Validation and Characterization of FGFR2 Rearrangements in Cholangiocarcinoma with Comprehensive Genomic Profiling.

Cholangiocarcinoma (CCA) is a heterogeneous biliary tract cancer with a poor prognosis. Approximately 30% to 50% of patients harbor actionable alterations, including FGFR2 rearrangements. Pemigatinib, a potent, selective fibroblast growth factor receptor (FGFR) FGFR1-3 inhibitor, is approved for previously treated, unresectable, locally advanced or metastatic CCA harboring FGFR2 fusions/rearrangements, as detected by a US Food and Drug Administration-approved test. The next-generation sequencing (NGS)-based FoundationOneCDx (F1CDx) was US Food and Drug Administration approved for detecting FGFR2 fusions or rearrangements. The precision and reproducibility of F1CDx in detecting FGFR2 rearrangements in CCA were examined. Analytical concordance between F1CDx and an externally validated RNA-based NGS (evNGS) test was performed. Identification of FGFR2 rearrangements in the screening population from the pivotal FIGHT-202 study (NCT02924376) was compared with F1CDx. The reproducibility and repeatability of F1CDx were 90% to 100%. Adjusted positive, negative, and overall percentage agreements were 87.1%, 99.6%, and 98.3%, respectively, between F1CDx and evNGS. Compared with evNGS, F1CDx had a positive predictive value of 96.2% and a negative predictive value of 98.5%. The positive percentage agreement, negative percentage agreement, overall percentage agreement, positive predictive value, and negative predictive value were 100% for F1CDx versus the FIbroblast Growth factor receptor inhibitor in oncology and Hematology Trial-202 (FIGHT-202) clinical trial assay. Of 6802 CCA samples interrogated, 9.2% had FGFR2 rearrangements. Cell lines expressing diverse FGFR2 fusions were sensitive to pemigatinib. F1CDx demonstrated sensitivity, reproducibility, and high concordance with clinical utility in identifying patients with FGFR2 rearrangements who may benefit from pemigatinib treatment.

RET rearrangements are actionable alterations in breast cancer.

Fusions involving the oncogenic gene RET have been observed in thyroid and lung cancers. Here we report RET gene alterations, including amplification, missense mutations, known fusions, novel fusions, and rearrangements in breast cancer. Their frequency, oncogenic potential, and actionability in breast cancer are described. Two out of eight RET fusions (NCOA4-RET and a novel RASGEF1A-RET fusion) and RET amplification were functionally characterized and shown to activate RET kinase and drive signaling through MAPK and PI3K pathways. These fusions and RET amplification can induce transformation of non-tumorigenic cells, support xenograft tumor formation, and render sensitivity to RET inhibition. An index case of metastatic breast cancer progressing on HER2-targeted therapy was found to have the NCOA4-RET fusion. Subsequent treatment with the RET inhibitor cabozantinib led to a rapid clinical and radiographic response. RET alterations, identified by genomic profiling, are promising therapeutic targets and are present in a subset of breast cancers.

Genomics of primary chemoresistance and remission induction failure in paediatric and adult acute myeloid leukaemia.

Cure rates of children and adults with acute myeloid leukaemia (AML) remain unsatisfactory partly due to chemotherapy resistance. We investigated the genetic basis of AML in 107 primary cases by sequencing 670 genes mutated in haematological malignancies. SETBP1, ASXL1 and RELN mutations were significantly associated with primary chemoresistance. We identified genomic alterations not previously described in AML, together with distinct genes that were significantly overexpressed in therapy-resistant AML. Defined gene mutations were sufficient to explain primary induction failure in only a minority of cases. Thus, additional genetic or molecular mechanisms must cause primary chemoresistance in paediatric and adult AML.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.

The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

Identification of potential drug targets for tuberous sclerosis complex by synthetic screens combining CRISPR-based knockouts with RNAi.

The tuberous sclerosis complex (TSC) family of tumor suppressors, TSC1 and TSC2, function together in an evolutionarily conserved protein complex that is a point of convergence for major cell signaling pathways that regulate mTOR complex 1 (mTORC1). Mutation or aberrant inhibition of the TSC complex is common in various human tumor syndromes and cancers. The discovery of novel therapeutic strategies to selectively target cells with functional loss of this complex is therefore of clinical relevance to patients with nonmalignant TSC and those with sporadic cancers. We developed a CRISPR-based method to generate homogeneous mutant Drosophila cell lines. By combining TSC1 or TSC2 mutant cell lines with RNAi screens against all kinases and phosphatases, we identified synthetic interactions with TSC1 and TSC2. Individual knockdown of three candidate genes (mRNA-cap, Pitslre, and CycT; orthologs of RNGTT, CDK11, and CCNT1 in humans) reduced the population growth rate of Drosophila cells lacking either TSC1 or TSC2 but not that of wild-type cells. Moreover, individual knockdown of these three genes had similar growth-inhibiting effects in mammalian TSC2-deficient cell lines, including human tumor-derived cells, illustrating the power of this cross-species screening strategy to identify potential drug targets.

Durable clinical benefit to trastuzumab and chemotherapy in a patient with metastatic colon adenocarcinoma harboring ERBB2 amplification.

Somatic ERBB2 amplification or activating mutations occur in approximately 2-5% of metastatic colorectal adenocarcinomas and are presumed to be oncogenic drivers, but limited evidence exists to suggest these lesions are sensitive to targeted monotherapy in patients. Here we present the case of a patient with advanced CRC with pulmonary metastases, who had progressed on both standard of care cytotoxic chemotherapy and anti-EGFR targeted therapy. Comprehensive genomic profiling (FoundationOne(®)) identified amplification of ERBB2 and a TP53 mutation in the metastatic lesion. Treatment with trastuzumab with a chemotherapy backbone elicited stable disease/minor response in the patient over a one year course of therapy, reducing tumor burden and significantly improving quality of life. This report demonstrates the application of personalized targeted therapy guided by comprehensive genomic profiling in metastatic colorectal adenocarcinoma.

Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis.

BACKGROUND: RNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial. RESULTS: Significant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files. CONCLUSION: Online GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.

Integrating protein-protein interaction networks with phenotypes reveals signs of interactions.

A major objective of systems biology is to organize molecular interactions as networks and to characterize information flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the 'signs' of interactions (i.e., activation-inhibition relationships). We constructed a Drosophila melanogaster signed PPI network consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes enolase and aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation-inhibition relationships between physically interacting proteins within signaling pathways will affect our understanding of many biological functions, including signal transduction and mechanisms of disease.