ABSTRACT
Purpose: We address the need for effective stain domain adaptation methods in histopathology to enhance the performance of downstream computational tasks, particularly classification. Existing methods exhibit varying strengths and weaknesses, prompting the exploration of a different approach. The focus is on improving stain color consistency, expanding the stain domain scope, and minimizing the domain gap between image batches. Approach: We introduce a new domain adaptation method, Stain simultaneous augmentation and normalization (SAN), designed to adjust the distribution of stain colors to align with a target distribution. Stain SAN combines the merits of established methods, such as stain normalization, stain augmentation, and stain mix-up, while mitigating their inherent limitations. Stain SAN adapts stain domains by resampling stain color matrices from a well-structured target distribution. Results: Experimental evaluations of cross-dataset clinical estrogen receptor status classification demonstrate the efficacy of Stain SAN and its superior performance compared with existing stain adaptation methods. In one case, the area under the curve (AUC) increased by 11.4%. Overall, our results clearly show the improvements made over the history of the development of these methods culminating with substantial enhancement provided by Stain SAN. Furthermore, we show that Stain SAN achieves results comparable with the state-of-the-art generative adversarial network-based approach without requiring separate training for stain adaptation or access to the target domain during training. Stain SAN's performance is on par with HistAuGAN, proving its effectiveness and computational efficiency. Conclusions: Stain SAN emerges as a promising solution, addressing the potential shortcomings of contemporary stain adaptation methods. Its effectiveness is underscored by notable improvements in the context of clinical estrogen receptor status classification, where it achieves the best AUC performance. The findings endorse Stain SAN as a robust approach for stain domain adaptation in histopathology images, with implications for advancing computational tasks in the field.
ABSTRACT
Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.
ABSTRACT
Male breast cancer represents about 1% of all breast cancer diagnoses and, although there are some similarities between male and female breast cancer, the paucity of data available on male breast cancer makes it difficult to establish targeted therapies. To date, most male breast cancers (MBCs) are treated according to protocols established for female breast cancer (FBC). Thus, defining the transcriptional and epigenetic landscape of MBC with improved resolution is critical for developing better avenues for therapeutic intervention. In this study, we present matched transcriptional (scRNA-seq) and epigenetic (scATAC-seq) profiles at single-cell resolution of two treatment naïve MBC tumors processed immediately after surgical resection. These data enable the detection of differentially expressed genes between male and female breast tumors across immune, stromal, and malignant cell types, to highlight several genes that may have therapeutic implications. Notably, MYC target genes and mTORC1 signaling genes were significantly upregulated in the malignant cells of MBC compared to the female counterparts. To understand how the regulatory landscape of MBC gives rise to these male-specific gene expression patterns, we leveraged the scATAC-seq data to systematically link changes in chromatin accessibility to changes in gene expression within each cell type. We observed cancer-specific rewiring of several salient enhancers and posit that these enhancers have a higher regulatory load than lineage-specific enhancers. We highlight two examples of previously unannotated cancer-cell-specific enhancers of ANXA2 and PRDX4 gene expression and show evidence for super-enhancer regulation of LAMB3 and CD47 in male breast cancer cells. Overall, this dataset annotates clinically relevant regulatory networks in male breast tumors, providing a useful resource that expands our current understanding of the gene expression programs that underlie the biology of MBC.
Subject(s)
Mammary Neoplasms, Animal , Regulatory Sequences, Nucleic Acid , Female , Male , Animals , Chromatin , Epigenomics , Epigenesis, GeneticABSTRACT
Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.
Subject(s)
Breast Neoplasms , Carcinoma, Ductal, Breast , Carcinoma, Intraductal, Noninfiltrating , Female , Humans , Breast Neoplasms/pathology , Carcinoma, Ductal, Breast/genetics , Carcinoma, Intraductal, Noninfiltrating/genetics , Carcinoma, Intraductal, Noninfiltrating/pathology , Disease Progression , Genomics/methods , Single-Cell Gene Expression Analysis , Cell Line, TumorABSTRACT
Developmentally programmed polyploidy (whole-genome duplication) of cardiomyocytes is common across evolution. Functions of such polyploidy are essentially unknown. Here, in both Drosophila larvae and human organ donors, we reveal distinct polyploidy levels in cardiac organ chambers. In Drosophila, differential growth and cell cycle signal sensitivity leads the heart chamber to reach a higher ploidy/cell size relative to the aorta chamber. Cardiac ploidy-reduced animals exhibit reduced heart chamber size, stroke volume and cardiac output, and acceleration of circulating hemocytes. These Drosophila phenotypes mimic human cardiomyopathies. Our results identify productive and likely conserved roles for polyploidy in cardiac chambers and suggest that precise ploidy levels sculpt many developing tissues. These findings of productive cardiomyocyte polyploidy impact efforts to block developmental polyploidy to improve heart injury recovery.
Subject(s)
Drosophila , Myocytes, Cardiac , Animals , Humans , Polyploidy , Ploidies , Cell CycleABSTRACT
The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.
Subject(s)
Breast , Gene Expression Profiling , Single-Cell Analysis , Adult , Female , Humans , Breast/cytology , Breast/immunology , Breast/metabolism , Breast Neoplasms/metabolism , Breast Neoplasms/pathology , Endothelial Cells/classification , Endothelial Cells/metabolism , Epithelial Cells/classification , Epithelial Cells/metabolism , Genomics , ImmunityABSTRACT
The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.
ABSTRACT
Developmentally programmed polyploidy (whole-genome-duplication) of cardiomyocytes is common across evolution. Functions of such polyploidy are essentially unknown. Here, we reveal roles for precise polyploidy levels in cardiac tissue. We highlight a conserved asymmetry in polyploidy level between cardiac chambers in Drosophila larvae and humans. In Drosophila , differential Insulin Receptor (InR) sensitivity leads the heart chamber to reach a higher ploidy/cell size relative to the aorta chamber. Cardiac ploidy-reduced animals exhibit reduced heart chamber size, stroke volume, cardiac output, and acceleration of circulating hemocytes. These Drosophila phenotypes mimic systemic human heart failure. Using human donor hearts, we reveal asymmetry in nuclear volume (ploidy) and insulin signaling between the left ventricle and atrium. Our results identify productive and likely conserved roles for polyploidy in cardiac chambers and suggest precise ploidy levels sculpt many developing tissues. These findings of productive cardiomyocyte polyploidy impact efforts to block developmental polyploidy to improve heart injury recovery.
ABSTRACT
Ductal carcinoma in situ (DCIS) of the breast is a non-obligate precursor of Invasive Ductal Carcinoma (IDC) and thus the identification of features that may predict DCIS progression would be of potential clinical value. Experimental mouse models can be used to address this challenge by studying DCIS-to-IDC biology. Here we utilize single cell RNA sequencing (scRNAseq) on the C3Tag genetically engineered mouse model that forms DCIS-like precursor lesions and for which many lesions progress into end-stage basal-like molecular subtype IDC. We also perform bulk RNAseq analysis on 10 human synchronous DCIS-IDC pairs comprised of estrogen receptor (ER) positive and ER-negative subsets and utilize 2 additional public human DCIS data sets for comparison to our mouse model. By identifying malignant cells using inferred DNA copy number changes from the murine C3Tag scRNAseq data, we show the existence of cancer cells within the C3Tag pre-DCIS, DCIS, and IDC-like tumor specimens. These cancer cells were further classified into proliferative, hypoxic, and inflammatory subpopulations, which change in frequency in DCIS versus IDC. The C3Tag tumor progression model was also associated with increase in Cancer-Associated Fibroblasts and decrease in activated T cells in IDC. Importantly, we translate the C3Tag murine genomic findings into human DCIS where we find common features only with human basal-like DCIS, suggesting there are intrinsic subtype unique DCIS features. This study identifies several tumor and microenvironmental features associated with DCIS progression and may also provide genomic signatures that can identify progression-prone DCIS within the context of human basal-like breast cancers.
ABSTRACT
Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.
Subject(s)
Ovarian Neoplasms/genetics , Ovarian Neoplasms/metabolism , RNA, Small Cytoplasmic/genetics , Aged , Carcinogenesis , Chromatin/metabolism , Enhancer Elements, Genetic , Epithelial-Mesenchymal Transition , Female , Gastrointestinal Stromal Tumors/genetics , Gene Library , Genetic Techniques , Genomics , Humans , Kaplan-Meier Estimate , Middle Aged , Oncogenes , Ovary/metabolism , Proteomics , RNA-Seq , Regulatory Elements, Transcriptional , Transcription Factors/metabolism , TranscriptomeABSTRACT
Primary liver cancer, consisting of both cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC), is the second leading cause of cancer deaths worldwide. Our goal is to genomically characterize rare HCC subclasses to provide insight into disease biology. Leveraging The Cancer Genome Atlas (TCGA) to perform a combined analysis of CCA (n = 36) and HCC (n = 275), we integrated multiple genomic platforms, to assess transcriptional profiles, mutational signatures, and copy number patterns to uncover underlying etiology and linage specific patterns. We identified two molecular classes distinct from prototypical HCC tumors. The first, CCA-Like, although histologically indistinguishable from HCC, had enrichment of CCA mutations (IDH1, BAP1), mutational signatures, and transcriptional patterns (SOX9, KRT19). CCA-Like, however, retained a copy number landscape similar to HCC, suggesting a hepatocellular linage. The second, Blast-Like, is enriched in TP53 mutations, HBV infection, exposure related mutational signatures and transcriptionally similar to hepatoblasts. Although these subclasses are molecularly distinct, they both have a worse progression-free survival compared to classical HCC tumors, yet are clinically treated the same. The identification of and characterization of CCA-Like and Blast-Like subclasses advance our knowledge of HCC as well as represents an urgent need for the identification of class specific biomarkers and targeted therapy.
Subject(s)
Carcinoma, Hepatocellular/genetics , DNA Copy Number Variations , Liver Neoplasms/genetics , Mutation , Transcription, Genetic , HumansABSTRACT
Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.
Subject(s)
Breast Neoplasms/genetics , Breast Neoplasms/pathology , Single-Cell Analysis , Transcriptome/genetics , B-Lymphocytes/immunology , B7-H1 Antigen/genetics , Biomarkers, Tumor/genetics , Breast Neoplasms/immunology , CD8-Positive T-Lymphocytes/immunology , Endothelial Cells/metabolism , Female , Gene Expression Regulation, Neoplastic/genetics , Humans , Macrophages/cytology , Macrophages/immunology , Membrane Proteins/genetics , Myeloid Cells/immunology , Myeloid Cells/metabolism , Sequence Analysis, RNA , Tumor Microenvironment , Tumor Suppressor Proteins/geneticsABSTRACT
Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.
Subject(s)
Sequence Analysis, RNA/methods , Single-Cell Analysis/methods , Software , Algorithms , Animals , Cell Line , Cluster Analysis , Gene Expression , Genomics , Humans , Mammary Glands, Animal , Mice , RNA-Seq , WorkflowABSTRACT
Breast cancer is classified into multiple distinct histologic types, and many of the rarer types have limited characterization. Here, we extend The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset with additional histologic type annotations, in a total of 1063 breast cancers. We analyze this extended dataset to define transcriptomic and genomic profiles of six rare special histologic types: cribriform, micropapillary, mucinous, papillary, metaplastic, and invasive carcinoma with medullary pattern. We show the broader applicability of our constructed special histologic type gene signatures in the TCGA Pan-Cancer Atlas dataset with a predictive model that detects mucinous histologic type across cancers of other organ systems. Using a normal mammary cell differentiation score analysis, we order histologic types into a continuum from stem cell-like to luminal progenitor-like to mature luminal-like. Finally, we classify TCGA-BRCA into 12 consensus groups based on integrated genomic and histological features. We present a rich openly accessible resource of histologic and genomic characterization of TCGA-BRCA to enable studies of the range of breast cancers.
ABSTRACT
PURPOSE: Immunotherapy has recently been shown to improve outcomes for advanced PD-L1-positive triple-negative breast cancer (TNBC) in the Impassion130 trial, leading to FDA approval of the first immune checkpoint inhibitor in combination with taxane chemotherapy. To further develop predictive biomarkers and improve therapeutic efficacy of the combination, interrogation of the tumor immune microenvironment before therapy as well as during each component of treatment is crucial. Here we use single-cell RNA sequencing (scRNA-seq) on tumor biopsies to assess immune cell changes from two patients with advanced TNBC treated in a prospective trial at predefined serial time points, before treatment, on taxane chemotherapy and on chemo-immunotherapy. METHODS: Both patients (one responder and one progressor) received the trial therapy, in cycle 1 nab-paclitaxel given as single agent, in cycle 2 nab-paclitaxel in combination with pembrolizumab. Tumor core biopsies were obtained at baseline, 3 weeks (after cycle 1, chemotherapy alone) and 6 weeks (after cycle 2, chemo-immunotherapy). Single-cell RNA sequencing (scRNA-seq) of both cancer cells and infiltrating immune cells isolated were performed from fresh tumor core biopsy specimens by 10 × chromium sequencing. RESULTS: ScRNA-seq analysis showed significant baseline heterogeneity of tumor-infiltrating immune cell populations between the two patients as well as modulation of the tumor microenvironment by chemotherapy and immunotherapy. In the responding patient there was a population of PD-1high-expressing T cells which significantly decreased after nab-paclitaxel plus pembrolizumab treatment as well as a presence of tissue-resident memory T cells (TRM). In contrast, tumors from the patient with rapid disease progression showed a prevalent and persistent myeloid compartment. CONCLUSIONS: Our study provides a deep cellular analysis of on-treatment changes during chemo-immunotherapy for advanced TNBC, demonstrating not only feasibility of single-cell analyses on serial tumor biopsies but also the heterogeneity of TNBC and differences in on-treatment changes in responder versus progressor.
Subject(s)
Triple Negative Breast Neoplasms , Albumins , Antibodies, Monoclonal, Humanized , Antineoplastic Combined Chemotherapy Protocols/therapeutic use , Humans , Paclitaxel , Prospective Studies , Single-Cell Analysis , Triple Negative Breast Neoplasms/drug therapy , Triple Negative Breast Neoplasms/genetics , Tumor MicroenvironmentABSTRACT
Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.
Subject(s)
RNA-Seq/methods , Single-Cell Analysis/methods , Software/standards , Animals , Female , Gene Expression Regulation, Neoplastic , Humans , Islets of Langerhans/metabolism , MCF-7 Cells , Mammary Glands, Animal/metabolism , Mice , RNA-Seq/standards , Reference Standards , Single-Cell Analysis/standardsABSTRACT
BACKGROUND: Odontogenic keratocyst (OKC) is a clinical entity with characteristic microscopic features, high growth potential and propensity to recur. Aggressive behavior and higher tendency for recurrence have been attributed to greater proliferative activity of epithelial lining. The incidence of recurrence in various reported series ranges from 2.5% to 62%. OBJECTIVES: The objective of the study was to investigate the clinical behavior of OKC by evaluating p53, MDM2 expression, AgNOR staining and to ascertain if the expression of these markers correlate with the clinical outcome and tendency for recurrence. MATERIALS AND METHODS: All recurrent and nonrecurrent OKCs from the archives were included, and sections were subjected to AgNOR staining, p53 and MDM2 immunohistochemical staining. RESULTS AND CONCLUSION: There was a significant difference in the staining pattern of MDM2 and AgNOR in the recurrent group as compared to the nonrecurrent group. The higher expression of these markers in recurrent lesions may be important in order to consider additional surgical interventions to improve prognosis.
ABSTRACT
Mechanisms driving tumor progression from less aggressive subtypes to more aggressive states represent key targets for therapy. We identified a subset of luminal A primary breast tumors that give rise to HER2-enriched (HER2E) subtype metastases, but remain clinically HER2 negative (cHER2-). By testing the unique genetic and transcriptomic features of these cases, we developed the hypothesis that FGFR4 likely participates in this subtype switching. To evaluate this, we developed 2 FGFR4 genomic signatures using a patient-derived xenograft (PDX) model treated with an FGFR4 inhibitor, which inhibited PDX growth in vivo. Bulk tumor gene expression analysis and single-cell RNA sequencing demonstrated that the inhibition of FGFR4 signaling caused molecular switching. In the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohort, FGFR4-induced and FGFR4-repressed signatures each predicted overall survival. Additionally, the FGFR4-induced signature was an independent prognostic factor beyond subtype and stage. Supervised analysis of 77 primary tumors with paired metastases revealed that the FGFR4-induced signature was significantly higher in luminal/ER+ tumor metastases compared with their primaries. Finally, multivariate analysis demonstrated that the FGFR4-induced signature also predicted site-specific metastasis for lung, liver, and brain, but not for bone or lymph nodes. These data identify a link between FGFR4-regulated genes and metastasis, suggesting treatment options for FGFR4-positive patients, whose high expression is not caused by mutation or amplification.
Subject(s)
Breast Neoplasms , Cell Differentiation , Gene Expression Regulation, Neoplastic , Neoplasm Proteins/metabolism , Receptor, Fibroblast Growth Factor, Type 4/metabolism , Animals , Breast Neoplasms/classification , Breast Neoplasms/genetics , Breast Neoplasms/metabolism , Breast Neoplasms/pathology , Female , Humans , MCF-7 Cells , Mice , Mice, Inbred NOD , Mice, SCID , Neoplasm Metastasis , Neoplasm Proteins/genetics , Receptor, Fibroblast Growth Factor, Type 4/geneticsABSTRACT
This study identifies mechanisms mediating responses to immune checkpoint inhibitors using mouse models of triple-negative breast cancer. By creating new mammary tumor models, we find that tumor mutation burden and specific immune cells are associated with response. Further, we developed a rich resource of single-cell RNA-seq and bulk mRNA-seq data of immunotherapy-treated and non-treated tumors from sensitive and resistant murine models. Using this, we uncover that immune checkpoint therapy induces T follicular helper cell activation of B cells to facilitate the anti-tumor response in these models. We also show that B cell activation of T cells and the generation of antibody are key to immunotherapy response and propose a new biomarker for immune checkpoint therapy. In total, this work presents resources of new preclinical models of breast cancer with large mRNA-seq and single-cell RNA-seq datasets annotated for sensitivity to therapy and uncovers new components of response to immune checkpoint inhibitors.