Tumor fraction-guided cell-free DNA profiling in metastatic solid tumor patients Journal Article


Authors: Tsui, D. W. Y.; Cheng, M. L.; Shady, M.; Yang, J. L.; Stephens, D.; Won, H.; Srinivasan, P.; Huberman, K.; Meng, F.; Jing, X.; Patel, J.; Hasan, M.; Johnson, I.; Gedvilaite, E.; Houck-Loomis, B.; Socci, N. D.; Selcuklu, S. D.; Seshan, V. E.; Zhang, H.; Chakravarty, D.; Zehir, A.; Benayed, R.; Arcila, M.; Ladanyi, M.; Funt, S. A.; Feldman, D. R.; Li, B. T.; Razavi, P.; Rosenberg, J.; Bajorin, D.; Iyer, G.; Abida, W.; Scher, H. I.; Rathkopf, D.; Viale, A.; Berger, M. F.; Solit, D. B.
Article Title: Tumor fraction-guided cell-free DNA profiling in metastatic solid tumor patients
Abstract: Background: Cell-free DNA (cfDNA) profiling is increasingly used to guide cancer care, yet mutations are not always identified. The ability to detect somatic mutations in plasma depends on both assay sensitivity and the fraction of circulating DNA in plasma that is tumor-derived (i.e., cfDNA tumor fraction). We hypothesized that cfDNA tumor fraction could inform the interpretation of negative cfDNA results and guide the choice of subsequent assays of greater genomic breadth or depth. Methods: Plasma samples collected from 118 metastatic cancer patients were analyzed with cf-IMPACT, a modified version of the FDA-authorized MSK-IMPACT tumor test that can detect genomic alterations in 410 cancer-associated genes. Shallow whole genome sequencing (sWGS) was also performed in the same samples to estimate cfDNA tumor fraction based on genome-wide copy number alterations using z-score statistics. Plasma samples with no somatic alterations detected by cf-IMPACT were triaged based on sWGS-estimated tumor fraction for analysis with either a less comprehensive but more sensitive assay (MSK-ACCESS) or broader whole exome sequencing (WES). Results: cfDNA profiling using cf-IMPACT identified somatic mutations in 55/76 (72%) patients for whom MSK-IMPACT tumor profiling data were available. A significantly higher concordance of mutational profiles and tumor mutational burden (TMB) was observed between plasma and tumor profiling for plasma samples with a high tumor fraction (z-score≥5). In the 42 patients from whom tumor data was not available, cf-IMPACT identified mutations in 16/42 (38%). In total, cf-IMPACT analysis of plasma revealed mutations in 71/118 (60%) patients, with clinically actionable alterations identified in 30 (25%), including therapeutic targets of FDA-approved drugs. Of the 47 samples without alterations detected and low tumor fraction (z-score<5), 29 had sufficient material to be re-analyzed using a less comprehensive but more sensitive assay, MSK-ACCESS, which revealed somatic mutations in 14/29 (48%). Conversely, 5 patients without alterations detected by cf-IMPACT and with high tumor fraction (z-score≥5) were analyzed by WES, which identified mutational signatures and alterations in potential oncogenic drivers not covered by the cf-IMPACT panel. Overall, we identified mutations in 90/118 (76%) patients in the entire cohort using the three complementary plasma profiling approaches. Conclusions: cfDNA tumor fraction can inform the interpretation of negative cfDNA results and guide the selection of subsequent sequencing platforms that are most likely to identify clinically-relevant genomic alterations. © 2021, The Author(s).
Keywords: sequencing; plasma dna; noninvasive; cancer; liquid biopsy; molecular diagnostic
Journal Title: Genome Medicine
Volume: 13
ISSN: 1756-994X
Publisher: Biomed Central Ltd  
Date Published: 2021-05-31
Start Page: 96
Language: English
DOI: 10.1186/s13073-021-00898-8
PUBMED: 34059130
PROVIDER: scopus
PMCID: PMC8165771
DOI/URL:
Notes: Article -- Export Date: 1 July 2021 -- Source: Scopus
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MSK Authors
  1. Venkatraman Ennapadam Seshan
    346 Seshan
  2. Dean Bajorin
    604 Bajorin
  3. David Solit
    658 Solit
  4. Darren Richard Feldman
    275 Feldman
  5. Marc Ladanyi
    1189 Ladanyi
  6. Gopakumar Vasudeva Iyer
    235 Iyer
  7. Dana Elizabeth Rathkopf
    222 Rathkopf
  8. Agnes Viale
    235 Viale
  9. Ahmet Zehir
    311 Zehir
  10. Xiaohong Jing
    21 Jing
  11. Nicholas D Socci
    215 Socci
  12. Michael Forman Berger
    602 Berger
  13. Maria Eugenia Arcila
    554 Arcila
  14. Howard Scher
    1085 Scher
  15. Wassim Abida
    124 Abida
  16. Helen Hyeong-Eun Won
    105 Won
  17. Jonathan Eric Rosenberg
    408 Rosenberg
  18. Pedram Razavi
    118 Razavi
  19. Rym Benayed
    167 Benayed
  20. Samuel Aaron Funt
    89 Funt
  21. Bob Tingkan Li
    182 Li
  22. Li   Yang
    19 Yang
  23. Fanli   Meng
    22 Meng
  24. Wai Yi   Tsui
    44 Tsui
  25. Hongxin Zhang
    25 Zhang
  26. Michael Lain Cheng
    14 Cheng
  27. Juber Ahamad Abdul Bari Patel
    18 Patel
  28. Maha Shady
    8 Shady
  29. Maysun M Hasan
    16 Hasan