Deciphering Human Tumor Biology by Single-Cell Expression Profiling

Itay Tirosh*, Mario L. Suva

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

34 Citations (Scopus)

Abstract

Human tumors are complex ecosystems where diverse cancer and noncancer cells interact to determine tumor biology and response to therapies. Genomic and transcriptomic methods have traditionally profiled these intricate ecosystems as bulk samples, thereby masking individual cellular programs and the variability among them. Recent advances in single-cell profiling have paved the way for studying tumors at the resolution of individual cells, providing a compelling strategy to bridge gaps in our understanding of human tumors. Here, we review methodologies for single-cell expression profiling of tumors and the initial studies deploying them in clinical contexts. We highlight how these studies uncover new biology and provide insights into drug resistance, stem cell programs, metastasis, and tumor classifications. We also discuss areas of technology development in single-cell genomics that provide new tools to address key questions in cancer biology. These emerging studies and technologies have the potential to revolutionize our understanding and management of human malignancies.

Original languageEnglish
Title of host publicationANNUAL REVIEW OF CANCER BIOLOGY, VOL 3
EditorsT Jacks, CL Sawyers
PublisherAnnual Reviews Inc.
Pages151-166
Number of pages16
DOIs
Publication statusPublished - 2019

Publication series

SeriesAnnual Review of Cancer Biology
Volume3
ISSN2472-3428

Bibliographical note

We thank Leslie Gaffney for help with figure design. This work was supported by grants from the Howard Goodman Fellowship at Massachusetts General Hospital (MGH) (to M.L.S.), the Merkin Institute Fellowship at the Broad Institute of MIT and Harvard (M.L.S.), the Zuckerman STEM Leadership Program (I.T.), the Rising Tide Foundation (I.T.), the Human Frontiers Science Program (I.T.), the Mexican Friends New Generation (I.T.), the Benoziyo Endowment Fund for the Advancement of Science (I.T.), and start-up funds from the MGH Department of Pathology (M.L.S.) and the Weizmann Institute of Science (I.T.).

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