Glioblastoma signature in the DNA of blood-derived cells
Abstract
Current approach for the detection of cancer is based on identifying genetic mutations typical to tumor cells. This approach is effective only when cancer has already emerged, however, it might be in a stage too advanced for effective treatment. Cancer is caused by the continuous accumulation of mutations; is it possible to measure the time-dependent information of mutation accumulation and predict the emergence of cancer? We hypothesize that the mutation history derived from the tandem repeat regions in blood-derived DNA carries information about the accumulation of the cancer driver mutations in other tissues. To validate our hypothesis, we computed the mutation histories from the tandem repeat regions in blood-derived exomic DNA of 3874 TCGA patients with different cancer types and found a statistically significant signal with specificity ranging from 66% to 93% differentiating Glioblastoma patients from other cancer patients. Our approach and findings offer a new direction for future cancer prediction and early cancer detection based on information derived from blood-derived DNA.
Additional Information
© 2021 Jain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: April 26, 2021; Accepted: August 16, 2021; Published: September 8, 2021. The authors would like to thank Eytan Ruppin for his valuable advice and feedback. Source of funding: Caltech internal research funding (Mead New Adventures Fund) for Siddharth Jain, Bijan Mazaheri, Netanel Raviv. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions: Conceptualization: Jehoshua Bruck. Formal analysis: Siddharth Jain. Funding acquisition: Jehoshua Bruck. Investigation: Siddharth Jain, Bijan Mazaheri, Jehoshua Bruck. Methodology: Siddharth Jain. Software: Siddharth Jain, Bijan Mazaheri, Netanel Raviv. Supervision: Jehoshua Bruck. Validation: Siddharth Jain, Bijan Mazaheri. Writing – original draft: Siddharth Jain. Writing – review & editing: Siddharth Jain, Bijan Mazaheri, Netanel Raviv, Jehoshua Bruck. The authors have declared that no competing interests exist. Data and material availability: The BAM files for WXS samples of cancer patients used in the study were obtained from The Cancer Genome Atlas (TCGA) [15]. These files have controlled access and cannot be availed publicly. However, request to access TCGA controlled data can be made via dbGap [28] (accession code: phs000178.v1.p1). The metadata information for the analyzed samples is given in S1–S11 Files. The code and necessary documentation for the pipeline used is available at https://github.com/sidjain516/GBM-Classification.Attached Files
Published - journal.pone.0256831.pdf
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Additional details
- PMCID
- PMC8425531
- Eprint ID
- 111263
- Resolver ID
- CaltechAUTHORS:20211007-150341511
- Caltech Mead New Adventure Fund
- Created
-
2021-10-07Created from EPrint's datestamp field
- Updated
-
2021-10-07Created from EPrint's last_modified field