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Additional file 1: Supplementary Table S1. Cox proportional hazard models of physical activity quartiles with overall and disease-free survival in colorectal cancer patients. Supplementary Table S2. Cox proportional hazard models of combined physical activity (8.75 MET hrs/wk threshold) and BMI groups with overall and disease-free survival in colorectal cancer patients. Supplementary Table S3. Baseline study population characteristics by completed and missing physical activity data [n (%) if not otherwise stated]. Supplementary Figure S1. Individual and combined physical activity and BMI groups.

Associations of energy balance components, including physical activity and obesity, with colorectal cancer risk and mortality are well established. However, the gut microbiome has not been investigated as underlying mechanism. We investigated associations of physical activity, BMI, and combinations of physical activity/BMI with gut microbiome diversity and differential abundances among colorectal cancer patients. N=179 patients with colorectal cancer (stages I-IV) were included in the study. Pre-surgery stool samples were used to perform 16S rRNA gene sequencing (Illumina). Physical activity (MET hrs/wk) during the year before diagnosis was assessed by questionnaire and participants were classified as being active vs. inactive based on guidelines. BMI at baseline was abstracted from medical records. Patients were classified into four combinations of physical activity levels/BMI. Lower gut microbial diversity was observed among ‘inactive’ vs. ‘active’ patients (Shannon: P=0.01, Simpson: P=0.03), ‘obese’ vs. ‘normal weight’ patients (Shannon, Simpson, and Observed species: P=0.02, respectively), and ‘overweight/obese/inactive’ vs. ‘normal weight/active’ patients (Shannon: P=0.02, Observed species: P=0.04). Results differed by sex and tumor site. Two phyla and 12 genera (Actinobacteria and Fusobacteria, Adlercreutzia, Anaerococcus, Clostridium, Eubacterium, Mogibacteriaceae, Olsenella, Peptinophilus, Pyramidobacter, RFN20, Ruminococcus, Succinivibrio, Succiniclasticum) were differentially abundant across physical activity and BMI groups. This is the first evidence for associations of physical activity with gut microbiome diversity and abundances, directly among colorectal cancer patients. Our results indicate that physical activity may offset gut microbiome dysbiosis due to obesity. Alterations in gut microbiota may contribute mechanistically to the energy balance-colorectal cancer link and impact clinical outcomes.

Small cell lung cancer (SCLC) is a deadly neuroendocrine tumor for which there are no screening methods sensitive enough to facilitate early, effective intervention. We propose targeting the neuroendocrine tumor neoantigen CD133 via antibody-based early detection and PET (immunoPET) to facilitate earlier and more accurate detection of SCLC. Methods: RNA sequencing datasets, immunohistochemistry, flow cytometry, and Western blots were used to quantify CD133 expression in healthy and SCLC patients. CD133 was imaged in vivo using near-infrared fluorescence (NIRF) immunoimaging, and (89)Zr immunoPET. Anti(α)-CD133 autoantibody levels were measured in SCLC patient plasma using antibody microarrays. Results: Across 6 publicly available datasets, CD133 messenger RNA was found to be higher in SCLC tumors than in other tissues, including healthy or normal adjacent lung and non-SCLC samples. Critically, the upregulation of CD133 messenger RNA in SCLC was associated with a significant increase (hazard ratio, 2.62) in death. CD133 protein was expressed in primary human SCLC, in SCLC patient-derived xenografts, and in both SCLC cell lines tested (H82 and H69). Using an H82 xenograft mouse model, we first imaged CD133 expression with NIRF. Both in vivo and ex vivo NIRF clearly showed that a fluorophore-tagged αCD133 homed to lung tumors. Next, we validated the noninvasive visualization of subcutaneous and orthotopic H82 xenografts via immunoPET. An αCD133 antibody labeled with the positron-emitting radiometal (89)Zr demonstrated significant accumulation in tumor tissue while producing minimal uptake in healthy organs. Finally, plasma αCD133 autoantibodies were found in subjects from cohort studies up to 1 year before SCLC diagnosis. Conclusion: In light of these findings, we conclude that the presence of αCD133 autoantibodies in a blood sample followed by CD133-targeted (89)Zr-immunoPET could be an effective early detection screening strategy for SCLC.

Background: Bilirubin, a byproduct of hemoglobin breakdown and purported anti-oxidant, is thought to be cancer preventive. We conducted complementary serological and Mendelian randomization (MR) analyses to investigate whether alterations in circulating levels of bilirubin are associated with risk of colorectal cancer (CRC). We decided a priori to perform analyses separately in men and women based on suggestive evidence that associations may differ by sex. Methods: In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition (EPIC), pre-diagnostic unconjugated bilirubin (UCB, the main component of total bilirubin) concentrations were measured by high-performance liquid chromatography in plasma samples of 1386 CRC cases and their individually matched controls. Additionally, 115 single-nucleotide polymorphisms (SNPs) robustly associated (P < 5 × 10−8) with circulating total bilirubin were instrumented in a 2-sample MR to test for a potential causal effect of bilirubin on CRC risk in 52,775 CRC cases and 45,940 matched controls in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colon Cancer Family Registry (CCFR), and the Colorectal Transdisciplinary (CORECT) study. Results: The associations between circulating UCB levels and CRC risk differed by sex (Pheterogeneity = 0.008). Among men, higher levels of UCB were positively associated with CRC risk (odds ratio [OR] = 1.19, 95% confidence interval [CI] = 1.04–1.36; per 1-SD increment of log-UCB). In women, an inverse association was observed (OR = 0.86 (0.76–0.97)). In the MR analysis of the main UGT1A1 SNP (rs6431625), genetically predicted higher levels of total bilirubin were associated with a 7% increase in CRC risk in men (OR = 1.07 (1.02–1.12); P = 0.006; per 1-SD increment of total bilirubin), while there was no association in women (OR = 1.01 (0.96–1.06); P = 0.73). Raised bilirubin levels, predicted by instrumental variables excluding rs6431625, were suggestive of an inverse association with CRC in men, but not in women. These differences by sex did not reach formal statistical significance (Pheterogeneity ≥ 0.2). Conclusions: Additional insight into the relationship between circulating bilirubin and CRC is needed in order to conclude on a potential causal role of bilirubin in CRC development.

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.

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The evidence regarding correlations between various anthropometric characteristics and breast cancer risk among young women is mixed, and few studies have assessed these associations by subtype.This was a population-based, case-control study of 779 women with estrogen receptor (ER)-positive breast cancer; 182 women with ER-negative/human epidermal growth factor-2 (HER2)-negative/progesterone receptor-negative (triple-negative [TN]) breast cancer; and 60 women with ER-negative/HER2-overexpressing, invasive breast cancer ages 20 to 44 years who were diagnosed from 2004 to 2010 in the Seattle-Puget Sound metropolitan area; as well as 939 cancer-free controls. Associations between height and body mass index (BMI) at different time points in relation to breast cancer risk were assessed using polytomous logistic regression.Height, BMI at age 18 years, and BMI at the reference date were not related to the risks of ER-positive, TN, or HER2-overexpressing breast cancer. Changes in BMI from age 18 years to the reference date were not related to the risk of either ER-positive or HER2-overexpressing breast cancer. However, compared with women who had a BMI change from 0 to 4.9 kg/m(2) from age 18 years to the reference date, those who experienced a BMI increase ≥10 kg/m(2) during the same interval had a 2.0-fold (95% confidence interval, 1.2-fold to 3.3-fold increase) increased risk of TN breast cancer. For women with ER-positive disease, there was some evidence that parity modified the effect of BMI change (Pinteraction = .002), because a BMI increase of ≥10 kg/m(2) was associated with a reduced risk of ER-positive disease only among nulliparous women (odds ratio, 0.3; 95% confidence interval, 0.2-0.6).The correlations appear to differ substantially between BMI change and the risks of TN breast cancer and ER-positive breast cancer.

Improved diagnostic screening has led to earlier detection of many tumors, but screening may still miss many aggressive tumor types. Proteomic and genomic profiling studies of breast cancer samples have identified tumor markers that may help improve screening for more aggressive, rapidly growing breast cancers. To identify potential blood-based biomarkers for the early detection of breast cancer, we assayed serum samples via matrix-assisted laser desorption ionization–time of flight mass spectrometry from a rat model of mammary carcinogenesis. We found elevated levels of a fragment of the protein dermcidin (DCD) to be associated with early progression of N-methylnitrosourea-induced breast cancer, demonstrating significance at weeks 4 (p = 0.045) and 5 (p = 0.004), a time period during which mammary pathologies rapidly progress from ductal hyperplasia to adenocarcinoma. The highest serum concentrations were observed in rats bearing palpable mammary carcinomas. Increased DCD was also detected with immunoblotting methods in 102 serum samples taken from women just prior to breast cancer diagnosis. To validate these findings in a larger population, we applied a 32-gene in vitro DCD response signature to a dataset of 295 breast tumors and assessed correlation with intrinsic breast cancer subtypes and overall survival. The DCD-derived gene signature was significantly associated with subtype (p < 0.001) and poorer overall survival [HR (95 % CI) = 1.60 (1.01–2.51), p = 0.044]. In conclusion, these results present novel evidence that DCD levels may increase in early carcinogenesis, particularly among more aggressive forms of breast cancer.

Applying advanced proteomic technologies to prospectively collected specimens from large studies is one means of identifying preclinical changes in plasma proteins that are potentially relevant to the early detection of diseases such as breast cancer. We conducted 14 independent quantitative proteomics experiments comparing pooled plasma samples collected from 420 estrogen receptor-positive (ER(+)) breast cancer patients ≤17 months before their diagnosis and matched controls. Based on the more than 3.4 million tandem mass spectra collected in the discovery set, 503 proteins were quantified, of which 57 differentiated cases from controls with a P value of0.1. Seven of these proteins, for which quantitative ELISA assays were available, were assessed in an independent validation set. Of these candidates, epidermal growth factor receptor (EGFR) was validated as a predictor of breast cancer risk in an independent set of preclinical plasma samples for women overall [odds ratio (OR), 1.44; P = 0.0008] and particularly for current users of estrogen plus progestin (E + P) menopausal hormone therapy (OR, 2.49; P = 0.0001). Among current E + P users, the EGFR sensitivity for breast cancer risk was 31% with 90% specificity. Whereas the sensitivity and specificity of EGFR are insufficient for a clinically useful early detection biomarker, this study suggests that proteins that are elevated preclinically in women who go on to develop breast cancer can be discovered and validated using current proteomic technologies. Further studies are warranted to examine the role of EGFR and to discover and validate other proteins that could potentially be used for early detection of breast cancer.

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.

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.

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.