Genome-wide study of genetic polymorphisms predictive for outcome from first-line oxaliplatin-based chemotherapy in colorectal cancer patients.

We 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.

Mendelian randomisation study of smoking exposure in relation to breast cancer risk.

BACKGROUND: Despite a modest association between tobacco smoking and breast cancer risk reported by recent epidemiological studies, it is still equivocal whether smoking is causally related to breast cancer risk. METHODS: We applied Mendelian randomisation (MR) to evaluate a potential causal effect of cigarette smoking on breast cancer risk. Both individual-level data as well as summary statistics for 164 single-nucleotide polymorphisms (SNPs) reported in genome-wide association studies of lifetime smoking index (LSI) or cigarette per day (CPD) were used to obtain MR effect estimates. Data from 108,420 invasive breast cancer cases and 87,681 controls were used for the LSI analysis and for the CPD analysis conducted among ever-smokers from 26,147 cancer cases and 26,072 controls. Sensitivity analyses were conducted to address pleiotropy. RESULTS: Genetically predicted LSI was associated with increased breast cancer risk (OR 1.18 per SD, 95% CI: 1.07-1.30, P = 0.11 × 10-2), but there was no evidence of association for genetically predicted CPD (OR 1.02, 95% CI: 0.78-1.19, P = 0.85). The sensitivity analyses yielded similar results and showed no strong evidence of pleiotropic effect. CONCLUSION: Our MR study provides supportive evidence for a potential causal association with breast cancer risk for lifetime smoking exposure but not cigarettes per day among smokers.

Genotype-Based Gene Expression in Colon Tissue-Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients.

Colorectal cancer (CRC) survival has environmental and inherited components. The expression of specific genes can be inferred based on individual genotypes-so called expression quantitative trait loci. In this study, we used the PrediXcan method to predict gene expression in normal colon tissue using individual genotype data from 91 CRC patients and examined the correlation ρ between predicted and measured gene expression levels. Out of 5434 predicted genes, 58% showed a negative ρ value and only 16% presented a ρ higher than 0.10. We subsequently investigated the association between genotype-based gene expression in colon tissue for genes with ρ > 0.10 and survival of 4436 CRC patients. We identified an inverse association between the predicted expression of ARID3B and CRC-specific survival for patients with a body mass index greater than or equal to 30 kg/m2 (HR (hazard ratio) = 0.66 for an expression higher vs. lower than the median, p = 0.005). This association was validated using genotype and clinical data from the UK Biobank (HR = 0.74, p = 0.04). In addition to the identification of ARID3B expression in normal colon tissue as a candidate prognostic biomarker for obese CRC patients, our study illustrates the challenges of genotype-based prediction of gene expression, and the advantage of reassessing the prediction accuracy in a subset of the study population using measured gene expression data.

Learning high-order interactions for polygenic risk prediction.

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.

Validation of Genetic Markers Associated with Survival in Colorectal Cancer Patients Treated with Oxaliplatin-Based Chemotherapy.

BACKGROUND: Associations between candidate genetic variants and treatment outcomes of oxaliplatin, a drug commonly used for colorectal cancer patients, have been reported but not robustly established. This study aimed to validate previously reported prognostic and predictive genetic markers for oxaliplatin treatment outcomes and evaluate additional putative functional variants. METHODS: Fifty-three SNPs were selected based on previous reports (40 SNPs) or putative function in candidate genes (13 SNPs). We used data from 1,502 patients with stage II-IV colorectal cancer who received primary adjuvant chemotherapy, 37% of whom received oxaliplatin treatment. Multivariable Cox proportional hazards models for overall survival and progression-free survival were applied separately in stage II-III and stage IV patients. For predictive SNPs, differential outcomes according to the type of chemotherapy (oxaliplatin-based vs. others) were evaluated using an interaction term. For prognostic SNPs, the association was assessed solely in patients with oxaliplatin-based treatment. RESULTS: Twelve SNPs were predictive and/or prognostic at P < 0.05 with differential survival based on the type of treatment, in patients with stage II-III (GSTM5-rs11807, ERCC2-rs13181, ERCC2-rs1799793, ERCC5-rs2016073, XPC-rs2228000, P2RX7-rs208294, HMGB1-rs1360485) and in patients with stage IV (GSTM5-rs11807, MNAT1-rs3783819, MNAT1-rs4151330, CXCR1-rs2234671, VEGFA-rs833061, P2RX7-rs2234671). In addition, five novel putative functional SNPs were identified to be predictive (ATP8B3-rs7250872, P2RX7-rs2230911, RPA1-rs5030755, MGMT-rs12917, P2RX7-rs2227963). CONCLUSIONS: Some SNPs yielded prognostic and/or predictive associations significant at P < 0.05, however, none of the associations remained significant after correction for multiple testing. IMPACT: We did not robustly confirm previously reported SNPs despite some suggestive findings but identified further potential predictive SNPs, which warrant further investigation in well-powered studies.

Predictive Polygenic Score for Outcome after First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients Using Supervised Principal Component Analysis.

BACKGROUND: Associations between candidate germline genetic variants and treatment outcome of oxaliplatin, a drug commonly used for patients with colorectal cancer, have been reported but not robustly established. This study aimed to construct polygenic hazard scores (PHSs) as predictive markers for oxaliplatin treatment outcome by using a supervised principal component approach (PCA). METHODS: Genome-wide association analysis for overall survival, including interaction terms (SNP*treatment type) was carried out using two phase III trials, 3,098 resected stage III colon cancer (rCC) patients of NCCTG N0147 and 506 metastatic colorectal cancer (mCRC) patients of NCCTG N9741, separately. SNPs showing interaction with genome-wide significance (P < 5 × 10-8) were selected for PCA to derive a PHS. PHS interaction with treatment was included in Cox regression models to predict outcome. Replication of prediction models was performed in an independent cohort, DACHS. RESULTS: The two PHSs based on the first two principal components of selected SNPs (15SNPs for rCC and 13SNPs for mCRC) were used to construct interaction terms with treatment type and included in models adjusted for clinical covariables. However, in the DACHS study, the addition of the two PHS terms to clinical models did not improve the prediction error in either patients with rCC or mCRC. PHS interaction was also not replicated. CONCLUSIONS: The PHSs derived using principal components efficiently combined multiple predictive SNPs for estimating likelihood of benefit from oxaliplatin versus other treatment but could not be replicated. IMPACT: These results highlight the potential but also challenges in generating evidence for a predictive polygenic score for oxaliplatin efficacy.