FusionPathway: Prediction of pathways and therapeutic targets associated with gene fusions in cancer

Pharmacogenomics and Big Data in medical oncology: developments and challenges

Medical oncology, through conventional chemotherapy as well as targeted drugs, remains an important component of cancer patient management, particularly for systemic disease. Despite advances in all areas of medical oncology, certain challenges persist in the form of drug resistance and severe normal tissue toxicity. These unwanted effects can be counteracted through a patient-tailored treatment approach, which in chemotherapy is translated as pharmacogenomics. This research field investigates the way genetic makeup influences a patient's response to various drugs with the aim to minimize trial-and-error associated with drug administration. The paper introduces the role, advances and challenges of pharmacogenomics, highlighting the importance of Big Data mining to reveal the mechanisms behind drug-gene pair interaction for better patient outcomes. International consortiums have prioritized their focus on the clinical implementation of pharmacogenomics while tackling the challenges ahead: data standardization, ethical aspects and the education of physicians and patients alike to comprehend the power of pharmacogenomics to transform medical oncology.

Fusion proteins mediate alternation of protein interaction networks in cancers

Fusions of two different genes could lead to the production of chimeric RNAs, which could be translated into novel fusion (or chimeric) proteins. Fusion proteins often act as oncoproteins and drive cancer development, particularly in leukemia and lymphomas. Fusion proteins modify the existing protein-protein interaction (PPI) networks, which could eliminate some PPIs by removing protein domains in such fusions. This alternation of protein interaction networks could impact the signaling pathways and switch on the cancer-promoting activity that could drive the generation of cancer phenotypes and/or loss of controlled apoptosis. Thus, knowledge of the fusion proteins and their protein interaction networks could facilitate a deeper molecular understanding of cancer development, which could help to design new approaches for cancer therapies. Here, we discuss the structural features of fusion proteins and how they impact the PPI networks in cancers. Further, we discuss how to analyze the fusion protein-mediated alternation of PPI networks in cancers.

Multi-site desmoplastic small round cell tumors are genetically related and immune-cold

Desmoplastic small round cell tumor (DSRCT) is a highly aggressive soft tissue sarcoma that is characterized by the EWSR1-WT1 fusion protein. Patients present with hundreds of tumor implants in their abdominal cavity at various sites. To determine the genetic relatedness among these sites, exome and RNA sequencing were performed on 22 DSRCT specimens from 14 patients, four of whom had specimens from various tissue sites. Multi-site tumors from individual DSRCT patients had a shared origin and were highly related. Other than the EWSR1-WT1 fusion, very few secondary cancer gene mutations were shared among the sites. Among these, ARID1A, was recurrently mutated, which corroborates findings by others in DSRCT patients. Knocking out ARID1A in JN-DSRCT cells using CRISPR/CAS9 resulted in significantly lower cell proliferation and increased drug sensitivity. The transcriptome data were integrated using network analysis and drug target database information to identify potential therapeutic opportunities in EWSR1-WT1-associated pathways, such as PI3K and mTOR pathways. Treatment of JN-DSRCT cells with the PI3K inhibitor alpelisib and mTOR inhibitor temsirolimus reduced cell proliferation. In addition, the low mutation burden was associated with an immune-cold state in DSRCT. Together, these data reveal multiple genomic and immune features of DSRCT and suggest therapeutic opportunities in patients.

Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

Owing to a lack of response to the anti-PD1 therapy for most cancer patients, we develop a network approach to infer genes, pathways, and potential therapeutic combinations that are associated with tumor response to anti-PD1. Here, our prediction identifies genes and pathways known to be associated with anti-PD1, and is further validated by 6 CRISPR gene sets associated with tumor resistance to cytotoxic T cells and targets of the 36 compounds that have been tested in clinical trials for combination treatments with anti-PD1. Integration of our top prediction and TCGA data identifies hundreds of genes whose expression and genetic alterations that could affect response to anti-PD1 in each TCGA cancer type, and the comparison of these genes across cancer types reveals that the tumor immunoregulation associated with response to anti-PD1 would be tissue-specific. In addition, the integration identifies the gene signature to calculate the MHC I association immunoscore (MIAS) that shows a good correlation with patient response to anti-PD1 for 411 melanoma samples complied from 6 cohorts. Furthermore, mapping drug target data to the top genes in our association prediction identifies inhibitors that could potentially enhance tumor response to anti-PD1, such as inhibitors of the encoded proteins of CDK4, GSK3B, and PTK2.

Challenges in modeling EWS-FLI1-driven transgenic mouse model for Ewing sarcoma

EWS-FLI1 is a master regulator of Ewing sarcoma (ES) oncogenesis. Although EWS-FLI1 represents a clear therapeutic target, targeted therapeutic inhibitors are lacking. Scientific literature has indicated accumulating information pertaining to EWS-FLI1 translocation, pathogenesis, function, oncogenic partnerships, and potential clinical relevance. However, attempts to develop EWS-FLI1-driven human-like ES mouse models or in vivo systems ended up with limited success. Establishing such models as preclinical screening tools may accelerate the development of EWS-FLI1 targeted therapeutic inhibitors. This review summarizes the current scenario, which focuses on the limitations, challenges, and possible reasons for past failures in model development and also plausible interim alternatives.

The impact of fusion genes on cancer stem cells and drug resistance

With ever increasing evidences on the role of fusion genes as the oncogenic protagonists in myriad cancers, it's time to explore if fusion genes can be the next generational drug targets in meeting the current demands of higher drug efficacy. Eliminating cancer stem cells (CSC) has become the current focus; however, we have reached a standstill in drug development owing to the lack of effective strategies to eradicate CSC. We believe that fusion genes could be the novel targets to overcome this limitation. The intriguing feature of fusion genes is that it dominantly impacts every aspect of CSC including self-renewal, differentiation, lineage commitment, tumorigenicity and stemness. Given the clinical success of fusion gene-based drugs in hematological cancers, our attempt to target fusion genes in eradicating CSC can be rewarding. As fusion genes are expressed explicitly in cancer cells, eradicating CSC by targeting fusion genes provides yet an another advantage of negligible patient side effects since normal cells remain unaffected by the drug. We hereby delineate the latest evidences on how fusion genes regulate CSC and drug resistance.

annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions

BACKGROUND

Gene fusion events are significant sources of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic prioritization difficult. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require informed filtering, making it difficult for researchers and clinicians to rapidly discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy.

RESULTS

We developed annoFuse, an R package, and shinyFuse, a companion web application, to annotate, prioritize, and explore biologically-relevant expressed gene fusions, downstream of fusion calling. We validated annoFuse using a random cohort of TCGA RNA-Seq samples (N = 160) and achieved a 96% sensitivity for retention of high-confidence fusions (N = 603). annoFuse uses FusionAnnotator annotations to filter non-oncogenic and/or artifactual fusions. Then, fusions are prioritized if previously reported in TCGA and/or fusions containing gene partners that are known oncogenes, tumor suppressor genes, COSMIC genes, and/or transcription factors. We applied annoFuse to fusion calls from pediatric brain tumor RNA-Seq samples (N = 1028) provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project to determine recurrent fusions and recurrently-fused genes within different brain tumor histologies. annoFuse annotates protein domains using the PFAM database, assesses reciprocality, and annotates gene partners for kinase domain retention. As a standard function, reportFuse enables generation of a reproducible R Markdown report to summarize filtered fusions, visualize breakpoints and protein domains by transcript, and plot recurrent fusions within cohorts. Finally, we created shinyFuse for algorithm-agnostic interactive exploration and plotting of gene fusions.

CONCLUSIONS

annoFuse provides standardized filtering and annotation for gene fusion calls from STAR-Fusion and Arriba by merging, filtering, and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with data from the OpenPBTA project. We are expanding the package to be widely-applicable to other fusion algorithms and expect annoFuse to provide researchers a method for rapidly evaluating, prioritizing, and translating fusion findings in patient tumors.