Nascent Prostate Cancer Heterogeneity Drives Evolution and Resistance to Intense Hormonal Therapy

新生前列腺癌的异质性推动了其进化和对强化激素疗法的抵抗

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作者:Scott Wilkinson, Huihui Ye, Fatima Karzai, Stephanie A Harmon, Nicholas T Terrigino, David J VanderWeele, John R Bright, Rayann Atway, Shana Y Trostel, Nicole V Carrabba, Nichelle C Whitlock, Stephanie M Walker, Rosina T Lis, Houssein Abdul Sater, Brian J Capaldo, Ravi A Madan, James L Gulley, Guine

Background

Patients diagnosed with high risk localized prostate cancer have variable outcomes following surgery. Trials of intense neoadjuvant androgen deprivation therapy (NADT) have shown lower rates of recurrence among patients with minimal residual disease after treatment. The molecular features that distinguish exceptional responders from poor responders are not known.

Conclusions

A subset of prostate cancers exhibit greater histologic and genomic diversity at the time of diagnosis, and these localized tumors have greater fitness to resist therapy. Patient summary: Some prostate cancer tumors do not respond well to a hormonal treatment called androgen deprivation therapy (ADT). We used tumor volume and four other parameters to develop a model to identify tumors that will not respond well to ADT. Treatments other than ADT should be considered for these patients.

Objective

To identify genomic and histologic features associated with treatment resistance at baseline. Design, setting, and participants: Targeted biopsies were obtained from 37 men with intermediate- to high-risk prostate cancer before receiving 6 mo of ADT plus enzalutamide. Biopsy tissues were used for whole-exome sequencing and immunohistochemistry (IHC). Outcome measurements and statistical analysis: We assessed the relationship of molecular features with final pathologic response using a cutpoint of 0.05 cm3 for residual cancer burden to compare exceptional responders to incomplete and nonresponders. We assessed intratumoral heterogeneity at the tissue and genomic level, and compared the volume of residual disease to the Shannon diversity index for each tumor. We generated multivariate models of resistance based on three molecular features and one histologic feature, with and without multiparametric magnetic resonance imaging estimates of baseline tumor volume.

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