Pan-cancer Transcriptomic Predictors of Perineural Invasion Improve Occult Histopathologic Detection

Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathologic examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic un...

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Bibliographic Details
Published in:Clinical cancer research Vol. 27; no. 10; p. 2807
Main Authors: Guo, Jimmy A, Hoffman, Hannah I, Shroff, Stuti G, Chen, Peter, Hwang, Peter G, Kim, Daniel Y, Kim, Daniel W, Cheng, Stephanie W, Zhao, Daniel, Mahal, Brandon A, Alshalalfa, Mohammed, Niemierko, Andrzej, Wo, Jennifer Y, Loeffler, Jay S, Fernandez-Del Castillo, Carlos, Jacks, Tyler, Aguirre, Andrew J, Hong, Theodore S, Mino-Kenudson, Mari, Hwang, William L
Format: Journal Article
Language:English
Published: United States 15.05.2021
Subjects:
Biomarkers, Tumor
Computational Biology - methods
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Machine Learning
Neoplasm Invasiveness
Neoplasms - diagnosis
Neoplasms - genetics
Neoplasms - mortality
Nerve Tissue - pathology
Prognosis
Proportional Hazards Models
ROC Curve
Transcriptome
ISSN:1557-3265, 1557-3265
Online Access:Get more information
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Summary:Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathologic examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic underpinnings. A multivariate Cox regression was performed to validate associations between PNI and survival in 2,029 patients across 12 cancer types. Differential expression and gene set enrichment analysis were used to learn PNI-associated programs. Machine learning models were applied to build a PNI gene expression classifier. A blinded re-review of hematoxylin and eosin (H&E) slides by a board-certified pathologist helped determine whether the classifier could improve occult histopathologic detection of PNI. PNI associated with both poor overall survival [HR, 1.73; 95% confidence interval (CI), 1.27-2.36; < 0.001] and disease-free survival (HR, 1.79; 95% CI, 1.38-2.32; < 0.001). Neural-like, prosurvival, and invasive programs were enriched in PNI-positive tumors ( < 0.001). Although PNI-associated features likely reflect in part the increased presence of nerves, many differentially expressed genes mapped specifically to malignant cells from single-cell atlases. A PNI gene expression classifier was derived using random forest and evaluated as a tool for occult histopathologic detection. On a blinded H&E re-review of sections initially described as PNI negative, more specimens were reannotated as PNI positive in the high classifier score cohort compared with the low-scoring cohort ( = 0.03, Fisher exact test). This study provides salient biological insights regarding PNI and demonstrates a role for gene expression classifiers to augment detection of histopathologic features.
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ISSN:1557-3265
1557-3265
DOI:10.1158/1078-0432.CCR-20-4382