Genome Wide Association Study for Predictors of Progression Free Survival in Patients on Capecitabine, Oxaliplatin, Bevacizumab and Cetuximab in First-Line Therapy of Metastatic Colorectal Cancer

Characterization of Expression-Based Gene Clusters Gives Insights into Variation in Patient Response to Cancer Therapies

Background

Transcriptomics can reveal much about cellular activity, and cancer transcriptomics have been useful in investigating tumor cell behaviors. Patterns in transcriptome-wide gene expression can be used to investigate biological mechanisms and pathways that can explain the variability in patient response to cancer therapies.

Methods

We identified gene expression patterns related to patient drug response by clustering tumor gene expression data and selecting from the resulting gene clusters those where expression of cluster genes was related to patient survival on specific drugs. We then investigated these gene clusters for biological meaning using several approaches, including identifying common genomic locations and transcription factors whose targets were enriched in these clusters and performing survival analyses to support these candidate transcription factor-drug relationships.

Results

We identified gene clusters related to drug-specific survival, and through these, we were able to associate observed variations in patient drug response to specific known biological phenomena. Specifically, our analysis implicated 2 stem cell-related transcription factors, HOXB4 and SALL4, in poor response to temozolomide in brain cancers. In addition, expression of SNRNP70 and its targets were implicated in cetuximab response by 3 different analyses, although the mechanism remains unclear. We also found evidence that 2 cancer-related chromosomal structural changes may impact drug efficacy.

Conclusion

In this study, we present the gene clusters identified and the results of our systematic analysis linking drug efficacy to specific transcription factors, which are rich sources of potential mechanistic relationships impacting patient outcomes. We also highlight the most promising of these results, which were supported by multiple analyses and by previous research. We report these findings as promising avenues for independent validation and further research into cancer treatments and patient response.

Genetic Variants as Predictors of the Success of Colorectal Cancer Treatments

BACKGROUND

Some genetic polymorphisms (SNPs) have been proposed as predictors for different colorectal cancer (CRC) outcomes. This work aims to assess their performance in our cohort and find new SNPs associated with them.

METHODS

A total of 833 CRC cases were analyzed for seven outcomes, including the use of chemotherapy, and stratified by tumor location and stage. The performance of 63 SNPs was assessed using a generalized linear model and area under the receiver operating characteristic curve, and local SNPs were detected using logistic regressions.

RESULTS

In total 26 of the SNPs showed an AUC > 0.6 and a significant association (p < 0.05) with one or more outcomes. However, clinical variables outperformed some of them, and the combination of genetic and clinical data showed better performance. In addition, 49 suggestive (p < 5 × 10-6) SNPs associated with one or more CRC outcomes were detected, and those SNPs were located at or near genes involved in biological mechanisms associated with CRC.

CONCLUSIONS

Some SNPs with clinical data can be used in our population as predictors of some CRC outcomes, and the local SNPs detected in our study could be feasible markers that need further validation as predictors.

From Omic Layers to Personalized Medicine in Colorectal Cancer: The Road Ahead

Colorectal cancer is a major health concern since it is a highly diagnosed cancer and the second cause of death among cancers. Thus, the most suitable biomarkers for its diagnosis, prognosis, and treatment have been studied to improve and personalize the prevention and clinical management of colorectal cancer. The emergence of omic techniques has provided a great opportunity to better study CRC and make personalized medicine feasible. In this review, we will try to summarize how the analysis of the omic layers can be useful for personalized medicine and the existing difficulties. We will discuss how single and multiple omic layer analyses have been used to improve the prediction of the risk of CRC and its outcomes and how to overcome the challenges in the use of omic layers in personalized medicine.

Examining SNP-SNP interactions and risk of clinical outcomes in colorectal cancer using multifactor dimensionality reduction based methods

Background: SNP interactions may explain the variable outcome risk among colorectal cancer patients. Examining SNP interactions is challenging, especially with large datasets. Multifactor Dimensionality Reduction (MDR)-based programs may address this problem.

Objectives: 1) To compare two MDR-based programs for their utility; and 2) to apply these programs to sets of MMP and VEGF-family gene SNPs in order to examine their interactions in relation to colorectal cancer survival outcomes.

Methods: This study applied two data reduction methods, Cox-MDR and GMDR 0.9, to study one to three way SNP interactions. Both programs were run using a 5-fold cross validation step and the top models were verified by permutation testing. Prognostic associations of the SNP interactions were verified using multivariable regression methods. Eight datasets, including SNPs from MMP family genes (n = 201) and seven sets of VEGF-family interaction networks (n = 1,517 SNPs) were examined.

Results: ∼90 million potential interactions were examined. Analyses in the MMP and VEGF gene family datasets found several novel 1- to 3-way SNP interactions. These interactions were able to distinguish between the patients with different outcome risks (regression p-values 0.03–2.2E-09). The strongest association was detected for a 3-way interaction including CHRM3.rs665159_EPN1.rs6509955_PTGER3.rs1327460 variants.

Conclusion: Our work demonstrates the utility of data reduction methods while identifying potential prognostic markers in colorectal cancer.

Predicting drug response and toxicity in metastatic colorectal cancer: the role of germline markers

INTRODUCTION

Despite the introduction of targeted agents leading to therapeutic advances, clinical management of patients with metastatic colorectal cancer (mCRC) is still challenged by significant interindividual variability in treatment outcomes, both in terms of toxicity and therapy efficacy. The study of germline genetic variants could help to personalize and optimize therapeutic approaches in mCRC.

AREAS COVERED

A systematic review of pharmacogenetic studies in mCRC patients published on PubMed between 2011 and 2021, evaluating the role of germline variants as predictive markers of toxicity and efficacy of drugs currently approved for treatment of mCRC, was perfomed.

EXPERT OPINION

Despite the large amount of pharmacogenetic data published to date, only a few genetic markers (i.e., DPYD and UGT1A1 variants) reached the clinical practice, mainly to prevent the toxic effects of chemotherapy. The large heterogeneity of available studies represents the major limitation in comparing results and identifying potential markers for clinical use, the role of which remains exploratory in most cases. However, the available published findings are an important starting point for future investigations. They highlighted new promising pharmacogenetic markers within the network of inflammatory and immune response signaling. In addition, the emerging role of previously overlooked rare variants has been pointed out.

A comprehensive analysis of SNPs and CNVs identifies novel markers associated with disease outcomes in colorectal cancer

We aimed to examine the associations of a genome-wide set of single nucleotide polymorphisms (SNPs) and 254 copy number variations (CNVs) and/or insertion/deletions (INDELs) with clinical outcomes in colorectal cancer patients (n = 505). We also aimed to investigate whether their associations changed (e.g., appeared, diminished) over time. Multivariable Cox proportional hazards and piece-wise Cox regression models were used to examine the associations. The Cancer Genome Atlas (TCGA) datasets were used for replication purposes and to examine the gene expression differences between tumor and nontumor tissue samples. A common SNP (WBP11-rs7314075) was associated with disease-specific survival with P-value of 3.2 × 10-8 . Association of this region with disease-specific survival was also detected in the TCGA patient cohort. Two expression quantitative trait loci (eQTLs) were identified in this locus that were implicated in the regulation of ERP27 expression. Interestingly, expression levels of ERP27 and WBP11 were significantly different between colorectal tumors and nontumor tissues. Three SNPs predicted the risk of recurrent disease only after 5 years postdiagnosis. Overall, our study identified novel variants, one of which also showed an association in the TCGA dataset, but no CNVs/INDELs, that associated with outcomes in colorectal cancer. Three SNPs were candidate predictors of long-term recurrence/metastasis risk.

Pathological Complete Response After Neoadjuvant Therapy in Rectal Adenocarcinoma: a 5-Year Follow-up

Neoadjuvant therapy is the gold standard treatment of locally advanced rectal cancer. It may induce complete sterilization of tumor cell and decreases its local recurrence rate. While 15–20% of patients were found to have pathological complete response (pCR) with combined multimodal therapy, Asian data were generally scarce. pCR rate can indicate the suitability of applying the “watch-and-wait” strategy, which advocates deferment of surgery that can alleviate surgery-associated morbidity.To determine the percentage of pCR of rectal cancer after neoadjuvant therapy. Patients diagnosed with rectal cancer underwent treatment from 2013 to 2017 were retrieved retrospectively. Demographic data, tumor localization, pre- and post-operative pathological reports, neoadjuvant therapy, and pCR status were collected from patients’ records. A total of 242 out of 259 patients were treated with definitive rectal surgery. Mean age was 67.1 years old. Chinese ethnicity and male gender were predominant (n = 131, 54.1% and n = 146, 64.3% respectively). More than half (n = 124, 51.2%) had tumor located at mid or low rectum. Histologically, moderate differentiated adenocarcinoma was predominant (n = 227, 93.8%). Merely half (n = 123, 50.8%) of the patients received neoadjuvant chemoradiation therapy, but only 12 (9.8%) had a pCR. From follow-up on these 12 pCR patients, most had 2-year disease-free survival but 1 (8.3%) of the pCR had distant metastasis within 1-year post-surgery. The pathological complete response rate in our center was lower than reported. Stringent patient selection with close follow-up for patients should be carried out if the “watch-and-wait” strategy is implemented in our population.

RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials

MOTIVATION

When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting.

RESULTS

We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit. However, we find that RAINFOREST is able to identify a subgroup comprising 27.7% of the patients that do benefit, with a hazard ratio of 0.69 (P = 0.04) in favor of cetuximab. The method is not specific to colorectal cancer and could aid in reanalysis of clinical trial data and provide a more personalized approach to cancer treatment, also when there is no clear link between a single variant and treatment benefit.

AVAILABILITY AND IMPLEMENTATION

The R code used to produce the results in this paper can be found at github.com/jubels/RAINFOREST. A more configurable, user-friendly Python implementation of RAINFOREST is also provided. Due to restrictions based on privacy regulations and informed consent of participants, phenotype and genotype data of the CAIRO2 trial cannot be made freely available in a public repository. Data from this study can be obtained upon request. Requests should be directed toward Prof. Dr. H.J. Guchelaar (h.j.guchelaar@lumc.nl).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic Variant Associated With Survival of Patients With Stage II-III Colon Cancer

BACKGROUND & AIMS

Many genetic variants have been associated with colorectal cancer risk, although few have been associated with survival times of patients. Identification of genetic variants associated with survival times might improve our understanding of disease progression and aid in outcome prediction. We performed a genome-wide association study to identify variants associated with colon cancer survival time.

METHODS

We performed a post hoc analysis of data from NCCTG N0147 (Alliance), a randomized phase 3 trial of patients with resected stage III colon cancer, and from NSABP C-08 (NRG), a phase 3 trial that compared therapy regimens for patients with resected stage II or III colon cancer. Genotype analyses were performed on DNA from blood samples from 4974 patients. We used Cox proportional hazards regression to evaluate the association of each single nucleotide polymorphism with times of overall survival and disease-free survival, adjusting for age at diagnosis, sex, treatment group, and principal components of genetic ancestry. We performed the analysis for studies N0147 and C-08 separately, and results were combined in a fixed-effects meta-analysis.

RESULTS

A locus on chromosome 7p15.2 was significantly associated with overall survival time (P ≤ 5x10-08). The most significant variant at this locus, rs76766811 (P = 1.6x10-08), is common among African Americans (minor allele frequency, approximately 18%) but rare in European Americans (minor allele frequency <0.1%). Within strata of self-reported ancestry, this variant was associated with times of overall survival and disease-free survival in only African Americans (hazard ratio for overall survival, 2.82; 95% CI, 1.88-4.23; P = 5.0x10-07 and hazard ratio for disease-free survival, 2.27; 95% CI, 1.62-3.18; P = 1.8x10-06).

CONCLUSIONS

In an analysis of data from 2 trials of patients with stage II or III colon cancer, we identified rs76766811 as a potential prognostic variant in African American patients. This finding should be confirmed in additional study populations. ClinicalTrials.gov Identifiers: NCT00096278 (NSABP C-08) and NCT00079274 (NCCTG N0147).

Identification of key genes in osteosarcoma by meta‑analysis of gene expression microarray

Osteosarcoma (OS) is one of the most malignant tumors in children and young adults. To better understand the underlying mechanism, five related datasets deposited in the Gene Expression Omnibus were included in the present study. The Bioconductor ‘limma’ package was used to identify differentially expressed genes (DEGs) and the ‘Weighted Gene Co-expression Network Analysis’ package was used to construct a weighted gene co-expression network to identify key modules and hub genes, associated with OS. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes overrepresentation analyses were used for functional annotation. The results indicated that 1,405 genes were dysregulated in OS, including 927 upregulated and 478 downregulated genes, when the cut off value was set at a ≥2 fold-change and an adjusted P-value of P<0.01 was used. Functional annotation of DEGs indicated that these genes were involved in the extracellular matrix (ECM) and that they function in several processes, including biological adhesion, ECM organization, cell migration and leukocyte migration. These findings suggested that dysregulation of the ECM shaped the tumor microenvironment and modulated the OS hallmark. Genes assigned to the yellow module were positively associated with OS and could contribute to the development of OS. In conclusion, the present study has identified several key genes that are potentially druggable genes or therapeutics targets in OS. Functional annotations revealed that the dysregulation of the ECM may contribute to OS development and, therefore, provided new insights to improve our understanding of the mechanisms underlying OS.

Genome wide association study to identify predictors for severe skin toxicity in colorectal cancer patients treated with cetuximab

EGFR-antibodies are associated with significant skin toxicity, including acneiform rash and folliculitis. It remains impossible to predict the occurrence of severe skin toxicity due to the lack of predictive markers. Here, we present the first genome-wide association study (GWAS) to find single nucleotide polymorphisms (SNPs) associated with EGFR inhibitor-induced skin toxicity using data of the multicentre randomized phase III CAIRO2 trial (clinicaltrials.gov NCT00208546). In this study, advanced or metastatic colorectal cancer patients were treated with capecitabine, oxaliplatin and bevacizumab with or without cetuximab. Germline DNA was available in 282 of the 368 patients in the cetuximab arm. Mild skin toxicity occurred in 195 patients (i.e. CTC grade 1 or 2, respectively 91 and 104 patients) and severe skin toxicity (i.e. grade 3) in 36 patients. Grade 4 skin toxicity did not occur. None of the SNPs reached the formal genome wide threshold for significance of 5x10(-8), though SNPs of at least 8 loci did show moderate association (p-value between 5x10(-7) and 5x10(-5)) with the occurrence of grade 3 (severe) skin toxicity. These SNPs did not overlap with SNPs associated with cetuximab efficacy as found in a previous GWAS in the same CAIRO2 cohort. If formally proven by replication, the SNPs associated with severe EGFR induced skin toxicity may be helpful to predict the occurrence and severity of skin toxicity in patients that will receive cetuximab and allow for adequate information on the risk of skin toxicity and prophylactic measurements.

Genome-wide scan of the effect of common nsSNPs on colorectal cancer survival outcome

BACKGROUND

We conducted a genome-wide scan to identify non-synonymous SNPs (nsSNPs) that might influence survival after a diagnosis of colorectal cancer (CRC).

METHODS

We genotyped 7679 nsSNPs in 1939 Scottish patients from the Scottish Colorectal Cancer Study recruited soon after a CRC diagnosis and prospectively followed for survival outcomes. All-cause and CRC-specific survival analyses were conducted using Cox proportional hazard models adjusted for stage, age and sex for all cancer cases, after cancer type stratification and assuming additive and recessive models of inheritance. For all the SNPs that had a p-value < 0.10 a meta-analysis was performed combining the results of the discovery set and a replication set of 899 Scottish CRC patients. The p-value threshold of significance was set as at p < 10-8.

RESULTS

897 and 894 nsSNPs were associated with all-cause and CRC-specific mortality, respectively, at a p-value level < 0.10 in the discovery set. Meta-analysis of the results from the discovery and replication sets was performed overall and for cancers of colon and rectum separately and none of the variants reached a p-value < 10-8.

CONCLUSIONS

This large scale well-powered analysis demonstrates that common nsSNPs are not associated with CRC prognosis overall.