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The theory of cancer immunoediting, which describes the dynamic interactions between tumors and host immune cells that shape the character of each compartment, is foundational for understanding cancer immunotherapy. Few models exist that facilitate in-depth study of each of the three canonical phases of immunoediting: elimination, equilibrium, and escape. Here, we utilized NPK-C1, a transplantable prostate tumor model that we found recapitulated the three phases of immunoediting spontaneously in immunocompetent animals. Given that a significant portion of NPK-C1 tumors reliably progressed to the escape phase, we were able to delineate cell types and mechanisms differentially prevalent in equilibrium versus escape phases. Using high-dimensional flow cytometry, we found that activated CD4+ effector T cells were enriched in regressing tumors, highlighting a role for CD4+ T cells in antitumor immunity. CD8+ T cells were also important for NPK-C1 control, specifically, central memory–like cytotoxic CD8+ T cells. Regulatory T cells (Treg), as a whole, were counterintuitively enriched in regressing tumors; however, high-dimensional analysis revealed their significant phenotypic diversity, with a number of Treg subpopulations enriched in progressing tumors. In the myeloid compartment, we found that iNOS+ dendritic cell (DC)–like cells are enriched in regressing tumors, whereas CD103+ DCs were associated with late-stage tumor progression. In total, these analyses of the NPK-C1 model provide novel insights into the roles of lymphoid and myeloid populations throughout the cancer immunoediting process and highlight a role for multidimensional, flow-based analyses to more deeply understand immune cell dynamics in the tumor microenvironment.

Precision oncology therapies are significantly hindered by the emergence of genetically or epigenetically-distinct cellular sub-populations, presenting either pre-existing or acquired drug resistance. For example, patients affected by advanced prostate cancer (PC), which is initially driven by aberrant activation of androgen receptor (AR) signals, often respond to Androgen Deprivation Therapy (ADT). Unfortunately, long-term treatment leads to the emergence of ADT-resistant tumors, termed Castration Resistant Prostate Cancer (CRPC), and poor outcome. Next-generation AR inhibitors extend CRPC patient survival, but frequently lead to the emergence of an even more aggressive Neuroendocrine PC (NEPC) phenotype, with exceedingly poor prognosis. Progression to NEPC is often determined by epigenetic-mediated reprogramming, rather than genomic alterations, resulting in cell-adaptive resistance, driven by aberrant activation of a handful of Master Regulator (MR) proteins. Consequently, better and more mechanistic understanding of cell-adaptive resistance is necessary to develop effective therapeutic strategies for CRPC. To address this challenge, we devised an RNA-based, high-throughput methodology, to systematically elucidate drug resistance mechanisms with single-cell resolution, and we applied it to study diverse, clonal populations that emerge following long-term treatment of PC LnCAP cells with next-generation AR inhibitors. Specifically, we identified Master Regulator (MR) proteins leading to the emergence of drug resistance, as well as clinically-available inhibitors able to reversing their activity, which were experimentally validated. We used PLATE-Seq, a low-cost, high-throughput RNA-Seq technology to profile more than 80 clonal, ADT-resistant populations, with single-cell resolution, and the VIPER algorithm to elucidate the MR proteins that mechanistically regulate their cell-adaptation programs. Our analysis revealed distinct cell-adaptive responses, associated with profound epigenetic reprogramming of prostate cancer cells. We used OncoTreat to prioritize a list of more than 400 FDA-approved and late-stage investigational drugs. Sensitivity to predicted drugs was confirmed in vitro and in vivo studies, in xenograft models derived from ADT- resistant clones are underway. We propose that this framework may represent a relevant advance to the study of cell-adaptive mechanism of drug resistance. Critically, this methodology is not limited to prostate cancer and can be applied to study arbitrary drug-resistance phenotypes. Citation Format: Prem Subramaniam, Alessandro Vasciaveo, Juan Arriaga, Jordan Kesner, Cory Abate-Shen, Andrea Califano. Targeting cell-adaptive mechanisms of drug resistance in lethal prostate cancer [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 1421.

Millions of individuals in United States are affected by Prostate Cancer (PCa), with tens of thousands dying every year, as therapeutic options for more advanced stages are not yet curative. A lack of suitable models of human cancer and inter-patient tumor heterogeneity often hinder the drug discovery process. In fact, the lost-in-translation phenomenon is one of the major causes of failure for compounds in the transitioning process from the workbench to the clinic. These limitations implicate considerable costs for the society in terms of both financial assets and human lives. To accelerate the drug discovery process, we devised and experimentally validated OncoLoop, a data-driven framework for the identification of clinically-relevant compounds that can be validated in preclinical models that optimally match patient-specific tumor dependencies. Specifically, we generated a set of Genetically-Engineered Mouse Models (GEMMs) of prostate cancer that recapitulate a wide variety of established prostate-cancer driver mutations. Their RNA profiles were then used to reverse-engineer a murine prostate cancer regulatory network, which was used to assess the activity profile of thousands of GEMM-specific regulatory proteins using the VIPER algorithm. We then assessed the match between the GEMM repertoire and several human prostate cancer databases–including from The Cancer Genome Atlas (TCGA) and Stand-Up To Cancer (SU2C) consortia–based on GEMM vs. patient Master Regulator (MR) protein conservation. This allowed selecting an optimally matched GEMM model, for virtually every patient in these databases, to serve as an in vivo surrogate model for screening patient-relevant drugs (i.e., a patient avatar). We then leveraged large-scale perturbational databases, comprising RNASeq profiles of 2 prostate cancer cell lines treated with >400 drugs, to prioritize drugs presenting the greatest ability to reverse MR protein activity in both the patient and in its optimally-matched GEMM, using the OncoTreat algorithm (Alvarez, et al. Nat Genet 2018). This formed a closed loop between each patient, its optimally matched GEMM and the drug(s) inferred to invert the activity of MR proteins in both the patient and the GEMM. OncoLoop selection of candidate drugs was validated in vivo. Notably, four out of five drugs were validated in the GEMM, including two inducing complete abrogation of tumor growth and two inducing significant tumor size reduction. In summary, we propose that given a patient's tumor biopsy, OncoLoop can identify an optimal GEMM model for in vivo validation and dramatically reduce the combinatorial search space of candidate drugs to a handful that may be effectively tested in that model. OncoLoop can be applied to any other cancer for which clinically-relevant tumor models are available. Citation Format: Alessandro Vasciaveo, Min Zou, Juan Arriaga, Andrea Califano, Cory Abate-Shen. OncoLoop: Closing the loop between patient-centered drug discovery and preclinical testing in precision-oncology [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 822.

The primary metastatic site of prostate cancer is the bone, which is associated with high morbidity and mortality. Despite this fact, very few genetically engineered mouse models recapitulate this phenotype, hindering our ability to model and understand the molecular drivers of bone metastasis. Our laboratory has generated a highly metastatic mouse model of castration-resistant prostate cancer based on the monoallelic loss of function of Nkx3.1, biallelic deletion of Pten, and oncogenic mutation of Kras in the prostate, termed the NPK model. By tracking these tumors with a bright fluorescent reporter, we now show that these tumors metastasize to bone with a penetrance of ~45% and display a bone distribution similar to that of human cancer. This model has thus allowed us to comprehensively profile the molecular alterations of bone metastatic lesions compared to the primary tumor and other metastatic sites. We found that differential gene expression profiles comparing bone metastases with primary tumors differ from those comparing lung metastases with primary tumors, suggesting that different biologic principles underlie metastasis to these two organ sites. Furthermore, gene-expression profiling demonstrated significant overlap with human bone metastases at the gene, master regulator, and pathway levels. Even though expression of the androgen receptor (AR) is maintained in the primary tumor and most lung metastases, bone lesions are AR-, without evidence of neuroendocrine (NE) differentiation, pointing to an AR-, NE- prostate cancer subtype, which is increasingly frequent in human prostate cancer. These results lay the groundwork for understanding the molecular drivers and pathways that are important for bone metastatic spread and may hopefully aid in selecting appropriate molecular targets to combat the metastatic disease. Citation Format: Juan Martin Arriaga, Cory Abate-Shen. A genetically engineered mouse model of de novo bone metastasis [abstract]. In: Proceedings of the AACR Special Conference: Advances in Modeling Cancer in Mice: Technology, Biology, and Beyond; 2017 Sep 24-27; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(10 Suppl):Abstract nr B01.