A genome-wide association study identifies single nucleotide polymorphisms associated with time-to-metastasis in colorectal cancer

Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

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.

Discovering Innate Driver Variants for Risk Assessment of Early Colorectal Cancer Metastasis

Metastasis is the main fatal cause of colorectal cancer (CRC). Although enormous efforts have been made to date to identify biomarkers associated with metastasis, there is still a huge gap to translate these efforts into effective clinical applications due to the poor consistency of biomarkers in dealing with the genetic heterogeneity of CRCs. In this study, a small cohort of eight CRC patients was recruited, from whom we collected cancer, paracancer, and normal tissues simultaneously and performed whole-exome sequencing. Given the exomes, a novel statistical parameter LIP was introduced to quantitatively measure the local invasion power for every somatic and germline mutation, whereby we affirmed that the innate germline mutations instead of somatic mutations might serve as the major driving force in promoting local invasion. Furthermore, via bioinformatic analyses of big data derived from the public zone, we identified ten potential driver variants that likely urged the local invasion of tumor cells into nearby tissue. Of them, six corresponding genes were new to CRC metastasis. In addition, a metastasis resister variant was also identified. Based on these eleven variants, we constructed a logistic regression model for rapid risk assessment of early metastasis, which was also deployed as an online server, AmetaRisk (http://www.bio-add.org/AmetaRisk). In summary, we made a valuable attempt in this study to exome-wide explore the genetic driving force to local invasion, which provides new insights into the mechanistic understanding of metastasis. Furthermore, the risk assessment model can assist in prioritizing therapeutic regimens in clinics and discovering new drug targets, and thus substantially increase the survival rate of CRC patients.

Genome-Wide Association Study of Fluorescent Oxidation Products Accounting for Tobacco Smoking Status in Adults from the French EGEA Study

Oxidative stress (OS) is the main pathophysiological mechanism involved in several chronic diseases, including asthma. Fluorescent oxidation products (FlOPs), a global biomarker of damage due to OS, is of growing interest in epidemiological studies. We conducted a genome-wide association study (GWAS) of the FlOPs level in 1216 adults from the case-control and family-based EGEA study (mean age 43 years old, 51% women, and 23% current smokers) to identify genetic variants associated with FlOPs. The GWAS was first conducted in the whole sample and then stratified according to smoking status, the main exogenous source of reactive oxygen species. Among the top genetic variants identified by the three GWAS, those located in BMP6 (p = 3 × 10⁻⁶), near BMPER (p = 9 × 10⁻⁶), in GABRG3 (p = 4 × 10⁻⁷), and near ATG5 (p = 2 × 10⁻⁹) are the most relevant because of both their link to biological pathways related to OS and their association with several chronic diseases for which the role of OS in their pathophysiology has been pointed out. BMP6 and BMPER are of particular interest due to their involvement in the same biological pathways related to OS and their functional interaction. To conclude, this study, which is the first GWAS of FlOPs, provides new insights into the pathophysiology of chronic OS-related diseases.

Genome-wide association study identifies tumor anatomical site-specific risk variants for colorectal cancer survival

Identification of new genetic markers may improve the prediction of colorectal cancer prognosis. Our objective was to examine genome-wide associations of germline genetic variants with disease-specific survival in an analysis of 16,964 cases of colorectal cancer. We analyzed genotype and colorectal cancer-specific survival data from a consortium of 15 studies. Approximately 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, adjusting for clinical factors and principal components. Additionally, we ran secondary analyses stratifying by tumor site and disease stage. We used a genome-wide p-value threshold of 5 × 10-8 to assess statistical significance. No variants were statistically significantly associated with disease-specific survival in the full case analysis or in the stage-stratified analyses. Three SNPs were statistically significantly associated with disease-specific survival for cases with tumors located in the distal colon (rs698022, HR = 1.48, CI 1.30-1.69, p = 8.47 × 10-9) and the proximal colon (rs189655236, HR = 2.14, 95% CI 1.65-2.77, p = 9.19 × 10-9 and rs144717887, HR = 2.01, 95% CI 1.57-2.58, p = 3.14 × 10-8), whereas no associations were detected for rectal tumors. Findings from this large genome-wide association study highlight the potential for anatomical-site-stratified genome-wide studies to identify germline genetic risk variants associated with colorectal cancer-specific survival. Larger sample sizes and further replication efforts are needed to more fully interpret these findings.

A genome-wide search for determinants of survival in 1926 patients with advanced colorectal cancer with follow-up in over 22,000 patients

BACKGROUND

While genome-wide association studies (GWAS) have identified germline variants influencing the risk of developing colorectal cancer (CRC), there has been limited examination of the possible role of inherited variation as a determinant of patient outcome.

PATIENTS AND METHODS

We performed a GWAS for overall survival (OS) in 1926 patients with advanced CRC from the COIN and COIN-B clinical trials. For single nucleotide polymorphisms (SNPs) showing an association with OS (P < 1.0 × 10-5), we conducted sensitivity analyses based on the time from diagnosis to death and sought independent replications in 5675 patients from the Study of Colorectal Cancer in Scotland (SOCCS) and 16,964 patients from the International Survival Analysis in Colorectal cancer Consortium (ISACC). We analysed the Human Protein Atlas to determine if ERBB4 expression was associated with survival in 438 patients with colon adenocarcinomas.

RESULTS

The most significant SNP associated with OS was rs79612564 in ERBB4 (hazard ratio [HR] = 1.24, 95% confidence interval [CI] = 1.16-1.32, P = 1.9 × 10-7). SNPs at 17 loci had suggestive associations for OS and all had similar effects on the time from diagnosis to death. No lead SNPs were independently replicated in the meta-analysis of all patients from SOCCS and ISACC. However, rs79612564 was significant in stage-IV patients from SOCCS (P = 2.1 × 10-2) but not ISACC (P = 0.89) and SOCCS combined with COIN and COIN-B attained genome-wide significance (P = 1.7 × 10-8). Patients with high ERBB4 expression in their colon adenocarcinomas had worse survival (HR = 1.50, 95% CI = 1.1-1.9, P = 4.6 × 10-2).

CONCLUSIONS

Genetic and expression data support a potential role for rs79612564 in the receptor tyrosine kinase ERBB4 as a predictive biomarker of survival.

Large-Scale Prospective Genome-Wide Association Study of Oxaliplatin in Stage II/III Colon Cancer and Neuropathy

IMPORTANCE

The severity of oxaliplatin (L-OHP)-induced peripheral sensory neuropathy (PSN) exhibits substantial interpatient variability, and some patients suffer from long-term, persisting PSN.

OBJECTIVE

To identify single-nucleotide polymorphisms (SNPs) predicting L-OHP-induced PSN using a genome-wide association study (GWAS) approach.

DESIGN, SETTING, PARTICIPANTS

A large prospective GWAS including 1,379 patients with stage II/III colon cancer who received L-OHP-based adjuvant chemotherapy (mFOLFOX6/CAPOX) under the phase II (JOIN/JFMC41) or the phase III (ACHIVE/JFMC47) trial.

MAIN OUTCOMES AND MEASURES

First, GWAS comparison of worst grade PSN (grade 0/1 vs. 2/3) was performed. Next, to minimize the impact of ambiguity in PSN grading, extreme PSN phenotypes were selected and analyzed by GWAS. SNPs that could predict time to recovery from PSN were also evaluated. In addition, SNPs associated with L-OHP-induced allergic reactions (AR) and time to disease recurrence were explored.

RESULTS

No SNPs exceeded the genome-wide significance (p < 5.0 × 10^-8) in either GWAS comparison of worst grade PSN, extreme PSN phenotypes, or time to recovery from PSN. Association study focusing on AR or time to disease recurrence also failed to reveal any significant SNPs.

CONCLUSION AND RELEVANCE

Our results highlight the challenges of utilizing SNPs for predicting susceptibility to L-OHP-induced PSN in daily clinical practice.

PretiMeth: precise prediction models for DNA methylation based on single methylation mark

Background

The computational prediction of methylation levels at single CpG resolution is promising to explore the methylation levels of CpGs uncovered by existing array techniques, especially for the 450 K beadchip array data with huge reserves. General prediction models concentrate on improving the overall prediction accuracy for the bulk of CpG loci while neglecting whether each locus is precisely predicted. This leads to the limited application of the prediction results, especially when performing downstream analysis with high precision requirements.

Results

Here we reported PretiMeth, a method for constructing precise prediction models for each single CpG locus. PretiMeth used a logistic regression algorithm to build a prediction model for each interested locus. Only one DNA methylation feature that shared the most similar methylation pattern with the CpG locus to be predicted was applied in the model. We found that PretiMeth outperformed other algorithms in the prediction accuracy, and kept robust across platforms and cell types. Furthermore, PretiMeth was applied to The Cancer Genome Atlas data (TCGA), the intensive analysis based on precise prediction results showed that several CpG loci and genes (differentially methylated between the tumor and normal samples) were worthy for further biological validation.

Conclusion

The precise prediction of single CpG locus is important for both methylation array data expansion and downstream analysis of prediction results. PretiMeth achieved precise modeling for each CpG locus by using only one significant feature, which also suggested that our precise prediction models could be probably used for reference in the probe set design when the DNA methylation beadchip update. PretiMeth is provided as an open source tool via https://github.com/JxTang-bioinformatics/PretiMeth.

Transcriptome sequencing analysis of mono‑ADP‑ribosylation in colorectal cancer cells

Colorectal cancer (CRC) is a global health concern. The role of epigenetics in tumors has garnered increasing interest. ADP ribosylation is an epigenetic modification that is associated with a variety of biological functions and diseases, and its association with tumor development and progression has been hypothesized. However, due to the limitations of available techniques and methods, ADP ribosylation of specific sites is difficult to determine. In previous studies, it was shown that arginine-117 of histone 3 (H3R117) in Lovo cells can be modified by mono-ADP-ribosylation. This site was mutated and Lovo cells overexpressing this mutant construct were established. In the present study, the expression of differentially expressed genes (DEGs) between untransfected Lovo cells and H3R117A Lovo cells was analyzed. A total of 58,174 DEGs were identified, of which 2,324 were significantly differentially expressed (q-value <0.05; fold change >2). Functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment was used to analyze the functions and possible roles of the DEGs. The DEGs were enriched in pathways associated with metabolic process, catalytic activity, organelle and chromatin structure, and dynamics. Through this comprehensive and systematic analysis, the role of mono-ADP-ribosylation in CRC was examined, providing a foundation for future studies.

The long-term survival characteristics of a cohort of colorectal cancer patients and baseline variables associated with survival outcomes with or without time-varying effects

BACKGROUND

Colorectal cancer is the third most common cancer in the world. In this study, we assessed the long-term survival characteristics and prognostic associations and potential time-varying effects of clinico-demographic variables and two molecular markers (microsatellite instability (MSI) and BRAF Val600Glu mutation) in a population-based patient cohort followed up to ~ 19 years.

METHODS

The patient cohort included 738 incident cases diagnosed between 1999 and 2003. Cox models were used to analyze the association between the variables and a set of survival outcome measures (overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS), metastasis-free survival (MFS), recurrence/metastasis-free survival (RMFS), and event-free survival (EFS)). Cox proportional hazard (PH) assumption was tested for all variables, and Cox models with time-varying effects were used if any departure from the PH assumption was detected.

RESULTS

During the follow-up, ~ 61% patients died from any cause, ~ 26% died from colorectal cancer, and ~ 10% and ~ 20% experienced recurrences and distant metastases, respectively. Stage IV disease and post-diagnostic recurrence or metastasis were strongly linked to risk of death from colorectal cancer. If a patient had survived the first 6 years without any disease-related event (i.e., recurrence, metastasis, or death from colorectal cancer), their risks became very minimal after this time period. Distinct sets of markers were associated with different outcome measures. In some cases, the effects by variables were constant throughout the follow-up. For example, MSI-high tumor phenotype and older age at diagnosis predicted longer MFS times consistently over the follow-up. However, in some other cases, the effects of the variables varied with time. For example, adjuvant radiotherapy treatment was associated with increased risk of metastasis in patients who received this treatment after 5.5 years post-diagnosis, but not before that.

CONCLUSIONS

This study describes the long-term survival characteristics of a prospective cohort of colorectal cancer patients, relationships between baseline variables and a detailed set of patient outcomes over a long time, and time-varying effects of a group of variables. The results presented advance our understanding of the long-term prognostic characteristics in colorectal cancer and are expected to inspire future studies and clinical care strategies.