Genome-wide association studies of survival in 1520 cancer patients treated with bevacizumab-containing regimens

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

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.

PRAME Is a Novel Target of Tumor-Intrinsic Gas6/Axl Activation and Promotes Cancer Cell Invasion in Hepatocellular Carcinoma

(1) Background: Activation of the receptor tyrosine kinase Axl by Gas6 fosters oncogenic effects in hepatocellular carcinoma (HCC), associating with increased mortality of patients. The impact of Gas6/Axl signaling on the induction of individual target genes in HCC and its consequences is an open issue. (2) Methods: RNA-seq analysis of Gas6-stimulated Axl-proficient or Axl-deficient HCC cells was used to identify Gas6/Axl targets. Gain- and loss-of-function studies as well as proteomics were employed to characterize the role of PRAME (preferentially expressed antigen in melanoma). Expression of Axl/PRAME was assessed in publicly available HCC patient datasets and in 133 HCC cases. (3) Results: Exploitation of well-characterized HCC models expressing Axl or devoid of Axl allowed the identification of target genes including PRAME. Intervention with Axl signaling or MAPK/ERK1/2 resulted in reduced PRAME expression. PRAME levels were associated with a mesenchymal-like phenotype augmenting 2D cell migration and 3D cell invasion. Interactions with pro-oncogenic proteins such as CCAR1 suggested further tumor-promoting functions of PRAME in HCC. Moreover, PRAME showed elevated expression in Axl-stratified HCC patients, which correlates with vascular invasion and lowered patient survival. (4) Conclusions: PRAME is a bona fide target of Gas6/Axl/ERK signaling linked to EMT and cancer cell invasion in HCC.

Multifactor dimensionality reduction method identifies novel SNP interactions in the WNT protein interaction networks that are associated with recurrence risk in colorectal cancer

Background

Interactions among genetic variants are rarely studied but may explain a part of the variability in patient outcomes.

Objectives

In this study, we aimed to identify 1 to 3 way interactions among SNPs from five Wnt protein interaction networks that predict the 5-year recurrence risk in a cohort of stage I-III colorectal cancer patients.

Methods

423 patients recruited to the Newfoundland Familial Colorectal Cancer Registry were included. Five Wnt family member proteins (Wnt1, Wnt2, Wnt5a, Wnt5b, and Wnt11) were selected. The BioGRID database was used to identify the proteins interacting with each of these proteins. Genotypes of the SNPs located in the interaction network genes were retrieved from a genome-wide SNP genotype data previously obtained in the patient cohort. The GMDR 0.9 program was utilized to examine 1-, 2-, and 3-SNP interactions using a 5-fold cross validation step. Top GMDR 0.9 models were assessed by permutation testing and, if significant, prognostic associations were verified by multivariable logistic regression models.

Results

GMDR 0.9 has identified novel 1, 2, and 3-way SNP interactions associated with 5-year recurrence risk in colorectal cancer. Nine of these interactions were multi loci interactions (2-way or 3-way). Identified interaction models were able to distinguish patients based on their 5-year recurrence-free status in multivariable regression models. The significance of interactions was the highest in the 3-SNP models. Several of the identified SNPs were eQTLs, indicating potential biological roles of the genes they were associated with in colorectal cancer recurrence.

Conclusions

We identified novel interacting genetic variants that associate with 5-year recurrence risk in colorectal cancer. A significant portion of the genes identified were previously linked to colorectal cancer pathogenesis or progression. These variants and genes are of interest for future functional and prognostic studies. Our results provide further evidence for the utility of GMDR models in identifying novel prognostic biomarkers and the biological importance of the Wnt pathways in colorectal cancer.

Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer

Ovarian cancer (OC) is the most lethal gynecologic cancer due primarily to its asymptomatic nature and late stage at diagnosis. The development of non-invasive markers is an urgent priority. We report the accurate detection of epithelial OC using Artificial Intelligence (AI) and genome-wide epigenetic analysis of circulating cell free tumor DNA (cfTDNA). In a prospective study, we performed genome-wide DNA methylation profiling of cytosine (CpG) markers. Both conventional logistic regression and six AI platforms were used for OC detection. Further, we performed Gene Set Enrichment Analysis (GSEA) analysis to elucidate the molecular pathogenesis of OC. A total of 179,238 CpGs were significantly differentially methylated (FDR p-value < 0.05) genome-wide in OC. High OC diagnostic accuracies were achieved. Conventional logistic regression achieved an area under the ROC curve (AUC) [95% CI] 0.99 [± 0.1] with 95% sensitivity and 100% specificity. Multiple AI platforms each achieved high diagnostic accuracies (AUC = 0.99-1.00). For example, for Deep Learning (DL)/AI AUC = 1.00, sensitivity = 100% and 88% specificity. In terms of OC pathogenesis: GSEA analysis identified 'Adipogenesis' and 'retinoblastoma gene in cancer' as the top perturbed molecular pathway in OC. This finding of epigenomic dysregulation of molecular pathways that have been previously linked to cancer adds biological plausibility to our results.

ALOX12: A Novel Insight in Bevacizumab Response, Immunotherapy Effect, and Prognosis of Colorectal Cancer

Colorectal cancer is a highly malignant cancer with poor prognosis and mortality rates. As the first biological agent approved for metastatic colorectal cancer (mCRC), bevacizumab was confirmed to exhibit good performance when combined with chemotherapy and immunotherapy. However, the efficacy of both bevacizumab and immunotherapy is highly heterogeneous across CRC patients with different stages. Thus, exploring a novel biomarker to comprehensively assess the prognosis and bevacizumab and immunotherapy response of CRC is of great significance. In our study, weighted gene co-expression network analysis (WGCNA) and the receiver operating characteristic (ROC) curves were employed to identify bevacizumab-related genes. After verification in four public cohorts and our internal cohort, ALOX12 was identified as a key gene related to bevacizumab response. Prognostic analysis and in vitro experiments further demonstrated that ALOX12 was closely associated with the prognosis, tumor proliferation, invasion, and metastasis. Multi-omics data analysis based on mutation and copy number variation (CNV) revealed that RYR3 drove the expression of ALOX12 and the deletion of 17p12 inhibited ALOX12 expression, respectively. Moreover, we interrogated the relationship between ALOX12 and immune cells and checkpoints. The results exhibited that high ALOX12 expression predicted a higher immune infiltration and better immunotherapy response, which was further validated in Tumor Immune Dysfunction and Exclusion (TIDE) and Subclass Mapping (SubMap) methods. Above all, our study provides a stable biomarker for clinical protocol optimization, prognostic assessment, precise treatment, and individualized treatment of CRC.

Polygenic Risk Scores for Blood Pressure to Assess the Risk of Severe Bevacizumab-Induced Hypertension in Cancer Patients (Alliance)

Hypertension is a common bevacizumab-induced toxicity. No markers are available to predict patients at risk of developing hypertension. We hypothesized that genetic risk of essential hypertension, as measured by a blood pressure polygenic risk score (PRS), would be associated with risk of severe bevacizumab-induced hypertension. PRSs were calculated for 1,027 bevacizumab-treated patients of European descent with cancer from four clinical trials (Alliance for Clinical Trials in Oncology (Alliance) / Cancer and Leukemia Group B (CALGB) 80303, 40503, 90401, 40502) using summary systolic blood pressure (SBP) and diastolic blood pressure (DBP) genome-wide association results obtained from 757,601 individuals of European descent. The association between PRS and grade 3 bevacizumab-induced hypertension (Common Toxicity Criteria for Adverse Events version 3) in each trial was performed by multivariable logistic regression. Fixed-effect meta-analyses odds ratios (ORs) per standard deviation (SD) of the association of PRS (quantitative) and hypertension across trials were estimated by inverse-variance weighting. PRSs were additionally stratified into quintiles, with the bottom quintile as the referent group. The OR of the association between hypertension and each quintile vs. the referent group was determined by logistic regression. The most significant PRS (quantitative)-hypertension association included up to 67 single-nucleotide variants (SNPs) associated with SBP (P = 0.0077, OR per SD = 1.31, 95% confidence interval (CI), 1.07-1.60), and up to 53 SNPs associated with DBP (P = 0.0209, OR per SD = 1.27, 95% CI, 1.04-1.56). Patients in the top quintile had a higher risk of developing bevacizumab-induced hypertension compared with patients in the bottom quintile using SNPs associated with SBP (P = 4.75 × 10^-4 , OR = 3.72, 95% CI, 1.84-8.16) and DBP (P = 0.076, OR = 1.83, 95% CI, 0.95-3.64). Genetic variants associated with essential hypertension, mainly SBP, increase the risk of severe bevacizumab-induced hypertension.

PRUNE2 inhibits progression of colorectal cancer in vitro and in vivo

Prune homolog 2 with BCH domain (PRUNE2) is associated with prostate cancer, neuroblastoma, glioblastoma and melanoma; however, the function of PRUNE2 in colorectal cancer (CRC) remains unknown. The present study aimed to evaluate the effects of PRUNE2 on the development of CRC. The biological function of PRUNE2 in CRC cell lines was investigated by using Cell Counting Kit-8, colony formation, flow cytometry and Transwell assay. Additionally, a mouse model was established to investigate the effect of PRUNE2 on metastasis of CRC cells. The expression levels of PRUNE2 were lower in CRC compared with adjacent normal tissue and this expression pattern was associated with poor relapse-free survival probability. PRUNE2 overexpression significantly decreased cell proliferation and invasion, increased cell apoptosis and arrested the cell cycle. Consistently, it increased the protein expression levels of pro-apoptosis genes and decreased the expression of antiapoptotic proteins. PRUNE2 knockdown had the opposite effects. Furthermore, PRUNE2 overexpression decreased the tumorigenicity of CRC cells. In conclusion, PRUNE2 decreased cell survival, proliferation, invasion and tumorigenicity and promoted apoptosis, suggesting that PRUNE2 may function as a tumor-suppressive gene in CRC.