Protein kinase inhibitors in metastatic colorectal cancer. Let's pick patients, tumors, and kinase inhibitors to piece the puzzle together!

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

Predictive and prognostic markers in the treatment of metastatic colorectal cancer (mCRC): personalized medicine at work

This article clarifies prognostic and predictive markers in the treatment of colorectal cancer. Multiple chemotherapeutic drugs are approved for metastatic colorectal cancer (mCRC), but available guidelines are often not helpful in directing drug selections. It would be desirable to define patient populations before chemotherapy by biomarkers that predict outcome and toxicities. RAS mutational evaluation remains the only established biomarker analysis in the treatment of mCRC. BRAF mutant tumors are associated with poor outcome. Chemotherapeutic combination therapies still remain the most active treatments in the armamentarium, and future trials should address the need to prospectively investigate and validate biomarkers.

Genes involved in pericyte-driven tumor maturation predict treatment benefit of first-line FOLFIRI plus bevacizumab in patients with metastatic colorectal cancer

Pericytes are crucial for angiogenesis. The impact of pericyte function to bevacizumab efficacy in mCRC treatment has not been comprehensively examined. This retrospective study investigated germline polymorphisms in genes related to early pericyte maturation to predict bevacizumab efficacy in 424 patients of two clinical trials treated first line with FOLFIRI+bevacizumab. Eight single-nucleotide polymorphisms (SNPs) were tested for potential biomarker value: RGS5 (regulator of G-protein signaling 5; rs1056515, rs2661280), PDGFR-β (platelet-derived growth factor receptor-β; rs2229562, rs2302273), CSPG4 (chondroitin sulfate proteoglycan NG2; rs8023621, rs1127648) and RALBP1 (RalA binding protein 1; rs10989, rs329007). For progression-free survival (PFS), PDGFR-β (rs2302273) was able to define significantly different patient cohorts in uni- and multivariate testing. RALPB1 (rs329007) showed predictive value for tumor response. The C allele in RGS5 (rs2661280) predicted longer overall survival and CSPG4 rs1127648 was associated with differences in PFS, but for both value was lost when multivariate analysis was applied. A comprehensive statistical analysis revealed that the biomarker value of the SNPs was dependent on primary tumor location. This is the first study to identify pericyte germline polymorphisms associated with clinical outcome in mCRC patients treated first line with FOLFIRI+bevacizumab. The differences seen with regard to primary tumor location may lead to further research to understand the clinical outcome differences seen in right- and left-sided colon cancer.