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  5. Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures

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Preprint
en
2023

Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures

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en
2023
DOI: 10.21203/rs.3.rs-1638211/v1

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Shahneen Sandhu
Shahneen Sandhu

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A. M. Mahedi Hasan
Paolo Cremaschi
Daniel Wetterskog
+16 more

Abstract

<title>Abstract</title> Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient relationships of metastases that evade treatment, we performed genome-wide copy number profiling and bespoke approaches targeting the androgen receptor (<italic>AR</italic>) on 142 metastatic regions from 10 organs harvested post-mortem from nine men who died from prostate cancer. We identified diverse and patient-unique alterations clustering around the <italic>AR</italic> in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of <italic>AR</italic>-neutral clones. Using the genomic boundaries of pan-autosome copy number change, we confirmed a common clone of origin across metastases and diagnostic biopsies; and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations and cluster-specific <italic>AR</italic> gene architectures. Copy number boundaries identified treatment-selected clones with distinct lethal trajectories.

How to cite this publication

A. M. Mahedi Hasan, Paolo Cremaschi, Daniel Wetterskog, Anuradha Jayaram, Stephen Q. Wong, Scott Williams, Anupama Pasam, Anna Trigos, Blanca Trujillo, Emily Grist, Stefanie Friedrich, Osvaldas Vainauskas, Marina Parry, Mazlina Ismail, Wout Devlies, Anna Wingate, Stefano Lise, Shahneen Sandhu, Gerhardt Attard (2023). Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures. , DOI: https://doi.org/10.21203/rs.3.rs-1638211/v1.

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Publication Details

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Preprint

Year

2023

Authors

19

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.21203/rs.3.rs-1638211/v1

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