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Improved Tumor Purity Metrics in Next-generation Sequencing for Clinical Practice: The Integrated Interpretation of Neoplastic Cellularity and Sequencing Results (IINCaSe) Approach

Improved Tumor Purity Metrics in Next-generation Sequencing for Clinical Practice: The Integrated... Neoplastic cellularity contributes to the analytic sensitivity of most present technologies for mutation detection, such that they underperform when stroma and inflammatory cells dilute a cancer specimen’s variant fraction. Thus, tumor purity assessment by light microscopy is used to determine sample adequacy before sequencing and to interpret the significance of negative results and mutant allele fraction afterwards. However, pathologist estimates of tumor purity are imprecise and have limited reproducibility. With the advent of massively parallel sequencing, large amounts of molecular data can be analyzed by computational purity algorithms. We retrospectively compared tumor purity of 3 computational algorithms with neoplastic cellularity using hematoxylin and eosin light microscopy to determine which was best for clinical evaluation of molecular profiling. Data were analyzed from 881 cancer patients from a clinical trial cohort, LCCC1108 (UNCseq), whose tumors had targeted massively parallel sequencing. Concordance among algorithms was poor, and the specimens analyzed had high rates of algorithm failure partially due to variable tumor purity. Computational tumor purity estimates did not add value beyond the pathologist’s estimate of neoplastic cellularity microscopy. To improve present methods, we propose a semiquantitative, clinically applicable strategy based on mutant allele fraction and copy number changes present within a given specimen, which when combined with the morphologic tumor purity estimate, guide the interpretation of next-generation sequencing results in cancer patients. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Immunohistochemistry & Molecular Morphology Wolters Kluwer Health

Improved Tumor Purity Metrics in Next-generation Sequencing for Clinical Practice: The Integrated Interpretation of Neoplastic Cellularity and Sequencing Results (IINCaSe) Approach

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References (23)

Publisher
Wolters Kluwer Health
Copyright
Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc.
ISSN
1541-2016
DOI
10.1097/PAI.0000000000000684
Publisher site
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Abstract

Neoplastic cellularity contributes to the analytic sensitivity of most present technologies for mutation detection, such that they underperform when stroma and inflammatory cells dilute a cancer specimen’s variant fraction. Thus, tumor purity assessment by light microscopy is used to determine sample adequacy before sequencing and to interpret the significance of negative results and mutant allele fraction afterwards. However, pathologist estimates of tumor purity are imprecise and have limited reproducibility. With the advent of massively parallel sequencing, large amounts of molecular data can be analyzed by computational purity algorithms. We retrospectively compared tumor purity of 3 computational algorithms with neoplastic cellularity using hematoxylin and eosin light microscopy to determine which was best for clinical evaluation of molecular profiling. Data were analyzed from 881 cancer patients from a clinical trial cohort, LCCC1108 (UNCseq), whose tumors had targeted massively parallel sequencing. Concordance among algorithms was poor, and the specimens analyzed had high rates of algorithm failure partially due to variable tumor purity. Computational tumor purity estimates did not add value beyond the pathologist’s estimate of neoplastic cellularity microscopy. To improve present methods, we propose a semiquantitative, clinically applicable strategy based on mutant allele fraction and copy number changes present within a given specimen, which when combined with the morphologic tumor purity estimate, guide the interpretation of next-generation sequencing results in cancer patients.

Journal

Applied Immunohistochemistry & Molecular Morphology – Wolters Kluwer Health

Published: Nov 1, 2019

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