Open AccessUsing single‐cell multiple omics approaches to resolve tumor heterogeneity
Author(s) -
Ortega Michael A.,
Poirion Olivier,
Zhu Xun,
Huang Sijia,
Wolfgruber Thomas K.,
Sebra Robert,
Garmire Lana X.
Publication year - 2017
Publication title -
clinical and translational medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.125
H-Index - 1
ISSN - 2001-1326
DOI - 10.1186/s40169-017-0177-y
Subject(s) - computational biology , biology , genetic heterogeneity , profiling (computer programming) , metastasis , deconvolution , tumor microenvironment , tumor heterogeneity , computer science , cancer , bioinformatics , genetics , gene , phenotype , algorithm , operating system
It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next‐generation sequencing and computational biology have utilized single‐cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single‐cell multi‐omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single‐cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra‐tumour features and developing more personalized cancer treatments.