Cited by 122Cancer Diagnosis and TreatmentCancer Genomics and DiagnosticsGenomics and Rare Diseases

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Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary

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Moon, Intae, LoPiccolo, Jaclyn, Baca, Sylvan C., Sholl, Lynette M., Kehl, Kenneth L., Hassett, Michael J., Liu, David, Schrag, Deborah, Gusev, Alexander·2023·Nature Medicine

DOI 10.1038/s41591-023-02482-6PMC PMC11484892

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Abstract

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.

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Authors: Moon, I., LoPiccolo, J., Baca, S. C., Sholl, L. M., Kehl, K. L., Hassett, M. J., Liu, D., Schrag, D., & Gusev, A.
Affiliations: Harvard University (US)
Dana-Farber Cancer Institute (US)
Brigham and Women's Hospital (US)
Broad Institute (US)
Memorial Sloan Kettering Cancer Center (US)
Corresponding author: Gusev, Alexander
ORCID: Moon, Intae
LoPiccolo, Jaclyn
Baca, Sylvan C.
Sholl, Lynette M.
Kehl, Kenneth L.
Hassett, Michael J.
Liu, David
Schrag, Deborah
Gusev, Alexander
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2023-08
Volume: 29
Issue: 8
Pages: 2057-2067
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-023-02482-6
PMC: PMC11484892
OpenAlex: W4385628901
Cited by: 122
References: 52
Citations by year: 2026: 32 · 2025: 44 · 2024: 42 · 2023: 4
FWCI: 14.88
Type: journal article
Language: en
Update notices: correction (2023-11-15)
Open access: green
dx.doi.org
Licence: cc-by
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Medicine (publishedVersion)
Research Square (acceptedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
PubMed Central (submittedVersion, cc-by)
Funders: Dana-Farber Cancer Institute
Emerson Collective
National Cancer Institute (R01CA227237, R01CA244569, R01CA259058, P30CA008748, T32CA009172)
Topics: Health Sciences › Medicine › Oncology › Cancer Diagnosis and Treatment
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords: cancer of unknown primary, targeted next-generation sequencing, treatment response prediction, clinical decision support, cancer subgroups, survival outcomes

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Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary

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