Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images

IMPLICATIONS

Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.

Divergent immune microenvironments in two tumor nodules from a patient with mismatch repair-deficient prostate cancer

Patients with prostate cancer (PC) generally do not respond favorably to immune checkpoint inhibitors, which may be due to a low abundance of tumor-infiltrating lymphocytes even when mutational load is high. Here, we identified a patient who presented with high-grade primary prostate cancer with two adjacent tumor nodules. While both nodules were mismatch repair-deficient (MMRd), exhibited pathogenic MSH2 and MSH6 alterations, had a high tumor mutational burden (TMB), and demonstrated high microsatellite instability (MSI), they had markedly distinct immune phenotypes. The first displayed a dense infiltrate of lymphocytes ("hot nodule"), while the second displayed significantly fewer infiltrating lymphocytes ("cold nodule"). Whole-exome DNA analysis found that both nodules shared many identical mutations, indicating that they were derived from a single clone. However, the cold nodule appeared to be sub-clonal relative to the hot nodule, suggesting divergent evolution of the cold nodule from the hot nodule. Whole-transcriptome RNA analysis found that the cold nodule demonstrated lower expression of genes related to antigen presentation (HLA) and, paradoxically, classical tumor immune tolerance markers such as PD-L1 (CD274) and CTLA-4. Immune cell deconvolution suggested that the hot nodule was enriched not only in CD8+ and CD4 + T lymphocytes, but also in M1 macrophages, activated NK cells, and γδ T cells compared to the cold nodule. This case highlights that MMRd/TMB-high PC can evolve to minimize an anti-tumor immune response, and nominates downregulation of antigen presentation machinery (HLA loss) as a potential mechanism of adaptive immune evasion in PC.

Distinct mesenchymal cell states mediate prostate cancer progression

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

Concomitant assessment of PD-1 and CD56 expression identifies subsets of resting cord blood Vδ2 T cells with disparate cytotoxic potential

Vγ9Vδ2 T lymphocytes are programmed for broad antimicrobial responses with rapid production of Th1 cytokines even before birth, and thus thought to play key roles against pathogens in infants. The process regulating Vδ2 cell acquisition of cytotoxic potential shortly after birth remains understudied. We observed that perforin production in cord blood Vδ2 cells correlates with phenotypes defined by the concomitant assessment of PD-1 and CD56. Bulk RNA sequencing of sorted Vδ2 cell fractions indicated that transcripts related to cytotoxic activity and NK function are enriched in the subset with the highest proportion of perforin+ cells. Among differentially expressed transcripts, IRF8, previously linked to CD8 T cell effector differentiation and NK maturation, has the potential to mediate Vδ2 cell differentiation towards cytotoxic effectors. Our current and past results support the hypothesis that distinct mechanisms regulate Vδ2 cell cytotoxic function before and after birth, possibly linked to different levels of microbial exposure.

TP53 structure-function relationships in metastatic castrate-sensitive prostate cancer and the impact of APR-246 treatment

PURPOSE

Despite well-informed work in several malignancies, the phenotypic effects of TP53 mutations in metastatic castration-sensitive prostate cancer (mCSPC) progression and metastasis are not clear. We characterized the structure-function and clinical impact of TP53 mutations in mCSPC.

PATIENTS AND METHODS

We performed an international retrospective review of men with mCSPC who underwent next-generation sequencing and were stratified according to TP53 mutational status and metastatic burden. Clinical outcomes included radiographic progression-free survival (rPFS) and overall survival (OS) evaluated with Kaplan-Meier and multivariable Cox regression. We also utilized isogenic cancer cell lines to assess the effect of TP53 mutations and APR-246 treatment on migration, invasion, colony formation in vitro, and tumor growth in vivo. Preclinical experimental observations were compared using t-tests and ANOVA.

RESULTS

Dominant-negative (DN) TP53 mutations were enriched in patients with synchronous (vs. metachronous) (20.7% vs. 6.3%, p < 0.01) and polymetastatic (vs. oligometastatic) (14.4% vs. 7.9%, p < 0.01) disease. On multivariable analysis, DN mutations were associated with worse rPFS (hazards ratio [HR] = 1.97, 95% confidence interval [CI]: 1.31-2.98) and overall survival [OS] (HR = 2.05, 95% CI: 1.14-3.68) compared to TP53 wild type (WT). In vitro, 22Rv1 TP53 R175H cells possessed stronger migration, invasion, colony formation ability, and cellular movement pathway enrichment in RNA sequencing analysis compared to 22Rv1 TP53 WT cells. Treatment with APR-246 reversed the effects of TP53 mutations in vitro and inhibited 22Rv1 TP53 R175H tumor growth in vivo in a dosage-dependent manner.

CONCLUSIONS

DN TP53 mutations correlated with worse prognosis in prostate cancer patients and higher metastatic potential, which could be counteracted by APR-246 treatment suggesting a potential future therapeutic avenue.

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants

Structural variants (SVs) represent a major source of genetic variation associated with phenotypic diversity and disease susceptibility. While long-read sequencing can discover over 20,000 SVs per human genome, interpreting their functional consequences remains challenging. Existing methods for identifying disease-related SVs focus on deletion/duplication only and cannot prioritize individual genes affected by SVs, especially for noncoding SVs. Here, we introduce PhenoSV, a phenotype-aware machine-learning model that interprets all major types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse genomic features and employs a transformer-based architecture to predict their impacts under a multiple-instance learning framework. With phenotype information, PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates PhenoSV's superior performance over competing methods. Applications in diseases suggest that PhenoSV can determine disease-related genes from SVs. A web server and a command-line tool for PhenoSV are available at https://phenosv.wglab.org .

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

BACKGROUND

While the efficacy of neoadjuvant chemotherapy (NACT) in treating triple-negative breast cancer (TNBC) is generally accepted, not all patients derive benefit from this preoperative treatment. Presently, there are no validated biomarkers to predict the NACT response, and previous attempts to develop predictive classifiers based on gene expression data have not demonstrated clinical utility. However, predictive models incorporating biological constraints have shown increased robustness and improved performance compared to agnostic classifiers.

METHODS

We used the preoperative transcriptomic profiles from 298 patients with TNBC to train and test a rank-based classifier, k-top scoring pairs, to predict whether the patient will have pathological complete response (pCR) or residual disease (RD) following NACT. To reduce overfitting and enhance the signature's interpretability, we constrained the training process to genes involved in the Notch signaling pathway. Subsequently, we evaluated the signature performance on two independent cohorts with 75 and 71 patients. Finally, we assessed the prognostic value of the signature by examining its association with relapse-free survival (RFS) using Kaplan‒Meier (KM) survival estimates and a multivariate Cox proportional hazards model.

RESULTS

The final signature consists of five gene pairs, whose relative ordering can be predictive of the NACT response. The signature has a robust performance at predicting pCR in TNBC patients with an area under the ROC curve (AUC) of 0.76 and 0.85 in the first and second testing cohorts, respectively, outperforming other gene signatures developed for the same purpose. Additionally, the signature was significantly associated with RFS in an independent TNBC patient cohort even after adjusting for T stage, patient age at the time of diagnosis, type of breast surgery, and menopausal status.

CONCLUSION

We introduce a robust gene signature to predict pathological complete response (pCR) in patients with TNBC. This signature applies easily interpretable, rank-based decision rules to genes regulated by the Notch signaling pathway, a known determinant in breast cancer chemoresistance. The robust predictive and prognostic performance of the signature make it a strong candidate for clinical implementation, aiding in the stratification of TNBC patients undergoing NACT.

Optimal fluid volume for detecting malignancy in serous effusions: a single institution experience

INTRODUCTION

Detection of malignant cells in serous fluids is an indicator of advanced stage of malignancy and is critical in clinical management decisions and prompt treatment initiation. The minimum volume which is ideal for detecting malignancy in serous fluid is not well established. In this study, we aim to identify optimal volume that will be ideal for adequate cytopathological diagnosis.

MATERIALS AND METHODS

A total of 1597 samples of serous fluids from 1134 patients were included in the study. Samples were diagnosed based on International System for Reporting Serous Fluid Cytopathology (ISRSFC). Clinicopathologic results from different diagnostic groups were compared and statistically analyzed.

RESULTS

Pleural fluids comprised 890 (55.7%) specimens, followed by 456 (28.6%) peritoneal, 128 (8%) ascites, and 123 (7.7%) pericardial fluid specimens. The majority were negative for malignancy (1138, 71.3%), followed by malignant (376, 23.5%), atypical (59, 3.7%), and suspicious for malignancy (24, 1.5%). Malignancy was identified in sample with volumes from 5 mL to 5000 mL. Rate of detection of malignant cells increased significantly with higher sample volumes. For malignancy detection the optimal volume for overall serous fluid is 70 mL. Pericardial fluid is an exception, with lower mean volume and significantly lower proportion of cases with malignant diagnosis.

CONCLUSIONS

Our study indicates that higher fluid volumes have a higher rate of malignancy detection and a low false-negative rate. We recommend a minimum of 70 mL of serous fluid for optimal cytopathologic examination and malignancy detection. Pericardial fluid is an exception, with lower mean volume and thus lower requirement.

Tumor-Specific CD8+ T Cells from the Bone Marrow Resist Exhaustion and Exhibit Increased Persistence in Tumor-Bearing Hosts as Compared to Tumor Infiltrating Lymphocytes

Immunotherapy is now an integral aspect of cancer therapy. Strategies employing adoptive cell therapy (ACT) have seen the establishment of chimeric antigen receptor (CAR)-T cells using peripheral blood lymphocytes as well as tumor infiltrating lymphocytes (TILs) with significant clinical results. Despite these successes, the limitations of the current strategies are also emerging and novel approaches are needed. The bone marrow (BM) is an immunological niche that houses T cells with specificity for previously encountered antigens, including tumor-associated antigens from certain solid cancers. This study sought to improve our understanding of tumor-specific BM T cells in the context of solid tumors by comparing them with TILs, and to assess whether there is a rationale for using the BM as a source of T cells for ACT against solid malignancies. Herein, we demonstrate that T cells from the BM appear superior to TILs as a source of cells for cellular therapy. Specifically, they possess a memory-enriched phenotype and exhibit improved effector function, greater persistence within a tumor-bearing host, and the capacity for increased tumor infiltration. Taken together, these data provide a foundation for further exploring the BM as a source of tumor-specific T cells for ACT in solid malignancies.

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species

Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture.

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously

MOTIVATION

Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes.

RESULTS

Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance.

AVAILABILITY AND IMPLEMENTATION

The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.

Abstract 1343: Mesenchymal cell populations associated with different stages of prostate cancer progression in mice and human

Mesenchymal cells in the prostate cancer (PCa) tumor microenvironment (TME) contribute to the biological and clinical history of PCa. Indeed, mesenchymal cells heavily interact with cancer cells, immune cells, and the other cellular and non-cellular components of the TME to favor or hinder carcinogenesis and tumor progression. Using a comprehensive array of genetically engineered mouse models (GEMMs) of prostate cancer, 8 mesenchymal populations with different transcriptional programs are preferentially enriched in specific GEMMs at different stages of PCa. Here, we determine the transferability of this mesenchymal cluster designation from mice PCa models to human PCa cases. To this end, we compared: a) Tmprss2-ERG (T-ERG) mouse and ERG+ human cases; b) Pb4-Cre+/-;Ptenf/f;LSL-MYCN+/+;Rb1f/f (PRN) mouse and PCa bone metastasis. We generated scRNA-seq data for &gt; 8000 mesenchymal cells from ERG+ (n=6) and ERG- (n=3) PCa patients, and we retrieved data for bone metastasis mesenchymal cells (osteoblasts, osteoclasts, endothelial cells, pericytes; 1,872 total cells) from GSE143791. To transfer the stromal mouse clusters’ labels to human data, human gene symbols were converted to their mouse counterparts, then both datasets were restricted to overlapping genes. For the human PCa cases, label transfer was performed through ‘ingest’ using the scRNA-seq data from the mouse T-ERG model as reference for the human ERG+ cases and data from the remaining GEMMs as reference for the human ERG- cases. For bone metastases cases, mouse stromal data from all GEMMs were used to project the 8 stromal clusters to the mesenchymal cells in the bone metastases microenvironment. Not surprisingly, ERG+ human samples were enriched (&gt; 60% of total stromal cells) in mouse stroma clusters predominantly present in T-ERG mouse model, characterized by the expression of Wnt regulators and AR. Common populations to all murine models, representing myofibroblasts and immunomodulatory fibroblasts (expressing Gpx3, C3, C7, Cfh), were also commonly present in patients, irrespectively to the ERG status. In the PCa bone metastases, mesenchymal clusters enriched in the PRN model were strongly represented in human bone metastases, comprising &gt; 60% of total stromal cells. These cells were characterized by high expression of POSTN and MKI67, as well as bone-specific genes like BGN. Altogether, these findings suggest that our mesenchymal cluster designation developed using GEMMs can be meaningfully applied to human PCa, and that the different transcriptional programs we identified in distinct mesenchymal population are conserved across species. This lays the foundation for the utilization of defined genetically-engineered models in defining the interactions and cross-talks between different mesenchymal populations in relation to cancer and immune cells and other components of the TME in human prostate cancer.

Citation Format: Mohamed Omar, Hubert Pakula, Filippo Pederzoli, Giuseppe N. Fanelli, Tania Panellinni, Ryan Carelli, Silvia Rodrigues, Caroline Fidalgo-Ribeiro, Pier V. Nuzzo, Lucie V. Emmenis, Mohammad Mohammad, Madhavi Jere, Caitlin Unkenholz, David Rickman, Christopher Barbieri, Brian Robinson, Luigi Marchionni, Massimo Loda. Mesenchymal cell populations associated with different stages of prostate cancer progression in mice and human [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1343.

Abstract 5414: Automated detection of high-grade urothelial carcinoma from urine cytology slides using attention-based deep learning

Urine cytology has long been an effective and non-invasive test for the detection of bladder urothelial carcinomas (UC) routinely performed in cases of unexplained hematuria or for monitoring patients with UC. In cytopathology practice, urine cytology specimens are examined manually with a light microscope to identify morphologic features associated with different diagnostic categories based on the Paris System (TPS) for Reporting Urinary Cytology. Specifically, the diagnosis of high-grade urothelial carcinoma (HGUC) requires the identification of &gt; 5-10 cells with a nuclear/cytoplasm ratio of 0.7 or greater and hyperchromasia together with coarse chromatin or irregular nuclear membranes. However, the task of identifying HGUC involves a substantial degree of manual review and is often associated with intra-and inter-observer variability. To address this, we have designed an accurate and efficient deep learning system capable of automatically distinguishing between HGUC and non-HGUC using digitized cytology slides. Our model has been developed using a retrospective cohort of 158 digitized urine ThinPrep cytology slides consisting of HGUC (n=98) and negative for HGUC (n=60). The model was then prospectively validated on a cohort of 105 urine cytology slides that were also independently reviewed prospectively in a blinded manner by a cytopathologist and cytotechnologist. Our system uses Otsu’s method for automatic image thresholding followed by dividing images into non-overlapping tiles of 500 × 500 pixels at the highest magnification. Subsequently, we use a pre-trained ResNet50 model to extract features which are used for training our attention-based multiple instance learning framework. For the training task, our retrospective cohort (158 slides) has been divided into 10 different splits each consisting of training (70%), validation (15%), and testing (15%) sets. The training and validation sets were used for the model training and optimalization, respectively, while the testing set was used for assessing the performance. This process yielded 10 different models with an average Area Under the ROC Curve (AUC) of 0.80 in the testing set. The best performing model had an AUC of 0.90 and an accuracy of 0.88. This model was subsequently validated prospectively in an independent testing cohort with 105 slides. In the prospective testing cohort, the model was able to accurately distinguish between HGUC and non-HGUC with an AUC of 0.83, accuracy of 0.76, sensitivity of 0.89, and specificity of 0.62. Additionally, our system can detect slide regions with high attention score for HGUC which are enriched in atypical urothelial cells. These findings show that our system can be utilized to assist cytopathologists in assessing urine cytology slides and to detect regions with high-diagnostic relevance for further assessment which is expected to reduce the time needed for manual review.

Citation Format: Mohamed Omar, David Kim, Luigi Marchionni, Momin T. Siddiqui. Automated detection of high-grade urothelial carcinoma from urine cytology slides using attention-based deep learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5414.

Distinct mesenchymal cell states mediate prostate cancer progression

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

Distinct mesenchymal cell states mediate prostate cancer progression

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

Using biological constraints to improve prediction in precision oncology

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle-invasive disease, response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training to features capturing specific biological mechanisms; and agnostic, in which the training did not use any a priori biological information. Mechanistic models had a similar or better testing performance than their agnostic counterparts, with enhanced interpretability. Our findings support the use of biological constraints to develop robust gene signatures with high translational potential.

Recurrent germline variant in ATM associated with familial myeloproliferative neoplasms

Genetic predisposition (familial risk) in the myeloproliferative neoplasms (MPNs) is more common than the risk observed in most other cancers, including breast, prostate, and colon. Up to 10% of MPNs are considered to be familial. Recent genome-wide association studies have identified genomic loci associated with an MPN diagnosis. However, the identification of variants with functional contributions to the development of MPN remains limited. In this study, we have included 630 MPN patients and whole genome sequencing was performed in 64 individuals with familial MPN to uncover recurrent germline predisposition variants. Both targeted and unbiased filtering of single nucleotide variants (SNVs) was performed, with a comparison to 218 individuals with MPN unselected for familial status. This approach identified an ATM L2307F SNV occurring in nearly 8% of individuals with familial MPN. Structural protein modeling of this variant suggested stabilization of inactive ATM dimer, and alteration of the endogenous ATM locus in a human myeloid cell line resulted in decreased phosphorylation of the downstream tumor suppressor CHEK2. These results implicate ATM, and the DNA-damage response pathway, in predisposition to MPN.

Hyaluronan driven by epithelial aPKC deficiency remodels the microenvironment and creates a vulnerability in mesenchymal colorectal cancer

Mesenchymal colorectal cancer (mCRC) is microsatellite stable (MSS), highly desmoplastic, with CD8+ T cells excluded to the stromal periphery, resistant to immunotherapy, and driven by low levels of the atypical protein kinase Cs (aPKCs) in the intestinal epithelium. We show here that a salient feature of these tumors is the accumulation of hyaluronan (HA) which, along with reduced aPKC levels, predicts poor survival. HA promotes epithelial heterogeneity and the emergence of a tumor fetal metaplastic cell (TFMC) population endowed with invasive cancer features through a network of interactions with activated fibroblasts. TFMCs are sensitive to HA deposition, and their metaplastic markers have prognostic value. We demonstrate that in vivo HA degradation with a clinical dose of hyaluronidase impairs mCRC tumorigenesis and liver metastasis and enables immune checkpoint blockade therapy by promoting the recruitment of B and CD8+ T cells, including a proportion with resident memory features, and by blocking immunosuppression.

REPAC: analysis of alternative polyadenylation from RNA-sequencing data

Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, availability of these data are limited compared to RNA-sequencing data. We developed REPAC, a framework for the analysis of APA from RNA-sequencing data. Using REPAC, we investigate the landscape of APA caused by activation of B cells. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA.

Association of self-identified race and genetic ancestry with the immunogenomic landscape of primary prostate cancer

The genomic and immune landscapes of prostate cancer differ by self-identified race. However, few studies have examined the genome-wide copy number landscape and immune content of matched cohorts with genetic ancestry data and clinical outcomes. Here, we assessed prostate cancer somatic copy number alterations (sCNA) and tumor immune content of a grade-matched, surgically treated cohort of 145 self-identified Black (BL) and 145 self-identified White (WH) patients with genetic ancestry estimation. A generalized linear model adjusted with age, preoperative prostate-specific antigen (PSA), and Gleason Grade Group and filtered for germline copy number variations (gCNV) identified 143 loci where copy number varied significantly by percent African ancestry, clustering on chromosomes 6p, 10q, 11p, 12p, and 17p. Multivariable Cox regression models adjusted for age, preoperative PSA levels, and Gleason Grade Group revealed that chromosome 8q gains (including MYC) were significantly associated with biochemical recurrence and metastasis, independent of genetic ancestry. Finally, Treg density in BL and WH patients was significantly correlated with percent genome altered, and these findings were validated in the TCGA cohort. Taken together, our findings identify specific sCNA linked to genetic ancestry and outcome in primary prostate cancer and demonstrate that Treg infiltration varies by global sCNA burden in primary disease.

A FOXO1-dependent transcription network is a targetable vulnerability of mantle cell lymphomas

Targeting lineage-defined transcriptional dependencies has emerged as an effective therapeutic strategy in cancer treatment. Through screening for molecular vulnerabilities of mantle cell lymphoma (MCL), we identified a set of transcription factors (TFs) including FOXO1, EBF1, PAX5, and IRF4 that are essential for MCL propagation. Integrated chromatin immunoprecipitation and sequencing (ChIP-Seq) with transcriptional network reconstruction analysis revealed FOXO1 as a master regulator that acts upstream in the regulatory TF hierarchy. FOXO1 is both necessary and sufficient to drive MCL lineage commitment through supporting the lineage-specific transcription programs. We further show that FOXO1, but not its close paralog FOXO3, can reprogram myeloid leukemia cells and induce B-lineage gene expression. Finally, we demonstrate that cpd10, a small molecule identified from an enriched FOXO1 inhibitor library, induces a robust cytotoxic response in MCL cells in vitro and suppresses MCL progression in vivo. Our findings establish FOXO1 inhibition as a therapeutic strategy targeting lineage-driven transcriptional addiction in MCL.

Donor-derived acute myeloid leukemia in solid organ transplantation

We report the transmission of acute myeloid leukemia (AML) undetected at donation from a deceased organ donor to two kidneys and one liver recipients. We reviewed the medical records, and performed molecular analyses and whole exome sequencing (WES) to ascertain AML donor origin and its molecular evolution. The liver recipient was diagnosed 11 months after transplantation and died from complications 2 months later. The two kidney recipients (R1 and R2) were diagnosed 19 and 20 months after transplantation and both received treatment for leukemia. R1 died of complications 11 months after diagnosis, while R2 went into complete remission for 44 months, before relapsing. R2 died 10 months later of complications from allogenic bone marrow transplantation. Microsatellite analysis demonstrated donor chimerism in circulating cells from both kidney recipients. Targeted molecular analyses and medical records revealed NPM1 mutation present in the donor and recipients, while FLT3 was mutated only in R1. These findings were confirmed by WES, which revealed additional founder and clonal mutations, and HLA genomic loss in R2. In conclusion, we report the first in-depth genomic analysis of AML transmission following solid organ transplantation, revealing distinct clonal evolution, and providing a potential molecular explanation for tumor escape.

ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin

Tumor phenotype is shaped both by transforming genomic alterations and the normal cell-of-origin. We identified a cell-of-origin associated prognostic gene expression signature, ET-9, that correlates with remarkably shorter overall and relapse free breast cancer survival, 8.7 and 6.2 years respectively. The genes associated with the ET-9 signature are regulated by histone deacetylase 7 (HDAC7) partly through ZNF92, a previously unexplored transcription factor with a single PubMed citation since its cloning in 1990s. Remarkably, ZNF92 is distinctively over-expressed in breast cancer compared to other tumor types, on a par with the breast cancer specificity of the estrogen receptor. Importantly, ET-9 signature appears to be independent of proliferation, and correlates with outcome in lymph-node positive, HER2+, post-chemotherapy and triple-negative breast cancers. These features distinguish ET-9 from existing breast cancer prognostic signatures that are generally related to proliferation and correlate with outcome in lymph-node negative, ER-positive, HER2-negative breast cancers. Our results suggest that ET-9 could be also utilized as a predictive signature to select patients for HDAC inhibitor treatment.

Hobotnica: exploring molecular signature quality

A Molecular Features Set (MFS), is a result of a vast diversity of bioinformatics pipelines. The lack of a “gold standard” for most experimental data modalities makes it difficult to provide valid estimation for a particular MFS's quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrices (DM) and their power to distinguish between phenotypes.

The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS's quality. 

Here we propose Hobotnica, an approach to estimate MFSs quality by their ability to stratify data, and assign them significance scores, that allow for collating various signatures and comparing their quality for contrasting groups.

Rapid detection of SARS-CoV-2 variants of concern by single nucleotide polymorphism genotpying using TaqMan assays

We evaluated the performance of SARS-CoV-2 TaqMan real-time reverse-transcription PCR (RT-qPCR) assays (ThermoFisher) for detecting 2 nonsynonymous spike protein mutations, E484K and N501Y. Assay accuracy was evaluated by whole genome sequencing (WGS). Residual nasopharyngeal SARS-CoV-2 positive samples (N = 510) from a diverse patient population in New York City submitted for routine SARS-CoV-2 testing during January-April 2020 were used. We detected 91 (18%) N501Y and 101 (20%) E484K variants. Four samples (0.8%) were positive for both variants. The assay had nearly perfect concordance with WGS in the validation subset, detecting B.1.1.7 and B.1.526 variants among others. Sensitivity and specificity ranged from 0.95 to 1.00. Positive and negative predictive values were 0.98−1.00. TaqMan genotyping successfully predicted the presence of B.1.1.7, but had significantly lower sensitivity, 62% (95% CI, 0.53, 0.71), for predicting B.1.526 sub-lineages lacking E484K. This approach is rapid and accurate for detecting SARS-CoV-2 variants and can be rapidly implemented in routine clinical setting.

Abstract 1219: Unraveling alternative polyadenylation in prostate cancer with CORE-PAD

Mechanisms that control gene expression at the RNA level are often referred to as post-transcriptional regulation (PTR) mechanisms. Splicing and polyadenylation (PA) are well-known examples of PTR that can regulate not only gene expression but also their function. Alternative Polyadenylation (APA) has already been shown to be essential to many biological processes (e.g., proliferation, cell differentiation, etc) and has also been implicated in the development and progression of multiple diseases (e.g., cancer, hematological and immune disorders, etc.). Although several sequencing methods have been developed to sequence only the transcript termination site (TTS), the number of publicly available data derived from these methods is extremely limited in comparison to traditional RNA-Seq data. To overcome this limitation, we created a new framework - Compositional Regression of Polyadenylation Differences (CORE-PAD) - for the study of differential APA events using traditional bulk RNA-seq data. Through simulated data, we showed that CORE-PAD has higher accuracy than other methods (accuracy = 0.98) in detecting APA events. We applied CORE-PAD across prostate cancer (PCa) samples with impaired CDK12 and “wild” samples. We found multiple genes presented differential PA site usage, including DNA repair genes. Most notably, we notice that many genes that exhibit differential APA were not differentially expressed at gene level, meaning they are potentially “silent drivers” that cannot be capture through standard differential gene expression analysis. These findings highlight the importance of studying APA, which can help shed light into another layer of regulation occurring between transcription and translation. This is especially important since these events can be source of neoantigens or targeted mRNA degradation which could be explore for new treatments. Finally, the CORE-PAD framework was designed to take advantage of our recently published recount3 resource making over 750,000 RNA-Seq samples of human and mouse origin readily available for analysis, enabling studies of APA across thousands of phenotypes in an accurate and accessible way.

Citation Format: Eddie L. Imada, Christopher Wilks, Ben Langmead, Luigi Marchionni. Unraveling alternative polyadenylation in prostate cancer with CORE-PAD [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1219.

Abstract 3816: Functional atlas of prostate mesenchyme

Prostate cancer has a heterogeneous prognosis, and genetic alterations alone do not fully explain clinical behavior. We previously characterized the stroma of localized human prostates by Laser Capture Microdissection, and found that stroma was substantially different in prostates with and without tumor. Furthermore, a stromal gene signature reflecting bone remodeling was upregulated in high compared to low Gleason grade cases. To determine how stromal cells contribute to carcinogenesis and progression we study whether specific genetic alterations in the epithelium induce unique stromal changes. To do this, we utilized Genetically Engineered Mouse Models (GEMMs) representing common prostate cancer mutations and compared these to their wild-type conterparts: the Tmprss2-ERG fusion knock-in murine model induces histological alterations in the stroma in the absence of an epithelial phenotype; the Pten deletion mouse model (PtenKO) results in prostate intraepithelial neoplasia (PIN) but not invasive cancer; the Hi-Myc GEMM, leads to PIN and subsequently invasion; and the Pb4-Cre +/-;Pten f/f; LSL-MYCN +/+; Rb1 f/f (MNRPDKO) mouse model that leads to neuroendocrine prostate cancer (NEPC). We generated a comprehensive single-cell transcriptomic atlas of the mouse prostate cancer mesenchyme in these models. Using deep generative modeling followed by graph-based clustering and gene regulatory network inference, six (6) distinct subsets of fibroblasts and two (2) subsets of smooth muscle cells (myofibroblasts and pericytes) were identified. Notably, some subsets were common across all GEMMs and WT mice, while others aligned with specific genotypes. Moreover, we found a variable pattern of positive and negative Ar expressing cells between genotypes. Analysis by CellphoneDB of mesenchymal-epithelial communications revealed the complex cross-talk between mutated epithelial cells and the tumor microenvironment. Multiplex immunofluorescence phenotyping of mesenchymal cell confirmed the cluster subtypes by both expression and spatial location. Finally, stromal transcripts defining mesenchymal cluster subtypes associated with Tmprss2-ERG were conserved between mouse and human genotypes.This study lays the groundwork for understanding and ultimately targeting stromal-epithelial interactions in prostate cancer.

Citation Format: Hubert Pakula, Ryan Carelli, Nicolo Fanelli, Madhavi Jere, Caitlin Unkenholz, Mohamed Omar, Caroline Ribeiro- Fidalgo, Filippo Pederzoli, Cory Abate-Shen, David S. Rickman, Brian Robinson, Luigi Marchionni, Massimo Loda. Functional atlas of prostate mesenchyme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3816.

Abstract 5858: A multi-omics signature for patients’ risk classification in prostate cancer

Background: Effective biomarkers are urgently needed in the clinical settings. However, most biomarkers are currently developed from single type of omics data. The goal of this study is to identify prognostic prostate cancer signatures using transcriptomics and metabolomics profiles jointly aiming to capture wider spectrum of biological information.

Methods: In this study, we included 94 tumor and 48 adjacency normal samples with both transcriptomics and metabolomics profiles from Dana-Farber/Harvard Cancer Center SPORE Prostate Cancer Cohort. There were 85 patients being followed up with median length of 2.02 years including 3 lethal and 8 progression cases. We first constructed a multi-omics covering network that contained minimal set of variable pairs but sufficiently rich to account for observed inter-patient variations. The network was built on known gene-metabolite interaction pairs from Pathway Commons as prior knowledge. Next, we used a diffusion process with each of connected gene-metabolite pairs as seeds to identify candidate signatures in the network. Signature sizes were controlled under 5 by adjusting the tree height during pruning. Hierarchical clustering and survival analyses were then performed to stratify patients into two risk groups for disease-free survival probability. Only the prognostic signatures with power higher than 0.9 from bootstrapping were kept for external validation and functional analyses. Since to our best knowledge, there is no publicly available dataset with both transcriptomics and metabolomics to validate the multi-omics signature we identified. We thus trained a rank-based kTSP classifier using gene expression data in SPORE cohort as a surrogate signature and validated it in TCGA prostate cancer cohort independently.

Results: Constructed covering network consisted of 12 metabolites and 54 genes with inter-patient heterogeneities being captured efficiently. We identified one high-powered multi-omics signature (Gene: EGLN3; Metabolites: succinate, trans-4-hydroxyproline) that exhibited good prognostic value where high-risk patients had significant less time of disease-free survival (log rank test: p=0.019, power: 0.974). In addition, genomic variations were observed in different percentages of patients in high and low risk groups including NCOR1(SNV, 70.6% vs 96.3% p=0.025), NKX3.1(CNV, 29.6% vs 64.7% p=0.031) and TNFRSF10C (CNV, 29.6% vs 64.7%, p=0.031). No evidence indicated the patient grouping by the signature depend on Gleason scores (p=0.44). The surrogated gene signature contained 6 pairs of genes that can effectively classify TCGA patients into two prognostic groups (log rank test: p=0.048). Still, no evidence indicated the surrogate gene signature is associated with Gleason score (p=0.23) in TCGA dataset.

Conclusions: We identified a prognostic multi-omics signature (EGLN3, succinate, trans-4-hydroxyproline) with high statistical power.

Citation Format: Zhuoran Xu, Elisa Benedetti, Ryan Carelli, Jacob Rosenthal, Hubert Pakula, Mohamed Omar, Renato Umeton, David Brundage, Jan Krumsiek, Massimo Loda, Luigi Marchionni. A multi-omics signature for patients’ risk classification in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5858.

Abstract 462: Using attention-based deep multiple instance learning to identify key genetic alterations in prostate cancer from whole slide images

Prostate cancer (PCa) is associated with several genetic alterations which play an important role in the disease heterogeneity and clinical outcome. These alterations involve gene fusion between TMPRSS2 and members of the ETS family of transcription factors like ERG, ETV1, and ETV4 together with mutations or deletions in tumor suppressors like TP53 and PTEN. The expanding wealth of digital whole slide images (WSIs) and the increasing adoption of deep learning approaches offer a unique opportunity for pathologists to streamline the detection of these alterations. Here, we used 736 haematoxylin and eosin-stained WSIs from 494 primary PCa patients to identify several key genetic alterations including ERG, ETV1, and ETV4 fusion, PTEN loss, and TP53 and SPOP mutations. Using a custom segmentation pipeline, we identified tissue regions and tiled them into high-resolution (10X magnification) patches (256X256 pixels) which were passed to our deep multiple instance learning framework. Using a pre-trained ResNet50 model, we extracted informative features which were subsequently used for training to predict slide-level labels and to detect slide regions with high diagnostic relevance. Using a 10-folds cross validation approach, we divided the data into training (80%), validation (10%) and testing (10%) sets. The training and validation data were used for training the model and hyperparameters tuning, respectively while the testing data was used to provide an unbiased evaluation of the models’ performance using the mean Area Under the Receiver Operating Characteristic (AUROC) across the ten testing folds as evaluation metric. We managed to accurately detect key molecular alterations including ERG fusion, ETV1 fusion, ETV4 fusion, and PTEN loss. Additionally, we were able to detect mutations in TP53 and SPOP together with the presence of androgen-receptor splice variant 7 (ARv7). In addition to slide-level classification, we also identified subregions with high attention score which can help pathologists identify the distinct morphological features associated with each genetic alteration. Finally, in order to examine the cellular structure associated with each genetic alteration, we used Hover-Net model to segment and classify the nuclei in the high-attention tiles. Our work highlights the utility of using WSIs to accurately identify key molecular alteration in cancer and their associated morphological and cellular features on the slide which would streamline the diagnostic process. To the best of our knowledge, this is the first study that uses routine WSIs to predict and characterize key genetic alterations in PCa.

Citation Format: Mohamed Omar, Zhuoran Xu, Ryan Carelli, Jacob Rosenthal, David Brundage, Daniela C. Salles, Eddie L. Imada, Renato Umeton, Edward M. Schaeffer, Brian D. Robinson, Tamara L. Lotan, Massimo Loda, Luigi Marchionni. Using attention-based deep multiple instance learning to identify key genetic alterations in prostate cancer from whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 462.

Precision health diagnostic and surveillance network uses S gene target failure (SGTF) combined with sequencing technologies to track emerging SARS‐CoV‐2 variants

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pandemic revealed a worldwide lack of effective molecular surveillance networks at local, state, and national levels, which are essential to identify, monitor, and limit viral community spread. SARS‐CoV‐2 variants of concern (VOCs) such as Alpha and Omicron, which show increased transmissibility and immune evasion, rapidly became dominant VOCs worldwide. Our objective was to develop an evidenced‐based genomic surveillance algorithm, combining reverse transcription polymerase chain reaction (RT‐PCR) and sequencing technologies to quickly identify highly contagious VOCs, before cases accumulate exponentially.

Methods

Deidentified data were obtained from 508,969 patients tested for coronavirus disease 2019 (COVID‐19) with the TaqPath COVID‐19 RT‐PCR Combo Kit (ThermoFisher) in four CLIA‐certified clinical laboratories in Puerto Rico (n = 86,639) and in three CLIA‐certified clinical laboratories in the United States (n = 422,330).

Results

TaqPath data revealed a frequency of S Gene Target Failure (SGTF) > 47% for the last week of March 2021 in both, Puerto Rico and US laboratories. The monthly frequency of SGTF in Puerto Rico steadily increased exponentially from 4% in November 2020 to 47% in March 2021. The weekly SGTF rate in US samples was high (>8%) from late December to early January and then also increased exponentially through April (48%). The exponential increase in SGFT prevalence in Puerto Rico was concurrent with a sharp increase in VOCs among all SARS‐CoV‐2 sequences from Puerto Rico uploaded to Global Influenza Surveillance and Response System (GISAID) (n = 461). Alpha variant frequency increased from <1% in the last week of January 2021 to 51.5% of viral sequences from Puerto Rico collected in the last week of March 2021.

Conclusions

According to the proposed evidence‐based algorithm, approximately 50% of all SGTF patients should be managed with VOCs self‐quarantine and contact tracing protocols, while WGS confirms their lineage in genomic surveillance laboratories. Our results suggest this workflow is useful for tracking VOCs with SGTF.

A robust and interpretable gene signature for predicting the lymph node status of primary T1/T2 oral cavity squamous cell carcinoma

Oral cavity squamous cell carcinoma (OSCC) affects more than 30 000 individuals in the United States annually, with smoking and alcohol consumption being the main risk factors. Management of early-stage tumors usually includes surgical resection followed by postoperative radiotherapy in certain cases. The cervical lymph nodes (LNs) are the most common site for local metastasis, and elective neck dissection is usually performed if the primary tumor thickness is greater than 3.5 mm. However, postoperative histological examination often reveals that many patients with early-stage disease are negative for neck nodal metastasis, posing a pressing need for improved risk stratification to either avoid overtreatment or prevent the disease progression. To this end, we aimed to identify a primary tumor gene signature that can accurately predict cervical LN metastasis in patients with early-stage OSCC. Using gene expression profiles from 189 samples, we trained K-top scoring pairs models and identified six gene pairs that can distinguish primary tumors with nodal metastasis from those without metastasis. The signature was further validated on an independent cohort of 35 patients using real-time polymerase chain reaction (PCR) in which it achieved an area under the receiver operating characteristic (ROC) curve and accuracy of 90% and 91%, respectively. These results indicate that such signature holds promise as a quick and cost effective method for detecting patients at high risk of developing cervical LN metastasis, and may be potentially used to guide the neck treatment regimen in early-stage OSCC.

RNA-sequencing highlights differential regulated pathways involved in cell cycle and inflammation in orbitofacial neurofibromas

Although most commonly benign, neurofibromas (NFs) can have devastating functional and cosmetic effects in addition to the possibility of malignant transformation. Orbitofacial NFs, in particular, may cause progressive, disfiguring tumors of the lid, brow, temple, face, and orbit, and clinical evidence suggests that they may have increased local aggressiveness compared to NFs developing at other sites. The purpose of this study was to identify biological differences between orbitofacial NFs and those occurring at other anatomic sites. We performed RNA-sequencing in orbitofacial (n = 10) and non-orbitofacial (n = 9) NFs. Differential gene expression analysis demonstrated that a variety of gene sets including genes involved in cell proliferation, interferon, and immune-related pathways were enriched in orbitofacial NF. Comparisons with publicly available databases of various Schwann cell tumors and malignant peripheral nerve sheath tumor (MPNST) revealed a significant overlap of differentially expressed genes between orbitofacial versus non-orbitofacial NF and plexiform NF versus MPNST. In summary, we identified gene expression differences between orbitofacial NF and NFs occurring at other locations. Further investigation may be warranted, given that orbitofacial NF are notoriously difficult to treat and associated with disproportionate morbidity.

Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use-cases. PathML is publicly available at www.pathml.com.

Hobotnica: exploring molecular signature quality

A Molecular Features Set (MFS), is a result of a vast diversity of bioinformatics pipelines. The lack of a "gold standard" for most experimental data modalities makes it difficult to provide valid estimation for a particular MFS's quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrices (DM) and their power to distinguish between phenotypes. The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS's quality.  Here we propose Hobotnica, an approach to estimate MFSs quality by their ability to stratify data, and assign them significance scores, that allow for collating various signatures and comparing their quality for contrasting groups.

Host Blood Gene Signatures Can Detect the Progression to Severe and Cerebral Malaria

Malaria is a major international public health problem that affects millions of patients worldwide especially in sub-Saharan Africa. Although many tests have been developed to diagnose malaria infections, we still lack reliable diagnostic biomarkers for the identification of disease severity, especially in endemic areas where the diagnosis of cerebral malaria is very difficult and requires the exclusion of all other possible causes. Previous host and pathogen transcriptomic studies have not yielded homogenous results that can be harnessed into a reliable diagnostic tool. Here we utilized a multi-cohort analysis approach using machine-learning algorithms to identify blood gene signatures that can distinguish severe and cerebral malaria from moderate and non-cerebral cases. Using a Regularized Random Forest model, we identified 28-gene and 32-gene signatures that can reliably distinguish severe and cerebral malaria, respectively. We tested the specificity of both signatures against other common infectious diseases to ensure the signatures reliability and suitability as diagnostic markers. The severe and cerebral malaria gene-signatures were further integrated through k-top scoring pairs classifiers into ten and nine gene pairs that could distinguish severe and cerebral malaria, respectively. These signatures have various implications that can be utilized as blood diagnostic tools for malaria severity in endemic countries.

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Transcriptional landscape of PTEN loss in primary prostate cancer

BACKGROUND

PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa.

METHODS

Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas.

RESULTS

The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression.

CONCLUSION

We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

Abstract 173: Efficient representations of tumor diversity with paired DNA-RNA aberrations

In this work we develop a framework which allows for a systematic analysis of joint DNA and putative downstream RNA effects in cancer data cohorts. Using the Reactome database, we extract gene pairs that are linked by known mechanistic connections. Such pairs, which we refer to as 'Source Target Pairs' or STPs, consist of a source gene for which we examine aberrant activity in the DNA profile, and a target gene that is affected by said source gene, for which we examine aberrant activity in the RNA profile.

Using TCGA data for six different cancer types (breast, colon, kidney, liver, lung and prostate), we use mutation and copy number variation information to compile DNA aberrant activity data. For the same cancer cohorts, we use RNASeq gene expression data to quantify RNA aberrant activity via the previous 'divergence' method we have developed. In the divergence framework, normal samples from the same cancer are used to estimate a normal range of expression for target genes of interest and deviation from the normal range is assumed to indicate aberrant activity which may result from upstream DNA aberrations. Then for a given sample, an STP can be represented as a binary variable, indicating presence or absence of joint DNA-RNA aberrant activity.

We utilize integer programming to discover a small set of such STPs for each cancer type such that every sample displays aberrant activity in at least one STP. We refer to these reduced STP configurations as 'minimal coverings' of that cancer. These configurations then allow for the quantification of heterogeneity for that cancer type, as well as for phenotypical groups of interest. This is made possible due to the fact that sample to sample variability can be compared via the entropy of the distribution of the minimal covering, where the small number of STPs in such a configuration makes the computation more tractable.

Our results reveal many known putative drivers of cancer, as well as identify some novel genes of interest for further consideration. Comparison of heterogeneity across phenotypes of interest show higher entropy in more pathological phenotypes, indicating increasing heterogeneity with severity of disease.

Citation Format: Qian Ke, Wikum Dinalankara, Laurent Younes, Donald Geman, Luigi Marchionni. Efficient representations of tumor diversity with paired DNA-RNA aberrations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 173.

Abstract 2251: Gene expression analysis by RNA-sequencing highlights differential regulated pathways involved in cell cycle and inflammation in orbitofacial neurofibromas

Although most commonly benign, neurofibromas (NFs) can have devastating functional and cosmetic effects in addition to the possibility of malignant transformation. Orbitofacial NFs in particular may cause progressive, disfiguring tumors of the lid, brow, temple, face and orbit, and anecdotal evidence suggest that they may have increased local aggressiveness compared to NFs developing at other sites. The purpose of this study was to identify biological differences between orbitofacial NFs and those occurring at other anatomic sites. We performed global RNA-sequencing in orbitofacial (n=10) and non-orbitofacial (n=9) NFs. A total of 880 mRNA transcripts were differentially expressed between the two groups (adjusted p&gt;0.05), The top 10 genes relatively overexpressed in orbitofacial NF included NEFL, TREM2, CST1, GAP43, ADORA3, MIA, SYT6, FCGR3A, SPP1, and FCGR1A. The top 10 genes relatively underexpressed in orbitofacial NF included XG, WISP2, MMP27, CXCL14, MFAP5, APLNR, MYOC, SLITRK6, STMN2, and TDRD1. Gene enrichment analyses demonstrated a variety of gene sets differentially affected including pathways involved in cell proliferation, interferon and immune related pathways. Comparisons with publicly available databases of various Schwann cell tumors and models using CAT plots demonstrated the highest overlap with differentially expressed genes in plexiform NF vs MPNST (&gt;10%). In summary, we identified gene expression differences between orbitofacial NF and NFs occurring at other anatomic locations. Further investigation may be warranted, given that orbitofacial NF are notoriously difficult to treat and associated with disproportionate morbidity.

Citation Format: Eddie L. Imada, Deepak P. Edward, Antje Arnold, Hailah Al-Hussain, Diego Strianese, Luigi Marchionni, Fausto J. Rodriguez. Gene expression analysis by RNA-sequencing highlights differential regulated pathways involved in cell cycle and inflammation in orbitofacial neurofibromas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2251.

covid19census: U.S. and Italy COVID-19 metrics and other epidemiological data

Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the ‘covid19census’ package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes.

Database URL:https://github.com/c1au6i0/covid19census

Influenza Vaccination and COVID-19 Mortality in the USA: An Ecological Study

The COVID-19 mortality rate is higher in the elderly and in those with pre-existing chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infections; thus, an annual influenza vaccination is recommended for them. In this study, we explore a possible county-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data up to 14 December 2020 and US population health data at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the COVID-19 mortality rate as the outcome variable. We adjusted for an array of potential confounders using different propensity score regression methods. Results show that, on the county level, influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19, using different methodologies for confounding adjustment. These findings point to the need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level to investigate any underlying biological mechanisms.

Abstract PR-008: Identification and characterization of the molecular mechanisms of SCLC chemo-radiation resistance

Small cell lung cancer (SCLC) is among the most aggressive form of lung malignancies and accounts for 15-20% of all lung cancers. It has the tendency to metastasize early, thus limited-stage SCLC patients still receive systemic treatment with chemo-radiotherapy (chemoXRT) for their localized disease. SCLC is exceptionally sensitive to chemoXRT and exhibits high response rates; however, the recurrence rate is almost 100% and patients relapse with tumors that resist further treatments. Elucidating mechanisms of chemoXRT resistance in SCLC is needed to develop improved therapies and positively impact patient outcomes. To better interrogate mechanisms of chemoXRT resistance, we developed a SCLC patient-derived xenograft (PDX) in vivo system for the major molecular subtypes of SCLC (classic and variant). Briefly, PDX tumor bearing mice were treated with: 1) vehicle control; 2) cisplatin plus etoposide (EP); 3) radiotherapy (XRT); and 4) both EP/XRT. A major response was observed within the EP/XRT arm compared to vehicle or single therapy arms. Whole transcriptome profiling among all treatment arms revealed molecular pathways and biological processes associated with chemoXRT resistance. Also, by comparing our data with two previous SCLC patient cohort studies, we identified gene candidates for functional validation of chemoXRT resistance (i.e. ST6GAL1, TNIK and SOHLH2). To enable real-time cellular and molecular analysis of PDX behavior ex vivo and to validate SCLC chemoXRT resistance candidate genes, we established a novel PDX organoid (PDO) model to study the molecular underpinnings of XRT resistance in SCLC. Classic and variant SCLC PDOs still retained the cellular, DNA and RNA markers consistent with their parental PDX molecular subtype classification using array comparative genomic hybridization and RNA-sequencing. We aim to utilize our novel SCLC PDX/PDO models as a tool to identify and validate candidates for chemoXRT resistance to be used as biomarkers and targets to combat chemoXRT resistance in SCLC.

Citation Format: Francesca Anna Carrieri, Nick Connis, Eloise Grasset, Eddie Luidy-Imada, Andrew Ewald, Luigi Marchionni, Christine Hann, Phuoc T. Tran. Identification and characterization of the molecular mechanisms of SCLC chemo-radiation resistance [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr PR-008.

Plasma cells are enriched in localized prostate cancer in Black men and are associated with improved outcomes

Black men die more often of prostate cancer yet, interestingly, may derive greater survival benefits from immune-based treatment with sipuleucel-T. Since no signatures of immune-responsiveness exist for prostate cancer, we explored race-based immune-profiles to identify vulnerabilities. Here we show in multiple independent cohorts comprised of over 1,300 patient samples annotated with either self-identified race or genetic ancestry, prostate tumors from Black men or men of African ancestry have increases in plasma cell infiltrate and augmented markers of NK cell activity and IgG expression. These findings are associated with improved recurrence-free survival following surgery and nominate plasma cells as drivers of prostate cancer immune-responsiveness.

Newly Identified Members of FGFR1 Splice Variants Engage in Cross-talk with AXL/AKT Axis in Salivary Adenoid Cystic Carcinoma

Adenoid cystic carcinoma (ACC) is the second most common malignancy of the salivary gland. Although characterized as an indolent tumor, ACC often leads to incurable metastatic disease. Patients with ACC respond poorly to currently available therapeutic drugs and factors contributing to the limited response remain unknown. Determining the role of molecular alterations frequently occurring in ACC may clarify ACC tumorigenesis and advance the development of effective treatment strategies. Applying Splice Expression Variant Analysis and outlier statistics on RNA sequencing of primary ACC tumors and matched normal salivary gland tissues, we identified multiple alternative splicing events (ASE) of genes specific to ACC. In ACC cells and patient-derived xenografts, FGFR1 was a uniquely expressed ASE. Detailed PCR analysis identified three novel, truncated, intracellular domain-lacking FGFR1 variants (FGFR1v). Cloning and expression analysis suggest that the three FGFR1v are cell surface proteins, that expression of FGFR1v augmented pAKT activity, and that cells became more resistant to pharmacologic FGFR1 inhibitor. FGFR1v-induced AKT activation was associated with AXL function, and inhibition of AXL activity in FGFR1v knockdown cells led to enhanced cytotoxicity in ACC. Moreover, cell killing effect was increased by dual inhibition of AXL and FGFR1 in ACC cells. This study demonstrates that these previously undescribed FGFR1v cooperate with AXL and desensitize cells to FGFR1 inhibitor, which supports further investigation into combined FGFR1 and AXL inhibition as an effective ACC therapy.This study identifies several FGFR1 variants that function through the AXL/AKT signaling pathway independent of FGF/FGFR1, desensitizing cells to FGFR1 inhibitor suggestive of a potential resistance mechanism in ACC. SIGNIFICANCE: This study identifies several FGFR1 variants that function through the AXL/AKT signaling pathway independent of FGF/FGFR1, desensitizing cells to FGFR1 inhibitor, suggestive of a potential resistance mechanism in ACC.

Identifying Personalized Metabolic Signatures in Breast Cancer

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

GULP1 regulates the NRF2-KEAP1 signaling axis in urothelial carcinoma

Disruption of the KEAP1-NRF2 pathway results in the transactivation of NRF2 target genes, consequently inducing cell proliferation and other phenotypic changes in cancer cells. Here, we demonstrated that GULP1 was a KEAP1-binding protein that maintained actin cytoskeleton architecture and helped KEAP1 to sequester NRF2 in the cytoplasm. In urothelial carcinoma of the bladder (UCB), silencing of GULP1 facilitated the nuclear accumulation of NRF2, led to constitutive activation of NRF2 signaling, and conferred resistance to the platinum drug cisplatin. Knockdown of GULP1 in UCB cells promoted tumor cell proliferation in vitro and enhanced tumor growth in vivo. In primary UCB, GULP1 silencing was more prevalent in muscle-invasive UCB compared to nonmuscle-invasive UCB. GULP1 knockdown cells showed resistance to cisplatin treatment. In parallel with decreased GULP1 expression, we observed increased expression of NRF2, HMOX1, and other candidate antioxidant genes in cisplatin-resistant cells. Furthermore, low or no expression of GULP1 was observed in most cisplatin nonresponder cases. Silencing of GULP1 was associated with GULP1 promoter hypermethylation in cell lines and primary tumors, and a high frequency of GULP1 promoter methylation was observed in multiple sets of primary clinical UCB samples. Together, our findings demonstrate that GULP1 is a KEAP1-binding protein that regulates KEAP1-NRF2 signaling in UCB and that promoter hypermethylation of GULP1 is a potential mechanism of GULP1 silencing.

Abstract 3926: Establishment of patient-derived organoids as ex vivo tool to characterize the molecular mechanisms of SCLC chemo-radiation resistance

Small cell lung cancer (SCLC) is the most aggressive form of lung malignancies and accounts for 15-20% of all lung cancers. It has the tendency to metastasize early, thus limited-stage SCLC patients receive systemic chemo-radiotherapy (XRT) treatments. SCLC is exceptionally sensitive to XRT and exhibits high response rates; however, the recurrence rate is almost 100% and patients relapse with tumors that resist further chemotherapy. Clearly, elucidating the mechanisms of chemo-radiation resistance in SCLC will contribute to understanding how SCLC resists further treatments, to develop improved therapies and positively impact patient outcomes. Significant limitations for SCLC therapeutic development have been the lack of germane reliable and tractable model systems. Recent advances in establishing 3D organotypic culture have shown that this model can preserve the majority of pathways, key genes, histology and behavior of in situ tumors. Furthermore, patient-derived organoids (PDO) represent a powerful preclinical model that enable real-time cellular and molecular analysis of patient-derived xenograft (PDX) behavior ex vivo. Here, we present a novel patient-derived cancer organoid model to study the molecular underpinnings of XRT resistance in SCLC. Classic and variant SCLC PDX tumor tissues were isolated from mice and mechanically dissociated. Derived organoids were cultured in basal organoid medium. PDOs have been characterized using the SCLC molecular subtype classification reported in literature. RNA for transcriptomic analyses has been obtained to further characterize gene expression profiles of primary PDXs and PDOs, and to reconstruct gene regulatory network associated with XRT resistance. A SCLC PDX served as in vivo system to characterize the response to chemo-radiation resistance. Briefly, PDX tumor bearing mice were treated with: 1) vehicle control; 2) Cisplatin 5mg/kg on d1 plus Etoposide 8mg/kg on d1-2 (EP); 3) Radiotherapy 3Gy x1 on d3 (RT); and 4) both EP/RT. Whole transcriptome profiling among all treatments arms reveals molecular pathways and biological processes associated with XRT resistance. Also, by comparing our data with two previous SCLC patient cohort studies, we identified ideal candidates for functional analyses. SCLC XRT resistance candidate genes will be tested by either treating PDOs with small molecule inhibitors or by cDNA/shRNA lentiviral infection. To assess changes in chemo-radiation sensitivity, chemo-radiation protocols have been established and immunofluorescence staining for Ki67, γH2AX and cleaved caspase 3 served as markers for proliferation, DNA damage and apoptosis, respectively. Although further in-depth characterization is required, we aim to utilize our novel SCLC PDO model as a tool to identify candidate biomarkers to be used for developing therapy responses and translational research.

Citation Format: Francesca A. Carrieri, Nick Connis, Eloise M. Grasset, Isaac S. Chan, Eddie Luidy-Imada, Christine Lam, Hailun Wang, Andrew J. Ewald, Luigi Marchionni, Christine L. Hann, Phuoc T. Tran. Establishment of patient-derived organoids as ex vivo tool to characterize the molecular mechanisms of SCLC chemo-radiation resistance [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3926.

Abstract 2535: Screening PTEN-loss associated lncRNAs in prostate cancer

Phosphatase and tensin homologue (PTEN) is a tumor suppressor gene that is frequently inactivated by deletion in prostate cancer (PCa). Occurring in around 20% of primary PCa tumors, and up to 50% in castration resistant tumors, it is the most frequent genomic aberration in PCa. Loss of PTEN activates the phosphoinositide 3-kinase-RAC-alpha serine/threonine-protein kinase (PI3K-AKT) pathway, which is associated with poor clinical outcomes. Despite the consequences of PTEN loss being well studied, most of what is known is restricted to protein-coding genes, with relatively little information about the role of non-coding genes. Using our recently created resource - the FC-R2 expression atlas, which encompasses expression levels for thousands of lncRNAs recently unveiled by the FANTOM consortium - we analyzed differential gene expression of PTEN-null vs PTEN-intact tumors with the goal of characterizing the molecular landscape of PTEN loss. First, we generated a consensus signature using two large PCa cohorts with experimentally validated PTEN status by Immunohistochemistry (IHC), applying a meta-analysis approach. This signature encompassed mainly protein coding genes due it being microarray based. In order to expand this signature beyond the coding genes, we relied on FC-R2-based TCGA-PRAD data. Since PTEN status was not available by IHC, we opted to call the status based on CNV. Then, we proceed to generate a PTEN-null signature using a generalized linear model approach. Both signatures were compared for concordance using correspondence-at-the-top plots and hypergeometric confidence intervals. Gene set enrichment analysis was performed in both signatures using a collection of obtained from the MSigDB database in order to characterize pathways involved in this event. Our results showed that the signature based on IHC validated samples agreed significantly with the CNV-based signature from TCGA for the genes in common. In the differential gene expression analysis on the TCGA cohort we observed 203 significant coding genes and 171 significant non-coding genes (FDR ≤ 0.01, LogFC ≥ 1). Notably, we identified several lncRNAs that have not been associated with PCa or PTEN loss, these include many classes of non-coding RNAs characterized by the FANTOM consortium such as: enhancers and promoters genes. Gene set enrichment analysis revealed that PTEN-null tumors are associated with epithelial-mesenchymal transition suggesting a possible role for these lncRNAs. In conclusion, by leveraging our resources, we were able to obtain comprehensive landscape of the PTEN loss in PCa for both the coding and non-coding counterpart. Furthermore, the association of many lncRNAs with PTEN loss was observed, many recently annotated by the FANTOM consortium, which can help us understand how genes are regulated in this event. In this work we show that despite being widely studied, there are still many components of PTEN loss in the form of lncRNAs highlighting potential markers for PTEN loss and clinical outcomes.

Citation Format: Eddie Luidy Imada, Diego Fernando Sanchez, Wikum Dinalankara, Tamara Lotan, Luigi Marchionni. Screening PTEN-loss associated lncRNAs in prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2535.