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In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases. While transcriptomics have enhanced our understanding for cancer, spatial transcriptomics enable the characterisation of cellular interactions. Here, the authors integrate single cell data with spatial information for HER2 + tumours and develop tools for the prediction of interactions between tumour-infiltrating cells.

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. A multi-omic atlas of breast cancers, integrating single-cell RNA sequencing, spatial transcriptomics and immunophenotyping, identifies nine ecotypes associated with cellular heterogeneity and prognosis.

High-throughput single-cell RNA sequencing is a powerful technique but only generates short reads from one end of a cDNA template, limiting the reconstruction of highly diverse sequences such as antigen receptors. To overcome this limitation, we combined targeted capture and long-read sequencing of T-cell-receptor (TCR) and B-cell-receptor (BCR) mRNA transcripts with short-read transcriptome profiling of barcoded single-cell libraries generated by droplet-based partitioning. We show that Repertoire and Gene Expression by Sequencing (RAGE-Seq) can generate accurate full-length antigen receptor sequences at nucleotide resolution, infer B-cell clonal evolution and identify alternatively spliced BCR transcripts. We apply RAGE-Seq to 7138 cells sampled from the primary tumor and draining lymph node of a breast cancer patient to track transcriptome profiles of expanded lymphocyte clones across tissues. Our results demonstrate that RAGE-Seq is a powerful method for tracking the clonal evolution from large numbers of lymphocytes applicable to the study of immunity, autoimmunity and cancer. Single cell RNA sequencing generates short reads from one end of a template, providing incomplete transcript coverage and limiting identification of diverse sequences such as antigen receptors. Here the authors combine long read nanopore sequencing with short read profiling of barcoded libraries to generate full-length antigen receptor sequences.

The role of the androgen receptor (AR) in estrogen receptor (ER)-α-positive breast cancer is controversial, constraining implementation of AR-directed therapies. Using a diverse, clinically relevant panel of cell-line and patient-derived models, we demonstrate that AR activation, not suppression, exerts potent antitumor activity in multiple disease contexts, including resistance to standard-of-care ER and CDK4/6 inhibitors. Notably, AR agonists combined with standard-of-care agents enhanced therapeutic responses. Mechanistically, agonist activation of AR altered the genomic distribution of ER and essential co-activators (p300, SRC-3), resulting in repression of ER-regulated cell cycle genes and upregulation of AR target genes, including known tumor suppressors. A gene signature of AR activity positively predicted disease survival in multiple clinical ER-positive breast cancer cohorts. These findings provide unambiguous evidence that AR has a tumor suppressor role in ER-positive breast cancer and support AR agonism as the optimal AR-directed treatment strategy, revealing a rational therapeutic opportunity. Functional interplay of sex hormones in estrogen receptor–positive breast cancer unveils the therapeutic potential of androgen receptor agonists.

The cellular and molecular basis of stromal cell recruitment, activation and crosstalk in carcinomas is poorly understood, limiting the development of targeted anti-stromal therapies. In mouse models of triple negative breast cancer (TNBC), Hedgehog ligand produced by neoplastic cells reprograms cancer-associated fibroblasts (CAFs) to provide a supportive niche for the acquisition of a chemo-resistant, cancer stem cell (CSC) phenotype via FGF5 expression and production of fibrillar collagen. Stromal treatment of patient-derived xenografts with smoothened inhibitors (SMOi) downregulates CSC markers expression and sensitizes tumors to docetaxel, leading to markedly improved survival and reduced metastatic burden. In the phase I clinical trial EDALINE, 3 of 12 patients with metastatic TNBC derived clinical benefit from combination therapy with the SMOi Sonidegib and docetaxel chemotherapy, with one patient experiencing a complete response. These studies identify Hedgehog signaling to CAFs as a novel mediator of CSC plasticity and an exciting new therapeutic target in TNBC. Stromal cell recruitment, activation and crosstalk with cancer cells is poorly understood. Here, the authors demonstrate that cancer cell-derived Hedgehog ligand  triggers stromal remodeling that in turn induces a cancer-stem-cell like, drug-resistant phenotype of nearby cancer cells while treatment with smoothened inhibitors reverses these phenotypes.

Cancers evade the immune system through the process of cancer immunoediting. While immune checkpoint inhibitors are effective for reactivating tumour immunity in some cancer types, many other solid cancers, including breast cancer, remain largely non-responsive. Understanding how non-responsive cancers evade immunity and whether this occurs at the clonal level will improve immunotherapeutic design. Here we use DNA barcoding to track murine mammary cancer cell clones during immunoediting and determine clonal transcriptional profiles that allow immune evasion following anti-PD1 plus anti-CTLA4 immunotherapy. Clonal diversity is significantly restricted by immunotherapy treatment in both primary tumours and metastases, demonstrating selection for pre-existing breast cancer cell populations and ongoing immunoediting during metastasis and treatment. Immunotherapy resistant clones express a common gene signature associated with poor survival of basal-like breast cancer patient cohorts. At least one of these genes has an existing small molecule that can potentially be used to improve immunotherapy response. Understanding the molecular mechanisms of cancer immunoediting could provide insight into resistance to immunotherapy. Here, DNA barcoding provides evidence of ongoing immunoediting during metastasis and treatment with anti-PD1 and anti-CTLA4, and identifies cancer cell clones with unique immune evasive phenotypes.

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). S ome methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME. Multiple computational approaches have been developed for the deconvolution of cells in the tumour microenvironment (TME) using bulk RNA-seq data. Here, the authors use breast cancer single-cell RNA-seq data to produce simulated bulk data, with which they compare the performance of nine TME deconvolution methods.

Acquired resistance to endocrine therapy is responsible for half of the therapeutic failures in the treatment of breast cancer. Recent findings have implicated increased expression of the ETS transcription factor ELF5 as a potential modulator of estrogen action and driver of endocrine resistance, and here we provide the first insight into the mechanisms by which ELF5 modulates estrogen sensitivity. Using chromatin immunoprecipitation sequencing we found that ELF5 binding overlapped with FOXA1 and ER at super enhancers, enhancers and promoters, and when elevated, caused FOXA1 and ER to bind to new regions of the genome, in a pattern that replicated the alterations to the ER/FOXA1 cistrome caused by the acquisition of resistance to endocrine therapy. RNA sequencing demonstrated that these changes altered estrogen-driven patterns of gene expression, the expression of ER transcription-complex members, and 6 genes known to be involved in driving the acquisition of endocrine resistance. Using rapid immunoprecipitation mass spectrometry of endogenous proteins, and proximity ligation assays, we found that ELF5 interacted physically with members of the ER transcription complex, such as DNA-PKcs. We found 2 cases of endocrine-resistant brain metastases where ELF5 levels were greatly increased and ELF5 patterns of gene expression were enriched, compared to the matched primary tumour. Thus ELF5 alters ER-driven gene expression by modulating the ER/FOXA1 cistrome, by interacting with it, and by modulating the expression of members of the ER transcriptional complex, providing multiple mechanisms by which ELF5 can drive endocrine resistance.

Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

We have previously shown that during pregnancy the E-twenty-six (ETS) transcription factor ELF5 directs the differentiation of mammary progenitor cells toward the estrogen receptor (ER)-negative and milk producing cell lineage, raising the possibility that ELF5 may suppress the estrogen sensitivity of breast cancers. To test this we constructed inducible models of ELF5 expression in ER positive luminal breast cancer cells and interrogated them using transcript profiling and chromatin immunoprecipitation of DNA followed by DNA sequencing (ChIP-Seq). ELF5 suppressed ER and FOXA1 expression and broadly suppressed ER-driven patterns of gene expression including sets of genes distinguishing the luminal molecular subtype. Direct transcriptional targets of ELF5, which included FOXA1, EGFR, and MYC, accurately classified a large cohort of breast cancers into their intrinsic molecular subtypes, predicted ER status with high precision, and defined groups with differential prognosis. Knockdown of ELF5 in basal breast cancer cell lines suppressed basal patterns of gene expression and produced a shift in molecular subtype toward the claudin-low and normal-like groups. Luminal breast cancer cells that acquired resistance to the antiestrogen Tamoxifen showed greatly elevated levels of ELF5 and its transcriptional signature, and became dependent on ELF5 for proliferation, compared to the parental cells. Thus ELF5 provides a key transcriptional determinant of breast cancer molecular subtype by suppression of estrogen sensitivity in luminal breast cancer cells and promotion of basal characteristics in basal breast cancer cells, an action that may be utilised to acquire antiestrogen resistance.