Random survival forests identify pathways with polymorphisms predictive of survival in KRAS mutant and KRAS wild-type metastatic colorectal cancer patients

Associations between Single Nucleotide Polymorphisms from the Genes of Chemokines and the CXCR2 Chemokine Receptor and an Increased Risk of Endometrial Cancer

Significant relationships with endometrial cancer were demonstrated, both for CCL2, CCL5, and CXCL8 chemokines and for the chemokine receptor CXCR2. The reported case-control study of genetic associations was designed to establish the role of selected single nucleotide polymorphisms (SNPs) of the CCL2, CCL5, CXCL8, and CXCR2 genes in the onset and progression of endometrial cancer. This study was conducted on 282 women, including 132 (46.8%) patients with endometrial cancer and 150 (53.2%) non-cancerous controls. The genotypes for CCL2 rs4586, CCL5 rs2107538 and rs2280789, CXCL8 rs2227532 and -738 T>A, and CXCR2 rs1126580 were determined, using PCR-RFLP assays. The AA homozygotes in CCL5 rs2107538 were associated with more than a quadruple risk of endometrial cancer (p ≤ 0.050). The GA heterozygotes in the CXCR2 SNP were associated with approximately threefold higher cancer risk (p ≤ 0.001). That association also remained significant after certain adjustments, carried out for age, diabetes mellitus, arterial hypertension, or endometrial thickness above 5 mm (p ≤ 0.050). The A-A haplotypes for the CCL5 polymorphisms and T-A-A haplotypes for the CCL2 and CCL5 SNPs were associated with about a twofold risk of endometrial cancer (p ≤ 0.050). In conclusion, CCL2 rs4586, CCL5 rs2107538 and rs2280789, and CXCR2 rs1126580 demonstrated significant associations with an increased risk of endometrial cancer.

Pharmacogenetics Role of Genetic Variants in Immune-Related Factors: A Systematic Review Focusing on mCRC

Pharmacogenetics plays a key role in personalized cancer treatment. Currently, the clinically available pharmacogenetic markers for metastatic colorectal cancer (mCRC) are in genes related to drug metabolism, such as DPYD for fluoropyrimidines and UGT1A1 for irinotecan. Recently, the impact of host variability in inflammatory and immune-response genes on treatment response has gained considerable attention, opening innovative perspectives for optimizing tailored mCRC therapy. A literature review was performed on the predictive role of immune-related germline genetic biomarkers on pharmacological outcomes in patients with mCRC. Particularly, that for efficacy and toxicity was reported and the potential role for clinical management of patients was discussed. Most of the available data regard therapy effectiveness, while the impact on toxicity remains limited. Several studies focused on the effects of polymorphisms in genes related to antibody-dependent cellular cytotoxicity (FCGR2A, FCGR3A) and yielded promising but inconclusive results on cetuximab efficacy. The remaining published data are sparse and mainly hypothesis-generating but suggest potentially interesting topics for future pharmacogenetic studies, including innovative gene-drug interactions in a clinical context. Besides the tumor immune escape pathway, genetic markers belonging to cytokines/interleukins (IL-8 and its receptors) and angiogenic mediators (IGF1) seem to be the best investigated and hopefully most promising to be translated into clinical practice after validation.

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.