Author
Listed:
- Cameron Herberts
(University of British Columbia)
- Matti Annala
(University of British Columbia
Tampere University and Tays Cancer Center)
- Joonatan Sipola
(Tampere University and Tays Cancer Center)
- Sarah W. S. Ng
(University of British Columbia)
- Xinyi E. Chen
(University of British Columbia)
- Anssi Nurminen
(Tampere University and Tays Cancer Center)
- Olga V. Korhonen
(Tampere University and Tays Cancer Center)
- Aslı D. Munzur
(University of British Columbia)
- Kevin Beja
(University of British Columbia)
- Elena Schönlau
(University of British Columbia)
- Cecily Q. Bernales
(University of British Columbia)
- Elie Ritch
(University of British Columbia)
- Jack V. W. Bacon
(University of British Columbia)
- Nathan A. Lack
(University of British Columbia
Koç University
Koç University)
- Matti Nykter
(Tampere University and Tays Cancer Center)
- Rahul Aggarwal
(University of California San Francisco
University of California San Francisco)
- Eric J. Small
(University of California San Francisco
University of California San Francisco)
- Martin E. Gleave
(University of British Columbia)
- David A. Quigley
(University of California San Francisco
University of California San Francisco
University of California San Francisco)
- Felix Y. Feng
(University of California San Francisco
University of California San Francisco
University of California San Francisco
University of California San Francisco)
- Kim N. Chi
(University of British Columbia
BC Cancer)
- Alexander W. Wyatt
(University of British Columbia
BC Cancer)
Abstract
> Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring1. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete2–12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.
Suggested Citation
Cameron Herberts & Matti Annala & Joonatan Sipola & Sarah W. S. Ng & Xinyi E. Chen & Anssi Nurminen & Olga V. Korhonen & Aslı D. Munzur & Kevin Beja & Elena Schönlau & Cecily Q. Bernales & Elie Ritch , 2022.
"Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer,"
Nature, Nature, vol. 608(7921), pages 199-208, August.
Handle:
RePEc:nat:nature:v:608:y:2022:i:7921:d:10.1038_s41586-022-04975-9
DOI: 10.1038/s41586-022-04975-9
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Nicolette M. Fonseca & Corinne Maurice-Dror & Cameron Herberts & Wilson Tu & William Fan & Andrew J. Murtha & Catarina Kollmannsberger & Edmond M. Kwan & Karan Parekh & Elena Schönlau & Cecily Q. Bern, 2024.
"Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer,"
Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Xian Sun & Dongshuo Yin & Fei Qin & Hongfeng Yu & Wanxuan Lu & Fanglong Yao & Qibin He & Xingliang Huang & Zhiyuan Yan & Peijin Wang & Chubo Deng & Nayu Liu & Yiran Yang & Wei Liang & Ruiping Wang & C, 2023.
"Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery,"
Nature Communications, Nature, vol. 14(1), pages 1-13, December.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:608:y:2022:i:7921:d:10.1038_s41586-022-04975-9. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.