Enhanced specificity of clinical high-sensitivity tumor mutation profiling in cell-free DNA via paired normal sequencing using MSK-ACCESS Journal Article


Authors: Brannon, A. R.; Jayakumaran, G.; Diosdado, M.; Patel, J.; Razumova, A.; Hu, Y.; Meng, F.; Haque, M.; Sadowska, J.; Murphy, B. J.; Baldi, T.; Johnson, I.; Ptashkin, R.; Hasan, M.; Srinivasan, P.; Rema, A. B.; Rijo, I.; Agarunov, A.; Won, H.; Perera, D.; Brown, D. N.; Samoila, A.; Jing, X.; Gedvilaite, E.; Yang, J. L.; Stephens, D. P.; Dix, J. M.; DeGroat, N.; Nafa, K.; Syed, A.; Li, A.; Lebow, E. S.; Bowman, A. S.; Ferguson, D. C.; Liu, Y.; Mata, D. A.; Sharma, R.; Yang, S. R.; Bale, T.; Benhamida, J. K.; Chang, J. C.; Dogan, S.; Hameed, M. R.; Hechtman, J. F.; Moung, C.; Ross, D. S.; Vakiani, E.; Vanderbilt, C. M.; Yao, J. J.; Razavi, P.; Smyth, L. M.; Chandarlapaty, S.; Iyer, G.; Abida, W.; Harding, J. J.; Krantz, B.; O’Reilly, E.; Yu, H. A.; Li, B. T.; Rudin, C. M.; Diaz, L.; Solit, D. B.; Arcila, M. E.; Ladanyi, M.; Loomis, B.; Tsui, D.; Berger, M. F.; Zehir, A.; Benayed, R.
Article Title: Enhanced specificity of clinical high-sensitivity tumor mutation profiling in cell-free DNA via paired normal sequencing using MSK-ACCESS
Abstract: Circulating cell-free DNA from blood plasma of cancer patients can be used to non-invasively interrogate somatic tumor alterations. Here we develop MSK-ACCESS (Memorial Sloan Kettering - Analysis of Circulating cfDNA to Examine Somatic Status), an NGS assay for detection of very low frequency somatic alterations in 129 genes. Analytical validation demonstrated 92% sensitivity in de-novo mutation calling down to 0.5% allele frequency and 99% for a priori mutation profiling. To evaluate the performance of MSK-ACCESS, we report results from 681 prospective blood samples that underwent clinical analysis to guide patient management. Somatic alterations are detected in 73% of the samples, 56% of which have clinically actionable alterations. The utilization of matched normal sequencing allows retention of somatic alterations while removing over 10,000 germline and clonal hematopoiesis variants. Our experience illustrates the importance of analyzing matched normal samples when interpreting cfDNA results and highlights the importance of cfDNA as a genomic profiling source for cancer patients. © 2021, The Author(s).
Keywords: adult; human tissue; major clinical study; cancer patient; sensitivity and specificity; gene frequency; germ line; patient care; clinical evaluation; clonal hematopoiesis; human; male; female; article; protein fingerprinting
Journal Title: Nature Communications
Volume: 12
ISSN: 2041-1723
Publisher: Nature Publishing Group  
Date Published: 2021-01-01
Start Page: 3770
Language: English
DOI: 10.1038/s41467-021-24109-5
PUBMED: 34145282
PROVIDER: scopus
PMCID: PMC8213710
DOI/URL:
Notes: Article -- Export Date: 1 July 2021 -- Source: Scopus
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MSK Authors
  1. Meera Hameed
    213 Hameed
  2. Khedoudja Nafa
    222 Nafa
  3. David Solit
    658 Solit
  4. Helena Alexandra Yu
    186 Yu
  5. James Joseph Harding
    162 Harding
  6. Jinjuan Yao
    44 Yao
  7. Marc Ladanyi
    1190 Ladanyi
  8. Gopakumar Vasudeva Iyer
    235 Iyer
  9. Christine Gi-Yun Moung
    19 Moung
  10. Snjezana Dogan
    158 Dogan
  11. Ahmet Zehir
    311 Zehir
  12. Xiaohong Jing
    21 Jing
  13. Eileen O'Reilly
    566 O'Reilly
  14. Michael Forman Berger
    602 Berger
  15. Maria Eugenia Arcila
    554 Arcila
  16. Efsevia Vakiani
    225 Vakiani
  17. Wassim Abida
    124 Abida
  18. Dara Stacy Ross
    98 Ross
  19. Helen Hyeong-Eun Won
    105 Won
  20. Angela Rose Brannon
    45 Brannon
  21. Ivelise A Rijo
    19 Rijo
  22. Aliaksandra Samoila
    22 Samoila
  23. Aijazuddin Syed
    37 Syed
  24. Charles Rudin
    346 Rudin
  25. Jaclyn Frances Hechtman
    204 Hechtman
  26. Pedram Razavi
    118 Razavi
  27. Rym Benayed
    167 Benayed
  28. Tessara   Baldi
    12 Baldi
  29. Jason Chih-Peng Chang
    84 Chang
  30. Bob Tingkan Li
    182 Li
  31. Lillian   Smyth
    42 Smyth
  32. Li   Yang
    20 Yang
  33. Fanli   Meng
    22 Meng
  34. Wai Yi   Tsui
    44 Tsui
  35. Mohammad Haque Haque
    5 Haque
  36. Benjamin Krantz
    6 Krantz
  37. Luis Alberto Diaz
    99 Diaz
  38. Juber Ahamad Abdul Bari Patel
    18 Patel
  39. Anita S Bowman
    28 Bowman
  40. David Norman Brown
    58 Brown
  41. Tejus Bale
    53 Bale
  42. Maysun M Hasan
    16 Hasan
  43. Dilmi Chathurika Perera
    4 Perera
  44. Yu Hu
    4 Hu
  45. Ying Liu
    15 Liu
  46. Douglas Alexander Mata
    24 Mata
  47. Emily Schapira Lebow
    13 Lebow
  48. Rohit Kumar Sharma
    3 Sharma
  49. Soo Ryum Yang
    28 Yang
  50. Jenna-Marie C Dix
    2 Dix
  51. Brian J Murphy
    1 Murphy
  52. Alan Hon Lun Li
    1 Li