Cancer Driver Mutations Research Articles

Abstract 5002: Next-generation mRNA-LNP vaccine prototype achieves tumor clearance in a GBM mouse model

Abstract Glioblastoma (GBM) is the most prevalent and aggressive primary brain tumor type with an abysmal prognosis, featuring an immunologically dampened tumor microenvironment and limited neoantigen production. Novel therapeutic strategies are urgently needed. A well-characterized GBM-associated mutation that can elicit an anti-tumor immune response is EGFRvIII mutation. The cancer-driver mutation is detected in approximately 30% of patients at the time of diagnosis and plays a pivotal role in the emergence of GBM. EGFRvIII is an in-frame epidermal growth factor receptor (EGFR) deletion generating a constitutively active oncogenic protein. Its highly immunogenic nature represents an ideal focal point for cutting-edge mRNA-LNP candidate therapeutics. To evaluate proprietary lipid nanoparticle (LNP) compositions for their capacity to induce anti-GBM immunity, we developed a murine transplantable GBM tumor model by neonatal electroporation. The somatically engineered glioblastomas harbor two targeted CRISPR knockouts and stably overexpress EGFRvIII to yield transplantable cell lines for orthotopic tumor initiation. One week after intracranial tumor cell implantation of 6-8 week-old female mice (day 0; n=10), half the animals were administered either buffer (control cohort) or our novel anti-EGFRvIII mRNA-LNP vaccine (experimental cohort). The regimen involved four intramuscular injections before monitoring tumor progression by MRI on day 30. The imaging revealed a high tumor burden in all control group animals, compared to no detectable tumor cells in vaccine-protected mice. All control group animals were humanely euthanized by day 38 due to symptoms associated with the growing brain tumor mass. The asymptomatic experimental animals received a fifth anti-EGFRvIII vaccination before MRI-based examinations on day 43 and day 78 confirmed that no tumor cells could be detected in vaccine-protected mice. The results highlight the potency and potential of our proprietary anti-EGFRvIII mRNA-LNP vaccine as a novel anticancer therapeutic. In addition, the established tumor model represents a valuable tool to test future mRNA-LNP designs. Outstanding cellular and molecular investigations will reveal vaccine-mediated mechanistic insights, allowing for the co-advancement of the tumor model and next-generation multi-target mRNA-LNP vaccine candidates. Citation Format: Robert Nechanitzky, Shannon Snelling Snelling, Kristofor Ellestad, Xueqing Lun, Kirstin Olsen, Yu Wu, Yury Karpov, Jun Liu, Matthew Gold, Haritha Menon, Rajesh Krishnan, Robert Georgantas, Pamela S. Ohashi, Douglas J. Mahoney, Jennifer A. Chan, Natalia Martin-Orozco. Next-generation mRNA-LNP vaccine prototype achieves tumor clearance in a GBM mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5002.

Abstract 1627: Persistence and dynamics of mutation, selection, and epistasis during the somatic evolution of low-risk, high-risk, and metastatic prostate cancer

Abstract Driver genes and mutations responsible for prostate cancer tumorigenesis have been identified, but research has not previously quantified the effects of prostate cancer driver mutations, or how the magnitude of those effects vary with cancer stage and epistatic interactions among genes. Using 2699 prostate cancer tumor exomes, genomes, and panel sequences (1648 primary tumors and 1051 metastatic samples), we calculated the cancer effect size (CES)—the each mutation confers to lower-risk (Grade Group 1/2) and higher-risk (Grade Group 3/4/5) tumorigenesis and metastasis. The lower-risk, higher-risk, and metastatic tumors exhibited similar neutral rates at which mutations were introduced into DNA, with these underlying gene mutation rates generally increasing from lower-risk to higher risk to metastatic tumors. However, lower risk, higher risk, and metastatic tumors noticeably differed in the genes and specific somatic variants that were most highly selected for within each group. Therefore, to assess whether differences in natural selection that we observed among low-risk, high-risk, and metastatic tumor types were at least partly due to epistatic interactions, we applied a model of pairwise epistasis that quantified the effect of the presence or absence of a mutation in one gene on the selection for mutations in another gene. We found significant evidence of epistatic effects among driver genes. Our results indicate a very early role for SPOP in the development of prostate cancer: strength of selection for SPOP mutations is tightly associated with the degree to which the mutation ablates contact with the BRD3, and these disruptive mutations in turn increase the selection for mutations in several other tumor suppressors and oncogenes such as RHOA. Previously identified gene-gene interactions such as that between SPOP and PIK3CA were supported by this analysis, and we also identified novel gene-gene interactions such as those between AR and MUC16 occurring during metastasis. In conclusion, we have demonstrated that rates of mutation and strengths of selection on these driver mutations vary dynamically along the trajectories to lower-risk, higher-risk, and metastatic prostate cancer, and that powerful selective epistasis among driver genes provides an explanation for these differences. Because precision therapeutic targeting of somatic variant genes is fundamentally targeting the selective benefit obtained from that variant at the time of treatment, understanding these changes in stage-specific and somatic genotype-specific selective strengths will enable better therapeutic targeting in translational and clinical science. Citation Format: Moein Rajaei, Alexander Yang, Christopher N. Cross, J Nick Fisk, Elizabeth B. Perry, Jeffrey D. Mandell, Stephen G. Gaffney, Takafumi N. Yamaguchi, Julie Livingstone, Vincent L. Cannataro, Peter Humphrey, Jose Costa, Paul C. Boutros, Jeffrey P. Townsend. Persistence and dynamics of mutation, selection, and epistasis during the somatic evolution of low-risk, high-risk, and metastatic prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1627.

Abstract 1252: AI-derived predictions improve identification of real-world cancer driver mutations

Abstract AI-based variant effect predictors (VEPs), such as AlphaMissense, which is based on the protein structure prediction method AlphaFold, have gained significant attention for their potential to predict pathogenic effects of mutations. However, the utility of these methods for identifying pathogenic mutations in cancer remains unknown. We sought to quantify the prevalence of variants of unknown significance (VUSs) across the pan-cancer, multi-institution AACR GENIE cohort (N=160,965 samples) and test the utility of VEPs (SIFT, PolyPhen, MutationAssessor, REVEL, CADD, and AlphaMissense), using publicly available mutation annotations from each method, for 1) Annotating known pathogenic somatic cancer variants across the GENIE cohort, using known benign single nucleotide polymorphisms as negative controls 2) Identifying VUSs associated with overall survival (OS) in two cohorts of patients with non-small cell cancer (NSCLC), the MSK-IMPACT NCSLC (N=8,690) and non-MSK AACR GENIE Biopharma Collaborative NSCLC (N=977) cohorts, and 3) Identifying VUSs associated with known genomic patterns of mutual exclusivity in the MSK-IMPACT NSCLC cohort. In the GENIE cohort, 79% of mutations identified were VUSs according to an FDA-recognized molecular knowledge database, with a wide range of VUS frequencies across genes. Among VEPs trained without prior human-annotated knowledge, CADD and AlphaMissense had the highest AUROCs (0.940, 95%CI 0.937-0.943 and 0.917, 95%CI 0.913-0.922 respectively) for predicting known cancer drivers, demonstrating the power of protein structure modeling or functional genomic data for enhancing cancer drivers prediction. All VEPs identified known driver mutations in tumor suppressor genes more accurately than oncogenes, highlighting the challenge in identifying gain-of-function mutations. We tested whether VUSs reclassified as pathogenic by VEPs are associated with prognosis using inverse probability of treatment-weighted Cox’s propensity hazard models controlling for tumor mutational burden, treatment history, and other factors. VUSs reclassified as pathogenic by AlphaMissense and other VEPs in KEAP1 and SMARCA4 correlated with worse OS, comparable to that of known oncogenic mutations in those genes. Patients with tumors containing VUSs annotated as benign had similar OS to patients without analogous driver mutations. Finally, VUSs reclassified as oncogenic by AlphaMissense in genes within the RTK/RAS and NRF2 pathways followed expected patterns of mutual exclusivity, further suggesting biological validity. Despite not being trained to predict somatic effects in cancer, AI-derived mutation annotations can broaden the subset of annotated pathogenic variants in cancer and contribute to a more complete understanding of cancer genetics. Real-world outcomes and alteration patterns are additional benchmarks to consider when assessing the utility of VEPs in cancer. Citation Format: Thinh N. Tran, Chris Fong, Karl Pichotta, Anisha Luthra, Ronglai Shen, Yuan Chen, Michele Waters, Susie Kim, Gregory Riely, Debyani Chakravarty, Nikolaus Schultz, Justin Jee. AI-derived predictions improve identification of real-world cancer driver mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1252.

Abstract 909: Enhancing genomic analysis in cancer diagnostics: A machine learning approach for removing artifacts in FFPE specimens

Abstract Formalin-Fixed Paraffin-Embedded (FFPE) specimens, widely utilized in clinical cancer diagnostics, present significant challenges by introducing artifacts into genomic data. This study aimed to profile these FFPE-induced genomic alterations, with a particular focus on single-nucleotide variants (SNVs), small insertions and deletions (indels), and copy-number variations (CNVs), and to develop computational methods for filtering out such artifacts. Our primary focus was twofold: first, to comprehensively characterize the unique error profile of FFPE specimens observed through whole-genome sequencing (WGS), and second, to construct artifact classifiers and noise filters for SNV/indels and CNVs. We utilized machine learning (ML) and signal-processing techniques on a dataset of FFPE and matched fresh-frozen (FF) samples. The dataset of 52 FFPE-FF pairs were obtained from four different medical institutes and from various cancers including liver, breast, colon, stomach, and lung cancer, with varied FFPE archiving times. We also analyzed additional FFPE-only samples to refine our methods. Our methodology incorporated advanced computational approaches, including stacking ensemble, transfer learning, and wavelet transform, to enhance robustness and accuracy. The method's design was centered around the notion of not only achieving high performance in distinguishing true signals from FFPE-induced artifacts but also addressing the real-world challenges posed by the varying quality and conditions of clinical FFPE samples. In the analysis, we found peculiar patterns of FFPE-specific error profile, including well-known cytosine deamination and novel mutational signatures. The classifier, building on our findings, effectively differentiated true genomic variants from FFPE-induced artifacts for both SNVs and indels, demonstrating a sensitivity of 0.97, specificity of 0.87, and an F1 score of 0.94 for SNVs. For indels, it achieved a sensitivity of 0.91, specificity of 0.91, and an F1 score of 0.91. The CNV filter notably enhanced the signal-to-noise ratio (SNR) of CNV depth profiles, increasing it from 13dB to 17.5dB on average. Furthermore, we conducted evaluations on two critical measures in cancer and clinical genomics: homologous recombination deficiency (HRD) and tumor mutational burden (TMB), achieving post-filtering concordance rates of 0.99 for HRD—correctly identifying all 8 HRD-positive patients in our dataset—and 0.96 for SNV-based TMB and 0.87 for indel-based TMB. Additionally, a post hoc procedure for sensitive detection of cancer driver mutations resulted in concordance rates of 0.94 for SNVs, 0.91 for indels, and 0.95 for oncogene amplification. Taken together, our study advances FFPE WGS analysis in cancer diagnostics by effectively filtering artifacts and addressing challenges with older, degraded samples, enhancing clinical applicability. Citation Format: Joonoh Lim, Seongyeol Park, Won-Chul Lee, Ryul Kim, Sangmoon Lee, Jeong Seok Lee, Brian Baek-Lok Oh, Young Seok Ju. Enhancing genomic analysis in cancer diagnostics: A machine learning approach for removing artifacts in FFPE specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 909.

Abstract 6569: KLIPP: Precision targeting of cancer with CRISPR

Abstract Current cancer therapeutic treatments are typically associated with significant collateral damage to normal tissues and the emergence of treatment resistance. Thus, there is an unmet need to develop therapies that are uniquely cancer specific and unhindered by the emergence of resistance. We have developed an RNA therapy approach based on CRISPR technology, known as “KLIPP”, that targets cancer-specific structural variant junctions (SVJs), a so far untapped cancer “Achilles heel”, that are highly abundant across cancer types. KLIPP uses a “split enzyme” system consisting of dCas9 conjugated to an endonuclease, Fok1, that requires dimerization for its activity. By designing sgRNAs that bind to sequences flanking the cancer-specific SVJs, Fok1-dCas9 dimers are formed, leading to the induction of DNA double strand breaks (DSBs) specifically at SVJs in cancer cells. We verify induction of DSBs by γH2AX foci staining in the nucleus of KLIPP-treated cells and resulting loss of clonogenic survival and induction of apoptosis, while cells lacking the targeted SVJs are unaffected. Importantly, we show elimination of orthotopic bladder cancer tumors in vivo after turning on expression of the KLIPP reagents by doxycycline in the drinking water. Finally, we show successful lipid nanoparticle delivery of KLIPP reagents to orthotopic bladder tumors in vivo using catheter-mediated delivery, leading to specific targeting of bladder cancer cells. Independently of the cancer-driving mutations, the omnipresence of cancer-specific SVJs is exploited by KLIPP to force cancer cells into apoptosis while leaving normal cells unperturbed. We believe that the successful implementation of precision KLIPP therapy could be transformative for the treatment of cancer patients. Citation Format: Mats Ljungman, Huibin Yang, Radhika Suhas Hulbatte, Natalie Gratsch, Alan Kelher, John Raupp, Yin Wang, Kristen Hong Dorsey, Troy Halseth, Anna Schwendeman, Philip Palmbos. KLIPP: Precision targeting of cancer with CRISPR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6569.

Concomitant double-fusion of PLEKHA7-ALK and INPP5D-ALK reveals favorable alectinib sensitivity in lung adenocarcinoma: a case report and literature review

Anaplastic lymphoma kinase (ALK) gene fusion is a classic driver mutation in non-small cell lung cancer (NSCLC); however, ALK double-fusion variants in NSCLC have rarely been reported. In this study, we reported a case with extremely uncommon ALK double-fusion variants. A 32-year-old female diagnosed with lung adenocarcinoma, who had developed multiple intrapulmonary and brain metastases, experienced worsening of her condition despite undergoing prior chemotherapy. Subsequent testing using next-generation sequencing (NGS) detected the presence of PLEKHA7-ALK and INPP5D-ALK double-fusion. The prescription of alectinib revealed potent efficacy and resulted in an increase in the survival rate. This case presented two uncommon and concomitant ALK fusion partners in NSCLC; more importantly, the INPP5D-ALK subtype has not been reported, therefore this study broadens the spectrum of ALK double-fusion variants and provides insight into the use of ALK inhibitors for the treatment of NSCLC in patients with double ALK fusions.

Rare subclonal sequencing of breast cancers indicates putative metastatic driver mutations are predominately acquired after dissemination

BackgroundEvolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear.MethodsTo ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 “metastasis-specific” mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition.ResultsThe ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable.ConclusionsOur results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.

Insights into the mechanisms and structure of breakage-fusion-bridge cycles in cervical cancer using long-read sequencing

Cervical cancer is caused by human papillomavirus (HPV) infection, has few approved targeted therapeutics, and is the most common cause of cancer death in low-resource countries. We characterized 19 cervical and four head and neck cancer cell lines using long-read DNA and RNA sequencing and identified the HPV types, HPV integration sites, chromosomal alterations, and cancer driver mutations. Structural variation analysis revealed telomeric deletions associated with DNA inversions resulting from breakage-fusion-bridge (BFB) cycles. BFB is a common mechanism of chromosomal alterations in cancer, and our study applies long-read sequencing to this important chromosomal rearrangement type. Analysis of the inversion sites revealed staggered ends consistent with exonuclease digestion of the DNA after breakage. Some BFB events are complex, involving inter- or intra-chromosomal insertions or rearrangements. None of the BFB breakpoints had telomere sequences added to resolve the dicentric chromosomes, and only one BFB breakpoint showed chromothripsis. Five cell lines have a chromosomal region 11q BFB event, with YAP1-BIRC3-BIRC2 amplification. Indeed, YAP1 amplification is associated with a 10-year-earlier age of diagnosis of cervical cancer and is three times more common in African American women. This suggests that individuals with cervical cancer and YAP1-BIRC3-BIRC2 amplification, especially those of African ancestry, might benefit from targeted therapy. In summary, we uncovered valuable insights into the mechanisms and consequences of BFB cycles in cervical cancer using long-read sequencing.

Abstract PR007: Molecular subtyping of lung adenocarcinoma premalignant lesions identifies features associated with aggressive disease

Abstract Lung cancer is the leading cause of cancer-related death. The five year survival rate had remained stagnant for decades, but more effective screening protocols using low-dose computed tomographic screening for high-risk individuals, along with progress in treatment for advanced-stage lung cancer with targeted therapy and immunotherapy, have helped decrease lung cancer mortality. However, improved approaches to lung cancer prevention and early detection are still urgently needed. Adenocarcinoma (LUAD) is the most common form of lung cancer. Following surgical resection of pre-invasive adenocarcinoma in situ, the five-year survival rate approaches 100%. This emphasizes the need for improved understanding of characteristics of aggressive premalignant lesions that would benefit from early interventions. In contrast to the well-characterized histopathologic sequence of lung squamous cell carcinoma, atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) are the only known precursors in the sequence of LUAD pathogenesis. The features of AAH and AIS that lead to invasive LUAD are poorly characterized. The relationship of AIS and AAH to the histologically diverse subtypes of LUAD, ranging from the typically slow growing lepidic to aggressive solid subtypes, are also unclear. We hypothesized that transcriptomic changes in subsets of premalignant lesions are linked to distinct genomic and clinicopathologic features of malignant disease. To investigate this hypothesis, we performed exome sequencing and bulk RNA sequencing of laser capture microdissected tissue from tumor margins that included premalignant lesions, invasive tumor, and adjacent normal tissues. With our analysis, we discovered de novo subtypes of LUAD premalignant lesions based on gene expression. One of these subtypes had gene expression alterations similar to aggressive invasive LUAD solid tumors. This molecular adenomatous premalignant lesion subtype was also associated with an increased accumulation of cancer driver mutations. This subtype was further characterized by altered expression of immune-related pathways. Molecular signatures measured in adenomatous PML may thus enhance our understanding of pathway dysregulation and mutational heterogeneity occurring during LUAD carcinogenesis, and implicate immunotherapeutic strategies to prevent their progression to cancer. Citation Format: Kelley Anderson, Linh Tran, Kostyantyn Krysan, William Wallace, Gregory Fishbein, Emily Green, Gang Liu, Hanqiao Liu, Erin Kane, Sarah Mazzilli, Steven Dubinett, Avrum Spira, Marc Lenburg, Jennifer Beane. Molecular subtyping of lung adenocarcinoma premalignant lesions identifies features associated with aggressive disease [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR007.

Subclonal Cancer Driver Mutations Are Prevalent in the Unresected Peritumoral Edema of Adult Diffuse Gliomas.

True2 is a next-generation sequencing workflow that facilitates unbiased discovery of somatic mutations across the full range of variant allele frequencies, which could help identify residual disease vulnerabilities for targeted adjuvant therapies.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

A phase II telemedicine study of pemigatinib in adult patients with unresectable or metastatic pancreas cancer with FGFR2 gene fusions or other FGFR genetic alterations.

TPS717 Background: Advanced pancreas cancer has a poor prognosis with few effective targeted therapies. Given this current state, novel therapies for pancreas cancer are an unmet need. Genomic alterations in FGFR, including fusions and point mutations are known driver mutations in cancer. FGFR kinase inhibitors have been FDA approved in cholangiocarcinoma and urothelial carcinoma, however, have not been well studied in other cancers. Our team previously identified and treated four patients with advanced pancreas cancer and FGFR2 fusions with FGFR kinase inhibitors resulting in durable responses and prolonged survival. Additionally, through a review of Foundation Medicine’s clinical genomic database, consisting of 30,229 pancreas cancer tumors, we determined that FGFR1-3 fusions have a prevalence of approximately 1% in pancreas cancer, most commonly in FGFR2 (0.8%). Our observations provide preliminary evidence that FGFR targeted therapy results in excellent clinical outcomes. We hypothesize that FGFR altered pancreas cancer treated with a FGFR kinase inhibitor will result in durable responses. We developed a phase 2, telemedicine trial of pemigatinib, an FDA approved FGFR kinase inhibitor, in FGFR altered pancreas cancer. Since the COVID-19 pandemic, telemedicine is used in routine oncology care and clinical trials and is associated with cost savings for patients. The growing adoption of this technology provides an opportunity to enhance the study of ultra-rare cancers. Methods: The trial consists of unresectable or metastatic pancreas cancer of any histology in two cohorts: 1) Primary cohort of FGFR2 fusions (n=30) and 2) exploratory cohort of other known activating mutations in FGFR (n=10). Patients are identified from across the United States through Foundation Medicine, Caris Life Sciences, and the Pancreatic Cancer Action Network. Eligible patients will be treated with oral pemigatinib through telemedicine by investigators at The Ohio State University in collaboration with local oncologists for supportive care. This trial uses single-stage phase 2 design with sample size calculations based on an expected overall response rate of ≥ 33% and <10% as unfavorable response, at one‐sided Type I error of 0.05, 90% power and up to 13% attrition rate. Results: The primary objective is overall response rate. Secondary objectives are overall survival, progression free survival, and duration of response. Exploratory objectives are analysis of correlation of circulating tumor DNA with response to treatment and development of resistance. Conclusions: FGFR kinase inhibitorshave the potential to be an effective targeted therapy in pancreas cancer. Additionally, telemedicine is a novel tool that can aid in the study of ultra-rare cancers and reduce barriers to patient participation in clinical trials.

Abstract B013: A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS)

Abstract Deleterious mutations in homology-directed repair pathway genes impact both future cancer risk and therapeutic intervention options for cancer diagnosis and prognosis. Homology-directed repair pathway is a high-fidelity DNA repair pathway for resolving double-strand breaks (DSBs) throughout the human genome. BRCA2, as a homology-directed repair factor, is essential to ensure genome integrity through an error-free process. Deleterious BRCA2 mutations increase the lifetime risk of breast, ovarian, melanoma, prostate, and pancreatic cancers. Deleterious BRCA2 mutations cover only 25% of the total BRCA2 mutations identified in the population by genetic testing whereas 60% are mutations with unknown connection to the disease called Variants of Uncertain Significance (VUS). The majority of BRCA2 VUS are missense mutations with one point mutation which could potentially alter BRCA2 cellular localization and/or nuclear functions. Thus, evaluating BRCA2 cellular localization and function in BRCA2 missense carriers and tumors is urgently needed to provide clinical guidance regarding cancer risk and treatment options. We have identified a paradoxical relationship between BRCA2 nuclear import and treatment. Deleterious missense mutations in the DNA binding domain of BRCA2 direct the protein to the cytosol which increase sensitivity to PARP inhibitors (PARPi) and platinum drugs. Therefore, although BRCA2 mislocalization might be a cause of cancer, keeping it out of the nucleus allows for much better treatment outcomes. Here, we present how BRCA2 cancer-driver missense mutations lead to cytosolic mislocalization and how BRCA2 localization can be used as a diagnostic tool to stratify patients. Our robust multi-step molecular approach will guide future clinical decision rules surrounding BRCA2 VUS. By providing reliable and accurate functional assays to correctly evaluate the pathogenicity of BRCA2 VUS, our goal is to improve personalized medicine by providing actionable information to patients and their physicians. Citation Format: Cole Niedmann, Judit Jimenez Sainz. A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B013.

Intratumor genetic heterogeneity and head and neck cancer relapse

BackgroundHead and neck squamous cell carcinomas are treated by surgery, radiotherapy (RT), chemoradiotherapy (CRT) or combinations thereof, but locoregional recurrences (LRs) occur in 30–40% of treated patients. We have previously shown that in approximately half of the LRs after CRT, cancer driver mutations are not shared with the index tumor. AimTo investigate two possible explanations for these genetically unrelated relapses, treatment-induced genetic changes and intratumor genetic heterogeneity. MethodsTo investigate treatment-induced clonal DNA changes, we compared copy number alterations (CNAs) and mutations between primary and recurrent xenografted tumors after treatment with (C)RT. Intratumor genetic heterogeneity was studied by multi-region sequencing on DNA from 31 biopsies of 11 surgically treated tumors. ResultsInduction of clonal DNA changes by (C)RT was not observed in the xenograft models. Multi-region sequencing demonstrated variations in CNA profiles between paired biopsies of individual tumors, with copy number heterogeneity scores varying from 0.027 to 0.333. In total, 32 cancer driver mutations could be identified and were shared in all biopsies of each tumor. Remarkably, multi-clonal mutations in these same cancer driver genes were observed in 6 of 11 tumors. Genetically distinct heterogeneous cell cultures could also be established from single tumors, with different biomarker profiles and drug sensitivities. ConclusionIntratumor genetic heterogeneity at the level of the cancer driver mutations might explain the discordant mutational profiles in LRs after CRT, while there are no indications in xenograft models that these changes are induced by CRT.

Tissue-Predisposition to Cancer Driver Mutations.

Driver mutations are considered the cornerstone of cancer initiation. They are defined as mutations that convey a competitive fitness advantage, and hence, their mutation frequency in premalignant tissue is expected to exceed the basal mutation rate. In old terms, that translates to "the survival of the fittest" and implies that a selective process underlies the frequency of cancer driver mutations. In that sense, each tissue is its own niche that creates a molecular selective pressure that may favor the propagation of a mutation or not. At the heart of this stands one of the biggest riddles in cancer biology: the tissue-predisposition to cancer driver mutations. The frequency of cancer driver mutations among tissues is non-uniform: for instance, mutations in APC are particularly frequent in colorectal cancer, and 99% of chronic myeloid leukemia patients harbor the driver BCR-ABL1 fusion mutation, which is rarely found in solid tumors. Here, we provide a mechanistic framework that aims to explain how tissue-specific features, ranging from epigenetic underpinnings to the expression of viral transposable elements, establish a molecular basis for selecting cancer driver mutations in a tissue-specific manner.

BRAFV600E mutation and DSS treatment synergize to induce cecal tumor formation in mice

BRAF mutation is a driver mutation in colorectal cancer (CRC), and BRAFV600E mutation is found in 10–15 % of all CRCs. BRAF mutant CRCs in patients are primarily localized in the right colon, including the cecum. However, in the Vill-Cre;BRAFV600E/+ mice, adenomas mainly developed in the small intestines of the mice, and no tumor formed in the cecum. The mice model of BRAFV600E-mutant CRC with tumors in the cecum is lacking. Dextran Sulfate Sodium (DSS) treatment induces colitis in mice. Acute DSS treatment does not lead to tumor formation. We show that DSS treatment and BRAFV600E mutation synergistically induced cecal tumorigenesis, and cecal tumors formed within three months after five-day DSS treatment. The location of the adenomas supports the patient relevance of the model. Our BRAFV600E/DSS model provides a valuable in vivo model for future identification and validation of novel therapeutic approaches for treating BRAF-mutant CRC. Our results are consistent with the notion that BRAFV600E mutation is an oncogenic event that can shift controlled regeneration to unrestrained oncogenesis.

Detecting driver mutations in colorectal cancer through big data analysis

Detecting driver mutations in colorectal cancer through big data analysis

Detecting Driver Mutations in Colorectal Cancer through Big Data Analysis

Detecting Driver Mutations in Colorectal Cancer through Big Data Analysis

CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.

Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation

Loss-of-function of DDX3X is a leading cause of neurodevelopmental disorders (NDD) in females. DDX3X is also a somatically mutated cancer driver gene proposed to have tumour promoting and suppressing effects. We perform saturation genome editing of DDX3X, testing in vitro the functional impact of 12,776 nucleotide variants. We identify 3432 functionally abnormal variants, in three distinct classes. We train a machine learning classifier to identify functionally abnormal variants of NDD-relevance. This classifier has at least 97% sensitivity and 99% specificity to detect variants pathogenic for NDD, substantially out-performing in silico predictors, and resolving up to 93% of variants of uncertain significance. Moreover, functionally-abnormal variants can account for almost all of the excess nonsynonymous DDX3X somatic mutations seen in DDX3X-driven cancers. Systematic maps of variant effects generated in experimentally tractable cell types have the potential to transform clinical interpretation of both germline and somatic disease-associated variation.