The clinical efficacy of therapeutic monoclonal antibodies for breast and colorectal cancer has greatly contributed to the improvement of patients’ outcomes by individualizing their treatments according to their genomic background. However, primary or acquired resistance to treatment reduces its efficacy. In this context, the identification of biomarkers predictive of drug response would support research and development of new alternative treatments. Biomarkers play a major role in the genomic revolution, supporting disease diagnosis and treatment decision-making. Currently, several molecular biomarkers of treatment response for breast and colorectal cancer have been described. However, information on these biomarkers is scattered across several resources, and needs to be identified, collected and properly integrated to be fully exploited to inform monitoring of drug response in patients. Therefore, there is a need of resources that offer biomarker data in a harmonized manner to the user to support the identification of actionable biomarkers of response to treatment in cancer. ResMarkerDB was developed as a comprehensive resource of biomarkers of drug response in colorectal and breast cancer. It integrates data of biomarkers of drug response from existing repositories, and new data extracted and curated from the literature (referred as ResCur). ResMarkerDB currently features 266 biomarkers of diverse nature. Twenty-five percent of these biomarkers are exclusive of ResMarkerDB. Furthermore, ResMarkerDB is one of the few resources offering non-coding DNA data in response to drug treatment. The database contains more than 500 biomarker-drug-tumour associations, covering more than 100 genes. ResMarkerDB provides a web interface to facilitate the exploration of the current knowledge of biomarkers of response in breast and colorectal cancer. It aims to enhance translational research efforts in identifying actionable biomarkers of drug response in cancer.

Background: Major Depression is the leading cause of impairment worldwide. The understanding of its molecular underpinnings is key to identifying new potential biomarkers and drug targets to alleviate its burden in society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of Major Depression associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with Major Depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis using GTEx data and alteration of transcription factor binding sites with pattern matching approaches and chromatin accessibility data.Results: The fine-mapping of major depression genetic variants uncovered putative causally associated variants whose proximal genes were linked with Major Depression pathophysiology. Four genetic variants altering the expression of 5 genes were found by colocalization analysis, highlighting the role of SLC12A5, involved in chlorine homeostasis in neurons, and MYRF, related with central nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of the genomic variant rs62259947 in modulating the expression of P4HTM through the alteration of YY1 binding site, altogether regulating hypoxia response.Conclusions: The combination of GWAS signals, cis-eQTL, transcription factor binding site information and active regulatory regions in the chromatin, enabled the prioritization of putative causal genetic variants in Major Depression. Importantly, our pipeline can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.

Understanding the molecular basis of major depression is critical for identifying new potential biomarkers and drug targets to alleviate its burden on society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of major depression-associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with major depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis and alteration of transcription factor binding sites. The fine-mapping process uncovered putative causally associated variants whose proximal genes were linked with major depression pathophysiology. Four colocalizing genetic variants altered the expression of five genes, highlighting the role of SLC12A5 in neuronal chlorine homeostasis and MYRF in nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of rs62259947 in modulating P4HTM expression by altering the YY1 binding site, altogether regulating hypoxia response. Overall, our pipeline could prioritize putative causal genetic variants in major depression. More importantly, it can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.