Cargando…

Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity

PURPOSE: Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor heterogeneity serves as a reservoir for resistance, which often occurs as a result of the select...

Descripción completa

Detalles Bibliográficos
Autores principales: Ferrall-Fairbanks, Meghan C., Ball, Markus, Padron, Eric, Altrock, Philipp M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Clinical Oncology 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873939/
https://www.ncbi.nlm.nih.gov/pubmed/30995123
http://dx.doi.org/10.1200/CCI.18.00074
_version_ 1783472753919131648
author Ferrall-Fairbanks, Meghan C.
Ball, Markus
Padron, Eric
Altrock, Philipp M.
author_facet Ferrall-Fairbanks, Meghan C.
Ball, Markus
Padron, Eric
Altrock, Philipp M.
author_sort Ferrall-Fairbanks, Meghan C.
collection PubMed
description PURPOSE: Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor heterogeneity serves as a reservoir for resistance, which often occurs as a result of the selection of minor cellular subclones. On the level of gene expression, clonal heterogeneity can only be revealed using high-dimensional single-cell methods. We propose using a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution. MATERIALS AND METHODS: We focused on individual patient samples that were probed with single-cell RNA (scRNA) sequencing to describe heterogeneity. We developed a pipeline to analyze single-cell data via sample normalization, clustering, and mathematical interpretation using a generalized diversity measure, as well as to exemplify the utility of this platform using single-cell data. RESULTS: We focused on three sources of patient scRNA sequencing data: two healthy bone marrow (BM) donors, two patients with acute myeloid leukemia—each sampled before and after BM transplantation, four samples of presorted lineages—and six patients with lung carcinoma with multiregion sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified the ways in which these samples differed. Whereas a widely used Shannon diversity index sometimes reveals fewer differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparison of pre– and post–BM transplantation acute myeloid leukemia samples did not reveal differences in heterogeneity, although biological differences can exist. CONCLUSION: GDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach can be widely applied to quantify heterogeneity across samples and conditions.
format Online
Article
Text
id pubmed-6873939
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher American Society of Clinical Oncology
record_format MEDLINE/PubMed
spelling pubmed-68739392019-12-03 Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity Ferrall-Fairbanks, Meghan C. Ball, Markus Padron, Eric Altrock, Philipp M. JCO Clin Cancer Inform ORIGINAL REPORT PURPOSE: Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor heterogeneity serves as a reservoir for resistance, which often occurs as a result of the selection of minor cellular subclones. On the level of gene expression, clonal heterogeneity can only be revealed using high-dimensional single-cell methods. We propose using a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution. MATERIALS AND METHODS: We focused on individual patient samples that were probed with single-cell RNA (scRNA) sequencing to describe heterogeneity. We developed a pipeline to analyze single-cell data via sample normalization, clustering, and mathematical interpretation using a generalized diversity measure, as well as to exemplify the utility of this platform using single-cell data. RESULTS: We focused on three sources of patient scRNA sequencing data: two healthy bone marrow (BM) donors, two patients with acute myeloid leukemia—each sampled before and after BM transplantation, four samples of presorted lineages—and six patients with lung carcinoma with multiregion sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified the ways in which these samples differed. Whereas a widely used Shannon diversity index sometimes reveals fewer differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparison of pre– and post–BM transplantation acute myeloid leukemia samples did not reveal differences in heterogeneity, although biological differences can exist. CONCLUSION: GDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach can be widely applied to quantify heterogeneity across samples and conditions. American Society of Clinical Oncology 2018-04-17 /pmc/articles/PMC6873939/ /pubmed/30995123 http://dx.doi.org/10.1200/CCI.18.00074 Text en © 2019 by American Society of Clinical Oncology https://creativecommons.org/licenses/by/4.0/ Licensed under the Creative Commons Attribution 4.0 License: https://creativecommons.org/licenses/by/4.0/
spellingShingle ORIGINAL REPORT
Ferrall-Fairbanks, Meghan C.
Ball, Markus
Padron, Eric
Altrock, Philipp M.
Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity
title Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity
title_full Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity
title_fullStr Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity
title_full_unstemmed Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity
title_short Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity
title_sort leveraging single-cell rna sequencing experiments to model intratumor heterogeneity
topic ORIGINAL REPORT
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873939/
https://www.ncbi.nlm.nih.gov/pubmed/30995123
http://dx.doi.org/10.1200/CCI.18.00074
work_keys_str_mv AT ferrallfairbanksmeghanc leveragingsinglecellrnasequencingexperimentstomodelintratumorheterogeneity
AT ballmarkus leveragingsinglecellrnasequencingexperimentstomodelintratumorheterogeneity
AT padroneric leveragingsinglecellrnasequencingexperimentstomodelintratumorheterogeneity
AT altrockphilippm leveragingsinglecellrnasequencingexperimentstomodelintratumorheterogeneity