliplatin‐based chemotherapy vs chemotherapy without oxaliplatin. We used data from two phase III trials, NCCTG N0147 and NCCTG N9741 and a population‐based patient cohort, DACHS. Multivariable Cox proportional hazards models were employed, including an interaction term between each SNP and type of treatment for overall survival (OS) and progression‐free survival. The analysis was performed for studies individually, and the results were combined using fixed‐effect meta‐analyses separately for resected stage III colon cancer (3098 patients from NCCTG N0147 and 549 patients from DACHS) and mCRC (505 patients from NCCTG N9741 and 437 patients from DACHS). We further performed gene‐based analysis as well as in silico bioinformatics analysis for CRC‐relevant functional genomic annotation of identified loci. In stage III colon cancer patients, a locus on chr22 (rs11912167) was associated with significantly poorer OS after oxaliplatin‐based chemotherapy vs chemotherapy without oxaliplatin (Pinteraction < 5 × 10−8). For mCRC patients, three loci on chr1 (rs1234556), chr12 (rs11052270) and chr15 (rs11858406) were found to be associated with differential OS (P < 5 × 10−7). The locus on chr1 located in the intronic region of RCSD1 was replicated in an independent cohort of 586 mCRC patients from ALGB/SWOG 80405 (Pinteraction = .04). The GWA gene‐based analysis yielded for RCSD1 the most significant association with differential OS in mCRC (P = 6.6 × 10−6). With further investigation into its biological mechanisms, this finding could potentially be used to individualize first‐line treatment and improve clinical outcomes.

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 &gt; 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.