Details

Autor(en) / Beteiligte

• Helzer, K.T.
• Sharifi, M.N.
• Sperger, J.M.
• Shi, Y.
• Annala, M.
• Bootsma, M.L.
• Reese, S.R.
• Taylor, A.
• Kaufmann, K.R.
• Krause, H.K.
• Schehr, J.L.
• Sethakorn, N.
• Kosoff, D.
• Kyriakopoulos, C.
• Burkard, M.E.
• Rydzewski, N.R.
• Yu, M.
• Harari, P.M.
• Bassetti, M.
• Blitzer, G.
• Floberg, J.
• Sjöström, M.
• Quigley, D.A.
• Dehm, S.M.
• Armstrong, A.J.
• Beltran, H.
• McKay, R.R.
• Feng, F.Y.
• O’Regan, R.
• Wisinski, K.B.
• Emamekhoo, H.
• Wyatt, A.W.
• Lang, J.M.
• Zhao, S.G.

Titel

Fragmentomic analysis of circulating tumor DNA-targeted cancer panels

Ist Teil von

• Annals of oncology, 2023-09, Vol.34 (9), p.813-825

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner. We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n = 320). Each cohort was split 70%/30% into training and validation sets. In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99. To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost. •cfDNA fragmentomics in targeted cancer gene panels, not just WGS, can infer key phenotypic features of cancer.•Fragmentomic models of standard targeted cfDNA panels can distinguish between cancer types and subtypes.•Fragmentomic models of standard targeted cfDNA panels can distinguish cancer versus normal.