Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

PURPOSE: The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community. EXPERIMENTAL DESIGN: We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models. RESULTS: We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia. CONCLUSIONS: Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

Microglia promote anti-tumour immunity and suppress breast cancer brain metastasis.

Breast cancer brain metastasis (BCBM) is a lethal disease with no effective treatments. Prior work has shown that brain cancers and metastases are densely infiltrated with anti-inflammatory, protumourigenic tumour-associated macrophages, but the role of brain-resident microglia remains controversial because they are challenging to discriminate from other tumour-associated macrophages. Using single-cell RNA sequencing, genetic and humanized mouse models, we specifically identify microglia and find that they play a distinct pro-inflammatory and tumour-suppressive role in BCBM. Animals lacking microglia show increased metastasis, decreased survival and reduced natural killer and T cell responses, showing that microglia are critical to promote anti-tumour immunity to suppress BCBM. We find that the pro-inflammatory response is conserved in human microglia, and markers of their response are associated with better prognosis in patients with BCBM. These findings establish an important role for microglia in anti-tumour immunity and highlight them as a potential immunotherapy target for brain metastasis.

A spatially resolved single-cell genomic atlas of the adult human breast.

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.

A spatially resolved single cell genomic atlas of the adult human breast.

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.

Presentation of Human Neural Stem Cell Antigens Drives Regulatory T Cell Induction.

Transplantation of human neural stem cells (hNSCs) is a promising regenerative therapy to promote remyelination in patients with multiple sclerosis (MS). Transplantation of hNSCs has been shown to increase the number of CD4+CD25+Foxp3+ T regulatory cells (Tregs) in the spinal cords of murine models of MS, which is correlated with a strong localized remyelination response. However, the mechanisms by which hNSC transplantation leads to an increase in Tregs in the CNS remains unclear. We report that hNSCs drive the conversion of T conventional (Tconv) cells into Tregs in vitro. Conversion of Tconv cells is Ag driven and fails to occur in the absence of TCR stimulation by cognate antigenic self-peptides. Furthermore, CNS Ags are sufficient to drive this conversion in the absence of hNSCs in vitro and in vivo. Importantly, only Ags presented in the thymus during T cell selection drive this Treg response. In this study, we investigate the mechanisms by which hNSC Ags drive the conversion of Tconv cells into Tregs and may provide key insight needed for the development of MS therapies.

Preneoplastic stromal cells promote BRCA1-mediated breast tumorigenesis.

Women with germline BRCA1 mutations (BRCA1+/mut) have increased risk for hereditary breast cancer. Cancer initiation in BRCA1+/mut is associated with premalignant changes in breast epithelium; however, the role of the epithelium-associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here we show that the premalignant stromal niche promotes epithelial proliferation and mutant BRCA1-driven tumorigenesis in trans. Using single-cell RNA sequencing analysis of human preneoplastic BRCA1+/mut and noncarrier breast tissues, we show distinct changes in epithelial homeostasis including increased proliferation and expansion of basal-luminal intermediate progenitor cells. Additionally, BRCA1+/mut stromal cells show increased expression of pro-proliferative paracrine signals. In particular, we identify pre-cancer-associated fibroblasts (pre-CAFs) that produce protumorigenic factors including matrix metalloproteinase 3 (MMP3), which promotes BRCA1-driven tumorigenesis in vivo. Together, our findings demonstrate that precancerous stroma in BRCA1+/mut may elevate breast cancer risk through the promotion of epithelial proliferation and an accumulation of luminal progenitor cells with altered differentiation.

Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models.

Tissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

An in vitro vascularized micro-tumor model of human colorectal cancer recapitulates in vivo responses to standard-of-care therapy.

Around 95% of anti-cancer drugs that show promise during preclinical study fail to gain FDA-approval for clinical use. This failure of the preclinical pipeline highlights the need for improved, physiologically-relevant in vitro models that can better serve as reliable drug-screening and disease modeling tools. The vascularized micro-tumor (VMT) is a novel three-dimensional model system (tumor-on-a-chip) that recapitulates the complex human tumor microenvironment, including perfused vasculature, within a transparent microfluidic device, allowing real-time study of drug responses and tumor-stromal interactions. Here we have validated this microphysiological system (MPS) platform for the study of colorectal cancer (CRC), the second leading cause of cancer-related deaths, by showing that gene expression, tumor heterogeneity, and treatment responses in the VMT more closely model CRC tumor clinicopathology than current standard drug screening modalities, including 2-dimensional monolayer culture and 3-dimensional spheroids.

Single-Cell Transcriptome Analysis Workflow for Splenic Myeloid-Derived Suppressor Cells from Murine Breast Cancer Models.

Single-cell transcriptomics is a powerful tool to study previously unrealized cellular heterogeneity at the resolution of individual cells. Most of the previous knowledge in cell biology is based on data generated by bulk analysis methods, which provide averaged readouts that usually mask cellular heterogeneity. This approach is challenging when the biological effect of interest is limited to a subpopulation within a cell type. This may particularly apply immune cell populations as these cells are highly mobile and swiftly respond to changes in cytokines or chemokines. For example, in cancer certain subset of myeloid immune cells may acquire immunosuppressive features to suppress antitumor immune responses, and thus described as myeloid-derived suppressor cells (MDSCs). Advances in single-cell RNA sequencing (scRNAseq) allowed scientists to overcome this limitation and enable in-depth interrogation of these subsets of immune cells including MDSCs. Here, we provide a detailed protocol for using scRNAseq to explore MDSCs in the context of splenic myeloid cells from breast tumor-bearing mice in comparison to wildtype controls to define the unique molecular features of immunosuppressive myeloid cells.

Integrated Single-Cell Transcriptomics and Chromatin Accessibility Analysis Reveals Regulators of Mammary Epithelial Cell Identity.

The mammary epithelial cell (MEC) system is a bilayered ductal epithelium of luminal and basal cells, maintained by a lineage of stem and progenitor populations. Here, we used integrated single-cell transcriptomics and chromatin accessibility analysis to reconstruct the cell types of the mouse MEC system and their underlying gene regulatory features in an unbiased manner. We define differentiation states within the secretory type of luminal cells, which forms a continuous spectrum of general luminal progenitor and lactation-committed progenitor cells. By integrating single-cell transcriptomics and chromatin accessibility landscapes, we identify cis- and trans-regulatory elements that are differentially activated in the specific epithelial cell types and our newly defined luminal differentiation states. Our work provides a resource to reveal cis/trans-regulatory elements associated with MEC identity and differentiation that will serve as a reference to determine how the chromatin accessibility landscape changes during breast cancer.

Inhibition of PI3K by copanlisib exerts potent antitumor effects on Merkel cell carcinoma cell lines and mouse xenografts.

Merkel cell carcinoma (MCC) is a highly aggressive neuroendocrine skin cancer with steadily increasing incidence and poor prognosis. Despite recent success with immunotherapy, 50% of patients still succumb to their diseases. To date, there is no Food and Drug Administration-approved targeted therapy for advanced MCC. Aberrant activation of phosphatidylinositide-3-kinase (PI3K)/AKT/mTOR pathway is frequently detected in MCC, making it an attractive therapeutic target. We previously found PI3K pathway activation in human MCC cell lines and tumors and demonstrated complete clinical response in a Stage IV MCC patient treated with PI3K inhibitor idelalisib. Here, we found that both PI3K-α and -δ isoforms are abundantly expressed in our MCC cell lines and clinical samples; we therefore examined antitumor efficacy across a panel of five PI3K inhibitors with distinctive isoform-specificities, including idelalisib (PI3K-δ), copanlisib (PI3K-α/δ), duvelisib (PI3K-γ/δ), alpelisib (PI3K-α), and AZD8186 (PI3K-ß/δ). Of these, copanlisib exerts the most potent antitumor effects, markedly inhibiting cell proliferation, survival, and tumor growth by suppressing PI3K/mTOR/Akt activities in mouse models generated from MCC cell xenografts and patient-derived tumor xenografts. These results provide compelling preclinical evidence for application of copanlisib in advanced MCC with aberrant PI3K activation for which immunotherapy is insufficient, or patients who are unsuitable for immunotherapy.

Experimental Considerations for Single-Cell RNA Sequencing Approaches.

Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell biology is largely limited to data generated by bulk profiling methods, which only provide averaged read-outs that generally mask cellular heterogeneity. This averaged approach is particularly problematic when the biological effect of interest is limited to only a subpopulation of cells such as stem/progenitor cells within a given tissue, or immune cell subsets infiltrating a tumor. Great advances in single-cell RNA sequencing (scRNAseq) enabled scientists to overcome this limitation and allow for in depth interrogation of previously unexplored rare cell types. Due to the high sensitivity of scRNAseq, adequate attention must be put into experimental setup and execution. Careful handling and processing of cells for scRNAseq is critical to preserve the native expression profile that will ensure meaningful analysis and conclusions. Here, we delineate the individual steps of a typical single-cell analysis workflow from tissue procurement, cell preparation, to platform selection and data analysis, and we discuss critical challenges in each of these steps, which will serve as a helpful guide to navigate the complex field of single-cell sequencing.

Targeting the Mevalonate Pathway Suppresses VHL-Deficient CC-RCC through an HIF-Dependent Mechanism.

Clear cell renal cell carcinoma (CC-RCC) is a devastating disease with limited therapeutic options available for advanced stages. The objective of this study was to investigate HMG-CoA reductase inhibitors, also known as statins, as potential therapeutics for CC-RCC. Importantly, treatment with statins was found to be synthetically lethal with the loss of the von Hippel-Lindau (VHL) tumor suppressor gene, which occurs in 90% of CC-RCC driving the disease. This effect has been confirmed in three different CC-RCC cell lines with three different lipophilic statins. Inhibition of mevalonate synthesis by statins causes a profound cytostatic effect at nanomolar concentrations and becomes cytotoxic at low micromolar concentrations in VHL-deficient CC-RCC. The synthetic lethal effect can be fully rescued by both mevalonate and geranylgeranylpyrophosphate, but not by squalene, indicating that the effect is due to disruption of small GTPase isoprenylation and not the inhibition of cholesterol synthesis. Inhibition of Rho and Rho kinase (ROCK) signaling contributes to the synthetic lethality effect, and overactivation of hypoxia-inducible factor signaling resulting from VHL loss is required. Finally, statin treatment is able to inhibit both tumor initiation and progression of subcutaneous 786-OT1-based CC-RCC tumors in mice. Thus, statins represent potential therapeutics for the treatment of VHL-deficient CC-RCC. Mol Cancer Ther; 17(8); 1781-92. ©2018 AACR.

Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity.

Breast cancer arises from breast epithelial cells that acquire genetic alterations leading to subsequent loss of tissue homeostasis. Several distinct epithelial subpopulations have been proposed, but complete understanding of the spectrum of heterogeneity and differentiation hierarchy in the human breast remains elusive. Here, we use single-cell mRNA sequencing (scRNAseq) to profile the transcriptomes of 25,790 primary human breast epithelial cells isolated from reduction mammoplasties of seven individuals. Unbiased clustering analysis reveals the existence of three distinct epithelial cell populations, one basal and two luminal cell types, which we identify as secretory L1- and hormone-responsive L2-type cells. Pseudotemporal reconstruction of differentiation trajectories produces one continuous lineage hierarchy that closely connects the basal lineage to the two differentiated luminal branches. Our comprehensive cell atlas provides insights into the cellular blueprint of the human breast epithelium and will form the foundation to understand how the system goes awry during breast cancer.

Approaches to identifying synthetic lethal interactions in cancer.

Targeting synthetic lethal interactions is a promising new therapeutic approach to exploit specific changes that occur within cancer cells. Multiple approaches to investigate these interactions have been developed and successfully implemented, including chemical, siRNA, shRNA, and CRISPR library screens. Genome-wide computational approaches, such as DAISY, also have been successful in predicting synthetic lethal interactions from both cancer cell lines and patient samples. Each approach has its advantages and disadvantages that need to be considered depending on the cancer type and its molecular alterations. This review discusses these approaches and examines case studies that highlight their use.