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African-American (AA) men are more likely to be diagnosed with and die from prostate cancer than European American (EA) men. Despite the central role of the androgen receptor (AR) transcription factor in prostate cancer, little is known about the contribution of epigenetics to observed racial disparities. We performed AR chromatin immunoprecipitation sequencing on primary prostate tumors from AA and EA men, finding that sites with greater AR binding intensity in AA relative to EA prostate cancer are enriched for lipid metabolism and immune response genes. Integration with transcriptomic and metabolomic data demonstrated coinciding upregulation of lipid metabolism gene expression and increased lipid levels in AA prostate cancer. In a metastatic prostate cancer cohort, upregulated lipid metabolism associated with poor prognosis. These findings offer the first insights into ancestry-specific differences in the prostate cancer AR cistrome. The data suggest a model whereby increased androgen signaling may contribute to higher levels of lipid metabolism, immune response, and cytokine signaling in AA prostate tumors. Given the association of upregulated lipogenesis with prostate cancer progression, our study provides a plausible biological explanation for the higher incidence and aggressiveness of prostate cancer observed in AA men. Significance: With immunotherapies and inhibitors of metabolic enzymes in clinical development, the altered lipid metabolism and immune response in African-American men provides potential therapeutic opportunities to attenuate racial disparities in prostate cancer.

Supplementary figure 1. Validation of synthetic lethal interaction between OGT inhibition and AT7519 in LNCaP cells. Supplementary figure 2. Validation of synthetic lethal interaction between OSMI-2 and AT7519 in additional cell lines. Supplementary figure 3. Combination of OSMI-2 with AT7519 induces cells death in prostate cancer cells but not in cells derived from normal prostate epithelia. Supplementary figure 4. Characterization of RNA-seq data of cells treated with OGT inhibitor OSMI-2. Supplementary figure 5. OGT inhibition decreases phosphorylation of the carboxyterminal domain of RNA polymerase II. Supplementary figure 6. AT7519 enhances the effects of OGT inhibition by blocking the upregulation of OGT in PC3 cells. Supplementary figure 7. Summary of the RNA-seq data. Supplementary figure 8. Specific CDK9 inhibitor NVP2 is synthetically lethal in combination with OGT inhibition to LNCaP prostate cancer cells. Supplementary figure 9. OGT inhibition potentiates the anti-proliferative effects of CDK9 inhibitors Dinaciclib and SB1317 on prostate cancer cells. Supplementary figure 10. Knockdown of CDK9 sensitizes LNCaP prostate cancer cells to OGT inhibition. Supplementary figure 11. Combination of OSMI-2 with AT7519 further decreases the mRNA abundance of most mRNAs with half-life less than 4 hours. Supplementary figure 12. Determination of CDK9 inhibitor dose suitable for organoid experiments. Supplementary figure 13. Validation of synthetic lethality between inhibitors of OGT and CDK9 using models of castration-resistant prostate cancer (CRPC).

Supplementary Figure 3. Scatterplots for metabolites with high correlations between their abundances in tissue and serum.

O-GlcNAc transferase (OGT) is a nutrient-sensitive glycosyltransferase that is overexpressed in prostate cancer, the most common cancer in males. We recently developed a specific and potent inhibitor targeting this enzyme, and here, we report a synthetic lethality screen using this compound. Our screen identified pan-cyclin-dependent kinase (CDK) inhibitor AT7519 as lethal in combination with OGT inhibition. Follow-up chemical and genetic approaches identified CDK9 as the major target for synthetic lethality with OGT inhibition in prostate cancer cells. OGT expression is regulated through retention of the fourth intron in the gene and CDK9 inhibition blunted this regulatory mechanism. CDK9 phosphorylates carboxy-terminal domain (CTD) of RNA Polymerase II to promote transcription elongation. We show that OGT inhibition augments effects of CDK9 inhibitors on CTD phosphorylation and general transcription. Finally, the combined inhibition of both OGT and CDK9 blocked growth of organoids derived from patients with metastatic prostate cancer, but had minimal effects on normal prostate spheroids. We report a novel synthetic lethal interaction between inhibitors of OGT and CDK9 that specifically kills prostate cancer cells, but not normal cells. Our study highlights the potential of combining OGT inhibitors with other treatments to exploit cancer-specific vulnerabilities.Implications:The primary contribution of OGT to cell proliferation is unknown, and in this study, we used a compound screen to indicate that OGT and CDK9 collaborate to sustain a cancer cell–specific pro-proliferative program. A better understanding of how OGT and CDK9 cross-talk will refine our understanding of this novel synthetic lethal interaction.

Full results of 10-fold cross-validation performance assessment for all ML models and prediction tasks (mean +/- SD).

Supplementary figure legends Supplementary table 2

Supplementary Figure 4. Heatmap of the top twenty metabolites as identified by permutation feature importance analysis of AutoML model.

Supplementary Figure 2. Heatmap of top differential (unadjusted p

Supplemental Figure 1. Fresh radical prostatectomy specimens were inked and transverse sliced from apex to base.

SummaryAlterations 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.

The androgen receptor (AR) is the key oncogenic driver of prostate cancer, and despite implementation of novel AR targeting therapies, outcomes for metastatic disease remain dismal. There is an urgent need to better understand androgen-regulated cellular processes to more effectively target the AR dependence of prostate cancer cells through new therapeutic vulnerabilities. Transcriptomic studies have consistently identified lipid metabolism as a hallmark of enhanced AR signaling in prostate cancer, yet the relationship between AR and the lipidome remains undefined. Using mass spectrometry–based lipidomics, this study reveals increased fatty acyl chain length in phospholipids from prostate cancer cells and patient-derived explants as one of the most striking androgen-regulated changes to lipid metabolism. Potent and direct AR-mediated induction of ELOVL fatty acid elongase 5 (ELOVL5), an enzyme that catalyzes fatty acid elongation, was demonstrated in prostate cancer cells, xenografts, and clinical tumors. Assessment of mRNA and protein in large-scale data sets revealed ELOVL5 as the predominant ELOVL expressed and upregulated in prostate cancer compared with nonmalignant prostate. ELOVL5 depletion markedly altered mitochondrial morphology and function, leading to excess generation of reactive oxygen species and resulting in suppression of prostate cancer cell proliferation, 3D growth, and in vivo tumor growth and metastasis. Supplementation with the monounsaturated fatty acid cis-vaccenic acid, a direct product of ELOVL5 elongation, reversed the oxidative stress and associated cell proliferation and migration effects of ELOVL5 knockdown. Collectively, these results identify lipid elongation as a protumorigenic metabolic pathway in prostate cancer that is androgen-regulated, critical for metastasis, and targetable via ELOVL5.Significance:This study identifies phospholipid elongation as a new metabolic target of androgen action that is critical for prostate tumor metastasis.

Supplementary Data from The Prostate Cancer Androgen Receptor Cistrome in African American Men Associates with Upregulation of Lipid Metabolism and Immune Response

Supplementary figures 1-9 and legends.

African-American (AA) men are more likely to be diagnosed with and die from prostate cancer than European American (EA) men. Despite the central role of the androgen receptor (AR) transcription factor in prostate cancer, little is known about the contribution of epigenetics to observed racial disparities. We performed AR chromatin immunoprecipitation sequencing on primary prostate tumors from AA and EA men, finding that sites with greater AR binding intensity in AA relative to EA prostate cancer are enriched for lipid metabolism and immune response genes. Integration with transcriptomic and metabolomic data demonstrated coinciding upregulation of lipid metabolism gene expression and increased lipid levels in AA prostate cancer. In a metastatic prostate cancer cohort, upregulated lipid metabolism associated with poor prognosis. These findings offer the first insights into ancestry-specific differences in the prostate cancer AR cistrome. The data suggest a model whereby increased androgen signaling may contribute to higher levels of lipid metabolism, immune response, and cytokine signaling in AA prostate tumors. Given the association of upregulated lipogenesis with prostate cancer progression, our study provides a plausible biological explanation for the higher incidence and aggressiveness of prostate cancer observed in AA men.Significance:With immunotherapies and inhibitors of metabolic enzymes in clinical development, the altered lipid metabolism and immune response in African-American men provides potential therapeutic opportunities to attenuate racial disparities in prostate cancer.

Supplementary Figure from The Prostate Cancer Androgen Receptor Cistrome in African American Men Associates with Upregulation of Lipid Metabolism and Immune Response

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (Padjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (Padjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a “metabolite signature” for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. Implications: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

The androgen receptor (AR) is the key oncogenic driver of prostate cancer, and despite implementation of novel AR targeting therapies, outcomes for metastatic disease remain dismal. There is an urgent need to better understand androgen-regulated cellular processes to more effectively target the AR dependence of prostate cancer cells through new therapeutic vulnerabilities. Transcriptomic studies have consistently identified lipid metabolism as a hallmark of enhanced AR signaling in prostate cancer, yet the relationship between AR and the lipidome remains undefined. Using mass spectrometry–based lipidomics, this study reveals increased fatty acyl chain length in phospholipids from prostate cancer cells and patient-derived explants as one of the most striking androgen-regulated changes to lipid metabolism. Potent and direct AR-mediated induction of ELOVL fatty acid elongase 5 (ELOVL5), an enzyme that catalyzes fatty acid elongation, was demonstrated in prostate cancer cells, xenografts, and clinical tumors. Assessment of mRNA and protein in large-scale data sets revealed ELOVL5 as the predominant ELOVL expressed and upregulated in prostate cancer compared with nonmalignant prostate. ELOVL5 depletion markedly altered mitochondrial morphology and function, leading to excess generation of reactive oxygen species and resulting in suppression of prostate cancer cell proliferation, 3D growth, and in vivo tumor growth and metastasis. Supplementation with the monounsaturated fatty acid cis-vaccenic acid, a direct product of ELOVL5 elongation, reversed the oxidative stress and associated cell proliferation and migration effects of ELOVL5 knockdown. Collectively, these results identify lipid elongation as a protumorigenic metabolic pathway in prostate cancer that is androgen-regulated, critical for metastasis, and targetable via ELOVL5. Significance: This study identifies phospholipid elongation as a new metabolic target of androgen action that is critical for prostate tumor metastasis.

O-GlcNAc transferase (OGT) is a nutrient-sensitive glycosyltransferase that is overexpressed in prostate cancer, the most common cancer in males. We recently developed a specific and potent inhibitor targeting this enzyme, and here, we report a synthetic lethality screen using this compound. Our screen identified pan-cyclin-dependent kinase (CDK) inhibitor AT7519 as lethal in combination with OGT inhibition. Follow-up chemical and genetic approaches identified CDK9 as the major target for synthetic lethality with OGT inhibition in prostate cancer cells. OGT expression is regulated through retention of the fourth intron in the gene and CDK9 inhibition blunted this regulatory mechanism. CDK9 phosphorylates carboxy-terminal domain (CTD) of RNA Polymerase II to promote transcription elongation. We show that OGT inhibition augments effects of CDK9 inhibitors on CTD phosphorylation and general transcription. Finally, the combined inhibition of both OGT and CDK9 blocked growth of organoids derived from patients with metastatic prostate cancer, but had minimal effects on normal prostate spheroids. We report a novel synthetic lethal interaction between inhibitors of OGT and CDK9 that specifically kills prostate cancer cells, but not normal cells. Our study highlights the potential of combining OGT inhibitors with other treatments to exploit cancer-specific vulnerabilities.Implications:The primary contribution of OGT to cell proliferation is unknown, and in this study, we used a compound screen to indicate that OGT and CDK9 collaborate to sustain a cancer cell–specific pro-proliferative program. A better understanding of how OGT and CDK9 cross-talk will refine our understanding of this novel synthetic lethal interaction.

Patients with metastatic breast cancer (MBC) can develop malignant effusions (MEs) when excess fluid and tumor cells accumulate in the pleural or peritoneal space. MEs are associated with significant morbidity and mortality. Tropism of tumor cells in these spaces is poorly understood and tumor cells residing in these fluids are difficult to study with traditional methods due to low abundance. To overcome this limitation and understand the pathobiology of MEs in MBCs, we applied a label-free method to isolate tumor cells based on morphology using an artificial intelligence (AI) model This method enabled isolation of live cells for downstream molecular analysis and characterization of morphological heterogeneity. We collected ME samples from 9 MBC patients with cytology-confirmed MEs, including 3 triple negative and 6 hormone receptor-positive/HER2-negative breast cancers. ME cells were imaged and isolated using a Deepcell AI model trained on a separate cohort of MEs to isolate and enrich tumor cells based on deep learning representations of morphological features. Quantitative morphological features were then extracted from cell images to perform cluster analysis and visualized by dimensionality reduction projections. In parallel, flow cytometry (FC) was used to quantify % EpCAM+/Claudin4+ tumor cells in ME samples. Copy number (CN) analysis was performed before and after enrichment with as few as 200 cells from each sample. Tumor cell fraction (TCF) estimates were calculated from the AI classifier, FC, and CN analyses. CN profiling of malignant cells sorted by the AI classifier revealed genomic alterations common in breast cancer in 7 of 9 patients. We observed increased amplitude of genomic alterations in tumor-enriched vs. unenriched samples, indicating successful tumor cell enrichment in 14 out of 16 samples. We observed a significant correlation in TCF between the AI classifier and FC with a Spearman test (rho=0.568, p0.008). In some cases, the AI classifier reported higher TCF than FC. This could indicate that the AI model detected tumor cells missed by antibody labeling or false positives from morphologically similar cell types, such as macrophages and mesothelial cells. Remarkably, analysis of serial ME samples from the same patient revealed morphological shifts in cell populations which were consistent with changes in TCF by CN analysis. We demonstrate the feasibility of label-free identification and isolation of tumor cells in MEs using an AI classifier. CN profiling of isolated tumor cells verified enrichment from low initial frequencies. Future work will include expression analysis of ME tumor and immune cells, and analysis of organoids derived from MEs. Combining molecular and morphological profiling of MEs will enable new insights into their pathobiology and inform targeted treatment strategies for patients with this condition. Citation Format: Mark J. Magbanua, Laura Huppert, Janifer Cruz, Jackson Goudreau, Shaopu Zhou, Keyi Yin, Pavel Stejskal, Ryan Carelli, Julie Kim, Linda Hsie, Maggie Wang, Zhouyang Lian, Michael Phelan, Manisha Ray, Chassidy Johnson, Amy L. Delson, Ron Balassanian, Jennifer Rosenbluth, Laura van't Veer, Michelle Melisko, Hope S. Rugo, Mahyar Salek, Maddison Masaeli, Hani Goodarzi. Artificial intelligence-assisted morphology-based detection and enrichment of malignant effusion tumor cells as a method for molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB170.

Studying tumor heterogeneity at single cell resolution is crucial for elucidating tumor progression and treatment response. Classically, tumor cells are characterized with a limited set of biomarkers that cannot cover the full extent of the tumor cell heterogeneity. The advent of single cell genomics has enabled scientists to study heterogeneity at high resolution, but gene expression signatures may not translate to readily available biomarkers for functional studies. Multi-dimensional morphology analysis has the potential for higher resolution than cell surface biomarkers while also reducing cell manipulations. The Deepcell platform enables multi-dimensional morphology analysis of unlabeled single cells using artificial intelligence (AI), imaging, and microfluidics, allowing for higher resolution of population heterogeneity beyond protein expression markers. Using the Deepcell platform, we trained an AI model to identify and sort for malignant cells from non-small cell lung cancer cells (NSCLC) tumor biopsies based on morphology. scRNA-seq, CNV, and targeted mutation analysis verified enrichment of carcinoma cells. Sorted cells had gene expression signatures indicative of tumor cells (e.g. EpCAM expression), increased genomic alterations by CNV analysis, and increased allele frequency of P53 and KRAS mutations relative to pre-sorted samples. Overall, the data indicate brightfield images of cells can be used to detect macro-level changes in cell morphology resulting from the molecular events involved in tumorigenesis. In addition, we induced chemotherapeutic drug resistance of lung cancer cell lines in vitro, imaged the resulting cultures on the Deepcell platform, and used the bright-field cell images to generate multi-dimensional morphological embeddings that can be visualized by UMAP. The resulting UMAPs showed distinct clusters of cells for both the parent and resistant cell lines, indicating that there are morphological differences associated with drug resistance. We developed a random forest classifier to identify the top differential morphological features between parental and resistant cell lines. These top features can distinguish between the populations with high accuracy (87%). Finally, we performed multi-dimensional morphology analysis to compare lung adenocarcinoma (LA) and squamous cell carcinoma (SCC) cell lines. The classifier predicted the correct cell type with 75% accuracy, suggesting that differences between these two carcinomas are reflected in their morphology. Together, our data suggest that AI-detected morphological differences between cell populations may show a biologically significant link between morphology and phenotype. As such, multi-dimensional morphology analysis will bolster the understanding of complex disease states and tumor microenvironment, particularly in patient derived biopsies. Citation Format: Andreja Jovic, Kiran Saini, Ryan Carelli, Tiffine Pham, Christian Corona, Jeanette Mei, Michael Phelan, Stephane C. Boutet, Kevn Jacobs, Julie Kim, Manisha Ray, Chassidy Johnson, Nianzhen Li, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Ramathal. Multi-dimensional morphology analysis enables identification and label-free enrichment of heterogeneous tumor cell populations [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 2392.

Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity within the tumors. These characteristics have made a deeper understanding of melanomas challenging. Classically, melanoma cells are characterized with a limited set of protein biomarkers. Gene expression signatures and mutational analysis (e.g., BRAF and NRAS genotyping) can provide a more detailed view of heterogeneity but may not translate to readily available biomarkers for functional studies. The Deepcell platform enables multi-dimensional morphology analysis and enrichment of unlabeled single cells using artificial intelligence (AI), advanced imaging, and microfluidics, enabling high resolution profiling of population heterogeneity. Label-free multi-dimensional morphology analysis may have higher resolution than a limited set of protein biomarkers, minimizes perturbation to the transcriptome, and reduces cell handling steps. We used patient-derived cell lines and dissociated biopsy samples to train a Deepcell AI model to identify and sort for melanoma cells based on morphology alone. The model was tested on metastatic melanoma biopsies, with identification and enrichment of melanoma cells verified by various downstream assays, including scRNASeq. In addition to melanoma populations, the AI model classified various cells of the microenvironment, such as stromal cells and immune subtypes, based on morphology only. To further characterize tumor heterogeneity, we imaged >25 patient-derived melanoma cell lines representing melanocytic, mesenchymal, and intermediate phenotypic states on the Deepcell platform. Morphology analysis of these images revealed distinct clusters of cells for each phenotype, indicating that there are morphological differences associated with each state. We developed a random forest (RF) classifier to identify the top differential morphological features between the different cell lines, thereby providing a label-free means of phenotyping melanoma samples. The morphology analysis of the cell lines uncovered significant variability in pigmentation; an RF classifier distinguished between pigmented vs non-pigmented cells with >90% accuracy. Pigmentation is a hallmark of melanoma cells, and it has been associated with the melanocytic phenotype and differential drug response in vitro. However, there is not currently a robust method to profile and study pigmentation in live cells. We further investigated this observation by correlating morphological profiles, molecular, and functional information with the level of cell pigmentation. The Deepcell platform presents a new method for sorting and characterizing cellular heterogeneity using morphology, including pigmentation status. As such, multi-dimensional morphology analysis will bolster the understanding of complex melanoma states and tumor microenvironment, particularly in patient derived biopsies. Citation Format: Evelyn Lattmann, Aizhan Tastanova, Andreja Jovic, Kiran Saini, Tiffine Pham, Christian Corona, Jeanette Mei, Michael Phelan, Stephane C. Boutet, Ryan Carelli, Kevin B. Jacobs, Julie Kim, Manisha Ray, Chassidy Johnson, Nianzhen Li, Mahyar Salek, Maddison Masaeli, Mitchell P. Levesque. High dimensional morphology analysis reveals new insights in melanoma cell heterogeneity [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 5926.

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 > 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 (> 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 > 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.

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.

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.

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.

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.

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.

Summary The interferon (IFN) pathway is critical for cytotoxic T cell activation, which is central to tumor immunosurveillance and successful immunotherapy. We demonstrate here that PKCλ/ι inactivation results in the hyper-stimulation of the IFN cascade and the enhanced recruitment of CD8+ T cells that impaired the growth of intestinal tumors. PKCλ/ι directly phosphorylates and represses the activity of ULK2, promoting its degradation through an endosomal microautophagy-driven ubiquitin-dependent mechanism. Loss of PKCλ/ι results in increased levels of enzymatically active ULK2, which, by direct phosphorylation, activates TBK1 to foster the activation of the STING-mediated IFN response. PKCλ/ι inactivation also triggers autophagy, which prevents STING degradation by chaperone-mediated autophagy. Thus, PKCλ/ι is a hub regulating the IFN pathway and three autophagic mechanisms that serve to maintain its homeostatic control. Importantly, single-cell multiplex imaging and bioinformatics analysis demonstrated that low PKCλ/ι levels correlate with enhanced IFN signaling and good prognosis in colorectal cancer patients.

Heterostructures of topological insulators and ferromagnets offer new opportunities in spintronics and a route to novel anomalous Hall states. In one such structure, EuS/${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$, a dramatic enhancement of the Curie temperature was recently observed. We performed Raman spectroscopy on a similar set of thin films to investigate the magnetic and lattice excitations. Interfacial strain was monitored through its effects on the ${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$ phonon modes while the magnetic system was probed through the EuS Raman mode. Despite its appearance in bare EuS, the heterostructures lack the corresponding EuS Raman signal. Through numerical calculations we rule out the possibility of Fabry-Perot interference suppressing the mode. Direct measurements of the magnetic system also eliminate room temperature ordering from suppressing the mode. We therefore attribute the absence of a magnetic signal in EuS to a charge transfer with the ${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$. This could provide an additional pathway for manipulating the magnetic, optical, or electronic response of topological heterostructures.