Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the molecular mechanisms driving this plasticity remain unclear. Using murine organoid and genetically engineered mouse models, we investigated the causes of lineage plasticity in prostate cancer and its relationship to antiandrogen resistance. We found that plasticity initiates in an epithelial population defined by mixed luminal-basal phenotype and that it depends on elevated JAK and FGFR activity. Organoid cultures from patients with castration-resistant disease harboring mixed-lineage cells reproduce the dependency observed in mice, by upregulating luminal gene expression upon JAK and FGFR inhibitor treatment. Single-cell analysis confirms the presence of mixed lineage cells with elevated JAK/STAT and FGFR signaling in a subset of patients with metastatic disease, with implications for stratifying patients for clinical trials.

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene–peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here, we present single-cell aggregation of cell-states (SEACells), an algorithm for identifying metacells; overcoming the sparsity of single-cell data, while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying accurate, compact, and well-separated metacells in both RNA and ATAC modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and measure gene accessibility in each metacell. Metacell-level analysis scales to large datasets and are particularly well suited for patient cohorts, including facilitation of data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation, and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a COVID-19 patient cohort.

The inherent plasticity of tumor cells provides a mechanism of resistance to many molecularly targeted therapies, exemplified by adeno-to-neuroendocrine lineage transitions seen in prostate and lung cancer. Here we investigate the root cause of this lineage plasticity in a primary murine prostate organoid model that mirrors the lineage transition seen in patients. These cells lose luminal identity within weeks following deletion of Trp53 and Rb1, ultimately acquiring an Ar-negative, Syp+ phenotype after orthotopic in vivo transplantation. Single-cell transcriptomic analysis revealed progressive mixing of luminal-basal lineage features after tumor suppressor gene deletion, accompanied by activation of Jak/Stat and Fgfr pathway signaling and interferon-a and -g gene expression programs prior to any morphologic changes. Genetic or pharmacologic inhibition of Jak1/2 in combination with FGFR blockade restored luminal differentiation and sensitivity to antiandrogen therapy in models with residual AR expression. Collectively, we show lineage plasticity initiates quickly as a largely cell-autonomous process and, through newly developed computational approaches, identify a pharmacological strategy that restores lineage identity using clinical grade inhibitors.

The inherent plasticity of tumor cells provides a mechanism of resistance to many molecularly targeted therapies, exemplified by adeno-to-neuroendocrine lineage transitions seen in prostate and lung cancer. Here we investigate the root cause of this lineage plasticity in a primary murine prostate organoid model that mirrors the lineage transition seen in patients. These cells lose luminal identity within weeks following deletion of Trp53 and Rb1, ultimately acquiring an Ar-negative, Syp+ phenotype after orthotopic in vivo transplantation. We performed single-cell transcriptomic analysis of a time-course experiment on the prostate organoid following Trp53 and Rb1 deletion. Critical to this study, we developed SEACells, a method that enumerates distinct, highly granular cell states, allowing for robust transcriptomic quantification. Leveraging the SEACell platform, we developed several graph-based computational approaches based on Markov absorption, diffusion maps, and attributed stochastic block models to quantify dynamic changes in plasticity. These quantitative models independently confirmed rapid collapse of cell-type fidelity in the form of a mixed luminal-basal phenotype following tumor suppressor gene deletion. These methods compute metrics for plasticity that we correlated to candidate driver gene programs. Among the strongest plasticity correlates, Jak-Stat and Fgfr signaling stood out as gene programs activated early in the time-course prior to any corresponding morphological changes. We further developed a regression-based approach to nominate ligand-receptor interactions that activate downstream Jak-Stat signaling, which identified Fgf-Fgfr interactions that were functionally validated with growth factor addition and pharmacological inhibition. Most strikingly, genetic or pharmacologic inhibition of Jak1/2 in combination with Fgfr blockade not only reversed the plastic state and restored organoids to their wild-type morphology, but also re-sensitized drug-resistant cells to antiandrogen therapy in models with residual AR expression. We additionally confirm early activation of Jak/Stat transcriptional programs in an Rb1/Trp53/Pten-deleted genetically engineered mouse model undergoing substantial cell-type diversification under plasticity in the context of the tumor microenvironment. Collectively, we show that lineage plasticity initiates quickly as a largely cell-autonomous process that is further increased in the in vivo setting, and through newly developed computational approaches, we identify a pharmacological strategy that restores lineage identity using clinical grade inhibitors. Citation Format: Joseph M. Chan, Wouter R. Karthaus, Manu Setty, Jillian R. Love, Samir Zaidi, Jimmy Zhao, Zi-ning Choo, Sitara Persad, Justin LaClair, Kayla E. Lawrence, Ojasvi Chaudhary, Ignas Masilionis, Linas Mazutis, Ronan Chaligne, Dana Pe'er, Charles Sawyers. Reversal of lineage plasticity in RB1/TP53-deleted prostate cancer through FGFR and Janus kinase inhibition [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 1594.