The rapid evolution of cancer genome technology and computational analysis has engendered many fundamental cancer discoveries, thus transforming the scientific and clinical landscape in less than a decade. Increasingly, genetic alterations revealed by tumor genomic profiling guide diagnosis, treatment, and investigation in cancer. Despite these advances, many genomic technologies are unable to demonstrate drug targets or tumor-mediated drug resistance mechanisms that are not DNA-encoded. Furthermore, profiling approaches of bulk tumor samples only provide average signatures that do not reflect different tumor components and intrinsic heterogeneity of individual cell populations or cells. Emerging single-cell profiling technologies such as single-cell transcriptome analysis could overcome several challenges and provide a plethora of translational discovery opportunities. We recently provided proof- of-concept for application of single-cell RNA-seq in patient-derived tumor samples. To apply this technology more broadly in the translational oncology arena, we propose to create, optimize and implement a single-cell RNA-seq platform that can be deployed as a translational tool in the clinical oncology arena. In preliminary studies, we sequenced ~300 single cells from several melanoma tumors, including cancer and corresponding tumor-infiltrating cells (TILs). Transcriptome analysis revealed expression of key markers of melanoma, such as MITF and SOX10, and a stem-ness signature occurring in a fraction of melanoma cells. T cells expression reflected a spectrum of functional states, ranging from nave to highly anergic (`exhausted') cells. These preliminary results demonstrate that we have created each necessary component for an end-to-end (clinic-to- bench) workflow, which yields meaningful single-cell data. The goal of this research is to optimize current protocols to create standard operating procedures (SOPs) and merge individual components into a standardized workflow. From our clinical colleagues, we will receive clinical specimens (tumors, biopsies, malignant effusions) from diverse tumor types, including melanoma, lung, breast and ovarian cancer. We will deploy experimental protocols to extract disaggregated individual cells from each sample type and state-of-the-art single cell RNA-Seq to profile each cell. Existing computational pipelines will be optimized to best serve high-throughput single-cell analysis, and once standardized will be made publicly available via an existing online platform. Upon completion of this project, we expect to have created a robust, multi-component workflow for single-cell transcriptome analysis applicable across cancer and sample types, and to render this technology accessible to the entire oncology community.
Tumors are made of diverse cells. However, current technologies for measuring genes and their expression in tumors, in particular for those with therapeutically important genetic alterations, average measurements from all cells, and, thus, cannot read out information that is crucial to guide cancer precision medicine. This proposal aims to overcome these challenges by applying a new technology (single-cell transcriptome sequencing) that measures gene expression (and genetic) changes present in hundreds of individual cells extracted from tumor tissue. By implementing this technology as a clinical research tool, we will improve our ability to understand drug actions, dissect tumor response and drug resistance mechanisms, and develop new treatment strategies.
| Filbin, Mariella G; Tirosh, Itay; Hovestadt, Volker et al. (2018) Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360:331-335 |
| Ordovas-Montanes, Jose; Dwyer, Daniel F; Nyquist, Sarah K et al. (2018) Allergic inflammatory memory in human respiratory epithelial progenitor cells. Nature 560:649-654 |
| Mead, Benjamin E; Ordovas-Montanes, Jose; Braun, Alexandra P et al. (2018) Harnessing single-cell genomics to improve the physiological fidelity of organoid-derived cell types. BMC Biol 16:62 |
| Shalek, Alex K; Benson, Mikael (2017) Single-cell analyses to tailor treatments. Sci Transl Med 9: |
| Habib, Naomi; Avraham-Davidi, Inbal; Basu, Anindita et al. (2017) Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods 14:955-958 |
| Gierahn, Todd M; Wadsworth 2nd, Marc H; Hughes, Travis K et al. (2017) Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 14:395-398 |
| Puram, Sidharth V; Tirosh, Itay; Parikh, Anuraag S et al. (2017) Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell 171:1611-1624.e24 |
| Venteicher, Andrew S; Tirosh, Itay; Hebert, Christine et al. (2017) Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355: |
| Tirosh, Itay; Izar, Benjamin; Prakadan, Sanjay M et al. (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189-96 |
| Tirosh, Itay; Venteicher, Andrew S; Hebert, Christine et al. (2016) Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539:309-313 |
