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Cited by 90Cancer Diagnosis and TreatmentGene expression and cancer classificationCancer Genomics and Diagnostics

Application of a neural network whole transcriptome-based pan-cancer method for diagnosis of primary and metastatic cancers

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Grewal, Jasleen K., Tessier-Cloutier, Basile, Jones, Martin, Gakkhar, Sitanshu, Ma, Yussanne, Moore, Richard, Mungall, Andrew J., Zhao, Yongjun +8 more·2019·JAMA Network Open
DOI 10.1001/jamanetworkopen.2019.2597PMC PMC6487574
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Abstract

Importance: A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis. Objective: To determine the practical use of a whole transcriptome-based pan-cancer method in diagnosing primary and metastatic cancers and resolving complex diagnoses. Design, Setting, and Participants: This cross-sectional diagnostic study assessed Supervised Cancer Origin Prediction Using Expression (SCOPE), a machine learning method using whole-transcriptome RNA sequencing data. Training was performed on publicly available primary cancer data sets, including The Cancer Genome Atlas. Testing was performed retrospectively on untreated primary cancers and treated metastases from volunteer adult patients at BC Cancer in Vancouver, British Columbia, from January 1, 2013, to March 31, 2016, and testing spanned 10 822 samples and 66 output classes representing untreated primary cancers (n = 40) and adjacent normal tissues (n = 26). SCOPE's performance was demonstrated on 211 untreated primary mesothelioma cancers and 201 treatment-resistant metastatic cancers. Finally, SCOPE was used to identify the putative site of origin in 15 cases with initial presentation as cancers with unknown primary of origin. Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. Testing was performed on 211 patients with untreated primary mesothelioma (173 [82.0%] male; mean [SD] age, 64.5 [11.3] years); 201 patients with treatment-resistant cancers (141 [70.1%] female; mean [SD] age, 55.6 [12.9] years); and 15 patients with cancers of unknown primary of origin; among the treatment-resistant cancers, 168 were metastatic, and 33 were the primary presentation. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology. SCOPE achieved an overall mean (SD) accuracy rate of 86% (11%) and F1 score of 0.79 (0.12) on the 201 treatment-resistant cancers and matched 12 of 15 of the putative diagnoses for cancers with indeterminate diagnosis from conventional pathology. Conclusions and Relevance: These results suggest that machine learning approaches incorporating multiple tumor profiles can more accurately identify the cancerous state and discriminate it from normal cells. SCOPE uses the whole transcriptomes from normal and tumor tissues, and results of this study suggest that it performs well for rare cancer types, primary cancers, treatment-resistant metastatic cancers, and cancers of unknown primary of origin. Genes most relevant in SCOPE's decision making were examined, and several are known biological markers of respective cancers. SCOPE may be applied as an orthogonal diagnostic method in cases where the site of origin of a cancer is unknown, or when standard pathology assessment is inconclusive.

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  • Transcriptomic-based algorithms for cell-of-origin prediction have been developed, but they require expansion of clinical transcriptome profiling for generalizability assessments.

    “Transcriptomic-based algorithms have also been developed (136, 137), although these approaches will require expansion of clinical transcriptome profiling for assessments of generalizability.”

    From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [136]

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Authors
Grewal, J. K., Tessier-Cloutier, B., Jones, M., Gakkhar, S., Ma, Y., Moore, R., Mungall, A. J., Zhao, Y., Taylor, M. D., Gelmon, K., Lim, H., Renouf, D., Laskin, J., Marra, M., Yip, S., & Jones, S. J. M.
Affiliations
  • Canada's Michael Smith Genome Sciences Centre (CA)
  • Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
  • Hospital for Sick Children (CA)
  • BC Cancer Agency (CA)
  • University of British Columbia (CA)
Corresponding author
Jones, Steven J. M.
ORCID
Venue
JAMA Network Open
Publisher
American Medical Association (AMA)
Published
2019-04-26
Volume
2
Issue
4
Pages
e192597
ISSN
2574-3805 (ISSN-L)
DOI
10.1001/jamanetworkopen.2019.2597
PMC
PMC6487574
OpenAlex
W2941121701
Cited by
90
References
38
Citations by year
2026: 5 · 2025: 13 · 2024: 13 · 2023: 13 · 2022: 21 · 2021: 11 · 2020: 13 · 2019: 1
FWCI
4.79
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JAMA Network Open (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Oncology › Cancer Diagnosis and Treatment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
scope, cancer of unknown primary, metastatic cancer, mesothelioma, epithelioid mesothelioma, refractory cancer, molecular diagnosis, gene expression signatures
MeSH terms
Machine Learning, Exome Sequencing, Mesothelioma, Malignant, Adult, Cross-Sectional Studies, Female, Humans, Lung Neoplasms, Male, Mesothelioma, Middle Aged, Neoplasms, Biomarkers, Tumor, Neural Networks, Computer, Transcriptome

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