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Purpose: The National Cancer Institute-Molecular Analysis for Therapy Choice (NCI-MATCH) trial was implemented to identify actionable genetic alterations across cancer types and enroll patients accordingly onto treatment arms, irrespective of tumor histology. Using multiplex polymerase chain reaction (PCR) next-generation sequencing, NCI-MATCH genotyped 5,540 patients, discovering gene fusions in 202/5,540 tumors (3.65%). This result, substantially lower than the fusion detection prevalence of 8.5% across all patients with cancer screened at Massachusetts General Hospital's (MGH) clinical laboratories, supported reanalysis of NCI-MATCH samples identified as mutations-of-interest (MOI)-negative. The assay used by NCI-MATCH requires previous knowledge of both fusion genes, cannot detect novel fusions, and may underestimate fusion-positive patients. Anchored multiplex PCR (AMP) technology permits fusion detection with knowledge of just one gene of the fusion partners., Methods: Using AMP-based kits, we reprocessed 663 MOI-negative samples. 200 ng of RNA per sample were shipped from the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network biorepository to MGH (n = 319) and Yale University (n = 344), processed, and sequenced on the NextSeq550. Reported fusions were manually reviewed, and novel fusions orthogonally verified via reverse-transcription PCR and Sanger sequencing., Results: AMP identified 148 fusions in 142/663 MOI-negative patients (21% [95% CI, 18 to 25]), of which 28 were covered by the Oncomine Comprehensive Assay (OCA) panel but missed, while 120 were not covered by OCA. Among AMP-identified positive patients, 32 had actionable fusions, 24 contained novel fusions, and six had two fusion events. We identified fusions in 12/34 (35% [95% CI, 20 to 54]) cholangiocarcinomas and 43/109 (39% [95% CI, 30 to 49]) sarcomas., Conclusion: Technology and awareness of actionable fusions have improved since the NCI-MATCH trial. With AMP-based technology, we identified 142 patients with fusions not detected during NCI-MATCH screening, many potentially actionable. These striking data underscore the need to optimize the fusion-detection capabilities of genotyping assays used in precision medicine.

Purpose: The Wee1 kinase inhibitor adavosertib abrogates cell-cycle arrest, leading to cell death. Prior testing of twice-daily adavosertib in patients with advanced solid tumors determined the recommended phase II dose (RPh2D). Here, we report results for once-daily adavosertib., Patients and Methods: A 3 + 3 dose-escalation design was used, with adavosertib given once daily on days 1 to 5 and 8 to 12 in 21-day cycles. Molecular biomarkers of Wee1 activity, including tyrosine 15-phosphorylated Cdk1/2 (pY15-Cdk), were assessed in paired tumor biopsies. Whole-exome sequencing and RNA sequencing of remaining tumor tissue identified potential predictive biomarkers., Results: Among the 42 patients enrolled, the most common toxicities were gastrointestinal and hematologic; dose-limiting toxicities were grade 4 hematologic toxicity and grade 3 fatigue. The once-daily RPh2D was 300 mg. Six patients (14%) had confirmed partial responses: four ovarian, two endometrial. Adavosertib plasma exposures were similar to those from twice-daily dosing. On cycle 1 day 8 (pre-dose), tumor pY15-Cdk levels were higher than baseline in four of eight patients, suggesting target rebound during the day 5 to 8 dosing break. One patient who progressed rapidly had a tumor WEE1 mutation and potentially compensatory PKMYT1 overexpression. Baseline CCNE1 overexpression occurred in both of two responding patients, only one of whom had CCNE1 amplification, and in zero of three nonresponding patients., Conclusions: We determined the once-daily adavosertib RPh2D and observed activity in patients with ovarian or endometrial carcinoma, including two with baseline CCNE1 mRNA overexpression. Future studies will determine whether CCNE1 overexpression is a predictive biomarker for adavosertib., (©2021 American Association for Cancer Research.)

The use of large datasets has become ubiquitous in biomedical sciences. Researchers in the field of cancer genomics have, in recent years, generated large volumes of data from their experiments. Those responsible for production of this data often analyze a narrow subset of this data based on the research question they are trying to address: this is the case whether or not they are acting independently or in conjunction with a large-scale cancer genomics project. The reality of this situation creates the opportunity for other researchers to repurpose this data for different hypotheses if the data is made easily and freely available. New insights in biology resulting from more researchers having access to data they otherwise would be unable to generate on their own are a boon for the field. The following chapter reviews several cancer genomics-related databases and outlines the type of data they contain, as well as the methods required to access each database. While this list is not comprehensive, it should provide a basis for cancer researchers to begin exploring some of the many large datasets that are available to them.

Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited in scope and/or content. BioMuta, a database of cancer-associated single-nucleotide variations, and BioXpress, a database of cancer-associated differentially expressed genes and microRNAs, differ from other disease-associated variation and expression databases primarily through the aggregation of data across many studies into a single source with a unified representation and annotation of functional attributes. Early versions of these resources were initiated by pilot funding for specific research applications, but newly awarded funds have enabled hardening of these databases to production-level quality and will allow for sustained development of these resources for the next few years. Because both resources were developed using a similar methodology of integration, curation, unification, and annotation, we present BioMuta and BioXpress as allied databases that will facilitate a more comprehensive view of gene associations in cancer. BioMuta and BioXpress are hosted on the High-performance Integrated Virtual Environment (HIVE) server at the George Washington University at https://hive.biochemistry.gwu.edu/biomuta and https://hive.biochemistry.gwu.edu/bioxpress, respectively.