Trent, Jeffrey M.

Abstract Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, deadly form of ovarian cancer that uniformly harbors mutations in SMARCA4, a member of the SWI/SNF chromatin remodeling complex. SWI/SNF impacts RNA splicing, and dysregulation of splicing can generate immunogenic tumor antigens. Here, we explored the relationship between SMARCA4 loss and RNA splicing dysregulation. SCCOHT primary tumors harbored tumor-associated outlier splicing events compared to normal tissues. Many of the tumor events were retained introns encoding novel peptides predicted to bind to MHC-I complexes. Immune cells were observed in primary SCCOHT tumors, suggesting a potentially immune reactive tumor microenvironment. Mutations in several SWI/SNF subunits were associated with higher rates of outlier retained introns across tumor types in TCGA data. Interestingly, RNA sequencing of isogenic SCCOHT cell lines demonstrated a role for SMARCA4 in intron retention. Distinct protein-protein interactions between splicing factors identified in SCCOHT cell lines supported a role for SMARCA4 in splicing regulation. Further, SWI/SNF localized to genes which were differentially spliced. Mass spectrometry analyses confirmed expression of some of these novel peptides and a subset of these are predicted to bind to MHC-I complexes. A pool of these novel peptides derived from retained introns in SCCOHT triggered proliferation and expression of TNFa and INFb in primary human T cells. Together, these data suggest that SMARCA4 loss in SCCOHT leads to intron retention. Furthermore, T cell activation by novel peptides encoded by these tumor-specific splicing events suggests intron retention could be a source of tumor-associated antigens in SCCOHT. Citation Format: Elizabeth Raupach, Apurva Hegde, Krystine Garcia-Masnfield, Marice Alcantara, David Rose, Rebecca Halperin, Krystal Orlando, Jessica Lang, Ritin Sharma, Victoria David-Dirgo, Salvatore Facista, Rayvon Moore, Rochelle Kofman, Zoe Jensen, Victoria Zismann, Anthony Karnezis, Yemin Wang, Lynda Bennett, Timothy Whitsett, Marcin Kortylewski, William Hendricks, David Huntsman, Lorna Rodriguez-Rodriguez, Bernard Weissman, Jeffrey Trent, Patrick Pirrotte. Loss of SMARCA4 leads to intron retention and generation of tumor-associated antigens in small cell carcinoma of the ovary, hypercalcemic type [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B065.

Abstract Next-generation sequencing has transformed cancer care by providing essential insights for diagnosis, prognosis, and treatment. However, variability in testing timing, reporting practices, and interpretation challenges limits its clinical impact. This article highlights key opportunities to optimize somatic reporting, emphasizing the importance of timely testing throughout the cancer care continuum to maximize the diagnostic and therapeutic relevance of findings. Technical factors such as test design, sequencing depth, and the use of liquid biopsy substantially influence result accuracy and interpretation, underscoring the need for careful integration with clinical history. Standardized reporting practices that clearly delineate diagnostic, prognostic, and therapeutic findings can enhance the clinical utility of next-generation sequencing results. Streamlined formats and curated clinical trial data further support actionable decision making. Additionally, direct patient engagement and education are essential for empowering patients to navigate genomic testing and make informed decisions about their care. By leveraging multidisciplinary tumor boards, decision-support tools, and emerging artificial intelligence technologies, clinicians can better navigate the complexities of somatic reports. Standardization and clarity in reporting are critical to advancing precision oncology, empowering providers and patients to make informed treatment decisions and improve outcomes.

Abstract Introduction: The Patient Engagement-Cancer Genome Sequencing Research Center (PE-CGS; NCI U2C CA252973) based at the University of New Mexico Comprehensive Cancer Center (UNMCCC) with partners at Mayo Clinic Comprehensive Cancer Center, the Translational Genomics Research Institute, and the Black Hills for American Indian Health Research Center are developing culturally appropriate strategies and methods to address the underrepresentation of American Indians (AIs) in genomic research. Our efforts are directed towards the implementation of tailored cancer prevention, diagnosis, and treatment strategies that reduce cancer health disparities in Tribal communities. Methods: The PE-CGS Center has three units, each tasked with specific research components to engage AI participants in participatory, clinically-meaningful genomic research: 1) the Participant Engagement Unit; 2) the Genomics Characterization Unit; and 3) the Engagement Optimization Unit (EOU). The Center also has a Tribal Advisory Committee, composed of Tribal Governors, Presidents, and tribal health advocates and experts, who provide direction on all research matters. The PE-CGS EOU uses iterative rapid-cycle research techniques to optimize engagement and shares these strategies with other units for timely implementation. To date, the EOU has developed and implemented three key efforts. First, to aid in recruiting participants for the study, the EOU tailored a series (6) of participant-facing informational materials that include artwork from a local AI artist who combined her traditional and scientific knowledge to prepare complementary imagery. Second, the EOU is currently implementing a series of semi-structured discussions to document participant perceptions on criteria including engagement, communication, and education. Lastly, the EOU implemented a survey among UNMCCC providers on their return of results processes. Results: EOU discussions are ongoing and have provided important perspectives on participant satisfaction and areas for improvement. Participants have commended study staff on their efforts to make enrollment straightforward. Participants report discussing the study with family prior to enrolling, highlighting the importance of continuing to engage with the entire community on the goals of the study. The provider return of results survey is complete (n=42/62, response rate=68%); one key finding was that providers who return somatic test results rarely provide participants with a hard copy of the results or utilize complementary materials that might enhance their explanation of the test findings. Conclusion: The EOU serves an innovative function through its rapid-cycle methods to optimize participant engagement. Although participants are generally pleased with their study participation, there are numerous opportunities for improvement. The informational materials represent a novel contribution to the field of genomic research. Efforts will continue to implement optimized processes for effective engagement of AI communities in genomic research and cancer care delivery. Citation Format: Shoshana Adler Jaffe, Kendal Jacobson, Joseph Rodman, Cindy Blair, Debra MacKenzie, Mallery Quetawki, Tamar Ginossar, V. Shane Pankratz, Andrew Sussman, Ursa Brown-Glabermen, Jeffrey Trent, Cheryl Willman, Shiraz I. Mishra. The art of science: Painting a new image of genomic research in the indigenous communities of the Southwest [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B134.

Abstract Background: Engaging diverse populations in cancer genomics research is of critical importance and is a fundamental goal of the NCI Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. Established as part of the Cancer Moonshot, PE-CGS is a consortium of stakeholders including clinicians, scientists, genetic counselors, and representatives of potential study participants and their communities. Participant engagement is an ongoing, bidirectional, and mutually beneficial interaction between study participants and researchers. PE-CGS sought to set priorities in participant engagement for conducting the network's research. Methods: PE-CGS deliberatively engaged its stakeholders in the following four-phase process to set the network's research priorities in participant engagement: (i) a brainstorming exercise to elicit potential priorities; (ii) a 2-day virtual meeting to discuss priorities; (iii) recommendations from the PE-CGS External Advisory Panel to refine priorities; and (iv) a virtual meeting to set priorities. Results: Nearly 150 PE-CGS stakeholders engaged in the process. Five priorities were set: (i) tailor education and communication materials for participants throughout the research process; (ii) identify measures of participant engagement; (iii) identify optimal participant engagement strategies; (iv) understand cancer disparities in the context of cancer genomics research; and (v) personalize the return of genomics findings to participants. Conclusions: PE-CGS is pursuing these priorities to meaningfully engage diverse and underrepresented patients with cancer and posttreatment cancer survivors as participants in cancer genomics research and, subsequently, generate new discoveries. Impact: Data from PE-CGS will be shared with the broader scientific community in a manner consistent with participant informed consent and community agreement.

Genome-wide fragmentation patterns in cell-free DNA (cfDNA) in plasma are strongly influenced by cellular origin due to variation in chromatin accessibility across cell types. Such differences between healthy and cancer cells provide the opportunity for development of novel cancer diagnostics. Here, we investigated whether analysis of cfDNA fragment end positions and their surrounding DNA sequences reveals the presence of tumor-derived DNA in blood. We performed genome-wide analysis of cfDNA from 521 samples and analyzed sequencing data from an additional 2147 samples, including healthy individuals and patients with 11 different cancer types. We developed a metric based on genome-wide differences in fragment positioning, weighted by fragment length and GC content [information-weighted fraction of aberrant fragments (iwFAF)]. We observed that iwFAF strongly correlated with tumor fraction, was higher for DNA fragments carrying somatic mutations, and was higher within genomic regions affected by copy number amplifications. We also calculated sample-level means of nucleotide frequencies observed at genomic positions spanning fragment ends. Using a combination of iwFAF and nine nucleotide frequencies from three positions surrounding fragment ends, we developed a machine learning model to differentiate healthy individuals from patients with cancer. We observed an area under the receiver operative characteristic curve (AUC) of 0.91 for detection of cancer at any stage and an AUC of 0.87 for detection of stage I cancer. Our findings remained robust with as few as 1 million fragments analyzed per sample, demonstrating that analysis of fragment ends can become a cost-effective and accessible approach for cancer detection and monitoring.

Abstract Background Tumor heterogeneity underlies resistance and disease progression in glioblastoma (GBM), and tumors most commonly recur adjacent to the surgical resection margins in contrast non-enhancing (NE) regions. To date, no targeted therapies have meaningfully altered overall patient survival in the up-front setting. The aim of this study was to characterize intratumoral heterogeneity in recurrent GBM using bulk samples from primary resection and recurrent samples taken from contrast-enhancing (EN) and contrast NE regions. Methods Whole exome and RNA sequencing were performed on matched bulk primary and multiple recurrent EN and NE tumor samples from 16 GBM patients who received standard of care treatment alone or in combination with investigational clinical trial regimens. Results Private mutations emerge across multi-region sampling in recurrent tumors. Genomic clonal analysis revealed increased enrichment in gene alterations regulating the G2M checkpoint, Kras signaling, Wnt signaling, and DNA repair in recurrent disease. Subsequent functional studies identified augmented PI3K/AKT transcriptional and protein activity throughout progression, validated by phospho-protein levels. Moreover, a mesenchymal transcriptional signature was observed in recurrent EN regions, which differed from the proneural signature in recurrent NE regions. Conclusions Subclonal populations observed within bulk resected primary GBMs transcriptionally evolve across tumor recurrence (EN and NE regions) and exhibit aberrant gene expression of common signaling pathways that persist despite standard or targeted therapy. Our findings provide evidence that there are both adaptive and clonally mediated dependencies of GBM on key pathways, such as the PI3K/AKT axis, for survival across recurrences.

Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative was instigated to harness the power of multiple genomic approaches to further the understanding of multiple myeloma. To date, 137 samples from patients with newly diagnosed and relapsed myeloma (out of an estimated final total of 250 by the end of the year) have been subjected to expression profiling and array comparative genomic hybridization. To identify regions of recurrent copy number alteration with a high degree of confidence, we have used the Genomic Identification of Significant Targets In Cancer (GISTIC) algorithm, which detects such regions and assigns a probability to each. Application of GISTIC to the MMRC collection identified 14 significant regions of amplification and 15 significant regions of deletion. The algorithm further detects peaks of copy number change that contribute to each region’s significance. Of genes that were expressed in our dataset, a total of 64 across the genome were found to lie within the boundaries of significant amplification peaks and 30 were found to lie within significant deletion peaks. Given the likelihood that this gene collection is highly enriched for genes important in the pathogenesis of myeloma and potential therapeutic targets, it has been prioritized for further validation with the amplified genes submitted for arrayed RNAi and the deleted genes expedited for re-sequencing. In order to define a poor prognosis group, we developed and applied a model based on the gene expression dataset of Shaughnessy and colleagues. To identify pathways activated in poor prognosis disease, we applied Gene Set Enrichment Analysis with the Molecular Signatures Database. This demonstrated enrichment of multiple canonical pathways within the poor prognosis group, including those associated with proliferation, cell cycle progression and DNA repair. A search for recurrent copy number events associated with poor prognosis disease revealed that by far the most significant event, and the only one surviving correction for multiple hypothesis testing, was mono- or bi-allelic deletion of CDKN2C (p18). Interestingly, the poor prognosis group also demonstrated higher expression of p18 in those samples without bi-allelic deletion and of CDKN2A (p16). Deletion and increased expression of p18 in high proliferation index myeloma samples has previously been described. However, our analyses demonstrate the pre-eminence of p18 loss for defining poor prognosis disease compared to other recurrent copy number changes. Furthermore, it expands on work in other tumors demonstrating feedback mechanisms between cyclin dependent kinase inhibitor pathways, suggesting a more complex model of interplay between the pathways than previously described. In conclusion, the MMRC reference collection is proving an important tool in the fight to understand key genetic events in multiple myeloma and would ultimately be anticipated to contribute towards development of improved therapy for the disease.

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative is a three-year program to analyze tumor tissue from hundreds of multiple myeloma (MM) patients via gene expression profiling (GEP), comparative genomic hybridization (aCGH), and exon re-sequencing. In addition, RNAi knockdown of selected genes in MM tumor cell lines is being evaluated to identify potential new targets. All genomic data generated is scheduled for placement in an open-access Multiple Myeloma Genomics Portal pre-publication and in near real-time (www.broad.mit.edu/mmgp). Additionally, samples are also destined for drug validation and correlative science on clinical protocols as this study moves forward. This comprehensive project is spearheaded by the MMRC and conducted via collaboration with the Eli and Edythe L. Broad Institute of MIT and Harvard, the Translational Genomics Research Institute (TGen), Mayo Clinic Arizona, and The Dana-Farber Cancer Center. The study is supported by the collection from member institutions of the MMRC of bone marrow aspirates and matched peripheral blood samples from over 1000 patients. Specific genomic technologies that are currently being employed across this sample set include GEP using Affymetrix Human Genome U133A 2.0 Plus Arrays, and, in parallel, efforts to identify regions of genomic gain and loss are using Agilent Human Genome CGH arrays. In contrast to other large-scale genomic projects based on exon-sequencing of targeted gene sets, this project will be the first to perform genome-scale single molecule sequencing (SMS) of DNA from patient specimens. Results will be targeted against candidate classes of genes (e.g. kinases, phosphatases, known oncogenes and tumor suppressors), and genes from GEP or within candidate regions of copy gain or loss identified by the aCGH experiments. Mutations will be further validated in an independent set of patient specimens. Finally we will attempt to identify points of vulnerability of MM through systematic loss-of-function screens in myeloma cell lines using high-throughput RNA interference (using both shRNA and siRNA platforms). Importantly, data generated from this genomics initiative will ultimately be made public pre-publication through the established MMRC Multiple Myeloma Genomics Portal. Data from all aspects of this project (sample collection and analyte isolation, GEP, aCGH, SMS, RNAi and bioinformatics) will be described in this presentation. The power of this study is the comprehensive collection of gene expression, CGH, and genome sequencing on a single reference set of clinically annotated samples. The addition of RNAi screens makes this a very important and unique data resource, which we hope will help expedite the discovery of novel targeted agents for MM scientific community.