INVESTIGATING THE ECOLOGY AND EVOLUTION OF NORMAL BREAST TISSUES AND BREAST CANCER WITH SINGLE CELL GENOMICS

There is vast cellular heterogeneity in human breast tissues, with different transcriptional programs in the stromal, epithelial, and immune components, however, it remains unclear how their reprogramming and interplay leads to the progression of invasive phenotypes such as Triple- Negative Breast cancer (TNBC). To do define the microenvironmental alterations that occur during cancer, we first established a human breast cell atlas, a reference of normal breast cell types from disease free women. We profiled 535,941 cells from 62 women and 124,024 nuclei from 20 women revealing 11 major cell types and 52 cell states that reflect different biological functions that can be organized into 4 major spatial domains (adipose, connective, ducts, lobules). We then compared this atlas against one of the more aggressive subtypes of breast cancer, TNBC in which patients lack estrogen, progesterone, and HER2 growth receptors. Approximately 10-14% of all breast cancer patients are classified as TNBC, with a majority developing resistance to neoadjuvant chemotherapy (NAC) and having a 5-year survival of \u3c30%. Tumor evolution and changes in components of the tumor microenvironment (TME) such as cancer associated fibroblasts (CAFs), tumor associated macrophages (TAMs), and tumor endothelial cells (TECs), have shown to play a role in tumor progression. One knowledge gap is the role macroenvironmental changes confer on chemoresistance in TNBC patients. To order to investigate the transcriptional reprogramming of the TME, we performed single cell RNA sequencing on 100 treatment-naïve TNBC biopsies and compared its ecosystem to the normal breast ecosystem captured earlier. Our data suggest reprogramming and increased proportions of certain cell states such as TECs, CAFs, and effector T cells enrichment in TNBC and positive correlation with pathological complete response. Aside from transcriptional changes in normal and late stage invasive breast cancers microenvironments, we also investigated the genomic evolution in early stage breast cancer, Ductal Carcinoma In Situ (DCIS). DCIS is the most common form of pre-invasive breast cancer and despite treatment, a small fraction (5-10%) of DCIS patients present with invasive disease many years later. A fundamental question is whether the invasive disease recurring in the same breast is established by tumor cells in the initial DCIS or represents a new unrelated disease. Whole exome sequencing of 24 longitudinally matched DCIS and recurrent invasive breast cancer revealed clonally related recurrence in ~80% patients whereas ~20% pointed towards independent evolution which was also validated by single cell DNA sequencing in a subset of 4 cases. Overall, we established a breast cell atlas that provides an invaluable reference for the research community to under how normal cell types are reprogrammed in diseases such as TNBC. We established a secondary cell atlas of TNBC patients and compared the two atlases to characterize the transcriptomic features of progression and predictive of pathological complete response in TNBC samples. Lastly, we showed that not all DCIS may be precursors to invasive cancer which paves a way for accurate risk evaluation of DCIS, treatment de-escalation strategies, and the identification of predictive biomarkers

SCMarker: Ab initio marker selection for single cell transcriptome profiling.

Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker

Natural variations in expression of regulatory and detoxification related genes under limiting phosphate and arsenate stress in Arabidopsis thaliana

Abiotic stress including nutrient deficiency and heavy metal toxicity severely affects plant growth, development, and productivity. Genetic variations within and in between species are one of the important factors in establishing interactions and responses of plants with the environment. In the recent past, natural variations in Arabidopsis thaliana have been used to understand plant development and response towards different stresses at genetic level. Phosphorus (Pi) deficiency negatively affects plant growth and metabolism and modulates expression of the genes involved in Pi homeostasis. Arsenate, As(V), a chemical analogue of Pi, is taken up by the plants via phosphate transport system. Studies suggest that during Pi deficiency, enhanced As(V) uptake leads to increased toxicity in plants. Here, the natural variations in Arabidopsis have been utilized to study the As(V) stress response under limiting Pi condition. The primary root length was compared to identify differential response of three Arabidopsis accessions (Col-0, Sij-1 and Slavi-1) under limiting Pi and As(V) stress. To study the molecular mechanisms responsible for the differential response, comprehensive expression profiling of the genes involved in uptake, detoxification and regulatory mechanisms was carried out. Analysis suggests genetic variation-dependent regulatory mechanisms may affect differential response of Arabidopsis natural variants towards As(V) stress under limiting Pi condition. Therefore, it is hypothesized that detailed analysis of the natural variations under multiple stress conditions might help in the better understanding of the biological processes involved in stress tolerance and adaptation

Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5–10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.Pattern Recognition and Bioinformatic

Pre-existing Functional Heterogeneity of Tumorigenic Compartment as the Origin of Chemoresistance in Pancreatic Tumors

Summary: Adaptive drug-resistance mechanisms allow human tumors to evade treatment through selection and expansion of treatment-resistant clones. Here, studying clonal evolution of tumor cells derived from human pancreatic tumors, we demonstrate that in vitro cultures and in vivo tumors are maintained by a common set of tumorigenic cells that can be used to establish clonal replica tumors (CRTs), large cohorts of animals bearing human tumors with identical clonal composition. Using CRTs to conduct quantitative assessments of adaptive responses to therapeutics, we uncovered a multitude of functionally heterogeneous subpopulations of cells with differential degrees of drug sensitivity. High-throughput isolation and deep characterization of unique clonal lineages showed genetic and transcriptomic diversity underlying functionally diverse subpopulations. Molecular annotation of gemcitabine-naive clonal lineages with distinct responses to treatment in the context of CRTs generated signatures that can predict the response to chemotherapy, representing a potential biomarker to stratify patients with pancreatic cancer. : High-complexity lineage tracing shows that tumors growing in different environments are maintained by a common set of tumorigenic cells that enables the generation of clonal replica tumors (CRTs). Applying CRTs, Seth et al. unmask functional heterogeneity in response to therapeutics and identify a signature that predicts chemoresistance in pancreatic cancer. Keywords: tumor heterogeneity, functional heterogeneity, lineage tracing, clonal dynamics, clonal isolation, pancreatic cancer, drug resistance, subclonal gene signature, prognostic stratificatio

Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers