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Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license., Comment: Updated Figure 2 and Table A.5

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.

Evidence on the relationship between the vitamin D pathway and outcomes in melanoma is growing, although it is not always clear. We investigated the impact of measured levels of sun exposure at diagnosis on associations of vitamin D receptor gene (VDR) polymorphisms and melanoma-death in 3336 incident primary melanoma cases. Interactions between six SNPs and a common 3′ end haplotype were significant (p

Additional file 1 Supplementary tables 1–6, supplementary figures 1–3, supplementary text 1. Suppl. table 1 - Summary statistics for the genetic association with bilirubin levels, and sex-stratified colorectal cancer risk. Suppl. table 2 - Participating studies in the genetic consortia (GECCO/ CCFR/ and CORECT). Suppl. table 3 - Associations between unconjugated bilirubin (UCB) levels and colorectal cancer risk after different sensitivity analyses in the EPIC study. Suppl. table 4 - Results for the Mendelian randomization sensitivity analyses. Suppl. table 5 - Results for the Mendelian randomization sensitivity analyses: positive control outcomes for pancreatic cancer. Suppl. table 6 - Results for genome-wide associations of instruments with other phenotypes. Suppl. figure 1 - Cubic spline modeling of unconjugated bilirubin (UCB) levels in relation to colorectal cancer risk in the EPIC study. Suppl. figure 2 - Association between UGT1A1 polymorphism (rs6431625) and unconjugated bilirubin (UCB) levels in the EPIC study (with available GWAS data). Suppl. figure 3 - Directed acyclic graph (DAG) of the causal structure of associations between unconjugated bilirubin (UCB) levels in relation to colorectal cancer risk in the EPIC study. Suppl. text 1 - Specific funding sources and acknowledgements of participating studies.

Background: Previous genome-wide association studies (GWAS) have identified 42 loci (P

Background: Many cancers share specific genetic risk factors, including both rare high-penetrance mutations and common SNPs identified through genome-wide association studies (GWAS). However, little is known about the overall shared heritability across cancers. Quantifying the extent to which two distinct cancers share genetic origin will give insights to shared biological mechanisms underlying cancer and inform design for future genetic association studies.Methods: In this study, we estimated the pair-wise genetic correlation between six cancer types (breast, colorectal, lung, ovarian, pancreatic, and prostate) using cancer-specific GWAS summary statistics data based on 66,958 case and 70,665 control subjects of European ancestry. We also estimated genetic correlations between cancers and 14 noncancer diseases and traits.Results: After adjusting for 15 pair-wise genetic correlation tests between cancers, we found significant (P < 0.003) genetic correlations between pancreatic and colorectal cancer (rg = 0.55, P = 0.003), lung and colorectal cancer (rg = 0.31, P = 0.001). We also found suggestive genetic correlations between lung and breast cancer (rg = 0.27, P = 0.009), and colorectal and breast cancer (rg = 0.22, P = 0.01). In contrast, we found no evidence that prostate cancer shared an appreciable proportion of heritability with other cancers. After adjusting for 84 tests studying genetic correlations between cancer types and other traits (Bonferroni-corrected P value: 0.0006), only the genetic correlation between lung cancer and smoking remained significant (rg = 0.41, P = 1.03 × 10-6). We also observed nominally significant genetic correlations between body mass index and all cancers except ovarian cancer.Conclusions: Our results highlight novel genetic correlations and lend support to previous observational studies that have observed links between cancers and risk factors.Impact: This study demonstrates modest genetic correlations between cancers; in particular, breast, colorectal, and lung cancer share some degree of genetic basis. Cancer Epidemiol Biomarkers Prev; 26(9); 1427-35. ©2017 AACR.

Growing evidence from both epidemiology and basic science suggest an inverse association between Alzheimer’s disease (AD) and cancer. We examined the genetic relationship between AD and various cancer types using GWAS summary statistics from the IGAP and GAME-ON consortia. Sample size ranged from 9931 to 54,162; SNPs were imputed to the 1000 Genomes European panel. Our results based on cross-trait LD Score regression showed a significant positive genetic correlation between AD and five cancers combined (colon, breast, prostate, ovarian, lung; r g = 0.17, P = 0.04), and specifically with breast cancer (ER-negative and overall; r g = 0.21 and 0.18, P = 0.035 and 0.034) and lung cancer (adenocarcinoma, squamous cell carcinoma and overall; r g = 0.31, 0.38 and 0.30, P = 0.029, 0.016, and 0.006). Estimating the genetic correlation in specific functional categories revealed mixed positive and negative signals, notably stronger at annotations associated with increased enhancer activity. This suggests a role of gene expression regulators in the shared genetic etiology between AD and cancer, and that some shared variants modulate disease risk concordantly while others have effects in opposite directions. Due to power issues, we did not detect cross-phenotype associations at individual SNPs. This genetic overlap is not likely driven by a handful of major loci. Our study is the first to examine the co-heritability of AD and cancer leveraging large-scale GWAS results. The functional categories highlighted in this study need further investigation to illustrate the details of the genetic sharing and to bridge between different levels of associations.

A rare germline variant in the microphthalmia-associated transcription factor (MITF) gene, E318K, has been reported as associated with melanoma. We confirmed its independent association with melanoma [odds ratio (OR) 1.7, 95% confidence interval (CI) = 1.1, 2.7, P = 0.03]; adjusted for age, sex, center, age × sex interaction, pigmentation characteristics, family history of melanoma, and nevus density). In stratified analyses, carriage of MITF E318K was associated with melanoma more strongly in people with dark hair than fair hair (P for interaction, 0.03) and in those with no moles than some or many moles (P for interaction

Although nevus count is an established risk factor for melanoma, relationships between nevus number and patient and tumor characteristics have not been well studied and the influence of nevus count on melanoma-specific survival is equivocal. Using data from the Genes, Environment and Melanoma (GEM) study, a large population-based study of primary cutaneous melanoma, we evaluated associations between number of nevi and patient features, including sun-sensitivity summarized in a phenotypic index, and tumor characteristics. We also assessed the association of nevus count with melanoma-specific survival. Higher nevus counts were independently and positively associated with male gender and younger age at diagnosis, and they were inversely associated with lentigo maligna histology. We observed a borderline significant trend of poorer melanoma-specific survival with increasing quartile of nevus count, but little or no association between number of nevi and pigmentary phenotypic characteristics or prognostic tumor features.

Recent studies, including genome-wide association studies, have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 SNPs from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% confidence intervals were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma, while NCOA6 rs4911442 approached significance (P = 0.06). The GEM Study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma.

Melanocortin-1 receptor (MC1R) is a marker of melanoma risk in populations of European ancestry. However, MC1R effects on survival are much less studied. We investigated associations between variation at MC1R and survival in an international, population-based series of single primary melanoma patients enrolled into the Genes, Environment, and Melanoma study. MC1R genotype data was available for 2,200 participants with a first incident primary melanoma diagnosis. We estimated the association of MC1R genotypes with melanoma-specific survival (i.e., death caused by melanoma) and overall survival using COX proportional hazards modeling, adjusting for established prognostic factors for melanoma. We also conducted stratified analyses by Breslow thickness, tumor site, phenotypic index, and age. In addition, we evaluated haplotypes involving polymorphisms near the Agouti signaling protein gene (ASIP) locus for their impacts on survival. Melanoma-specific survival was inversely associated with carriage of MC1R variants in the absence of consensus alleles compared to carriage of at least one consensus allele (hazard ratio (HR) = 0.60; 95% confidence interval (CI): 0.40, 0.90). MC1R results for overall survival were consistent with no association. We did not observe any statistical evidence of heterogeneity of effect estimates in stratified analyses. We observed increased hazard of melanoma-specific death among carriers of the risk haplotype TG near the ASIP locus (HR = 1.37; 95% CI: 0.91, 2.04) when compared to carriers of the most common GG haplotype. Similar results were noted for overall survival. Upon examining the ASIP TG/TG diplotype, we observed considerably increased hazard of melanoma-specific death (HR = 5.11; 95% CI: 1.88, 13.88) compared to carriers of the most common GG/GG diplotype. Our data suggest improved melanoma-specific survival among carriers of two inherited MC1R variants.

IMPORTANCE Previous studies have reported that histopathologically amelanotic melanoma is associated with poorer survival than pigmented melanoma; however, small numbers of amelanotic melanomas, selected populations, lack of centralized pathologic review, or no adjustment for stage limit the interpretation or generalization of results from prior studies.OBJECTIVE To compare melanoma-specific survival between patients with histopathologically amelanotic and those with pigmented melanoma in a large international population-based study.DESIGN, SETTING, AND PARTICIPANTS Survival analysis with a median follow-up of 7.6 years.The study population comprised 2995 patients with 3486 invasive primary melanomas centrally scored for histologic pigmentation from the Genes, Environment, and Melanoma(GEM) Study, which enrolled incident cases of melanoma diagnosed in 1998 through 2003 from international population-based cancer registries.MAIN OUTCOMES AND MEASURES Clinicopathologic predictors and melanoma-specific survival of histologically amelanotic and pigmented melanoma were compared using generalized estimating equations and Cox regression models, respectively.RESULTS Of 3467 melanomas, 275 (8%) were histopathologically amelanotic. Female sex,nodular and unclassified or other histologic subtypes, increased Breslow thickness, presence of mitoses, severe solar elastosis, and lack of a coexisting nevus were independently associated with amelanotic melanoma (each P < .05). Amelanotic melanoma was generally ofa higher American Joint Committee on Cancer (AJCC) tumor stage at diagnosis (odds ratios[ORs] [95%CIs] between 2.9 [1.8-4.6] and 11.1 [5.8-21.2] for tumor stages between T1b and T3b and ORs [95%CIs] of 24.6 [13.6-44.4] for T4a and 29.1 [15.5-54.9] for T4b relative to T1a;P value for trend

BackgroundWe previously reported a significant association between higher UV radiation exposure before diagnosis and greater survival with melanoma in a population-based study in Connecticut. We sought to evaluate the hypothesis that sun exposure before diagnosis was associated with greater survival in a larger, international population-based study with more detailed exposure information.MethodsWe conducted a multicenter, international population-based study in four countries-Australia, Italy, Canada, and the United States-with 3,578 cases of melanoma with an average of 7.4 years of follow-up. Measures of sun exposure included sunburn, intermittent exposure, hours of holiday sun exposure, hours of water-related outdoor activities, ambient ultraviolet B (280-320 nm) dose, histologic solar elastosis, and season of diagnosis.ResultsResults were not strongly supportive of the earlier hypothesis. Having had any sunburn in 1 year within 10 years of diagnosis was inversely associated with survival; solar elastosis-a measure of lifetime cumulative exposure-was not. In addition, none of the intermittent exposure measures-water-related activities and sunny holidays-were associated with melanoma-specific survival. Estimated ambient UVB dose was not associated with survival.ConclusionAlthough there was an apparent protective effect of sunburns within 10 years of diagnosis, there was only weak evidence in this large, international, population-based study of melanoma that sun exposure before diagnosis is associated with greater melanoma-specific survival.ImpactThis study adds to the evidence that sun exposure before melanoma diagnosis has little effect on survival with melanoma.

Background & aimsHeritable factors contribute to the development of colorectal cancer. Identifying the genetic loci associated with colorectal tumor formation could elucidate the mechanisms of pathogenesis.MethodsWe conducted a genome-wide association study that included 14 studies, 12,696 cases of colorectal tumors (11,870 cancer, 826 adenoma), and 15,113 controls of European descent. The 10 most statistically significant, previously unreported findings were followed up in 6 studies; these included 3056 colorectal tumor cases (2098 cancer, 958 adenoma) and 6658 controls of European and Asian descent.ResultsBased on the combined analysis, we identified a locus that reached the conventional genome-wide significance level at less than 5.0 × 10(-8): an intergenic region on chromosome 2q32.3, close to nucleic acid binding protein 1 (most significant single nucleotide polymorphism: rs11903757; odds ratio [OR], 1.15 per risk allele; P = 3.7 × 10(-8)). We also found evidence for 3 additional loci with P values less than 5.0 × 10(-7): a locus within the laminin gamma 1 gene on chromosome 1q25.3 (rs10911251; OR, 1.10 per risk allele; P = 9.5 × 10(-8)), a locus within the cyclin D2 gene on chromosome 12p13.32 (rs3217810 per risk allele; OR, 0.84; P = 5.9 × 10(-8)), and a locus in the T-box 3 gene on chromosome 12q24.21 (rs59336; OR, 0.91 per risk allele; P = 3.7 × 10(-7)).ConclusionsIn a large genome-wide association study, we associated polymorphisms close to nucleic acid binding protein 1 (which encodes a DNA-binding protein involved in DNA repair) with colorectal tumor risk. We also provided evidence for an association between colorectal tumor risk and polymorphisms in laminin gamma 1 (this is the second gene in the laminin family to be associated with colorectal cancers), cyclin D2 (which encodes for cyclin D2), and T-box 3 (which encodes a T-box transcription factor and is a target of Wnt signaling to β-catenin). The roles of these genes and their products in cancer pathogenesis warrant further investigation.

Background A critical step in processing oligonucleotide microarray data is combining the information in multiple probes to produce a single number that best captures the expression level of a RNA transcript. Several systematic studies comparing multiple methods for array processing have used tightly controlled calibration data sets as the basis for comparison. Here we compare performances for seven processing methods using two data sets originally collected for disease profiling studies. An emphasis is placed on understanding sensitivity for detecting differentially expressed genes in terms of two key statistical determinants: test statistic variability for non-differentially expressed genes, and test statistic size for truly differentially expressed genes. Results In the two data sets considered here, up to seven-fold variation across the processing methods was found in the number of genes detected at a given false discovery rate (FDR). The best performing methods called up to 90% of the same genes differentially expressed, had less variable test statistics under randomization, and had a greater number of large test statistics in the experimental data. Poor performance of one method was directly tied to a tendency to produce highly variable test statistic values under randomization. Based on an overall measure of performance, two of the seven methods (Dchip and a trimmed mean approach) are superior in the two data sets considered here. Two other methods (MAS5 and GCRMA-EB) are inferior, while results for the other three methods are mixed. Conclusions Choice of processing method has a major impact on differential expression analysis of microarray data. Previously reported performance analyses using tightly controlled calibration data sets are not highly consistent with results reported here using data from human tissue samples. Performance of array processing methods in disease profiling and other realistic biological studies should be given greater consideration when comparing Affymetrix processing methods.

Physical activity has been associated with lower risks of breast and colorectal cancer in epidemiological studies; however, it is unknown if these associations are causal or confounded. In two-sample Mendelian randomisation analyses, using summary genetic data from the UK Biobank and GWA consortia, we found that a one standard deviation increment in average acceleration was associated with lower risks of breast cancer (odds ratio [OR]: 0.51, 95% confidence interval [CI]: 0.27 to 0.98, P-value = 0.04) and colorectal cancer (OR: 0.66, 95% CI: 0.48 to 0.90, P-value = 0.01). We found similar magnitude inverse associations for estrogen positive (ER+ve) breast cancer and for colon cancer. Our results support a potentially causal relationship between higher physical activity levels and lower risks of breast cancer and colorectal cancer. Based on these data, the promotion of physical activity is probably an effective strategy in the primary prevention of these commonly diagnosed cancers.

Additional file 2: Figure S1. Scatter plot of SNP-BMI and SNP-CRC associations. Figure S2. Scatter plot of SNP-BMI and SNP-CRC associations (female specific). Figure S3. Scatter plot of SNP-BMI and SNP-CRC associations (male specific). Figure S4. Scatter plot of SNP-BMI and SNP-colon cancer associations. Figure S5. Scatter plot of SNP-BMI and SNP-proximal colon cancer associations. Figure S6. Scatter plot of SNP-BMI and SNP-distal colon cancer associations. Figure S7. Scatter plot of SNP-BMI and SNP-rectal cancer associations. Figure S8. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC. Figure S9. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (female specific). Figure S10. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (male specific). Figure S11. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on colon cancer. Figure S12. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on proximal colon cancer. Figure S13. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on distal colon cancer. Figure S14. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on rectal cancer. Figure S15. Leave-one-out plot showing the association between BMI and CRC, following SNP-by-SNP removal from the model. Figure S16. Leave-one-out plot showing the association between BMI and CRC (femalespecific), following SNP-by-SNP removal from the model. Figure S17. Leave-one-out plot showing the association between BMI and CRC (malespecific), following SNP-by-SNP removal from the model. Figure S18. Leave-one-out plot showing the association between BMI and colon cancer, following SNP-by-SNP removal from the model. Figure S19. Leave-one-out plot showing the association between BMI and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S20. Leave-one-out plot showing the association between BMI and distal colon cancer, following SNP-by-SNP removal from the model. Figure S21. Leave-one-out plot showing the association between BMI and rectal cancer, following SNP-by-SNP removal from the model. Figure S22. Scatter plot of SNP-WHR and SNP-CRC associations. Figure S23. Scatter plot of SNP-WHR and SNP-CRC associations (female specific). Figure S24. Scatter plot of SNP-WHR and SNP-CRC associations (male specific). Figure S25. Scatter plot of SNP-WHR and SNP-colon cancer associations. Figure S26. Scatter plot of SNP-WHR and SNP-proximal colon cancer associations. Figure S27. Scatter plot of SNP-WHR and SNP-distal colon cancer associations. Figure S28. Scatter plot of SNP-WHR and SNP-rectal cancer associations. Figure S29. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC. Figure S30. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (female specific). Figure S31. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (male specific). Figure S32. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on colon cancer. Figure S33. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on proximal colon cancer. Figure S34. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on distal colon cancer. Figure S35. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on rectal cancer. Figure S36. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model. Figure S37. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (female specific). Figure S38. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (male specific). Figure S39. Leave-one-out plot showing the association between WHR and colon cancer, following SNP-by-SNP removal from the model. Figure S40. Leave-one-out plot showing the association between WHR and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S41. Leave-one-out plot showing the association between WHR and distal colon cancer, following SNP-by-SNP removal from the model. Figure S42. Leave-one-out plot showing the association between WHR and rectal cancer, following SNP-by-SNP removal from the model. Figure S43. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 1 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S44. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 2 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S45. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 3 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S46. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 4 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S47. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 5 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S48. Power curves for MR analyses, based on samples sizes for colorectal cancer in the present study (black), Thrift et al., 2015 (blue) and Jarvis et al., 2016 (purple). Upper and lower power curves describe genetic instruments explaining 5% and 0.3% of variance respectively for each study.

BACKGROUND: Substantial evidence supports an association between use of menopausal hormone therapy and decreased colorectal cancer (CRC) risk, indicating a role of exogenous sex hormones in CRC development. However, findings on endogenous oestrogen exposure and CRC are inconsistent. METHODS: We used a Mendelian randomisation approach to test for a causal effect of age at menarche and age at menopause as surrogates for endogenous oestrogen exposure on CRC risk. Weighted genetic risk scores based on 358 single-nucleotide polymorphisms associated with age at menarche and 51 single-nucleotide polymorphisms associated with age at menopause were used to estimate the association with CRC risk using logistic regression in 12,944 women diagnosed with CRC and 10,741 women without CRC from three consortia. Sensitivity analyses were conducted to address pleiotropy and possible confounding by body mass index. RESULTS: Genetic risk scores for age at menarche (odds ratio per year 0.98, 95% confidence interval: 0.95-1.02) and age at menopause (odds ratio 0.98, 95% confidence interval: 0.94-1.01) were not significantly associated with CRC risk. The sensitivity analyses yielded similar results. CONCLUSIONS: Our study does not support a causal relationship between genetic risk scores for age at menarche and age at menopause and CRC risk.

Salicylic acid (SA) has observationally been shown to decrease colorectal cancer (CRC) risk. Aspirin (acetylsalicylic acid, that rapidly deacetylates to SA) is an effective primary and secondary chemopreventive agent. Through a Mendelian randomization (MR) approach, we aimed to address whether levels of SA affected CRC risk, stratifying by aspirin use. A two-sample MR analysis was performed using GWAS summary statistics of SA (INTERVAL and EPIC-Norfolk, N = 14,149) and CRC (CCFR, CORECT, GECCO and UK Biobank, 55,168 cases and 65,160 controls). The DACHS study (4410 cases and 3441 controls) was used for replication and stratification of aspirin-use. SNPs proxying SA were selected via three methods: (1) functional SNPs that influence the activity of aspirin-metabolising enzymes; (2) pathway SNPs present in enzymes' coding regions; and (3) genome-wide significant SNPs. We found no association between functional SNPs and SA levels. The pathway and genome-wide SNPs showed no association between SA and CRC risk (OR: 1.03, 95% CI: 0.84-1.27 and OR: 1.08, 95% CI: 0.86-1.34, respectively). Results remained unchanged upon aspirin use stratification. We found little evidence to suggest that an SD increase in genetically predicted SA protects against CRC risk in the general population and upon stratification by aspirin use.

Physical activity has been associated with lower risks of breast and colorectal cancer in epidemiological studies; however, it is unknown if these associations are causal or confounded. In two-sample Mendelian randomisation analyses, using summary genetic data from the UK Biobank and GWA consortia, we found that a one standard deviation increment in average acceleration was associated with lower risks of breast cancer (odds ratio [OR]: 0.51, 95% confidence interval [CI]: 0.27 to 0.98, P-value = 0.04) and colorectal cancer (OR: 0.66, 95% CI: 0.48 to 0.90, P-value = 0.01). We found similar magnitude inverse associations for estrogen positive (ER+ve) breast cancer and for colon cancer. Our results support a potentially causal relationship between higher physical activity levels and lower risks of breast cancer and colorectal cancer. Based on these data, the promotion of physical activity is probably an effective strategy in the primary prevention of these commonly diagnosed cancers.

Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10- 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10- 4, replication P = 6.7 × 10- 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.

BACKGROUND: Chronic inflammation is a risk factor for colorectal cancer (CRC). Circulating C-reactive protein (CRP) is also moderately associated with CRC risk. However, observational studies are susceptible to unmeasured confounding or reverse causality. Using genetic risk variants as instrumental variables, we investigated the causal relationship between genetically elevated CRP concentration and CRC risk, using a Mendelian randomization approach. METHODS: Individual-level data from 30 480 CRC cases and 22 844 controls from 33 participating studies in three international consortia were used: the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Transdisciplinary Study (CORECT) and the Colon Cancer Family Registry (CCFR). As instrumental variables, we included 19 single nucleotide polymorphisms (SNPs) previously associated with CRP concentration. The SNP-CRC associations were estimated using a logistic regression model adjusted for age, sex, principal components and genotyping phases. An inverse-variance weighted method was applied to estimate the causal effect of CRP on CRC risk. RESULTS: Among the 19 CRP-associated SNPs, rs1260326 and rs6734238 were significantly associated with CRC risk (P = 7.5 × 10-4, and P = 0.003, respectively). A genetically predicted one-unit increase in the log-transformed CRP concentrations (mg/l) was not associated with increased risk of CRC [odds ratio (OR) = 1.04; 95% confidence interval (CI): 0.97, 1.12; P = 0.256). No evidence of association was observed in subgroup analyses stratified by other risk factors. CONCLUSIONS: In spite of adequate statistical power to detect moderate association, we found genetically elevated CRP concentration was not associated with increased risk of CRC among individuals of European ancestry. Our findings suggested that circulating CRP is unlikely to be a causal factor in CRC development.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10-8), breast and ovarian cancer (rg = 0.24, p = 7 × 10-5), breast and lung cancer (rg = 0.18, p =1.5 × 10-6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10-4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

BACKGROUND: The purpose of this study was to estimate precise age-specific tubo-ovarian carcinoma (TOC) and breast cancer (BC) risks for carriers of pathogenic variants in RAD51C and RAD51D. METHODS: We analyzed data from 6178 families, 125 with pathogenic variants in RAD51C, and 6690 families, 60 with pathogenic variants in RAD51D. TOC and BC relative and cumulative risks were estimated using complex segregation analysis to model the cancer inheritance patterns in families while adjusting for the mode of ascertainment of each family. All statistical tests were two-sided. RESULTS: Pathogenic variants in both RAD51C and RAD51D were associated with TOC (RAD51C: relative risk [RR] = 7.55, 95% confidence interval [CI] = 5.60 to 10.19; P = 5 × 10-40; RAD51D: RR = 7.60, 95% CI = 5.61 to 10.30; P = 5 × 10-39) and BC (RAD51C: RR = 1.99, 95% CI = 1.39 to 2.85; P = 1.55 × 10-4; RAD51D: RR = 1.83, 95% CI = 1.24 to 2.72; P = .002). For both RAD51C and RAD51D, there was a suggestion that the TOC relative risks increased with age until around age 60 years and decreased thereafter. The estimated cumulative risks of developing TOC to age 80 years were 11% (95% CI = 6% to 21%) for RAD51C and 13% (95% CI = 7% to 23%) for RAD51D pathogenic variant carriers. The estimated cumulative risks of developing BC to 80 years were 21% (95% CI = 15% to 29%) for RAD51C and 20% (95% CI = 14% to 28%) for RAD51D pathogenic variant carriers. Both TOC and BC risks for RAD51C and RAD51D pathogenic variant carriers varied by cancer family history and could be as high as 32-36% for TOC, for carriers with two first-degree relatives diagnosed with TOC, or 44-46% for BC, for carriers with two first-degree relatives diagnosed with BC. CONCLUSIONS: These estimates will facilitate the genetic counseling of RAD51C and RAD51D pathogenic variant carriers and justify the incorporation of RAD51C and RAD51D into cancer risk prediction models.