Tumors consist of millions of interacting cells, but our understanding of how those cells collectively respond to therapeutic intervention is limited. This is because we have traditionally divided tumors from the ?top-down? into distinct cell types and then studied each population separately, focusing primarily on cancer cells. Yet, recent studies have revealed that a cancer cell's behavior can be strongly affected by the molecules in its microenvironment and its interactions with other cells (intercellular circuits). Unfortunately, our inability to thoroughly measure and analyze each of these influences within the context of a tumor has limited our capacity to fully grasp the mechanisms by which cancer cells evade therapeutic interventions in vivo. To develop a deep and functional understanding of how these extrinsic tumor microenvironmental factors influence the response of individual cancer cells to drugs, we need new approaches that are capable of deeply and controllably profiling tumor cells and their interactions during treatment at single-cell resolution. In particular, we need methods for: (1) identifying individual tumor-resident cells and the molecules they secrete; (2) tracking the phenotypic responses (e.g., changes in size, shape, structure, transcriptome) of cancer cells to controlled perturbations (drugs) in the presence of defined and physiologically relevant factors (signaling molecules); and, (3) examining how cancer cell behaviors are directly impacted by physical interactions with other cells in the tumor microenvironment. This degree of integration and control would enable discovery of the regulators of in vivo cancer cell drug responses at unprecedented resolution. Here, we aim to achieve these goals by combining three different, cutting-edge experimental platforms ? single-cell RNA-Seq (scRNA-Seq), suspended microchannel resonators (SMRs), and nanowells ? to systematically examine how the extracellular factors present in leukemias and colon and pancreatic cancers influence drug responses. First, we will identify the signals and non-malignant cells present in each tumor type by performing scRNA-Seq on primary biopsies from Core 1. With Core 2, we will generate an atlas of implicated cell types/states and putative signaling molecules that may influence cancer cell drug responses in vivo (Aim 1). Second, we will use SMRs and nanowells to systematically uncover how these soluble factors and tumor cells inform on cancer cell drug responses (Aim 2); we will similarly examine the impact of previously implicated environmental factors as well as other cancer cells (of the same and different intrinsic states; from Project 1). By analyzing and modeling our results with Core 2, we will uncover previously unknown microenvironmental synergies (e.g., cytokines, receptor-ligand pairing) that may modulate cancer cell drug responses in vivo. Collectively, these aims will afford an unprecedented view of the tumor microenvironment and shed light on current therapeutic bottlenecks, while suggesting potential new and more effect therapeutic inroads for treating cancer.
| Luskin, Marlise R; Murakami, Mark A; Manalis, Scott R et al. (2018) Targeting minimal residual disease: a path to cure? Nat Rev Cancer 18:255-263 |
| 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 |
| Kimmerling, Robert J; Prakadan, Sanjay M; Gupta, Alejandro J et al. (2018) Linking single-cell measurements of mass, growth rate, and gene expression. Genome Biol 19:207 |
| Cetin, Arif E; Stevens, Mark M; Calistri, Nicholas L et al. (2017) Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation. Nat Commun 8:1613 |
| Shalek, Alex K; Benson, Mikael (2017) Single-cell analyses to tailor treatments. Sci Transl Med 9: |
