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Background Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. Methods We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app ‘Reverse the Odds’. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. Results Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. Conclusions Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery.

Candidate gene and genome-wide association studies (GWAS) have identified 15 independent genomic regions associated with bladder cancer risk. In search for additional susceptibility variants, we followed up on four promising single-nucleotide polymorphisms (SNPs) that had not achieved genome-wide significance in 6911 cases and 11 814 controls (rs6104690, rs4510656, rs5003154 and rs4907479, P < 1 × 10(-6)), using additional data from existing GWAS datasets and targeted genotyping for studies that did not have GWAS data. In a combined analysis, which included data on up to 15 058 cases and 286 270 controls, two SNPs achieved genome-wide statistical significance: rs6104690 in a gene desert at 20p12.2 (P = 2.19 × 10(-11)) and rs4907479 within the MCF2L gene at 13q34 (P = 3.3 × 10(-10)). Imputation and fine-mapping analyses were performed in these two regions for a subset of 5551 bladder cancer cases and 10 242 controls. Analyses at the 13q34 region suggest a single signal marked by rs4907479. In contrast, we detected two signals in the 20p12.2 region-the first signal is marked by rs6104690, and the second signal is marked by two moderately correlated SNPs (r(2) = 0.53), rs6108803 and the previously reported rs62185668. The second 20p12.2 signal is more strongly associated with the risk of muscle-invasive (T2-T4 stage) compared with non-muscle-invasive (Ta, T1 stage) bladder cancer (case-case P ≤ 0.02 for both rs62185668 and rs6108803). Functional analyses are needed to explore the biological mechanisms underlying these novel genetic associations with risk for bladder cancer.

Background: The majority of anal cancers (84-95%) are driven by infection with human papillomavirus (HPV). HPV-positive tumours show significantly better responses to chemoradiotherapy when compared to HPV-negative tumours. HPV infection is linked to alterations in DNA damage response proteins, including MRE11. MRE11 is a potential predictive biomarker for response to radiotherapy in muscle-invasive bladder cancer and may hold predictive power in other cancers. Methods: Using a previously reported cohort we evaluated the levels of MRE11 in anal cancer and assessed its predictive value in this disease. Results: We found no association between the level of MRE11 and relapse-free survival following chemo-radiotherapy. Conclusion: MRE11 has no predictive value in the analysis of relapse-free survival after chemoradiotherapy in anal cancer and does not add to the prognostic value of p16 and tumour infiltrating lymphocyte (TIL) scores. Further investigation into the role of DNA repair proteins in anal cancer is required.

Additional file 1: Figure S1. Similar bacterial components in the faecal and caecal microbiomes. Figure S2. Faecal butyrate levels and time taken for tumours to reach 50 mm3. Figure S3. Differences in composition of the gut microbiome when tumours reached 350 mm3. Figure S4. Individual mouse tumour growth curves. Figure S5. Cell survival analysis of RT112 bladder tumour cells treated with SCFAs and bacterial supernatants. Figure S6. Correlation of time to culling with B. acidifaciens or Parabacteroides genus abundance different groups. Figure S7. Effect of cage location of mice on relative abundance of B. acidifaciens and Parabacteroides genus. Table S1. Rodent diets used in the study with varying levels of cellulose or inulin per 4000 kcal. Table S2. Details mouse diets, cages, B. acidifaciens relative abundance and time of culling.

Additional file 1: Figure S1. Similar bacterial components in the faecal and caecal microbiomes. Figure S2. Faecal butyrate levels and time taken for tumours to reach 50 mm3. Figure S3. Differences in composition of the gut microbiome when tumours reached 350 mm3. Figure S4. Individual mouse tumour growth curves. Figure S5. Cell survival analysis of RT112 bladder tumour cells treated with SCFAs and bacterial supernatants. Figure S6. Correlation of time to culling with B. acidifaciens or Parabacteroides genus abundance different groups. Figure S7. Effect of cage location of mice on relative abundance of B. acidifaciens and Parabacteroides genus. Table S1. Rodent diets used in the study with varying levels of cellulose or inulin per 4000 kcal. Table S2. Details mouse diets, cages, B. acidifaciens relative abundance and time of culling.