Understanding cancer drug resistance with Sleeping Beauty functional genomic screens: Application to MAPK inhibition in cutaneous melanoma.

Combined BRAF and MEK inhibition is an effective treatment for BRAF-mutant cutaneous melanoma. However, most patients progress on this treatment due to drug resistance. Here, we applied the Sleeping Beauty transposon system to understand how melanoma evades MAPK inhibition. We found that the specific drug resistance mechanisms differed across melanomas in our genetic screens of five cutaneous melanoma cell lines. While drivers that reactivated MAPK were highly conserved, many others were cell-line specific. One such driver, VAV1, activated a de-differentiated transcriptional program like that of hyperactive RAC1, RAC1P29S. To target this mechanism, we showed that an inhibitor of SRC, saracatinib, blunts the VAV1-induced transcriptional reprogramming. Overall, we highlighted the importance of accounting for melanoma heterogeneity in treating cutaneous melanoma with MAPK inhibitors. Moreover, we demonstrated the utility of the Sleeping Beauty transposon system in understanding cancer drug resistance.

SRC-RAC1 signaling drives drug resistance to BRAF inhibition in de-differentiated cutaneous melanomas.

Rare gain-of-function mutations in RAC1 drive drug resistance to targeted BRAF inhibition in cutaneous melanoma. Here, we show that wildtype RAC1 is a critical driver of growth and drug resistance, but only in a subset of melanomas with elevated markers of de-differentiation. Similarly, SRC inhibition also selectively sensitized de-differentiated melanomas to BRAF inhibition. One possible mechanism may be the suppression of the de-differentiated state, as SRC and RAC1 maintained markers of de-differentiation in human melanoma cells. The functional differences between melanoma subtypes suggest that the clinical management of cutaneous melanoma can be enhanced by the knowledge of differentiation status. To simplify the task of classification, we developed a binary classification strategy based on a small set of ten genes. Using this gene set, we reliably determined the differentiation status previously defined by hundreds of genes. Overall, our study informs strategies that enhance the precision of BRAFi by discovering unique vulnerabilities of the de-differentiated cutaneous melanoma subtype and creating a practical method to resolve differentiation status.

Machine learning approach informs biology of cancer drug response.

BACKGROUND: The mechanism of action for most cancer drugs is not clear. Large-scale pharmacogenomic cancer cell line datasets offer a rich resource to obtain this knowledge. Here, we present an analysis strategy for revealing biological pathways that contribute to drug response using publicly available pharmacogenomic cancer cell line datasets. METHODS: We present a custom machine-learning based approach for identifying biological pathways involved in cancer drug response. We test the utility of our approach with a pan-cancer analysis of ML210, an inhibitor of GPX4, and a melanoma-focused analysis of inhibitors of BRAFV600. We apply our approach to reveal determinants of drug resistance to microtubule inhibitors. RESULTS: Our method implicated lipid metabolism and Rac1/cytoskeleton signaling in the context of ML210 and BRAF inhibitor response, respectively. These findings are consistent with current knowledge of how these drugs work. For microtubule inhibitors, our approach implicated Notch and Akt signaling as pathways that associated with response. CONCLUSIONS: Our results demonstrate the utility of combining informed feature selection and machine learning algorithms in understanding cancer drug response.

Src-Dependent DBL Family Members Drive Resistance to Vemurafenib in Human Melanoma.

The use of selective BRAF inhibitors (BRAFi) has produced remarkable outcomes for patients with advanced cutaneous melanoma harboring a BRAFV600E mutation. Unfortunately, the majority of patients eventually develop drug-resistant disease. We employed a genetic screening approach to identify gain-of-function mechanisms of BRAFi resistance in two independent melanoma cell lines. Our screens identified both known and unappreciated drivers of BRAFi resistance, including multiple members of the DBL family. Mechanistic studies identified a DBL/RAC1/PAK signaling axis capable of driving resistance to both current and next-generation BRAFis. However, we show that the SRC inhibitor, saracatinib, can block the DBL-driven resistance. Our work highlights the utility of our straightforward genetic screening method in identifying new drug combinations to combat acquired BRAFi resistance. SIGNIFICANCE: A simple, rapid, and flexible genetic screening approach identifies genes that drive resistance to MAPK inhibitors when overexpressed in human melanoma cells.

A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells.

BACKGROUND: The introduction of genome-wide shRNA and CRISPR libraries has facilitated cell-based screens to identify loss-of-function mutations associated with a phenotype of interest. Approaches to perform analogous gain-of-function screens are less common, although some reports have utilized arrayed viral expression libraries or the CRISPR activation system. However, a variety of technical and logistical challenges make these approaches difficult for many labs to execute. In addition, genome-wide shRNA or CRISPR libraries typically contain of hundreds of thousands of individual engineered elements, and the associated complexity creates issues with replication and reproducibility for these methods. RESULTS: Here we describe a simple, reproducible approach using the SB transposon system to perform phenotypic cell-based genetic screens. This approach employs only three plasmids to perform unbiased, whole-genome transposon mutagenesis. We also describe a ligation-mediated PCR method that can be used in conjunction with the included software tools to map raw sequence data, identify candidate genes associated with phenotypes of interest, and predict the impact of recurrent transposon insertions on candidate gene function. Finally, we demonstrate the high reproducibility of our approach by having three individuals perform independent replicates of a mutagenesis screen to identify drivers of vemurafenib resistance in cultured melanoma cells. CONCLUSIONS: Collectively, our work establishes a facile, adaptable method that can be performed by labs of any size to perform robust, genome-wide screens to identify genes that influence phenotypes of interest.

Validation of a Targeted RNA Sequencing Assay for Kinase Fusion Detection in Solid Tumors.

Kinase gene fusions are important drivers of oncogenic transformation and can be inhibited with targeted therapies. Clinical grade diagnostics using RNA sequencing to detect gene rearrangements in solid tumors are limited, and the few that are available require prior knowledge of fusion break points. To address this, we have analytically validated a targeted RNA sequencing assay (OSU-SpARKFuse) for fusion detection that interrogates complete transcripts from 93 kinase and transcription factor genes. From a total of 74 positive and 36 negative control samples, OSU-SpARKFuse had 93.3% sensitivity and 100% specificity for fusion detection. Assessment of repeatability and reproducibility revealed 96.3% and 94.4% concordance between intrarun and interrun technical replicates, respectively. Application of this assay on prospective patient samples uncovered OLFM4 as a novel RET fusion partner in a small-bowel cancer and led to the discovery of a KLK2-FGFR2 fusion in a patient with prostate cancer who subsequently underwent treatment with a pan-fibroblast growth factor receptor inhibitor. Beyond fusion detection, OSU-SpARKFuse has built-in capabilities for discovery research, including gene expression analysis, detection of single-nucleotide variants, and identification of alternative splicing events.

Cancer Driver Log (CanDL): Catalog of Potentially Actionable Cancer Mutations.

Massively parallel sequencing technologies have enabled characterization of genomic alterations across multiple tumor types. Efforts have focused on identifying driver mutations because they represent potential targets for therapy. However, because of the presence of driver and passenger mutations, it is often challenging to assign the clinical relevance of specific mutations observed in patients. Currently, there are multiple databases and tools that provide in silico assessment for potential drivers; however, there is no comprehensive resource for mutations with functional characterization. Therefore, we created an expert-curated database of potentially actionable driver mutations for molecular pathologists to facilitate annotation of cancer genomic testing. We reviewed scientific literature to identify variants that have been functionally characterized in vitro or in vivo as driver mutations. We obtained the chromosome location and all possible nucleotide positions for each amino acid change and uploaded them to the Cancer Driver Log (CanDL) database with associated literature reference indicating functional driver evidence. In addition to a simple interface, the database allows users to download all or selected genes as a comma-separated values file for incorporation into their own analysis pipeline. Furthermore, the database includes a mechanism for third-party contributions to support updates for novel driver mutations. Overall, this freely available database will facilitate rapid annotation of cancer genomic testing in molecular pathology laboratories for mutations.