Identifying tumor type and cell type-specific gene expression alterations in pediatric central nervous system tumors.

Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collect single nuclei RNA-seq data from 84,700 nuclei of 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues and characterize tumor heterogeneity and transcriptomic alterations. We distinguish cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observe pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identify transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. Here we address current gaps in understanding single nuclei gene expression profiles of previously under-investigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.

Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors.

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.

Pharmacological induction of chromatin remodeling drives chemosensitization in triple-negative breast cancer.

Targeted therapies have improved outcomes for certain cancer subtypes, but cytotoxic chemotherapy remains a mainstay for triple-negative breast cancer (TNBC). The epithelial-to-mesenchymal transition (EMT) is a developmental program co-opted by cancer cells that promotes metastasis and chemoresistance. There are no therapeutic strategies specifically targeting mesenchymal-like cancer cells. We report that the US Food and Drug Administration (FDA)-approved chemotherapeutic eribulin induces ZEB1-SWI/SNF-directed chromatin remodeling to reverse EMT that curtails the metastatic propensity of TNBC preclinical models. Eribulin induces mesenchymal-to-epithelial transition (MET) in primary TNBC in patients, but conventional chemotherapy does not. In the treatment-naive setting, but not after acquired resistance to other agents, eribulin sensitizes TNBC cells to subsequent treatment with other chemotherapeutics. These findings provide an epigenetic mechanism of action of eribulin, supporting its use early in the disease process for MET induction to prevent metastatic progression and chemoresistance. These findings warrant prospective clinical evaluation of the chemosensitizing effects of eribulin in the treatment-naive setting.

Hierarchal single-cell lineage tracing reveals differential fate commitment of CD8 T-cell clones in response to acute infection.

Generating balanced populations of CD8 effector and memory T cells is necessary for immediate and durable immunity to infections and cancer. Yet, a definitive understanding of CD8 differentiation remains unclear. We used CARLIN, a processive lineage recording mouse model with single-cell RNA-seq and TCR-seq to track endogenous antigen-specific CD8 T cells during acute viral infection. We identified a diverse repertoire of expanded T-cell clones represented by seven transcriptional states. TCR enrichment analysis revealed differential memory- or effector-fate biases within clonal populations. Shared Vb segments and amino acid motifs were found within biased categories despite high TCR diversity. Using single-cell CARLIN barcode-seq we tracked multi-generational clones and found that unlike unbiased or memory-biased clones, which stably retain their fate profiles, effector-biased clones could adopt memory- or effector-bias within subclones. Collectively, our study demonstrates that a heterogenous T-cell repertoire specific for a shared antigen is composed of clones with distinct TCR-intrinsic fate-biases.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity.

Background: Spatial transcriptomics involves studying the spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. While spatial gene expression is commonly amalgamated from 1-10 cells across 50-micron spots, recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. However, elucidating such information from histology alone presents a significant challenge but if solved can better permit spatial molecular analysis at cellular resolution for instances where Visium data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in whole slide images collected from a cohort of stage pT3 colorectal cancer patients. A cell graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA patterns through optimal transport methods, facilitating the analysis of cellular groupings and gene relationships. This approach leveraged spot-level expression as an intermediary to co-map histological and transcriptomic information at the single-cell level. Results: Our study demonstrated that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within a spot. Furthermore, our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional patch-based computer vision methods as well as other methods which did not incorporate single cell expression during the model fitting procedures. Topological nuances of single-cell expression within a Visium spot were preserved using the developed methodology. Conclusion: This innovative approach augments the resolution of spatial molecular assays utilizing histology as a sole input through synergistic co-mapping of histological and transcriptomic datasets at the single-cell level, anchored by spatial transcriptomics. While initial results are promising, they warrant rigorous validation. This includes collaborating with pathologists for precise spatial identification of distinct cell types and utilizing sophisticated assays, such as Xenium, to attain deeper subcellular insights.

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows.

The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.

Neoadjuvant Intratumoral Immunotherapy with Cowpea Mosaic Virus Induces Local and Systemic Antitumor Efficacy in Canine Mammary Cancer Patients.

The lack of optimal models to evaluate novel agents is delaying the development of effective immunotherapies against human breast cancer (BC). In this prospective open label study, we applied neoadjuvant intratumoral immunotherapy with empty cowpea mosaic virus-like particles (eCPMV) to 11 companion dogs diagnosed with canine mammary cancer (CMC), a spontaneous tumor resembling human BC. We found that two neoadjuvant intratumoral eCPMV injections resulted in tumor reduction in injected tumors in all patients and in noninjected tumors located in the ipsilateral and contralateral mammary chains of injected dogs. Tumor reduction was independent of clinical stage, tumor size, histopathologic grade, and tumor molecular subtype. RNA-seq-based analysis of injected tumors indicated a decrease in DNA replication activity and an increase in activated dendritic cell infiltration in the tumor microenvironment. Immunohistochemistry analysis demonstrated significant intratumoral increases in neutrophils, T and B lymphocytes, and plasma cells. eCPMV intratumoral immunotherapy demonstrated antitumor efficacy without any adverse effects. This novel immunotherapy has the potential for improving outcomes for human BC patients.

CDDO-Methyl Ester Inhibits BRAF Inhibitor Resistance and Remodels the Myeloid Compartment in BRAF-mutant Melanoma.

Approximately 50% of advanced melanomas harbor activating BRAF V600E mutations that are sensitive to BRAF inhibition. However, the duration of the response to BRAF inhibitors (BRAFi) has been limited due to the development of acquired resistance, which is preceded by recruitment of immunosuppressive myeloid cells and regulatory T cells (T regs ). While the addition of MAPK/ERK kinase 1 inhibitors (MEKi) prolongs therapeutic response to BRAF inhibition, most patients still develop resistance. Using a Braf V600E/+ /Pten -/- graft mouse model of melanoma, we now show that the addition of the methyl ester of the synthetic triterpenoid 2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oic acid (C-Me) to the BRAFi vemurafenib analog PLX4720 at resistance significantly reduces tumor burden. Dual treatment remodels the BRAFi resistant-tumor microenvironment (TME), reducing infiltration of T regs and tumor associated macrophages (TAMs), and attenuates immunosuppressive cytokine production. For the first time, we characterize myeloid populations using scRNA-seq in BRAFi-resistant tumors and demonstrate that restoration of therapeutic response is associated with significant changes in immune-activated myeloid subset representation. Collectively, these studies suggest that C-Me inhibits acquired resistance to BRAFi. Use of C-Me in combination with other therapies may both inhibit melanoma growth and enhance therapeutic responsiveness more broadly.

Pharmacological Induction of mesenchymal-epithelial transition chemosensitizes breast cancer cells and prevents metastatic progression.

The epithelial-mesenchymal transition (EMT) is a developmental program co-opted by tumor cells that aids the initiation of the metastatic cascade. Tumor cells that undergo EMT are relatively chemoresistant, and there are currently no therapeutic avenues specifically targeting cells that have acquired mesenchymal traits. We show that treatment of mesenchymal-like triple-negative breast cancer (TNBC) cells with the microtubule-destabilizing chemotherapeutic eribulin, which is FDA-approved for the treatment of advanced breast cancer, leads to a mesenchymal-epithelial transition (MET). This MET is accompanied by loss of metastatic propensity and sensitization to subsequent treatment with other FDA-approved chemotherapeutics. We uncover a novel epigenetic mechanism of action that supports eribulin pretreatment as a path to MET induction that curtails metastatic progression and the evolution of therapy resistance.

Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach.

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, granzyme B and fibronectin), immune suppression (eg, forkhead box P3), exhaustion and cytotoxicity (eg, CD8), Programmed death ligand 1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.

Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study.

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1-10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x-y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural networks to predict spatial RNA patterns at the Visium spots under the hypothesis that the transformer- and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model's ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, the results indicate that the transformer- and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications.

Tumor type and cell type-specific gene expression alterations in diverse pediatric central nervous system tumors identified using single nuclei RNA-seq.

Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collected single nuclei RNA-seq data from 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues (84,700 nuclei) and characterized tumor heterogeneity and transcriptomic alterations. We distinguished cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observed pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identified transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. In this study, we address current gaps in understanding single nuclei gene expression profiles of previously uninvestigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.

Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes.

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors we utilized a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identified a preponderance differential CpG hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like HDAC4 and IGF1R, were associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric CNS tumors.

Natural Antibodies Alert the Adaptive Immune System of the Presence of Transformed Cells in Early Tumorigenesis.

Recent studies have revealed a critical role for natural Abs (NAbs) in antitumor immune responses. However, the role of NAbs in cancer immunosurveillance remains unexplored, mainly because of the lack of in vivo models that mimic the early recognition and elimination of transforming cells. In this article, we propose a role for NAbs in alerting the immune system against precancerous neoantigen-expressing cells immediately after they escape intrinsic tumor suppression mechanisms. We identify four distinct reproducible, trackable, MHC-matched neoantigen-expressing cell models that do not form tumors as the end point. This amplified readout in the critical window prior to tumor formation allows investigation of new mediators of cancer immunosurveillance. We found that neoantigen-expressing cells adoptively transferred in NAb-deficient mice persisted, whereas they were eliminated in wild-type mice, indicating that the circulating NAb repertoire alerts the immune system to the presence of transformed cells. Moreover, immunity is mounted against immunogenic and nonimmunogenic neoantigens contained in the NAb-tagged cells, regardless of whether the NAb directly recognizes the neoantigens. Beyond these neoantigen-expressing model systems, we observed a significantly greater tumor burden in chemically and virally induced tumor models in NAb-deficient mice compared with wild-type mice. Restoration of the NAb repertoire in NAb-deficient mice elicited the recognition and elimination of neoantigen-expressing cells and cancer. These data show that NAbs are required and sufficient for elimination of transformed cells early in tumorigenesis. These models can now be used to investigate how NAbs stimulate immunity via recognition receptors to eliminate precancerous cells.

ScRNA-seq expression of IFI27 and APOC2 identifies four alveolar macrophage superclusters in healthy BALF.

Alveolar macrophages (AMs) reside on the luminal surface of the airways and alveoli, ensuring proper gas exchange by ingesting cellular debris and pathogens, and regulating inflammatory responses. Therefore, understanding the heterogeneity and diverse roles played by AMs, interstitial macrophages, and recruited monocytes is critical for treating airway diseases. We performed single-cell RNA sequencing on 113,213 bronchoalveolar lavage cells from four healthy and three uninflamed cystic fibrosis subjects and identified two MARCKS+LGMN+IMs, FOLR2+SELENOP+ and SPP1+PLA2G7+ IMs, monocyte subtypes, DC1, DC2, migDCs, plasmacytoid DCs, lymphocytes, epithelial cells, and four AM superclusters (families) based on the gene expression of IFI27 and APOC2 These four AM families have at least eight distinct functional members (subclusters) named after their differentially expressed gene(s): IGF1, CCL18, CXCL5, cholesterol, chemokine, metallothionein, interferon, and small-cluster AMs. Interestingly, the chemokine cluster further divides with each subcluster selectively expressing a unique combination of chemokines. One of the most striking observations, besides the heterogeneity, is the conservation of AM family members in relatively equal ratio across all AM superclusters and individuals. Transcriptional data and TotalSeq technology were used to investigate cell surface markers that distinguish resident AMs from recruited monocytes. Last, other AM datasets were projected onto our dataset. Similar AM superclusters and functional subclusters were observed, along with a significant increase in chemokine and IFN AM subclusters in individuals infected with COVID-19. Overall, functional specializations of the AM subclusters suggest that there are highly regulated AM niches with defined programming states, highlighting a clear division of labor.

Resident memory CD8+ T cells in regional lymph nodes mediate immunity to metastatic melanoma.

The nature of the anti-tumor immune response changes as primary tumors progress and metastasize. We investigated the role of resident memory (Trm) and circulating memory (Tcirm) cells in anti-tumor responses at metastatic locations using a mouse model of melanoma-associated vitiligo. We found that the transcriptional characteristics of tumor-specific CD8+ T cells were defined by the tissue of occupancy. Parabiosis revealed that tumor-specific Trm and Tcirm compartments persisted throughout visceral organs, but Trm cells dominated lymph nodes (LNs). Single-cell RNA-sequencing profiles of Trm cells in LN and skin were distinct, and T cell clonotypes that occupied both tissues were overwhelmingly maintained as Trm in LNs. Whereas Tcirm cells prevented melanoma growth in the lungs, Trm afforded long-lived protection against melanoma seeding in LNs. Expanded Trm populations were also present in melanoma-involved LNs from patients, and their transcriptional signature predicted better survival. Thus, tumor-specific Trm cells persist in LNs, restricting metastatic cancer.

Resident and circulating memory T cells persist for years in melanoma patients with durable responses to immunotherapy.

While T-cell responses to cancer immunotherapy have been avidly studied, long-lived memory has been poorly characterized. In a cohort of metastatic melanoma survivors with exceptional responses to immunotherapy, we probed memory CD8+ T-cell responses across tissues, and across several years. Single-cell RNA sequencing revealed three subsets of resident memory T (TRM) cells shared between tumors and distant vitiligo-affected skin. Paired T-cell receptor sequencing further identified clonotypes in tumors that co-existed as TRM in skin and as effector memory T (TEM) cells in blood. Clonotypes that dispersed throughout tumor, skin, and blood preferentially expressed a IFNG / TNF-high signature, which had a strong prognostic value for melanoma patients. Remarkably, clonotypes from tumors were found in patient skin and blood up to nine years later, with skin maintaining the most focused tumor-associated clonal repertoire. These studies reveal that cancer survivors can maintain durable memory as functional, broadly-distributed TRM and TEM compartments.

Plasma DNA as a "liquid biopsy" incompletely complements tumor biopsy for identification of mutations in a case series of four patients with oligometastatic breast cancer.

PURPOSE: Circulating tumor DNA in plasma may present a minimally invasive opportunity to identify tumor-derived mutations to inform selection of targeted therapies for individual patients, particularly in cases of oligometastatic disease where biopsy of multiple tumors is impractical. To assess the utility of plasma DNA as a "liquid biopsy" for precision oncology, we tested whether sequencing of plasma DNA is a reliable surrogate for sequencing of tumor DNA to identify targetable genetic alterations. METHODS: Blood and biopsies of 1-3 tumors were obtained from 4 evaluable patients with advanced breast cancer. One patient provided samples from an additional 7 tumors post-mortem. DNA extracted from plasma, tumor tissues, and buffy coat of blood were used for probe-directed capture of all exons in 149 cancer-related genes and massively parallel sequencing. Somatic mutations in DNA from plasma and tumors were identified by comparison to buffy coat DNA. RESULTS: Sequencing of plasma DNA identified 27.94 ± 11.81% (mean ± SD) of mutations detected in a tumor(s) from the same patient; such mutations tended to be present at high allelic frequency. The majority of mutations found in plasma DNA were not found in tumor samples. Mutations were also found in plasma that matched clinically undetectable tumors found post-mortem. CONCLUSIONS: The incomplete overlap of genetic alteration profiles of plasma and tumors warrants caution in the sole reliance of plasma DNA to identify therapeutically targetable alterations in patients and indicates that analysis of plasma DNA complements, but does not replace, tumor DNA profiling. TRIAL REGISTRATION: Subjects were prospectively enrolled in trial NCT01836640 (registered April 22, 2013).