Ontario Institute for Cancer Research

Immunotherapy of cancer is now an essential pillar of treatment for patients with many individual tumor types. Novel immune targets and technical advances are driving a rapid exploration of new treatment strategies incorporating immune agents in cancer clinical practice. Immunotherapies perturb a complex system of interactions among genomically unstable tumor cells, diverse cells within the tumor microenvironment including the systemic adaptive and innate immune cells. The drive to develop increasingly effective immunotherapy regimens is tempered by the risk of immune-related adverse events. Evidence-based biomarkers that measure the potential for therapeutic response and/or toxicity are critical to guide optimal patient care and contextualize the results of immunotherapy clinical trials. Responding to the lack of guidance on biomarker testing in early-phase immunotherapy clinical trials, we propose a definition and listing of essential biomarkers recommended for inclusion in all such protocols. These recommendations are based on consensus provided by the Society for Immunotherapy of Cancer (SITC) Clinical Immuno-Oncology Network (SCION) faculty with input from the SITC Pathology and Biomarker Committees and the Journal for ImmunoTherapy of Cancer readership. A consensus-based selection of essential biomarkers was conducted using a Delphi survey of SCION faculty. Regular updates to these recommendations are planned. The inaugural list of essential biomarkers includes complete blood count with differential to generate a neutrophil-to-lymphocyte ratio or systemic immune-inflammation index, serum lactate dehydrogenase and albumin, programmed death-ligand 1 immunohistochemistry, microsatellite stability assessment, and tumor mutational burden. Inclusion of these biomarkers across early-phase immunotherapy clinical trials will capture variation among trials, provide deeper insight into the novel and established therapies, and support improved patient selection and stratification for later-phase clinical trials.

A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a Kd below 10 µM and inhibited in vitro helicase activity. Overall, the CACHE #2 was successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultra-fast machine-learning models. The CACHE#2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts.

A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a Kd below 10 µM and inhibited in vitro helicase activity. Overall, the CACHE #2 was successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultra-fast machine-learning models. The CACHE#2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts.

A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a Kd below 10 µM and inhibited in vitro helicase activity. Overall, the CACHE #2 was successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultra-fast machine-learning models. The CACHE#2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts.

MYC-driven (MYC+) cancers are aggressive and often fatal. MYC dysregulation is a key event in these cancers, but overexpression of MYC alone is not always enough to cause cancer. Plasmocytoma Variant Translocation 1 (PVT1), a long non-coding RNA (lncRNA) adjacent to MYC on chromosome 8 is a rearrangement hotspot in many MYC+ cancers. In addition to being co-amplified with MYC, the genomic rearrangement at PVT1 involves translocation, which has had obscure functional consequences. We report that translocation at the PVT1 locus cause asymmetric enrichment of 5-PVT1 and loss of 3-PVT1. Despite being classified as a non-coding RNA, the retained 5- region of PVT1 generates a circular RNA (CircPVT1) that codes for the novel peptide we call Firefox (FFX). FFX augments AKT signaling and synergistically activates MYC and mTORC1 in these cells. Further, the 3- end of PVT1, which is lost during the translocation, codes for a tumor-suppressing micropeptide we named as Honeybadger (HNB). We demonstrate that HNB interacts with KRAS and disrupts the activation of KRAS effectors. Loss of HNB leads to activation of RAS/MAPK signaling pathway, and enhances MYC stability by promoting phosphorylation of MYC at Ser62. These findings identify PVT1 as a critical node that synchronizes MYC, AKT, and RAS-MAPK activities in cancer. Our study thus identifies a key mechanism by which rearrangements at the PVT1 locus activate additional oncogenic pathways that synergize with MYC to exacerbate the aggressiveness of MYC+ cancers. This newfound understanding explains the poor prognosis associated with MYC+ cancers and offers potential therapeutic targets that could be leveraged in treatment strategies for these cancers.

Background Improving the surgical outcomes for commonly occurring spinal neoplasms of extradural and intradural extramedullary origins requires precise intraoperative diagnosis provided by highly trained neuropathologists. Methods Through a retrospective study of n=319 patient specimens, verified where appropriate by learning curve analysis to be sufficient for statistically significant observations, we aimed to assess the utility of 10-second picosecond infrared laser mass spectrometry (PIRL-MS) for non-subjective diagnosis of major spinal tumour types of metastatic carcinoma, schwannoma and meningiomas. Results The sensitivity and specificity values of spinal tumour type diagnosis (based on n=182 independent specimens) were (93±1)% and (97±2)%, respectively. This classification utilizes n=41 cellular lipids including phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, and ceramides whose identities were established using high-resolution tandem mass spectrometry. Furthermore, the accuracy of diagnosis of a model that contained n=97 meningioma and n=106 schwannoma was not drastically influenced by the presence of n=54 additional intradural extramedullary spinal neoplasms of myxopapillary ependymoma, neurofibroma, paraganglioma and solitary fibrous tumour types in the differential diagnosis, confirming the generalizability and robustness of the identified molecular array in rendering correct classification even in the presence of data not seen previously by the model. Conclusions The identified lipids form a ‘molecular array’ for robust diagnosis of meningioma and schwannoma tumours by non-pathologists in a manner similar to genomic, transcriptomic or methylomic arrays used to diagnose brain cancer types, albeit on a much faster timescale of seconds as opposed to hours.

Newly diagnosed prostate cancers differ dramatically in mutational composition and lethality. The most accurate clinical predictor of lethality is tumor tissue architecture, quantified as tumor grade. To interrogate the evolutionary origins of prostate cancer heterogeneity, we analyzed 666 prostate tumor whole genomes. We identified a compendium of 223 recurrently mutated driver regions, most influencing downstream mutational processes and gene expression. We identified and validated individual germline variants that predispose tumors to acquire specific somatic driver mutations: these explain heterogeneity in disease presentation and ancestry differences. High-grade tumors have a superset of the drivers in lower-grade tumors, including increased frequency of BRCA2 and MYC mutations. Grade-associated driver mutations occur early in tumor evolution, and their earlier occurrence strongly predicts cancer relapse and metastasis. Our data suggest high- and low-grade prostate tumors both emerge from a common pre-malignant field, influenced by germline genomic context and stochastic mutation-timing.

Residual Cancer Burden (RCB) after neoadjuvant chemotherapy (NAC) is validated to predict event-free survival (EFS) in breast cancer but has not been studied for invasive lobular carcinoma (ILC). We studied patient-level data from a pooled cohort across 12 institutions. Associations between RCB index, class, and EFS were assessed in ILC and non-ILC with mixed effect Cox models and multivariable analyses. Recursive partitioning was used in an exploratory model to stratify prognosis by RCB components. Of 5106 patients, the diagnosis was ILC in 216 and non-ILC in 4890. Increased RCB index was associated with worse EFS in both ILC and non-ILC ( p = 0.002 and p < 0.001, respectively) and remained prognostic when stratified by receptor subtype and adjusted for age, grade, T category, and nodal status. Recursive partitioning demonstrated residual invasive cancer cellularity as most prognostic in ILC. These results underscore the utility of RCB for evaluating NAC response in those with ILC.

Background Waist circumference (WC) and its allometric counterpart, “a body shape index” (ABSI), are risk factors for colorectal cancer; however, it is uncertain whether associations with these body measurements are limited to specific molecular subtypes of the disease. Methods Data from 2,772 colorectal cancer cases and 3,521 controls were pooled from four cohort studies within the Genetics and Epidemiology of Colorectal Cancer Consortium. Four molecular markers (BRAF mutation, KRAS mutation, CpG island methylator phenotype, and microsatellite instability) were analyzed individually and in combination (Jass types). Multivariable logistic and multinomial logistic models were used to assess the associations of WC and ABSI with overall colorectal cancer risk and, in case-only analyses, to evaluate heterogeneity by molecular subtype, respectively. Results Higher WC (ORper 5 cm = 1.06, 95% confidence interval, 1.04–1.09) and ABSI (ORper 1-SD = 1.07, 95% confidence interval, 1.00–1.14) were associated with elevated colorectal cancer risk. There was no evidence of heterogeneity between the molecular subtypes. No difference was observed regarding the influence of WC and ABSI on the four major molecular markers in proximal colon, distal colon, and rectal cancers, as well as in early- and late-onset colorectal cancers. Associations did not differ in the Jass-type analysis. Conclusions Higher WC and ABSI were associated with elevated colorectal cancer risk; however, they do not differentially influence all four major molecular mutations involved in colorectal carcinogenesis but underscore the importance of maintaining a healthy body weight in colorectal cancer prevention. Impact The proposed results have potential utility in colorectal cancer prevention.

751 Background: Pancreatic ductal adenocarcinoma (PDAC) is associated with a hypercoagulable state leading to thrombosis. Risk models such as the Khorana score automatically classify PDAC as intermediate-high risk, and recent guidelines recommend consideration of thromboprophylaxis. However, little is known about the molecular correlates of PDAC for venous thromboembolism (VTE). Methods: We examined clinical and genomic data from the prospective multi-institution COMPASS trial (NCT02750657), which enrolled patients with treatment-naïve metastatic PDAC who underwent a fresh tumor biopsy for real-time whole genome and transcriptome sequencing. Laser capture microdissection was performed. Patients underwent restaging scans at 8-week intervals. Detailed chart review was conducted to focus on VTE risk factors, timing of VTE diagnosis, and anticoagulation. We also compared clinical and molecular factors based on timing of VTE diagnosis. Overall survival was defined as time from VTE to death. Results: Of 268 patients enrolled in the COMPASS trial, 166 patients had detailed clinical data available regarding VTE status. 82 patients (49%) developed a VTE, where 16 (20%) had breakthrough clots. Baseline epidemiological variables were similar for those with and without VTE, including no differences between age, sex, BMI, and baseline CA 19-9. No patients were on routine prophylactic anticoagulation. SMAD4 mutations were more frequently seen in the VTE subgroup (57% vs. 40%, p= 0.04), but no differences were seen in other driver genes (KRAS, TP53, CDKN2A), Moffitt subtype, or homologous recombination deficiency status. Patients who developed an early VTE (within 3 months) had a higher baseline CA 19-9 (median 4015 vs. 891, p= 0.02), and none were port-associated. A higher incidence of KRAS wildtype cases (10%) were observed in the early vs. later VTE groups (p= 0.03), but no differences in KRAS allelic status. There were no differences in burden of SNVs, indels, or SVs. Early VTE occurred in 46% of all basal cases, which are typically more aggressive, and only in 37% of classical subtypes. Overall survival was shorter with early VTE (HR 1.74, p= 0.02). Conclusions: A higher incidence of SMAD4 alterations were observed among patients with metastatic PDAC who developed VTE. VTE diagnosed earlier were associated with shorter survival, suggesting that early thromboprophylaxis should be considered at the time of diagnosis. Clinical trial information: NCT02750657 .

559 Background: Biliary tract cancers (BTC) are rare but have been implicated in hereditary cancer conditions such as Lynch syndrome (LS) and BRCA1 and BRCA2. Data from paired somatic and germline genetic testing on BTC have shown that ~5% may be related to germline pathogenic variants in BRCA1 and BRCA2. Identification of these germline pathogenic variants (PV) often provide patients with an opportunity for personalized treatment including platinum-based chemotherapies, PARP inhibitors and/or immunotherapy. Germline testing also provides crucial data to relatives regarding cancer risk and access to increased cancer surveillance and risk-reducing surgeries. Current guidelines recommend germline genetic testing when the likelihood of identifying a PV is >5%. The contribution of germline PV in BTC has not been described in a diverse patient population. Methods: In this prospective clinic-based study, individuals with BTC were referred by their oncologist for genetic counselling and testing. The majority of individuals were enrolled based on participation in the Legresley Biliary Registry, which recruits all individuals with BTC. Blood was sent for germline multi-gene panel testing. Results: Germline genetic testing was performed for 139 individuals. Average age of diagnosis was 57.8 years (range 28-84). The majority of individuals were diagnosed with intrahepatic cholangiocarcinoma (CCA) (54.7%), followed by extrahepatic CCA (20.9%) and gallbladder cancer (10.1%). The majority of individuals were male (56.1%). The population was ethnically diverse with 55% European, 24% Asian, 9% Middle Eastern/North African and 6% African. A minority of individuals had a previous cancer (18%) and 24.5% met current provincial eligibility criteria. Germline testing was complete on 136 individuals. 25/136 (14.7%) PV were identified in 20 individuals. 8/136 (5.9%) of the cohort carried PV in genes known to increase the risk for BTC ( BRCA1, BRCA2 and LS genes), 9/136 (6.6%) carried PV in other genes with clinically actionability (e.g. PALB2, ATM ), and 8/136 (5.9%) carried heterozygous PV in genes for recessive diseases. Variants of uncertain significance were identified in 42/136 (30.9%) and negative results were identified in 74/136 (54.4%). Personal and/or family history was not suggestive of the associated cancer condition in 9/16 (56%) of the PV cohort. Tumor profiling by whole genome sequencing on some individuals with PV found corresponding somatic mutational signatures, consistent with variant pathogenicity. Conclusions: Detection rates for PV in a diverse BTC cohort was up to 14.7%, including 5.9% among genes known to increase the risk for BTC. The majority of PV were found in individuals lacking personal and/or family history suggestive of the associated hereditary cancer condition. These results suggest that guidelines should be updated to recommend universal germline genetic testing for individuals with BTC.

Background Integrating germline genetic testing (GGT) recommendations from tumor testing into hereditary cancer clinics and precision oncology trials presents challenges that require multidisciplinary expertise and infrastructure. While there have been advancements in standardizing molecular tumor boards, the implementation of tumor profiling for germline-focused assessments has only recently gained momentum. However, this progress remains inconsistent across institutions, largely owing to a lack of systematic approaches for managing these findings. This study outlines the development of a clinical pathway for identifying potential germline variants from an institutional tumor-sequencing research program at Princess Margaret Cancer Centre. Methods Between August 2022 and August 2023, a clinical pathway led by a germline Molecular Tumor Board (gMTB) was established to review tumor genetic variants (TGVs) flagged as potential germline findings in patients with advanced cancer via a multigene panel. Eligibility for hereditary cancer syndrome investigation (‘germline criteria’) followed Cancer Care Ontario’s Hereditary Cancer Testing Criteria and clinical judgment. Germline-focused analysis of TGVs followed the European Society of Medical Oncology guidelines and similar published criteria (‘tumor-only criteria’). Results Of 243 tumor profiles, 83 (34.2%) had at least one TGV flagged by the genetic laboratory as potentially germline and were therefore referred to the gMTB for further review. Among these 83 cases, 47 (56.6%) met ‘germline criteria’ for GGT, regardless of the TGV assessment. A total of 127 TGVs were assessed in these 83 cases, of which 44 (34.6%) were considered germline relevant. Tier I TGVs, interpreted as pathogenic/likely pathogenic (P/LP) and found in most- or standard-actionable genes with high germline conversion rates (GCRs) in any context, were more likely to be considered germline relevant (p-value < 0.05). One confirmed germline variant was identified in nine patients meeting solely ‘tumor-only criteria’. Overall, 27/44 germline relevant TGVs underwent germline testing. We found a germline P/LP variant in 9 cases of the entire cohort, with a GCR of 33% (9/27). Conclusions Incorporating genetic counselors into gMTBs enhanced the integration of research findings into clinical care and improved the detection of disease-causing variants in patients outside traditional testing criteria.

765 Background: In resected pancreatic cancer (PDA), morphology from routine histopathology slides provides a rapid inexpensive biomarker that predicts overall survival and correlates with transcriptomic subtypes. In this study, we evaluated clinical and genomic associations of morphological subtypes in resected and advanced disease and validated the consistency of subtypes in patient-derived organoids (PDO) and mouse xenografts (PDXs) during in vitro and in vivo modeling. Methods: Our cohort included PDA tumor tissues from 152 resectable (stage I/II) and 228 advanced cases (Stage III/IV). Hematoxylin and eosin-stained slides were blindly reviewed by two pathologists and classified into subtypes based on Kalimuthu (1). Morphological subtypes were correlated with clinical and genomic data from whole-genome and transcriptome sequencing. Histological preparations from PDOs and PDXs obtained from pancreatic resections and metastases were reviewed using the same criteria. Results: Morphological subtypes were significantly associated with clinical patterns. Locally advanced PDA exhibited the highest proportion of glandular tumors. Metastatic tumors were enriched for non-glandular morphologies. The non-glandular morphologies were significantly associated with lower survival rates in both resected and advanced tumors. Furthermore, morphological subtypes were significantly associated with unique genomic alterations. Compared to glandular tumors, non-glandular tumors were associated with increased KRAS copy number, KRAS imbalances, and polyploid genomes. In advanced settings, glandular and non-glandular tumors mostly exhibited classical and basal-like transcriptional subtypes, respectively. Squamous tumors had the highest mutation burden and the highest proportion of Basal A signature (2). Using differential gene expression, we identified transcriptional signatures of the morphological subtypes. PDOs and PDXs maintained the morphological subtypes, although non-glandular tumors had a lower success rate for PDO establishment. Conclusions: Morphological subtypes have distinct clinical and genomic associations across all stages of pancreatic cancer, which can be consistently modeled both in vitro and in vivo . These results demonstrate that morphological subtyping offers a rapid and biologically-relevant classification of PDA that could be used for drug development and stratification to predict therapy selection. 1. Gut; 69:317-328 (2020). 2. Nat Gen; 52:231-240 (2020).

Prostate cancer is a common malignancy that in 5%–30% leads to treatment‐resistant and highly aggressive disease. Metastasis‐potential and treatment‐resistance is thought to rely on increased plasticity of the cancer cells—a mechanism whereby cancer cells alter their identity to adapt to changing environments or therapeutic pressures to create cellular heterogeneity. To understand the molecular basis of this plasticity, genomic studies have uncovered genetic variants to capture clonal heterogeneity of primary tumors and metastases. As cellular plasticity is largely driven by non‐genetic events, complementary studies in cancer epigenomics are now being conducted to identify chromatin variants. These variants, defined as genomic loci in cancer cells that show changes in chromatin state due to the loss or gain of epigenomic marks, inclusive of histone post‐translational modifications, DNA methylation and histone variants, are considered the fundamental units of epigenomic heterogeneity. In prostate cancer chromatin variants hold the promise of guiding the new era of precision oncology. In this review, we explore the role of epigenomic heterogeneity in prostate cancer, focusing on how chromatin variants contribute to tumor evolution and therapy resistance. We therefore discuss their impact on cellular plasticity and stochastic events, highlighting the value of single‐cell sequencing and liquid biopsy epigenomic assays to uncover new therapeutic targets and biomarkers. Ultimately, this review aims to support a new era of precision oncology, utilizing insights from epigenomics to improve prostate cancer patient outcomes.

The rapid increase in the number of reference-quality genome assemblies presents significant new opportunities for genomic research. However, the absence of standardized naming conventions for genome assemblies and annotations across datasets creates substantial challenges. Inconsistent naming hinders the identification of correct assemblies, complicates the integration of bioinformatics pipelines, and makes it difficult to link assemblies across multiple resources. To address this, we developed a specification for standardizing the naming of reference genome assemblies, to improve consistency across datasets and facilitate interoperability. This specification was created with FAIR (Findable, Accessible, Interoperable, and Reusable) practices in mind, ensuring that reference assemblies are easier to locate, access, and reuse across research communities. Additionally, it has been designed to comply with primary genomic data repositories, including members of the International Nucleotide Sequence Database Collaboration (INSDC) consortium, ensuring compatibility with widely used databases. While initially tailored to the agricultural genomics community, the specification is adaptable for use across different taxa. Widespread adoption of this standardized nomenclature would streamline assembly management, better enable cross-species analyses, and improve the reproducibility of research. It would also enhance natural language processing applications that depend on consistent reference assembly names in genomic literature, promoting greater integration and automated analysis of genomic data. This is a good time to consider more consistent genomic data nomenclature as many research communities and data resources are now finding themselves juggling multiple datasets from multiple data providers.

Purpose Brain temperature is tightly regulated and reflects a balance between cerebral metabolic heat production and heat transfer between the brain, blood, and external environment. Blood temperature and flow are critical to the regulation of brain temperature. Current methods for measuring in vivo brain and blood temperature are invasive and impractical for use in small animals. This work presents a methodology to measure both brain and arterial blood temperature in anesthetized mice by MRI using a paramagnetic lanthanide complex: thulium tetramethyl‐1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid (TmDOTMA‐). Methods A phase‐based imaging approach using a multi‐TE gradient echo sequence was used to measure the temperature‐dependent chemical shift difference between thulium tetramethyl‐1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid methyl protons and water, and from this calculate absolute temperature using calibration data. Results In a series of mice in which core body temperature was held stable but at different values within the range of 33° to 37°C, brain temperature away from the midline was independent of carotid artery blood temperature. In contrast, midline voxels correlated with carotid artery blood temperature, likely reflecting the preponderance of larger arteries and veins in this region. Conclusion These results are consistent with brain temperature being actively regulated. A limitation of the present implementation is that the spatial resolution in the brain is coarse relative to the size of the mouse brain, and further optimization is required for this method to be applied for finer spatial scale mapping or to characterize focal pathology.

Epigenetic therapies facilitate transcription of immunogenic repetitive elements that cull cancer cells through “viral mimicry” responses. Paradoxically, cancer-initiating events also facilitate transcription of repetitive elements. Contributions of repetitive element transcription toward cancer initiation, and the mechanisms by which cancer cells evade lethal viral mimicry responses during tumor initiation remain poorly understood. In this report, we characterize premalignant lesions of the fallopian tube along with syngeneic epithelial ovarian cancer models to explore the earliest events of tumorigenesis following the loss of the p53 tumor suppressor protein. We report that p53 loss permits the transcription of immunogenic repetitive elements and chronic viral mimicry activation that increases cellular tolerance of cytosolic nucleic acids and diminishes cellular immunogenicity. This selection process can be partially attenuated pharmacologically. Altogether, these results reveal that viral mimicry conditioning following p53 loss promotes immune evasion and may represent a pharmacologic target for early cancer interception. Significance Our landmark discovery of viral mimicry characterized repetitive elements as immunogenic stimuli that cull cancer cells. If expressed repetitive elements cull cancer cells, why does every human cancer express repetitive elements? Our report offers an exciting advancement toward understanding this paradox and how to exploit this mechanism for cancer interception.

Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors. To evaluate performance, we develop a comprehensive benchmarking workflow by generating highly multiplexed imaging data of cell line pellet standards with controlled cell content and marker expression and additionally established a score to quantify the biological plausibility of discovered cellular phenotypes on patient-derived tissue sections. Moreover, we generate spatial expression data of the human tonsil—a densely packed tissue prone to segmentation errors—and demonstrate cellular states captured by STARLING identify known cell types not visible with other methods and enable quantification of intra- and inter- individual heterogeneity.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid cancers; thus, identifying more effective therapies is a major unmet need. In this study, we characterized the super-enhancer (SE) landscape of human PDAC to identify drivers of the disease that might be targetable. This analysis revealed MICAL2 as an SE-associated gene in human PDAC, which encodes the flavin monooxygenase enzyme that induces actin depolymerization and indirectly promotes serum response factor transcription by modulating the availability of serum response factor coactivators such as myocardin-related transcription factors (MRTF-A and MRTF-B). MICAL2 was overexpressed in PDAC, and high-MICAL2 expression correlated with poor patient prognosis. Transcriptional analysis revealed that MICAL2 upregulates KRAS and epithelial–mesenchymal transition signaling pathways, contributing to tumor growth and metastasis. In loss- and gain-of-function experiments in human and mouse PDAC cells, MICAL2 promoted both ERK1/2 and AKT activation. Consistent with its role in actin depolymerization and KRAS signaling, loss of MICAL2 also inhibited macropinocytosis. MICAL2, MRTF-A, and MRTF-B influenced PDAC cell proliferation and migration and promoted cell-cycle progression in vitro. Importantly, MICAL2 supported in vivo tumor growth and metastasis. Interestingly, MRTF-B, but not MRTF-A, phenocopied MICAL2-driven phenotypes in vivo. This study highlights the multiple ways in which MICAL2 affects PDAC biology and provides a foundation for future investigations into the potential of targeting MICAL2 for therapeutic intervention. Significance: Characterization of the epigenomic landscape of pancreatic cancer to identify early drivers of tumorigenesis uncovered MICAL2 as a super-enhancer–associated gene critical for tumor progression that represents a potential pharmacologic target.