An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.
Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partial...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-06-01
|
| Series: | PLoS Biology |
| Online Access: | https://doi.org/10.1371/journal.pbio.3000797 |
| _version_ | 1819202980415537152 |
|---|---|
| author | Corey E Hayford Darren R Tyson C Jack Robbins Peter L Frick Vito Quaranta Leonard A Harris |
| author_facet | Corey E Hayford Darren R Tyson C Jack Robbins Peter L Frick Vito Quaranta Leonard A Harris |
| author_sort | Corey E Hayford |
| collection | DOAJ |
| description | Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment. |
| first_indexed | 2024-12-23T04:12:38Z |
| format | Article |
| id | doaj.art-97d002547d4149c08d67d30f0550022d |
| institution | Directory Open Access Journal |
| issn | 1544-9173 1545-7885 |
| language | English |
| last_indexed | 2024-12-23T04:12:38Z |
| publishDate | 2021-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Biology |
| spelling | doaj.art-97d002547d4149c08d67d30f0550022d2022-12-21T18:00:27ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852021-06-01196e300079710.1371/journal.pbio.3000797An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.Corey E HayfordDarren R TysonC Jack RobbinsPeter L FrickVito QuarantaLeonard A HarrisTumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment.https://doi.org/10.1371/journal.pbio.3000797 |
| spellingShingle | Corey E Hayford Darren R Tyson C Jack Robbins Peter L Frick Vito Quaranta Leonard A Harris An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. PLoS Biology |
| title | An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. |
| title_full | An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. |
| title_fullStr | An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. |
| title_full_unstemmed | An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. |
| title_short | An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. |
| title_sort | in vitro model of tumor heterogeneity resolves genetic epigenetic and stochastic sources of cell state variability |
| url | https://doi.org/10.1371/journal.pbio.3000797 |
| work_keys_str_mv | AT coreyehayford aninvitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT darrenrtyson aninvitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT cjackrobbins aninvitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT peterlfrick aninvitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT vitoquaranta aninvitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT leonardaharris aninvitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT coreyehayford invitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT darrenrtyson invitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT cjackrobbins invitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT peterlfrick invitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT vitoquaranta invitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability AT leonardaharris invitromodeloftumorheterogeneityresolvesgeneticepigeneticandstochasticsourcesofcellstatevariability |