RTK Inhibitors in Melanoma: From Bench to Bedside

MAPK (mitogen activated protein kinase) and PI3K/AKT (Phosphatidylinositol-3-Kinase and Protein Kinase B) pathways play a key role in melanoma progression and metastasis that are regulated by receptor tyrosine kinases (RTKs). Although RTKs are mutated in a small percentage of melanomas, several receptors were found up regulated/altered in various stages of melanoma initiation, progression, or metastasis. Targeting RTKs remains a significant challenge in melanoma, due to their variable expression across different melanoma stages of progression and among melanoma subtypes that consequently affect response to treatment and disease progression. In this review, we discuss in details the activation mechanism of several key RTKs: type III: c-KIT (mast/stem cell growth factor receptor); type I: EGFR (Epidermal growth factor receptor); type VIII: HGFR (hepatocyte growth factor receptor); type V: VEGFR (Vascular endothelial growth factor), structure variants, the function of their structural domains, and their alteration and its association with melanoma initiation and progression. Furthermore, several RTK inhibitors targeting the same receptor were tested alone or in combination with other therapies, yielding variable responses among different melanoma groups. Here, we classified RTK inhibitors by families and summarized all tested drugs in melanoma indicating the rationale behind the use of these drugs in each melanoma subgroups from preclinical studies to clinical trials with a specific focus on their purpose of treatment, resulted effect, and outcomes.

InferAMP, a python web app for copy number inference from discrete gene-level amplification signals noted in clinical tumor profiling reports

As somatic next-generation sequencing gene panel analysis in advanced cancer patients is becoming more routine, oncologists are frequently presented with reports containing lists of genes with increased copy number. Distinguishing which of these amplified genes, if any, might be driving tumor growth and might thus be worth considering targeting can be challenging. One particular issue is the frequent absence of genomic contextual information in clinical reports, making it very challenging to determine which reported genes might be co-amplified and how large any such amplicons might be. We describe a straightforward Python web app, InferAMP, into which healthcare professionals may enter lists of amplified genes from clinical reports. The tool reports (1) the likely size of amplified genomic regions, (2) which reported genes are co-amplified and (3) which other cancer-relevant genes that were not evaluated in the assay may also be co-amplified in the specimen. The tool is accessible for web queries at http://inferamp.org.

InferCNV, a python web app for copy number inference from discrete gene-level amplification signals noted in clinical tumor profiling reports

As somatic next-generation sequencing gene panel analysis in advanced cancer patients is becoming more routine, oncologists are frequently presented with reports containing lists of genes with increased copy number. Distinguishing which of these amplified genes, if any, might be driving tumor growth and might thus be worth considering targeting can be challenging. One particular issue is the frequent absence of genomic contextual information in clinical reports, making it very challenging to determine which reported genes might be co-amplified and how large any such amplicons might be. We describe a straightforward Python web app, InferCNV, into which healthcare professionals may enter lists of amplified genes from clinical reports. The tool reports (1) the likely size of amplified genomic regions, (2) which reported genes are co-amplified and (3) which other cancer-relevant genes that were not evaluated in the assay may also be co-amplified in the specimen. The tool is accessible for web queries at http://infercnv.org.

InferAMP, a python web app for copy number inference from discrete gene-level amplification signals noted in clinical tumor profiling reports

As somatic next-generation sequencing gene panel analysis in advanced cancer patients is becoming more routine, oncologists are frequently presented with reports containing lists of genes with increased copy number. Distinguishing which of these amplified genes, if any, might be driving tumor growth and might thus be worth considering targeting can be challenging. One particular issue is the frequent absence of genomic contextual information in clinical reports, making it very challenging to determine which reported genes might be co-amplified and how large any such amplicons might be. We describe a straightforward Python web app, InferAMP, into which healthcare professionals may enter lists of amplified genes from clinical reports. The tool reports (1) the likely size of amplified genomic regions, (2) which reported genes are co-amplified and (3) which other cancer-relevant genes that were not evaluated in the assay may also be co-amplified in the specimen. The tool is accessible for web queries at http://inferamp.org.