CellRank 2: unified fate mapping in multiview single-cell data

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.

Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers

Optimal transport (OT) and the related Wasserstein metric ( W ) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology. Software is available at http://wassersteinwormhole.readthedocs.io/en/latest/.

Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis

BACKGROUND & AIMS

Acinar cells produce digestive enzymes that impede transcriptomic characterization of the exocrine pancreas. Thus, single-cell RNA-sequencing studies of the pancreas underrepresent acinar cells relative to histological expectations, and a robust approach to capture pancreatic cell responses in disease states is needed. We sought to innovate a method that overcomes these challenges to accelerate study of the pancreas in health and disease.

METHODS

We leverage FixNCut, a single-cell RNA-sequencing approach in which tissue is reversibly fixed with dithiobis(succinimidyl propionate) before dissociation and single-cell preparation. We apply FixNCut to an established mouse model of acute pancreatitis, validate findings using GeoMx whole transcriptome atlas profiling, and integrate our data with prior studies to compare our method in both mouse and human pancreas datasets.

RESULTS

FixNCut achieves unprecedented definition of challenging pancreatic cells, including acinar and immune populations in homeostasis and acute pancreatitis, and identifies changes in all major cell types during injury and recovery. We define the acinar transcriptome during homeostasis and acinar-to-ductal metaplasia and establish a unique gene set to measure deviation from normal acinar identity. We characterize pancreatic immune cells, and analysis of T-cell subsets reveals a polarization of the homeostatic pancreas toward type-2 immunity. We report immune responses during acute pancreatitis and recovery, including early neutrophil infiltration, expansion of dendritic cell subsets, and a substantial shift in the transcriptome of macrophages due to both resident macrophage activation and monocyte infiltration.

CONCLUSIONS

FixNCut preserves pancreatic transcriptomes to uncover novel cell states during homeostasis and following pancreatitis, establishing a broadly applicable approach and reference atlas for study of pancreas biology and disease.

Microbial cancer immunotherapy reprograms hematopoietic stem cells to enhance anti-tumor immunity

Mycobacterium bovis BCG is the vaccine against tuberculosis and an immunotherapy for bladder cancer. When administered intravenously, BCG reprograms bone marrow hematopoietic stem and progenitor cells (HSPCs), leading to heterologous protection against infections. Whether HSPC-reprogramming contributes to the anti-tumor effects of BCG administered into the bladder is unknown. We demonstrate that BCG administered in the bladder in both mice and humans reprograms HSPCs to amplify myelopoiesis and functionally enhance myeloid cell antigen presentation pathways. Reconstitution of naive mice with HSPCs from bladder BCG-treated mice enhances anti-tumor immunity and tumor control, increases intratumor dendritic cell infiltration, reprograms pro-tumorigenic neutrophils, and synergizes with checkpoint blockade. We conclude that bladder BCG acts systemically, reprogramming HSPC-encoded innate immunity, highlighting the broad potential of modulating HSPC phenotypes to improve tumor immunity.

The covariance environment defines cellular niches for spatial inference

A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

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.

Deep generative model deciphers derailed trajectories in acute myeloid leukemia

Single-cell genomics has the potential to map cell states and their dynamics in an unbiased way in response to perturbations like disease. However, elucidating the cell-state transitions from healthy to disease requires analyzing data from perturbed samples jointly with unperturbed reference samples. Existing methods for integrating and jointly visualizing single-cell datasets from distinct contexts tend to remove key biological differences or do not correctly harmonize shared mechanisms. We present Decipher, a model that combines variational autoencoders with deep exponential families to reconstruct derailed trajectories ( https://github.com/azizilab/decipher ). Decipher jointly represents normal and perturbed single-cell RNA-seq datasets, revealing shared and disrupted dynamics. It further introduces a novel approach to visualize data, without the need for methods such as UMAP or TSNE. We demonstrate Decipher on data from acute myeloid leukemia patient bone marrow specimens, showing that it successfully characterizes the divergence from normal hematopoiesis and identifies transcriptional programs that become disrupted in each patient when they acquire NPM1 driver mutations.

Supervised discovery of interpretable gene programs from single-cell data

Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene-gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8+ T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs.

Progressive plasticity during colorectal cancer metastasis

Metastasis is the principal cause of cancer death, yet we lack an understanding of metastatic cell states, their relationship to primary tumor states, and the mechanisms by which they transition. In a cohort of biospecimen trios from same-patient normal colon, primary and metastatic colorectal cancer, we show that while primary tumors largely adopt LGR5 + intestinal stem-like states, metastases display progressive plasticity. Loss of intestinal cell states is accompanied by reprogramming into a highly conserved fetal progenitor state, followed by non-canonical differentiation into divergent squamous and neuroendocrine-like states, which is exacerbated by chemotherapy and associated with poor patient survival. Using matched patient-derived organoids, we demonstrate that metastatic cancer cells exhibit greater cell-autonomous multilineage differentiation potential in response to microenvironment cues than their intestinal lineage-restricted primary tumor counterparts. We identify PROX1 as a stabilizer of intestinal lineage in the fetal progenitor state, whose downregulation licenses non-canonical reprogramming.

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs new combination cancer therapy strategies

While regulatory T (Treg) cells are traditionally viewed as professional suppressors of antigen presenting cells and effector T cells in both autoimmunity and cancer, recent findings of distinct Treg cell functions in tissue maintenance suggest that their regulatory purview extends to a wider range of cells and is broader than previously assumed. To elucidate tumoral Treg cell 'connectivity' to diverse tumor-supporting accessory cell types, we explored immediate early changes in their single-cell transcriptomes upon punctual Treg cell depletion in experimental lung cancer and injury-induced inflammation. Before any notable T cell activation and inflammation, fibroblasts, endothelial and myeloid cells exhibited pronounced changes in their gene expression in both cancer and injury settings. Factor analysis revealed shared Treg cell-dependent gene programs, foremost, prominent upregulation of VEGF and CCR2 signaling-related genes upon Treg cell deprivation in either setting, as well as in Treg cell-poor versus Treg cell-rich human lung adenocarcinomas. Accordingly, punctual Treg cell depletion combined with short-term VEGF blockade showed markedly improved control of PD-1 blockade-resistant lung adenocarcinoma progression in mice compared to the corresponding monotherapies, highlighting a promising factor-based querying approach to elucidating new rational combination treatments of solid organ cancers.

Modeling collective cell behavior in cancer: Perspectives from an interdisciplinary conversation

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.

Cross-platform dataset of multiplex fluorescent cellular object image annotations

Defining cellular and subcellular structures in images, referred to as cell segmentation, is an outstanding obstacle to scalable single-cell analysis of multiplex imaging data. While advances in machine learning-based segmentation have led to potentially robust solutions, such algorithms typically rely on large amounts of example annotations, known as training data. Datasets consisting of annotations which are thoroughly assessed for quality are rarely released to the public. As a result, there is a lack of widely available, annotated data suitable for benchmarking and algorithm development. To address this unmet need, we release 105,774 primarily oncological cellular annotations concentrating on tumor and immune cells using over 40 antibody markers spanning three fluorescent imaging platforms, over a dozen tissue types and across various cellular morphologies. We use readily available annotation techniques to provide a modifiable community data set with the goal of advancing cellular segmentation for the greater imaging community.

Abstract 6421: Molecular profiles and single cell analysis identify immunogenic pancreatic ductal adenocarcinoma (iPDAC)

Most pancreatic ductal adenocarcinomas (PDAC) are lethal and resistant to immunotherapy. Thus, identifying the immunogenic subgroup (iPDAC) and therapeutic targets can save lives. Herein, we present molecular features of iPDAC. 3 cohorts (A, B, C) from 288 patients whose sequenced tumors (MSK-IMPACT) were classified by homologous recombination deficiency groups. MSI-H were excluded. Survival, tumor mutation burden, genomic instability score, and enriched pathways for each cohort are included in Table 1. Patients in A (BRCA1/2/PALB2) had longer survivals vs B/C. 61 samples were selected for bulk RNAseq analysis for A vs C. Gene Ontology was enriched for upregulated humoral, T cell, and neutrophil immunity. CIBERSORT suggested higher infiltration of gamma delta T (Tgd) cells (p=0.039) and neutrophils (p=0.012), but lower Treg (p=0.001). Multidimensional insights in cellular components of cancer, immune, stroma, and neural genes were obtained by single nuclear RNA (snRNAseq) analysis from 30 biopsies for A vs C. 10x Genomics Chromium platform for library and Scanpy for computational analysis after Cell Ranger pipelines were used. 61,868 nuclei were profiled from 18 (13 baseline and 5 matched longitudinal) samples after quality evaluation. UMAP accurately clustered cells from each patient. Long-term survivors (LTS) had heterogenous baseline immune cell infiltrates of plasma cells, neutrophils, and CD8 (+) cytotoxic T cells. In matched samples of LTS, evolution of more prominent CD8 (+) T cells, macrophage, plasma cell, and neutrophil were observed. Single nucleus T-Cell Receptor sequencing for clonal trajectory inference will be done to determine the associated single cell molecular features contributing to iPDAC and identify novel targets for future intervention.

Table 1. Cohort (Total: N=288) A: core HRD (BRCA1/2/PALB2) B: non-core HRD (ATM, BARD1, BLM, CHEK2, RAD50, RAD51C, RTEL1, MUTYH) C: others without HR-gene alterations Number (%) 48 (16.6) 19 (6.5) 221 (76) Median overall survival (95% confidence Interval) 33 months (3.6-64) 16 (11- not reached) 16 (14-18) Tumor Mutation Burden (TMB) 4.4 3.5 3.9 Genomic Instability Score (GIS, HRD score) 26 12 13 Gene Ongology term, enrichment score, adjusted p-value Adaptive immune response, GO:0002250, 0.49, 1.69e-10 Not included Reference to cohort A Humoral immune response, GO:0006959, 0.58, 1.67e-9 T cell activation, GO:0042110, 0.44, 2.75e-8 Neutrophil chemotaxis, GO:0030593, 0.73, 4.3e-10

Citation Format: Wungki Park, Catherine O'Connor, Shigeaki Umeda, Roshan Sharma, Yingjie Zhu, Elias-Ramzey Karnoub, Anna Varghese, Kevin C. Soares, Alejandro Jimemez, Asli Yavas, Kenneth H. Yu, Balachandran P. Vinod, Joanne F. Chou, Danny N. Khalil, Kelsen David, Hulya Sahin Ozkan, Olca Basturk, Marinela Capanu, Tal Nawy, Michael F. Berger, Ghassan K. Abou-Alfa, Jorge S. Reis-Filho, Ronan Chaligne, Nadeem Riaz, Dana Pe'er, Christine Iacobuzio-Donahue, Eileen M. O'Reilly. Molecular profiles and single cell analysis identify immunogenic pancreatic ductal adenocarcinoma (iPDAC) [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 6421.

Abstract 1167: Single-cell transcriptomic profiling of SCLC transformation reveals increased intratumoral diversity of variant and non-neuroendocrine subtypes

Lineage plasticity is the ability for a cell to change its phenotypic state under evolutionary pressure. This process mediates acquired resistance to EGFR-targeted therapies in lung adenocarcinoma (LUAD), through transformation to small cell lung cancer (SCLC), an aggressive cancer type of neuroendocrine (NE) histology. This transformed SCLC has worse clinical outcomes than either its LUAD or de novo SCLC counterparts, with less than one-year overall survival following transformation. Classical SCLC itself can display plasticity through interconversion between classical, variant, and non-NE transcriptional subtypes, with non-classical subtypes displaying increased metastasis and chemoresistance. It is poorly understood what factors drive SCLC transformation or subtype switching. Prior studies poorly model intratumoral heterogeneity because they profile tumors in bulk, which only estimates the average phenotype. However, SCLC transformation often results in admixed LUAD/SCLC tumors. We sought to capture the full intratumoral heterogeneity of SCLC transformation and identify potential molecular determinants of plasticity by applying single-cell RNA sequencing (scRNA-seq) to 41 tumors and patient-derived xenografts from 22 patients with transformed or combined LUAD/NE histology, with matched targeted DNA sequencing and 17 de novo SCLC tumors, 10 LUAD tumors with concurrent EGFR/RB1/TP53 mutations, and 4 tumor-adjacent normal lung samples for comparison. Of 234,322 single transcriptomes assessed, we found 89,113 NE cancer cells and 18,197 LUAD cancer cells. We confirmed that NE and LUAD components within the same tumor shared clonal mutations and common ancestry. Compared to de novo, transformed SCLC harbored greater phenotypic diversity across patients (p < 1 × 10−10), driven largely by enrichment of variant and non-NE subtypes (p < 0.02), including NEUROD1, POU2F3, and YAP1-high subtypes, the latter of which was completely absent in de novo. Within each tumor, transformed SCLC displayed higher intratumoral subtype diversity than de novo SCLC (likelihood ratio p < 0.025). After adjusting for SCLC subtype, differential expression and pathway analysis demonstrated that transformed SCLC is a distinct phenotype from de novo and shares features of the ancestral clone that include residual EGFR and NSCLC gene signatures, as well as pathways in neuronal stemness, MYC targets, AKT/MTOR signaling, JAK/STAT inflammation, and chromatin remodeling. In sum, we find increased intratumoral phenotypic diversity in transformed SCLC, including variant and non-NE subtypes, that may explain worse clinical outcomes. We show that transformed SCLC is a distinct phenotype from de novo, marked by pathways that may offer new potential drug targets to constrain plasticity, with the goal of restoring original sensitivity to targeted therapies.

Citation Format: Joseph Minhow Chan, Alvaro Quintanal-Villalonga, Amin Sabet, Parvathy Manoj, Ojasvi Chaudhary, Tianhao Xu, Ignas Masilionis, Jacklynn Egger, Noor Sohail, Jaeyoung Chun, Tal Nawy, Linas Mazutis, Triparna Sen, Ronan Chaligne, Helena Yu, Dana Pe'er, Charles Rudin. Single-cell transcriptomic profiling of SCLC transformation reveals increased intratumoral diversity of variant and non-neuroendocrine subtypes [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 1167.

STING inhibits the reactivation of dormant metastasis in lung adenocarcinoma

Metastasis frequently develops from disseminated cancer cells that remain dormant after the apparently successful treatment of a primary tumour. These cells fluctuate between an immune-evasive quiescent state and a proliferative state liable to immune-mediated elimination1-6. Little is known about the clearing of reawakened metastatic cells and how this process could be therapeutically activated to eliminate residual disease in patients. Here we use models of indolent lung adenocarcinoma metastasis to identify cancer cell-intrinsic determinants of immune reactivity during exit from dormancy. Genetic screens of tumour-intrinsic immune regulators identified the stimulator of interferon genes (STING) pathway as a suppressor of metastatic outbreak. STING activity increases in metastatic progenitors that re-enter the cell cycle and is dampened by hypermethylation of the STING promoter and enhancer in breakthrough metastases or by chromatin repression in cells re-entering dormancy in response to TGFβ. STING expression in cancer cells derived from spontaneous metastases suppresses their outgrowth. Systemic treatment of mice with STING agonists eliminates dormant metastasis and prevents spontaneous outbreaks in a T cell- and natural killer cell-dependent manner-these effects require cancer cell STING function. Thus, STING provides a checkpoint against the progression of dormant metastasis and a therapeutically actionable strategy for the prevention of disease relapse.

Leptomeningeal anti-tumor immunity follows unique signaling principles

Metastasis to the cerebrospinal fluid (CSF)-filled leptomeninges, or leptomeningeal metastasis (LM), represents a fatal complication of cancer. Proteomic and transcriptomic analyses of human CSF reveal a substantial inflammatory infiltrate in LM. We find the solute and immune composition of CSF in the setting of LM changes dramatically, with notable enrichment in IFN-γ signaling. To investigate the mechanistic relationships between immune cell signaling and cancer cells within the leptomeninges, we developed syngeneic lung, breast, and melanoma LM mouse models. Here we show that transgenic host mice, lacking IFN-γ or its receptor, fail to control LM growth. Overexpression of Ifng through a targeted AAV system controls cancer cell growth independent of adaptive immunity. Instead, leptomeningeal IFN-γ actively recruits and activates peripheral myeloid cells, generating a diverse spectrum of dendritic cell subsets. These migratory, CCR7+ dendritic cells orchestrate the influx, proliferation, and cytotoxic action of natural killer cells to control cancer cell growth in the leptomeninges. This work uncovers leptomeningeal-specific IFN-γ signaling and suggests a novel immune-therapeutic approach against tumors within this space.

Effect of Janus kinase (JAK) signaling inhibition on lineage plasticity and drug sensitivity in castrate resistant prostate cancer

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Background: Despite the remarkable successes of targeted cancer therapies, certain cancers, including lung, breast, and prostate cancer and melanoma, invariably become resistant to therapy. One mechanism of secondary resistance—lineage plasticity—arises when cells alter their identity and transition into aggressive states. In the case of prostate cancer, cells can acquire a neuroendocrine histology. This is often associated with a loss of tumor suppressor genes, such as TP53, RB1, and PTEN. However, while these genomic events initiate plasticity, tumor progression is not always associated with successive genomic alterations. This, in essence, not only poses a clinical challenge, but also confronts us with a wide-open biological question—what are the molecular underpinnings of lineage plasticity, and importantly, can the process be reversed? Methods: To study the temporal evolution of lineage plasticity and its relationship to androgen receptor signaling inhibitor (ARSI) resistance, we utilized genetically engineered mouse models (GEMMs) and murine organoids that were deleted for Tp53, Rb1, and/or Pten. Single cell RNA analyses were utilized to dissect which genes and pathways were up-regulated and most associated with the progression of plasticity. Plasticity-associated genes and pathways were perturbed using FDA-approved inhibitors or genetic editing tools. The presence of these pathways was confirmed in a subset of metastatic index lesions obtained by radiologically guided biopsies and as visualized by metabolic imaging. Furthermore, relevant findings were functionally validated in human tumor derived organoids (“tumoroids”). Results: Using GEMMs and organoid models, we found the lineage plasticity depended on increased Janus Kinase (JAK) and fibroblast growth factor receptor (FGFR) activity. Pharmacologic inhibition using FDA–approved inhibitors of JAK/STAT (ruxolitinib) and FGFR (erdafinitib), or through genetic knockdown, demonstrated increased androgen receptor (AR) signaling and restored ARSI sensitivity. These findings were further validated in a subset of ARSI-insensitive human tumoroids. By performing single cell RNA sequencing on mCRPC tumors biopsies, the presence of highly plastic JAK/STAT- and FGFR-high cells were confirmed, with implications for stratifying patients for clinical trials. Conclusions: JAK/STAT and FGFR signaling pathways promote lineage plasticity and result in complete insensitivity to androgen receptor signaling inhibitors (ARSIs). FDA-approved inhibitors of JAK/STAT (ruxolitinib) and FGFR (erdafitinib) synergize to reverse lineage plasticity and restore ARSI sensitivity.

Distinct tumor architectures for metastatic colonization of the brain

Brain metastasis is a dismal cancer complication, hinging on the initial survival and outgrowth of disseminated cancer cells. To understand these crucial early stages of colonization, we investigated two prevalent sources of cerebral relapse, triple-negative (TNBC) and HER2+ breast cancer (HER2BC). We show that these tumor types colonize the brain aggressively, yet with distinct tumor architectures, stromal interfaces, and autocrine growth programs. TNBC forms perivascular sheaths with diffusive contact with astrocytes and microglia. In contrast, HER2BC forms compact spheroids prompted by autonomous extracellular matrix components and segregating stromal cells to their periphery. Single-cell transcriptomic dissection reveals canonical Alzheimer's disease-associated microglia (DAM) responses. Differential engagement of tumor-DAM signaling through the receptor AXL suggests specific pro-metastatic functions of the tumor architecture in both TNBC perivascular and HER2BC spheroidal colonies. The distinct spatial features of these two highly efficient modes of brain colonization have relevance for leveraging the stroma to treat brain metastasis.

Abstract B016: Quantifying and dissecting pancreatic cancer cell phenotypic plasticity using lineage tracing, single-cell multiomics and CRISPR perturbations reveals novel regulators of plastic states

Pancreatic cancer is a lethal disease in part because tumor cells exist in distinct transcriptional phenotypes (e.g. basal and classical states), each with a selective ability to evade current chemotherapy regimens. Two major mechanisms have been suggested for treatment evasion: 1) intrinsic resistance of certain phenotypes to particular chemotherapy regimens and 2) plasticity of treatment sensitive phenotypes to adopt more resistant phenotypes. However, the relative contribution of these mechanisms to treatment resistance is still poorly understood. Whereas previous work has described the redistribution of tumor cell states under selective treatment pressure, there is no direct evidence that tumor cells exhibit phenotypic plasticity at steady state or with treatment. By leveraging technological advancements in single-cell methods, lineage tracing and functional genomics, we have now shown direct evidence of phenotypic state switching in human pancreatic cancer cell lines. By performing single-cell RNA-seq on 5 barcoded PDAC cell lines over a steady state timecourse and under chemotherapy selective pressure (>600k cells total), we identify unique plasticity phenotypes within these cell lines and infer regulators of these plastic states. We validate the role of several of these regulators using bulk phenotypic CRISPRi screens in these cell lines. We next perform CRISPRi perturbations along with lineage tracing and single-cell multiomics (>300k cells) to dissect the regulatory relationships that underlie these cell states. We identify several novel epithelial and mesenchymal biasing factors, including those with unique roles in the most plastic clones. Collectively, we nominate several regulators that bias PDAC cell states thus posing a paradigm whereby perturbations may be used to homogenize tumor populations towards treatment-sensitive phenotypes. We believe this approach combined with current chemotherapy regimens could benefit pancreatic cancer patients by targeting residual, resistant tumor cells in the localized and metastatic disease settings to improve patient survival.

Citation Format: Arnav Mehta, Lynn Bi, Aziz Al'Khafaji, Martin Jankowiak, Milan Parikh, Mehrtash Babadi, Alex Bloemendal, Marc Schwartz, Glen Munson, Joeseph Chan, Cassandra Burdziak, Elisa Donnard, Ryan Park, Chen Lu, Philippe Rigollet, Andrew Aguirre, Vidya Subramanian, Ray Jones, Eric S. Lander, David T. Ting, Dana Pe'er, Nir Hacohen. Quantifying and dissecting pancreatic cancer cell phenotypic plasticity using lineage tracing, single-cell multiomics and CRISPR perturbations reveals novel regulators of plastic states [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B016.

Abstract C012: Dissecting the reorganization of pancreatic tumor microenvironments after radiation and immunotherapy reveals insights into immunotherapy resistance

Immune checkpoint blockade (ICB) has revolutionized the treatment of many cancers but has been ineffective for the treatment of microsatellite stable (MSS) PDAC. The lack of efficacy of immunotherapies in PDAC is due to: 1) a desmoplastic tumor microenvironment (TME); 2) the presence of suppressive cells, including myeloid derived suppressor cells and regulatory T cells; and 3) the lack of antigen-presenting dendritic cells (DCs) that are important in priming an effective immune response to generate functionally effective tumor antigen-specific T cells. We recently completed a pilot study of dual ICB (Ipilumamab and Nivolumab) with radiation therapy (SBRT 8Gy for 3 fractions) in a cohort of 25 metastatic PDAC patients that had progressed on conventional chemotherapy; this combination conferred an impressive 18% ORR and 29% disease control rate measured on non-irradiated lesions (historical 0% ORR with ICB in PDAC). This led to a phase 2 study in 30 metastatic PDAC patients using this dual modality treatment paradigm. To understand the role of radiation and ICB in altering the PDAC tumor microenvironment we performed single-cell RNA-sequencing and TCR-sequencing (>180k cells), and single-nucleus RNA-sequencing (>300k cells) on 36 tumor biopsies (23 pre-treatment, 13 paired on-treatment between day 10 and 21) from patients undergoing treatment in our phase 2 study. Tumor tissue was taken from distinct tissue sites, including primary tumors in the pancreas, and liver and abdominal wall metastases. We identified distinct tumor cell state distributions within different tissues, and a redistribution of cells from basal/mesenchymal states to classical states after radiation. We identified several state-specific interferon stimulated gene programs thus cataloging distinct responses of epithelial cells with different transcriptional states. Importantly, we found a redistribution of T cells states towards proliferating and exhausted T cells with unique clonality after radiation. Additionally, the myeloid compartment after radiation was enriched for C1QC+ and MHCII+ macrophage subsets, as well as infiltrating CD16/CD16 monocytes and CD14 monocytes, each showing induction of unique sets of interferon stimulated genes (ISGs). We next sought to better understand immunotherapy resistance mechanisms within these PDAC patients despite finding strong ISG induction in several subsets. We analyzed covarying gene programs and identified multicellular communities of cells before and after radiation that underlie interaction networks associated with radiation. Together our data provides the most comprehensive single-cell atlas of paired biopsies to study tumor and immune cell states in the context of radiation and ICB response.

Citation Format: Arnav Mehta, Aparna Parikh, Milan Parikh, Ryan Park, Moshe Sade-Feldman, Lynn Bi, Nicole Carzo, Tarin M. Grillo, Islam Baiev, Olanike Asupoto, Irena Gushterova, Tom LaSalle, Anna Gonye, Emily Blaum, Sebastien Vigneau, Ronan Chaligne, Ana Lako, Thomas Lila, David Nelson, Caroline Porter, Orr Ashenberg, Karthik Jagadesh, William L. Hwang, Christopher Smillie, David P. Ryan, David T. Ting, Theodore Hong, Dana Pe'er, Nir Hacohen. Dissecting the reorganization of pancreatic tumor microenvironments after radiation and immunotherapy reveals insights into immunotherapy resistance [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C012.

MACHETE identifies interferon-encompassing chromosome 9p21.3 deletions as mediators of immune evasion and metastasis

The most prominent homozygous deletions in cancer affect chromosome 9p21.3 and eliminate CDKN2A/B tumor suppressors, disabling a cell-intrinsic barrier to tumorigenesis. Half of 9p21.3 deletions, however, also encompass a type I interferon (IFN) gene cluster; the consequences of this co-deletion remain unexplored. To functionally dissect 9p21.3 and other large genomic deletions, we developed a flexible deletion engineering strategy, MACHETE (molecular alteration of chromosomes with engineered tandem elements). Applying MACHETE to a syngeneic mouse model of pancreatic cancer, we found that co-deletion of the IFN cluster promoted immune evasion, metastasis and immunotherapy resistance. Mechanistically, IFN co-deletion disrupted type I IFN signaling in the tumor microenvironment, leading to marked changes in infiltrating immune cells and escape from CD8+ T-cell surveillance, effects largely driven by the poorly understood interferon epsilon. These results reveal a chromosomal deletion that disables both cell-intrinsic and cell-extrinsic tumor suppression and provide a framework for interrogating large deletions in cancer and beyond.

Protocol to dissociate, process, and analyze the human lung tissue using single-cell RNA-seq

We report a protocol for obtaining high-quality single-cell transcriptomics data from human lung biospecimens acquired from core needle biopsies, fine-needle aspirates, surgical resection, and pleural effusions. The protocol relies upon the brief mechanical and enzymatic disruption of tissue, enrichment of live cells by fluorescence-activated cell sorting (FACS), and droplet-based single-cell RNA sequencing (scRNA-seq). The protocol also details a procedure for analyzing the scRNA-seq data. For complete details on the use and execution of this protocol, please refer to Chan et al. (2021).

Abstract 1728: Single cell RNA-seq analysis reveals a role of pro-inflammatory tumor-associated macrophages in driving prostate cancer progression

Genomic amplification and increased expression of C-MYC are found in high-grade intraepithelial neoplasm (HGPIN) and primary prostate adenocarcinoma. What role C-MYC plays in the initiation and progression of prostate cancer remains unclear. A genetically engineered mouse model with a high level of human C-MYC protein expression under a probasin promoter (Hi-Myc model) was developed that captures prostate cancer progression from hyperplasia at 6-week-old, to HGPIN at 3-6 month-old, and ultimately to invasive adenocarcinoma at 8-month-old. Applying single-cell RNA sequencing (scRNA-seq) technology, we have carefully studied the temporal transcriptional change in MYC-expressing tumor-initiating cells and the tumor-infiltrating immune cells to understand the cell-intrinsic and -extrinsic mechanisms by which C-MYC promotes the transition from HGPIN to invasive prostate cancer. We have identified a particular type of luminal cells, Prom1+ L1 cells, as the likely tumor initiating cells in the Hi-Myc model. The MYC-expressing L1 tumor initiating cells were more abundant and cycling more often in the invasive cancer at 8-month-old, compared to the pre-invasive stage. Interestingly, significant infiltration of novel M1-like tumor-associated macrophages (TAMs) correlated with the histologic tumor progression from HGPIN to invasive cancer. Targeting TAMs with anti-CSF1R antibody delayed prostate cancer progression and invasion, which appears to be independent of CD8 T-cells. Mechanistically, TAMs expressed a high level of IL-1b, which stimulated Hi-Myc prostate cell proliferation in an organoid co-culture system. More importantly, depleting IL-1b with a neutralizing antibody slowed prostate cancer progression in Hi-Myc mice. Lastly, we found that increased macrophage gene expression was associated with higher Gleason scores and worse disease-free survival in a TCGA clinical patient cohort. In summary, we have identified a molecular mechanism involving pro-inflammatory TAMs and IL-1b in driving early prostate cancer progression in a MYC-driven prostate cancer model, opening a potential therapeutic avenue to target TAMs and pro-inflammatory cytokine in preventing early prostate cancer progression.

Citation Format: Jimmy L. Zhao, Joseph Chan, Max Land, Perianne Smith, Anuradha Gopalan, Michael Haffner, Dana Pe'er, Charles Sawyers. Single cell RNA-seq analysis reveals a role of pro-inflammatory tumor-associated macrophages in driving prostate cancer progression [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 1728.