Radical Collaboration: Reimagining Cancer Team Science.

SUMMARY: Here, we define a future of cancer team science adopting "radical collaboration"-in which six "Hallmarks of Cancer Collaboration" are utilized to propel cancer teams to reach new levels of productivity and impact in the modern era. This commentary establishes a playbook for cancer team science that can be readily adopted by others.

The CLCF1-CNTFR axis drives an immunosuppressive tumor microenvironment and blockade enhances the effects of established cancer therapies.

Tumors comprise a complex ecosystem consisting of many cell types that communicate through secreted factors. Targeting these intercellular signaling networks remains an important challenge in cancer research. Cardiotrophin-like cytokine factor 1 (CLCF1) is an interleukin-6 (IL-6) family member secreted by cancer-associated fibroblasts (CAFs) that binds to ciliary neurotrophic factor receptor (CNTFR), promoting tumor growth in lung and liver cancer1,2. A high-affinity soluble receptor (eCNTFR-Fc) that sequesters CLCF1 has anti-oncogenic effects3. However, the role of CLCF1 in mediating cell-cell interactions in cancer has remained unclear. We demonstrate that eCNTFR-Fc has widespread effects on both tumor cells and the tumor microenvironment and can sensitize cancer cells to KRAS inhibitors or immune checkpoint blockade. After three weeks of treatment with eCNTFR-Fc, there is a shift from an immunosuppressive to an immunostimulatory macrophage phenotype as well as an increase in activated T, NKT, and NK cells. Combination of eCNTFR-Fc and αPD1 was significantly more effective than single-agent therapy in a syngeneic allograft model, and eCNTFR-Fc sensitizes tumor cells to αPD1 in a non-responsive GEM model of lung adenocarcinoma. These data suggest that combining eCNTFR-Fc with KRAS inhibition or with αPD1 is a novel therapeutic strategy for lung cancer and potentially other cancers in which these therapies have been used but to date with only modest effect. Overall, we demonstrate the potential of cancer therapies that target cytokines to alter the immune microenvironment.

SOX17 enables immune evasion of early colorectal adenomas and cancers.

A hallmark of cancer is the avoidance of immune destruction. This process has been primarily investigated in locally advanced or metastatic cancer1-3; however, much less is known about how pre-malignant or early invasive tumours evade immune detection. Here, to understand this process in early colorectal cancers (CRCs), we investigated how naive colon cancer organoids that were engineered in vitro to harbour Apc-null, KrasG12D and Trp53-null (AKP) mutations adapted to the in vivo native colonic environment. Comprehensive transcriptomic and chromatin analyses revealed that the endoderm-specifying transcription factor SOX17 became strongly upregulated in vivo. Notably, whereas SOX17 loss did not affect AKP organoid propagation in vitro, its loss markedly reduced the ability of AKP tumours to persist in vivo. The small fraction of SOX17-null tumours that grew displayed notable interferon-γ (IFNγ)-producing effector-like CD8+ T cell infiltrates in contrast to the immune-suppressive microenvironment in wild-type counterparts. Mechanistically, in both endogenous Apc-null pre-malignant adenomas and transplanted organoid-derived AKP CRCs, SOX17 suppresses the ability of tumour cells to sense and respond to IFNγ, preventing anti-tumour T cell responses. Finally, SOX17 engages a fetal intestinal programme that drives differentiation away from LGR5+ tumour cells to produce immune-evasive LGR5- tumour cells with lower expression of major histocompatibility complex class I (MHC-I). We propose that SOX17 is a transcription factor that is engaged during the early steps of colon cancer to orchestrate an immune-evasive programme that permits CRC initiation and progression.

Deciphering cell states and genealogies of human haematopoiesis.

The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived haematopoietic stem cells (HSCs)1. Perturbations to this process underlie diverse diseases, but the clonal contributions to human haematopoiesis and how this changes with age remain incompletely understood. Although recent insights have emerged from barcoding studies in model systems2-5, simultaneous detection of cell states and phylogenies from natural barcodes in humans remains challenging. Here we introduce an improved, single-cell lineage-tracing system based on deep detection of naturally occurring mitochondrial DNA mutations with simultaneous readout of transcriptional states and chromatin accessibility. We use this system to define the clonal architecture of HSCs and map the physiological state and output of clones. We uncover functional heterogeneity in HSC clones, which is stable over months and manifests as both differences in total HSC output and biases towards the production of different mature cell types. We also find that the diversity of HSC clones decreases markedly with age, leading to an oligoclonal structure with multiple distinct clonal expansions. Our study thus provides a clonally resolved and cell-state-aware atlas of human haematopoiesis at single-cell resolution, showing an unappreciated functional diversity of human HSC clones and, more broadly, paving the way for refined studies of clonal dynamics across a range of tissues in human health and disease.

Inhalable point-of-care urinary diagnostic platform.

Although low-dose computed tomography screening improves lung cancer survival in at-risk groups, inequality remains in lung cancer diagnosis due to limited access to and high costs of medical imaging infrastructure. We designed a needleless and imaging-free platform, termed PATROL (point-of-care aerosolizable nanosensors with tumor-responsive oligonucleotide barcodes), to reduce resource disparities for early detection of lung cancer. PATROL formulates a set of DNA-barcoded, activity-based nanosensors (ABNs) into an inhalable format. Lung cancer-associated proteases selectively cleave the ABNs, releasing synthetic DNA reporters that are eventually excreted via the urine. The urinary signatures of barcoded nanosensors are quantified within 20 min at room temperature using a multiplexable paper-based lateral flow assay. PATROL detects early-stage tumors in an autochthonous lung adenocarcinoma mouse model with high sensitivity and specificity. Tailoring the library of ABNs may enable not only the modular PATROL platform to lower the resource threshold for lung cancer early detection tools but also the rapid detection of chronic pulmonary disorders and infections.

Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity.

DNA mismatch repair deficiency (MMRd) is associated with a high tumor mutational burden (TMB) and sensitivity to immune checkpoint blockade (ICB) therapy. Nevertheless, most MMRd tumors do not durably respond to ICB and critical questions remain about immunosurveillance and TMB in these tumors. In the present study, we developed autochthonous mouse models of MMRd lung and colon cancer. Surprisingly, these models did not display increased T cell infiltration or ICB response, which we showed to be the result of substantial intratumor heterogeneity of mutations. Furthermore, we found that immunosurveillance shapes the clonal architecture but not the overall burden of neoantigens, and T cell responses against subclonal neoantigens are blunted. Finally, we showed that clonal, but not subclonal, neoantigen burden predicts ICB response in clinical trials of MMRd gastric and colorectal cancer. These results provide important context for understanding immune evasion in cancers with a high TMB and have major implications for therapies aimed at increasing TMB.

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis.

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

A prime editor mouse to model a broad spectrum of somatic mutations in vivo.

Genetically engineered mouse models only capture a small fraction of the genetic lesions that drive human cancer. Current CRISPR-Cas9 models can expand this fraction but are limited by their reliance on error-prone DNA repair. Here we develop a system for in vivo prime editing by encoding a Cre-inducible prime editor in the mouse germline. This model allows rapid, precise engineering of a wide range of mutations in cell lines and organoids derived from primary tissues, including a clinically relevant Kras mutation associated with drug resistance and Trp53 hotspot mutations commonly observed in pancreatic cancer. With this system, we demonstrate somatic prime editing in vivo using lipid nanoparticles, and we model lung and pancreatic cancer through viral delivery of prime editing guide RNAs or orthotopic transplantation of prime-edited organoids. We believe that this approach will accelerate functional studies of cancer-associated mutations and complex genetic combinations that are challenging to construct with traditional models.

Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma.

Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

Multiscale profiling of protease activity in cancer.

Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. High-fidelity methods to profile enzyme activity are therefore critical to understanding and targeting the pathological roles of enzymes in cancer. Here, we present an integrated set of methods for measuring specific protease activities across scales, and deploy these methods to study treatment response in an autochthonous model of Alk-mutant lung cancer. We leverage multiplexed nanosensors and machine learning to analyze in vivo protease activity dynamics in lung cancer, identifying significant dysregulation that includes enhanced cleavage of a peptide, S1, which rapidly returns to healthy levels with targeted therapy. Through direct on-tissue localization of protease activity, we pinpoint S1 cleavage to the tumor vasculature. To link protease activity to cellular function, we design a high-throughput method to isolate and characterize proteolytically active cells, uncovering a pro-angiogenic phenotype in S1-cleaving cells. These methods provide a framework for functional, multiscale characterization of protease dysregulation in cancer.

Modeling diverse genetic subtypes of lung adenocarcinoma with a next-generation alveolar type 2 organoid platform.

Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD), the most common histological subtype, accounts for 40% of all cases. While existing genetically engineered mouse models (GEMMs) recapitulate the histological progression and transcriptional evolution of human LUAD, they are time-consuming and technically demanding. In contrast, cell line transplant models are fast and flexible, but these models fail to capture the full spectrum of disease progression. Organoid technologies provide a means to create next-generation cancer models that integrate the most advantageous features of autochthonous and transplant-based systems. However, robust and faithful LUAD organoid platforms are currently lacking. Here, we describe optimized conditions to continuously expand murine alveolar type 2 (AT2) cells, a prominent cell of origin for LUAD, in organoid culture. These organoids display canonical features of AT2 cells, including marker gene expression, the presence of lamellar bodies, and an ability to differentiate into the AT1 lineage. We used this system to develop flexible and versatile immunocompetent organoid-based models of KRAS, BRAF, and ALK mutant LUAD. Notably, organoid-based tumors display extensive burden and complete penetrance and are histopathologically indistinguishable from their autochthonous counterparts. Altogether, this organoid platform is a powerful, versatile new model system to study LUAD.

Deciphering the immunopeptidome in vivo reveals new tumour antigens.

Immunosurveillance of cancer requires the presentation of peptide antigens on major histocompatibility complex class I (MHC-I) molecules1-5. Current approaches to profiling of MHC-I-associated peptides, collectively known as the immunopeptidome, are limited to in vitro investigation or bulk tumour lysates, which limits our understanding of cancer-specific patterns of antigen presentation in vivo6. To overcome these limitations, we engineered an inducible affinity tag into the mouse MHC-I gene (H2-K1) and targeted this allele to the KrasLSL-G12D/+Trp53fl/fl mouse model (KP/KbStrep)7. This approach enabled us to precisely isolate MHC-I peptides from autochthonous pancreatic ductal adenocarcinoma and from lung adenocarcinoma (LUAD) in vivo. In addition, we profiled the LUAD immunopeptidome from the alveolar type 2 cell of origin up to late-stage disease. Differential peptide presentation in LUAD was not predictable by mRNA expression or translation efficiency and is probably driven by post-translational mechanisms. Vaccination with peptides presented by LUAD in vivo induced CD8+ T cell responses in naive mice and tumour-bearing mice. Many peptides specific to LUAD, including immunogenic peptides, exhibited minimal expression of the cognate mRNA, which prompts the reconsideration of antigen prediction pipelines that triage peptides according to transcript abundance8. Beyond cancer, the KbStrep allele is compatible with other Cre-driver lines to explore antigen presentation in vivo in the pursuit of understanding basic immunology, infectious disease and autoimmunity.

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution.

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Therapeutic avenues for cancer neuroscience: translational frontiers and clinical opportunities.

With increasing attention on the essential roles of the tumour microenvironment in recent years, the nervous system has emerged as a novel and crucial facilitator of cancer growth. In this Review, we describe the foundational, translational, and clinical advances illustrating how nerves contribute to tumour proliferation, stress adaptation, immunomodulation, metastasis, electrical hyperactivity and seizures, and neuropathic pain. Collectively, this expanding knowledge base reveals multiple therapeutic avenues for cancer neuroscience that warrant further exploration in clinical studies. We discuss the available clinical data, including ongoing trials investigating novel agents targeting the tumour-nerve axis, and the therapeutic potential for repurposing existing neuroactive drugs as an anti-cancer approach, particularly in combination with established treatment regimens. Lastly, we discuss the clinical challenges of these treatment strategies and highlight unanswered questions and future directions in the burgeoning field of cancer neuroscience.

A GATA4-regulated secretory program suppresses tumors through recruitment of cytotoxic CD8 T cells.

The GATA4 transcription factor acts as a master regulator of development of multiple tissues. GATA4 also acts in a distinct capacity to control a stress-inducible pro-inflammatory secretory program that is associated with senescence, a potent tumor suppression mechanism, but also operates in non-senescent contexts such as tumorigenesis. This secretory pathway is composed of chemokines, cytokines, growth factors, and proteases. Since GATA4 is deleted or epigenetically silenced in cancer, here we examine the role of GATA4 in tumorigenesis in mouse models through both loss-of-function and overexpression experiments. We find that GATA4 promotes non-cell autonomous tumor suppression in multiple model systems. Mechanistically, we show that Gata4-dependent tumor suppression requires cytotoxic CD8 T cells and partially requires the secreted chemokine CCL2. Analysis of transcriptome data in human tumors reveals reduced lymphocyte infiltration in GATA4-deficient tumors, consistent with our murine data. Notably, activation of the GATA4-dependent secretory program combined with an anti-PD-1 antibody robustly abrogates tumor growth in vivo.

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations1-4 as well as the makeup of the tumour microenvironment5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

Smarca4 Inactivation Promotes Lineage-Specific Transformation and Early Metastatic Features in the Lung.

SMARCA4/BRG1 encodes for one of two mutually exclusive ATPases present in mammalian SWI/SNF chromatin remodeling complexes and is frequently mutated in human lung adenocarcinoma. However, the functional consequences of SMARCA4 mutation on tumor initiation, progression, and chromatin regulation in lung cancer remain poorly understood. Here, we demonstrate that loss of Smarca4 sensitizes club cell secretory protein-positive cells within the lung in a cell type-dependent fashion to malignant transformation and tumor progression, resulting in highly advanced dedifferentiated tumors and increased metastatic incidence. Consistent with these phenotypes, Smarca4-deficient primary tumors lack lung lineage transcription factor activities and resemble a metastatic cell state. Mechanistically, we show that Smarca4 loss impairs the function of all three classes of SWI/SNF complexes, resulting in decreased chromatin accessibility at lung lineage motifs and ultimately accelerating tumor progression. Thus, we propose that the SWI/SNF complex via Smarca4 acts as a gatekeeper for lineage-specific cellular transformation and metastasis during lung cancer evolution. SIGNIFICANCE: We demonstrate cell-type specificity in the tumor-suppressive functions of SMARCA4 in the lung, pointing toward a critical role of the cell-of-origin in driving SWI/SNF-mutant lung adenocarcinoma. We further show the direct effects of SMARCA4 loss on SWI/SNF function and chromatin regulation that cause aggressive malignancy during lung cancer evolution.This article is highlighted in the In This Issue feature, p. 275.

Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer.

Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.