Systematic Assessment of Tumor Purity and Its Clinical Implications
The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, th...
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Published in | JCO precision oncology Vol. 4 |
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Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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United States
04.09.2020
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Abstract | The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor.
To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles.
We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types.
The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers. |
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AbstractList | The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor.
To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles.
We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types.
The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers. |
Author | Laird, Peter W Sander, Chris Xu, Andrew Wei Demichelis, Francesca Fox, Natalie S Prandi, Davide Park, Peter J Loda, Massimo Wang, Wenyi Haider, Syed Tyekucheva, Svitlana Ahn, Jaeil Pantazi, Angeliki Boutros, Paul C |
Author_xml | – sequence: 1 givenname: Syed surname: Haider fullname: Haider, Syed organization: The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom – sequence: 2 givenname: Svitlana surname: Tyekucheva fullname: Tyekucheva, Svitlana organization: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA – sequence: 3 givenname: Davide surname: Prandi fullname: Prandi, Davide organization: Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy – sequence: 4 givenname: Natalie S surname: Fox fullname: Fox, Natalie S organization: Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada – sequence: 5 givenname: Jaeil surname: Ahn fullname: Ahn, Jaeil organization: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC – sequence: 6 givenname: Andrew Wei surname: Xu fullname: Xu, Andrew Wei organization: Department of Biomedical Informatics, Harvard Medical School, Boston, MA – sequence: 7 givenname: Angeliki surname: Pantazi fullname: Pantazi, Angeliki organization: Brigham and Women's Hospital, Boston, MA – sequence: 8 givenname: Peter J surname: Park fullname: Park, Peter J organization: Department of Biomedical Informatics, Harvard Medical School, Boston, MA – sequence: 9 givenname: Peter W surname: Laird fullname: Laird, Peter W organization: Van Andel Research Institute, Grand Rapids, MI – sequence: 10 givenname: Chris surname: Sander fullname: Sander, Chris organization: Department of Cell Biology, Harvard Medical School, Boston, MA – sequence: 11 givenname: Wenyi surname: Wang fullname: Wang, Wenyi organization: The University of Texas MD Anderson Cancer Center Department of Bioinformatics and Computational Biology, Houston – sequence: 12 givenname: Francesca surname: Demichelis fullname: Demichelis, Francesca organization: Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, NY – sequence: 13 givenname: Massimo surname: Loda fullname: Loda, Massimo organization: Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA – sequence: 14 givenname: Paul C surname: Boutros fullname: Boutros, Paul C organization: Institute for Precision Health, University of California, Los Angeles, CA |
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