ArticlePLoS computational biology2018
A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 166 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
166 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-analysis reveals differences in somatic alterations by genetic ancestry across common cancers.Nature genetics · 2025Pooled it
- Germline and somatic testing for homologous repair deficiency in patients with prostate cancer (part 1 of 2).Prostate cancer and prostatic diseases · 2025Guideline
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- Nivolumab plus low-dose ipilimumab in hypermutated HER2-negative metastatic breast cancer: a phase II trial (NIMBUS).Nature communications · 2025Trial
- Neoadjuvant talazoparib in patients with germline BRCA1/2 mutation-positive, early-stage triple-negative breast cancer: exploration of tumor BRCA mutational status.Breast cancer (Tokyo, Japan) · 2024Trial
- Clinical and molecular characteristics of ARIEL3 patients who derived exceptional benefit from rucaparib maintenance treatment for high-grade ovarian carcinoma.Gynecologic oncology · 2022Trial
- Impaired Chromatin Remodeling Predicts Better Survival to Modified Gemcitabine and S-1 plus Nivolumab in Advanced Biliary Tract Cancer: A Phase II T1219 Study.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Trial
- The MDM2 antagonist idasanutlin in patients with polycythemia vera: results from a single-arm phase 2 study.Blood advances · 2022Trial
- Molecular and clinical determinants of response and resistance to rucaparib for recurrent ovarian cancer treatment in ARIEL2 (Parts 1 and 2).Nature communications · 2021Trial
- Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.Nature medicine · 2019Trial
- First-Line Nivolumab Plus Ipilimumab in Advanced Non-Small-Cell Lung Cancer (CheckMate 568): Outcomes by Programmed Death Ligand 1 and Tumor Mutational Burden as Biomarkers.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2019Trial
- Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden.The New England journal of medicine · 2018Trial
- Somatic likelihood tiering: an interpretable post-calling triage protocol for tumor-only whole-exome variant review.Briefings in bioinformatics · 2026Article
- Germline haploinsufficiency of MUTYH causes mutational signature SBS18 in multiple tumour types and specifically raises colorectal cancer risk.NPJ precision oncology · 2026Article
- Tissue-Specific Prevalence and Clonal Architecture ofbioRxiv : the preprint server for biology · 2026Article
- Comprehensive Genomic Profiling of Cutaneous Adnexal Carcinomas: A Genomic Landscape Study.Dermatopathology (Basel, Switzerland) · 2026Article
- Homologous recombination deficiency and genomic alterations in advanced prostate cancer: insights for precision therapy.The oncologist · 2026Article
- Cancer genomic profiling predicts pathogenicity ofmedRxiv : the preprint server for health sciences · 2026Article
- Intimal sarcomas (ISarc) of the cardiac chambers (CC) of the heart and great vessels (GV): a comprehensive genomic profiling (CGP) study.Cardio-oncology (London, England) · 2026Article
- Fanconi anemia complementation group C gene (FANCC) association with hereditary and sporadic renal tumors.The oncologist · 2026Article
106 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity), a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS) of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration's allele frequency (AF), taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x) using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs). Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10-75%). Overall, predictions were made in 85% of cases, with 95-99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants in cancer and found >50 that are in fact more likely to be germline.
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