TNF receptor-related factor 3 inactivation promotes the development of intrahepatic cholangiocarcinoma through NF-κB-inducing kinase-mediated hepatocyte transdifferentiation.

BACKGROUND AND AIMS: Intrahepatic cholangiocarcinoma (ICC) is a deadly but poorly understood disease, and its treatment options are very limited. The aim of this study was to identify the molecular drivers of ICC and search for therapeutic targets. APPROACH AND RESULTS: We performed a Sleeping Beauty transposon-based in vivo insertional mutagenesis screen in liver-specific Pten -deficient mice and identified TNF receptor-related factor 3 ( Traf3 ) as the most significantly mutated gene in murine ICCs in a loss-of-function manner. Liver-specific Traf3 deletion caused marked cholangiocyte overgrowth and spontaneous development of ICC in Pten knockout and KrasG12D mutant mice. Hepatocyte-specific, but not cholangiocyte-specific, Traf3 -deficient and Pten -deficient mice recapitulated these phenotypes. Lineage tracing and single-cell RNA sequencing suggested that these ICCs were derived from hepatocytes through transdifferentiation. TRAF3 and PTEN inhibition induced a transdifferentiation-like phenotype of hepatocyte-lineage cells into proliferative cholangiocytes through NF-κB-inducing kinase (NIK) up-regulation in vitro. Intrahepatic NIK levels were elevated in liver-specific Traf3 -deficient and Pten -deficient mice, and NIK inhibition alleviated cholangiocyte overgrowth. In human ICCs, we identified an inverse correlation between TRAF3 and NIK expression, with low TRAF3 or high NIK expression associated with poor prognosis. Finally, we showed that NIK inhibition by a small molecule inhibitor or gene silencing suppressed the growth of multiple human ICC cells in vitro and ICC xenografts in vivo. CONCLUSIONS: TRAF3 inactivation promotes ICC development through NIK-mediated hepatocyte transdifferentiation. The oncogenic TRAF3-NIK axis may be a potential therapeutic target for ICC.

Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression.

The systematic identification of genetic events driving cellular transformation and tumor progression in the absence of a highly recurrent oncogenic driver mutation is a challenge in cutaneous oncology. In cutaneous squamous cell carcinoma (cuSCC), the high UV-induced mutational burden poses a hurdle to achieve a complete molecular landscape of this disease. Here, we utilized the Sleeping Beauty transposon mutagenesis system to statistically define drivers of keratinocyte transformation and cuSCC progression in vivo in the absence of UV-IR, and identified both known tumor suppressor genes and novel oncogenic drivers of cuSCC. Functional analysis confirms an oncogenic role for the ZMIZ genes, and tumor suppressive roles for KMT2C, CREBBP and NCOA2, in the initiation or progression of human cuSCC. Taken together, our in vivo screen demonstrates an extremely heterogeneous genetic landscape of cuSCC initiation and progression, which can be harnessed to better understand skin oncogenic etiology and prioritize therapeutic candidates.

A Novel HRD Signature Is Predictive of FOLFIRINOX Benefit in Metastatic Pancreatic Cancer.

BACKGROUND: Pancreatic cancer (PC) represents an aggressive disease with median overall survival (OS) of less than 1 year in the front-line setting. FOLFIRINOX and gemcitabine and paclitaxel (GP) are standard of care options for these patients; however, optimal selection of therapy is challenging. METHODS: Comprehensive genomic profiling was performed on 8358 PC patients. Outcomes were available for 1149 metastatic PC patients treated with 1L FOLFIRINOX or GP. A scar-based measure of HRD was called using a machine learning-based algorithm incorporating copy number and indel features. RESULTS: A scar-based HRD signature (HRDsig) was identified in 9% of patients. HRDsig significantly co-occurred with biallelic alterations in BRCA1/2, PALB2, BARD1, and RAD51C/D, but encompassed a larger population than that defined by BRCA1/BRCA2/PALB2 (9% vs. 6%). HRDsig was predictive of 1L FOLFIRNOX chemotherapy benefit with doubled OS relative to gemcitabine and paclitaxel (GP) (rwOS aHR 0.37 [0.22-0.62]), including 25% of the population with long-term (2 year+) survival in a real-world cohort of patients. Less benefit from FOLFIRINOX was observed in the HRDsig(-) population. Predictive value was seen in both the BRCA1/2/PALB2 mutant and wildtype populations, suggesting additional value to mutational profiling. CONCLUSION: A scar-based HRD biomarker was identified in a significant fraction of PC patients and is predictive of FOLFIRINOX benefit. Incorporating a biomarker like HRDsig could identify the right patients for platinum chemotherapy and potentially reduce FOLFIRINOX use by over 40%, minimizing toxicities with similar survival outcomes. Confirmatory studies should be performed.

Molecular profiling of nonalcoholic fatty liver disease-associated hepatocellular carcinoma using SB transposon mutagenesis.

Nonalcoholic fatty liver disease (NAFLD) is the fastest rising cause of hepatocellular carcinoma (HCC) in Western countries; however, the molecular mechanisms that cause NAFLD-HCC remain elusive. To identify molecular drivers of NAFLD-HCC, we performed Sleeping Beauty (SB) transposon mutagenesis screens in liver-specific Pten knockout and in high-fat diet-fed mice, which are murine models of NAFLD-HCC. SB mutagenesis accelerated liver tumor formation in both models and identified 588 and 376 candidate cancer genes (CCGs), respectively; 257 CCGs were common to both screens and were enriched in signaling pathways known to be important for human HCC. Comparison of these CCGs with those identified in a previous SB screen of hepatitis B virus-induced HCC identified a core set of 141 CCGs that were mutated in all screens. Forty-one CCGs appeared specific for NAFLD-HCC, including Sav1, a component of the Hippo signaling pathway and the most frequently mutated gene identified in both NAFLD-HCC screens. Liver-specific deletion of Sav1 was found to promote hepatic lipid accumulation, apoptosis, and fibrogenesis, leading to the acceleration of hepatocarcinogenesis in liver-specific Pten mutant mice. Sav1/Pten double-mutant livers also showed a striking up-regulation of markers of liver progenitor cells (LPCs), along with synergistic activation of Yap, which is a major downstream effector of Hippo signaling. Lastly, Yap activation, in combination with Pten inactivation, was found to accelerate cell growth and sphere formation of LPCs in vitro and induce their malignant transformation in allografts. Our forward genetic screens in mice have thus identified pathways and genes driving the development of NAFLD-HCC.

SBCDDB: Sleeping Beauty Cancer Driver Database for gene discovery in mouse models of human cancers.

Large-scale oncogenomic studies have identified few frequently mutated cancer drivers and hundreds of infrequently mutated drivers. Defining the biological context for rare driving events is fundamentally important to increasing our understanding of the druggable pathways in cancer. Sleeping Beauty (SB) insertional mutagenesis is a powerful gene discovery tool used to model human cancers in mice. Our lab and others have published a number of studies that identify cancer drivers from these models using various statistical and computational approaches. Here, we have integrated SB data from primary tumor models into an analysis and reporting framework, the Sleeping Beauty Cancer Driver DataBase (SBCDDB, http://sbcddb.moffitt.org), which identifies drivers in individual tumors or tumor populations. Unique to this effort, the SBCDDB utilizes a single, scalable, statistical analysis method that enables data to be grouped by different biological properties. This allows for SB drivers to be evaluated (and re-evaluated) under different contexts. The SBCDDB provides visual representations highlighting the spatial attributes of transposon mutagenesis and couples this functionality with analysis of gene sets, enabling users to interrogate relationships between drivers. The SBCDDB is a powerful resource for comparative oncogenomic analyses with human cancer genomics datasets for driver prioritization.

SB Driver Analysis: a Sleeping Beauty cancer driver analysis framework for identifying and prioritizing experimentally actionable oncogenes and tumor suppressors.

Cancer driver prioritization for functional analysis of potential actionable therapeutic targets is a significant challenge. Meta-analyses of mutated genes across different human cancer types for driver prioritization has reaffirmed the role of major players in cancer, including KRAS, TP53 and EGFR, but has had limited success in prioritizing genes with non-recurrent mutations in specific cancer types. Sleeping Beauty (SB) insertional mutagenesis is a powerful experimental gene discovery framework to define driver genes in mouse models of human cancers. Meta-analyses of SB datasets across multiple tumor types is a potentially informative approach to prioritize drivers, and complements efforts in human cancers. Here, we report the development of SB Driver Analysis, an in-silico method for defining cancer driver genes that positively contribute to tumor initiation and progression from population-level SB insertion data sets. We demonstrate that SB Driver Analysis computationally prioritizes drivers and defines distinct driver classes from end-stage tumors that predict their putative functions during tumorigenesis. SB Driver Analysis greatly enhances our ability to analyze, interpret and prioritize drivers from SB cancer datasets and will continue to substantially increase our understanding of the genetic basis of cancer.

In vivo loss-of-function screens identify KPNB1 as a new druggable oncogene in epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a deadly cancer, and its prognosis has not been changed significantly during several decades. To seek new therapeutic targets for EOC, we performed an in vivo dropout screen in human tumor xenografts using a pooled shRNA library targeting thousands of druggable genes. Then, in follow-up studies, we performed a second screen using a genome-wide CRISPR/Cas9 library. These screens identified 10 high-confidence drug targets that included well-known oncogenes such as ERBB2 and RAF1, and novel oncogenes, notably KPNB1, which we investigated further. Genetic and pharmacological inhibition showed that KPNB1 exerts its antitumor effects through multiphase cell cycle arrest and apoptosis induction. Mechanistically, proteomic studies revealed that KPNB1 acts as a master regulator of cell cycle-related proteins, including p21, p27, and APC/C. Clinically, EOC patients with higher expression levels of KPNB1 showed earlier recurrence and worse prognosis than those with lower expression levels of KPNB1. Interestingly, ivermectin, a Food and Drug Administration-approved antiparasitic drug, showed KPNB1-dependent antitumor effects on EOC, serving as an alternative therapeutic toward EOC patients through drug repositioning. Last, we found that the combination of ivermectin and paclitaxel produces a stronger antitumor effect on EOC both in vitro and in vivo than either drug alone. Our studies have thus identified a combinatorial therapy for EOC, in addition to a plethora of potential drug targets.

PARP-1 activity (PAR) determines the sensitivity of cervical cancer to olaparib.

OBJECTIVES: Cervical cancer (CC) remains a major health problem worldwide. Poly (adenosine diphosphate [ADP]-ribose) polymerase (PARP) inhibitors (PARPi) have emerged as a promising class of chemotherapeutics in ovarian cancer. We explored the preclinical in vitro and in vivo activity of olaparib against multiple primary whole exome sequenced (WES) CC cells lines and xenografts. METHODS: Olaparib cell-cycle, apoptosis, homologous-recombination-deficiency (HRD), PARP trapping and cytotoxicity activity was evaluated against 9 primary CC cell lines in vitro. PARP and PAR expression were analyzed by Western blot assays. Finally, olaparib in vivo antitumor activity was tested against CC xenografts. RESULTS: While none of the cell lines demonstrated HRD, three out of 9 (33.3%) primary CC cell lines showed strong PARylation activity and demonstrated high sensitivity to olaparib in vitro treatment (cutoff IC50 values < 2 µM, p = 0.0012). Olaparib suppressed CC cell growth through cell cycle arrest in the G2/M phase and caused apoptosis (p < 0.0001). Olaparib activity in CC involved both PARP enzyme inhibition and trapping. In vivo, olaparib significantly impaired CC xenografts tumor growth (p = 0.0017) and increased overall animal survival (p = 0.008). CONCLUSIONS: A subset of CC primary cell lines is highly responsive to olaparib treatment in vitro and in vivo. High level of PARylation correlated with olaparib preclinical activity and may represent a useful biomarker for the identification of CC patients benefitting the most from PARPi.

Transposon mutagenesis identifies genes and cellular processes driving epithelial-mesenchymal transition in hepatocellular carcinoma.

Epithelial-mesenchymal transition (EMT) is thought to contribute to metastasis and chemoresistance in patients with hepatocellular carcinoma (HCC), leading to their poor prognosis. The genes driving EMT in HCC are not yet fully understood, however. Here, we show that mobilization of Sleeping Beauty (SB) transposons in immortalized mouse hepatoblasts induces mesenchymal liver tumors on transplantation to nude mice. These tumors show significant down-regulation of epithelial markers, along with up-regulation of mesenchymal markers and EMT-related transcription factors (EMT-TFs). Sequencing of transposon insertion sites from tumors identified 233 candidate cancer genes (CCGs) that were enriched for genes and cellular processes driving EMT. Subsequent trunk driver analysis identified 23 CCGs that are predicted to function early in tumorigenesis and whose mutation or alteration in patients with HCC is correlated with poor patient survival. Validation of the top trunk drivers identified in the screen, including MET (MET proto-oncogene, receptor tyrosine kinase), GRB2-associated binding protein 1 (GAB1), HECT, UBA, and WWE domain containing 1 (HUWE1), lysine-specific demethylase 6A (KDM6A), and protein-tyrosine phosphatase, nonreceptor-type 12 (PTPN12), showed that deregulation of these genes activates an EMT program in human HCC cells that enhances tumor cell migration. Finally, deregulation of these genes in human HCC was found to confer sorafenib resistance through apoptotic tolerance and reduced proliferation, consistent with recent studies showing that EMT contributes to the chemoresistance of tumor cells. Our unique cell-based transposon mutagenesis screen appears to be an excellent resource for discovering genes involved in EMT in human HCC and potentially for identifying new drug targets.

Transposon mutagenesis identifies genes that cooperate with mutant Pten in breast cancer progression.

Triple-negative breast cancer (TNBC) has the worst prognosis of any breast cancer subtype. To better understand the genetic forces driving TNBC, we performed a transposon mutagenesis screen in a phosphatase and tensin homolog (Pten) mutant mice and identified 12 candidate trunk drivers and a much larger number of progression genes. Validation studies identified eight TNBC tumor suppressor genes, including the GATA-like transcriptional repressor TRPS1 Down-regulation of TRPS1 in TNBC cells promoted epithelial-to-mesenchymal transition (EMT) by deregulating multiple EMT pathway genes, in addition to increasing the expression of SERPINE1 and SERPINB2 and the subsequent migration, invasion, and metastasis of tumor cells. Transposon mutagenesis has thus provided a better understanding of the genetic forces driving TNBC and discovered genes with potential clinical importance in TNBC.

Two-Step Forward Genetic Screen in Mice Identifies Ral GTPase-Activating Proteins as Suppressors of Hepatocellular Carcinoma.

BACKGROUND & AIMS: High-throughput sequencing technologies have identified thousands of infrequently mutated genes in hepatocellular carcinomas (HCCs). However, high intratumor and intertumor heterogeneity, combined with large numbers of passenger mutations, have made it difficult to identify driver mutations that contribute to the development of HCC. We combined transposon mutagenesis with a high-throughput screen of a small-hairpin RNA (shRNA) library to identify genes and pathways that contribute to HCC development. METHODS: Sleeping beauty transposons were mobilized in livers of transgenic mice predisposed to develop hepatocellular adenoma and HCC owing to expression of the hepatitis B virus surface antigen. This whole-genome mutagenesis technique was used to generate an unbiased catalogue of candidate cancer genes (CCGs). Pooled shRNA libraries targeting 250 selected CCGs then were introduced into immortalized mouse liver cells and the cells were monitored for their tumor-forming ability after injection into nude mice. RESULTS: Transposon-mediated mutagenesis identified 1917 high-confident CCGs and highlighted the importance of Ras signaling in the development of HCC. Subsequent pooled shRNA library screening of 250 selected CCGs validated 27 HCC tumor-suppressor genes. Individual shRNA knockdown of 4 of these genes (Acaa2, Hbs1l, Ralgapa2, and Ubr2) increased the proliferation of multiple human HCC cell lines in culture and accelerated the formation of xenograft tumors in nude mice. The ability of Ralgapa2 to promote HCC cell proliferation and tumor formation required its inhibition of Rala and Ralb. Dual inhibition of Ras signaling via Ral and Raf, using a combination of small-molecule inhibitor RBC8 and sorafenib, reduced the proliferation of HCC cells in culture and completely inhibited their growth as xenograft tumors in nude mice. CONCLUSIONS: In a 2-step forward genetic screen in mice, we identified members of the Ral guanosine triphosphatase-activating protein pathway and other proteins as suppressors of HCC cell proliferation and tumor growth. These proteins might serve as therapeutic targets for liver cancer.

Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers.

Molecular biomarkers are changes measured in biological samples that reflect disease states. Such markers can help clinicians identify types of cancer or stages of progression, and they can guide in tailoring specific therapies. Many efforts to identify biomarkers consider genes that mutate between normal and cancerous tissues or changes in protein or RNA expression levels. Here we define location biomarkers, proteins that undergo changes in subcellular location that are indicative of disease. To discover such biomarkers, we have developed an automated pipeline to compare the subcellular location of proteins between two sets of immunohistochemistry images. We used the pipeline to compare images of healthy and tumor tissue from the Human Protein Atlas, ranking hundreds of proteins in breast, liver, prostate, and bladder based on how much their location was estimated to have changed. The performance of the system was evaluated by determining whether proteins previously known to change location in tumors were ranked highly. We present a number of candidate location biomarkers for each tissue, and identify biochemical pathways that are enriched in proteins that change location. The analysis technology is anticipated to be useful not only for discovering new location biomarkers but also for enabling automated analysis of biomarker distributions as an aid to determining diagnosis.

Coactivators enable glucocorticoid receptor recruitment to fine-tune estrogen receptor transcriptional responses.

Nuclear receptors (NRs) are central regulators of pathophysiological processes; however, how their responses intertwine is still not fully understood. The aim of this study was to determine whether and how steroid NRs can influence each other's activity under co-agonist treatment. We used a unique system consisting of a multicopy integration of an estrogen receptor responsive unit that allows direct visualization and quantification of estrogen receptor alpha (ERα) DNA binding, co-regulator recruitment and transcriptional readout. We find that ERα DNA loading is required for other type I nuclear receptors to be co-recruited after dual agonist treatment. We focused on ERα/glucocorticoid receptor interplay and demonstrated that it requires steroid receptor coactivators (SRC-2, SRC-3) and the mediator component MED14. We then validated this cooperative interplay on endogenous target genes in breast cancer cells. Taken together, this work highlights another layer of mechanistic complexity through which NRs cross-talk with each other on chromatin under multiple hormonal stimuli.

Source, co-occurrence, and prognostic value of PTEN mutations or loss in colorectal cancer.

Somatic PTEN mutations are common and have driver function in some cancer types. However, in colorectal cancers (CRCs), somatic PTEN-inactivating mutations occur at a low frequency (~8-9%), and whether these mutations are actively selected and promote tumor aggressiveness has been controversial. Analysis of genomic data from ~53,000 CRCs indicates that hotspot mutation patterns in PTEN partially reflect DNA-dependent selection pressures, but also suggests a strong selection pressure based on protein function. In microsatellite stable (MSS) tumors, PTEN alterations co-occur with mutations activating BRAF or PI3K, or with TP53 deletions, but not in CRC with microsatellite instability (MSI). Unexpectedly, PTEN deletions are associated with poor survival in MSS CRC, whereas PTEN mutations are associated with improved survival in MSI CRC. These and other data suggest use of PTEN as a prognostic marker is valid in CRC, but such use must consider driver mutation landscape, tumor subtype, and category of PTEN alteration.

Pan-Cancer Analysis of Copy-Number Features Identifies Recurrent Signatures and a Homologous Recombination Deficiency Biomarker to Predict Poly (ADP-Ribose) Polymerase Inhibitor Response.

PURPOSE: Copy-number (CN) features reveal the molecular state of cancers and may have predictive and prognostic value in the treatment of cancer. We sought to apply published CN analysis methods to a large pan-cancer data set and characterize ubiquitous CN signatures across tumor types, including potential utility for treatment selection. METHODS: We analyzed the landscape of CN features in 260,333 pan-cancer samples. We examined the association of 10 signatures with genomic alterations and clinical characteristics and trained a machine learning classifier using CN and insertion and deletion features to detect homologous recombination deficiency signature (HRDsig) positivity. Clinical outcomes were assessed using a real-world clinicogenomic database (CGDB) of comprehensive genomic profiling linked to deidentified, electronic health record-derived clinical data. RESULTS: CN signatures were prevalent across cancer types and associated with diverse processes including focal tandem duplications, seismic amplifications, genome-wide loss of heterozygosity (gLOH), and HRD. Our novel HRDsig outperformed gLOH in predicting BRCAness and effectively distinguished biallelic BRCA and homologous recombination-repair wild-type (HRRwt) samples pan-tumor, demonstrating high sensitivity to detect biallelic BRCA in ovarian (93%) and other HRD-associated cancers (80%-87%). Pan-tumor prevalence of HRDsig was 6.4%. HRRwt cases represented a significant fraction of the HRDsig-positive cohort, likely reflecting a population with nongenomic mechanisms of HRD. In ovarian and prostate CGDBs, HRDsig identified more patients than gLOH and had predictive value for poly (ADP-ribose) polymerase inhibitor (PARPi) benefit. CONCLUSION: Tumor CN profiles are informative, revealing diverse processes active in cancer. We describe the landscape of 10 CN signatures in a large pan-cancer cohort, including two associated with HRD. We trained a machine learning-based HRDsig that robustly identified BRCAness and associated with biallelic BRCA pan-tumor, and was predictive of PARPi benefit in real-world ovarian and prostate data sets.

Integrative Analysis of a Large Real-World Cohort of Small Cell Lung Cancer Identifies Distinct Genetic Subtypes and Insights into Histologic Transformation.

Small cell lung cancer (SCLC) is a recalcitrant neuroendocrine carcinoma with dismal survival outcomes. A major barrier in the field has been the relative paucity of human tumors studied. Here we provide an integrated analysis of 3,600 "real-world" SCLC cases. This large cohort allowed us to identify new recurrent alterations and genetic subtypes, including STK11-mutant tumors (1.7%) and TP53/RB1 wild-type tumors (5.5%), as well as rare cases that were human papillomavirus-positive. In our cohort, gene amplifications on 4q12 are associated with increased overall survival, whereas CCNE1 amplification is associated with decreased overall survival. We also identify more frequent alterations in the PTEN pathway in brain metastases. Finally, profiling cases of SCLC containing oncogenic drivers typically associated with NSCLC demonstrates that SCLC transformation may occur across multiple distinct molecular cohorts of NSCLC. These novel and unsuspected genetic features of SCLC may help personalize treatment approaches for this fatal form of cancer. SIGNIFICANCE: Minimal changes in therapy and survival outcomes have occurred in SCLC for the past four decades. The identification of new genetic subtypes and novel recurrent mutations as well as an improved understanding of the mechanisms of transformation to SCLC from NSCLC may guide the development of personalized therapies for subsets of patients with SCLC. This article is highlighted in the In This Issue feature, p. 1501.

Comprehensive genomic profiling and treatment patterns across ancestries in advanced prostate cancer: a large-scale retrospective analysis.

BACKGROUND: Men of African ancestry experience the greatest burden of prostate cancer globally, but they are under-represented in genomic and precision medicine studies. Therefore, we sought to characterise the genomic landscape, comprehensive genomic profiling (CGP) utilisation patterns, and treatment patterns across ancestries in a large, diverse, advanced prostate cancer cohort, to determine the impact of genomics on ancestral disparities. METHODS: In this large-scale retrospective analysis, the CGP-based genomic landscape was evaluated in biopsy sections from 11 741 patients with prostate cancer, with ancestry inferred using a single nucleotide polymorphism-based approach. Admixture-derived ancestry fractions for each patient were also interrogated. Independently, clinical and treatment information was retrospectively reviewed for 1234 patients in a de-identified US-based clinicogenomic database. Prevalence of gene alterations, including actionable gene alterations, was assessed across ancestries (n=11 741). Furthermore, real-world treatment patterns and overall survival was assessed in the subset of patients with linked clincogenomic information (n=1234). FINDINGS: The CGP cohort included 1422 (12%) men of African ancestry and 9244 (79%) men of European ancestry; the clinicogenomic database cohort included 130 (11%) men of African ancestry and 1017 (82%) men of European ancestry. Men of African ancestry received more lines of therapy before CGP than men of European ancestry (median of two lines [IQR 0-8] vs one line [0-10], p=0·029). In genomic analyses, ancestry-specific mutational landscapes were observed, but the prevalence of alterations in AR, the DNA damage response pathway, and other actionable genes were similar across ancestries. Similar genomic landscapes were observed in analyses that accounted for admixture-derived ancestry fractions. After undergoing CGP, men of African ancestry were less likely to receive a clinical study drug compared with men of European ancestry (12 [10%] of 118 vs 246 [26%] of 938, p=0·0005). INTERPRETATION: Similar rates of gene alterations with therapy implications suggest that differences in actionable genes (including AR and DNA damage response pathway genes) might not be a main driver of disparities across ancestries in advanced prostate cancer. Later CGP utilisation and a lower rate of clinical trial enrolment observed in men of African ancestry could affect genomics, outcomes, and disparities. FUNDING: American Society for Radiation Oncology, Department of Defense, Flatiron Health, Foundation Medicine, Prostate Cancer Foundation, and Sylvester Comprehensive Cancer Center.

RBFOX2 deregulation promotes pancreatic cancer progression and metastasis through alternative splicing.

RNA splicing is an important biological process associated with cancer initiation and progression. However, the contribution of alternative splicing to pancreatic cancer (PDAC) development is not well understood. Here, we identify an enrichment of RNA binding proteins (RBPs) involved in splicing regulation linked to PDAC progression from a forward genetic screen using Sleeping Beauty insertional mutagenesis in a mouse model of pancreatic cancer. We demonstrate downregulation of RBFOX2, an RBP of the FOX family, promotes pancreatic cancer progression and liver metastasis. Specifically, we show RBFOX2 regulates exon splicing events in transcripts encoding proteins involved in cytoskeletal remodeling programs. These exons are differentially spliced in PDAC patients, with enhanced exon skipping in the classical subtype for several RBFOX2 targets. RBFOX2 mediated splicing of ABI1, encoding the Abelson-interactor 1 adapter protein, controls the abundance and localization of ABI1 protein isoforms in pancreatic cancer cells and promotes the relocalization of ABI1 from the cytoplasm to the periphery of migrating cells. Using splice-switching antisense oligonucleotides (AONs) we demonstrate the ABI1 ∆Ex9 isoform enhances cell migration. Together, our data identify a role for RBFOX2 in promoting PDAC progression through alternative splicing regulation.

Tumor sequencing of African ancestry reveals differences in clinically relevant alterations across common cancers.

Cancer genomes from patients with African (AFR) ancestry have been poorly studied in clinical research. We leverage two large genomic cohorts to investigate the relationship between genomic alterations and AFR ancestry in six common cancers. Cross-cancer type associations, such as an enrichment of MYC amplification with AFR ancestry in lung, breast, and prostate cancers, and depletion of BRAF alterations are observed in colorectal and pancreatic cancers. There are differences in actionable alterations, such as depletion of KRAS G12C and EGFR L858R, and enrichment of ROS1 fusion with AFR ancestry in lung cancers. Interestingly, in lung cancer, KRAS mutations are less common in both smokers and non-smokers with AFR ancestry, whereas the association of TP53 mutations with AFR ancestry is only seen in smokers, suggesting an ancestry-environment interaction that modifies driver rates. Our study highlights the need to increase representation of patients with AFR ancestry in drug development and biomarker discovery.

Comparison of PIK3CA Mutation Prevalence in Breast Cancer Across Predicted Ancestry Populations.

PURPOSE: Understanding the differences in biomarker prevalence that may exist among diverse populations is invaluable to accurately forecast biomarker-driven clinical trial enrollment metrics and to advance inclusive research and health equity. This study evaluated the frequency and types of PIK3CA mutations (PIK3CAmut) detected in predicted genetic ancestry subgroups across breast cancer (BC) subtypes. METHODS: Analyses were conducted using real-world genomic data from adult patients with BC treated in an academic or community setting in the United States and whose tumor tissue was submitted for comprehensive genomic profiling. RESULTS: Of 36,151 patients with BC (median age, 58 years; 99% female), the breakdown by predicted genetic ancestry was 75% European, 14% African, 6% Central/South American, 3% East Asian, and 1% South Asian. We demonstrated that patients of African ancestry are less likely to have tumors that harbor PIK3CAmut compared with patients of European ancestry with estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+/HER2-) BC (37% [949/2,593] v 44% [7,706/17,637]; q = 4.39E-11) and triple-negative breast cancer (8% [179/2,199] v 14% [991/7,072]; q = 6.07E-13). Moreover, we found that PIK3CAmut were predominantly composed of hotspot mutations, of which mutations at H1047 were the most prevalent across BC subtypes (35%-41% ER+/HER2- BC; 43%-61% HER2+ BC; 40%-59% triple-negative breast cancer). CONCLUSION: This analysis established that tumor PIK3CAmut prevalence can differ among predicted genetic ancestries across BC subtypes on the basis of the largest comprehensive genomic profiling data set of patients with cancer treated in the United States. This study highlights the need for equitable representation in research studies, which is imperative to ensuring better health outcomes for all.