Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Veracyte Inc, San Diego, California, USA.
Cancer. 2023 Jul 15;129(14):2169-2178. doi: 10.1002/cncr.34790. Epub 2023 Apr 14.
Prostate cancer (PCa) is a clinically heterogeneous disease. The creation of an expression-based subtyping model based on prostate-specific biological processes was sought.
Unsupervised machine learning of gene expression profiles from prospectively collected primary prostate tumors (training, n = 32,000; evaluation, n = 68,547) was used to create a prostate subtyping classifier (PSC) based on basal versus luminal cell expression patterns and other gene signatures relevant to PCa biology. Subtype molecular pathways and clinical characteristics were explored in five other clinical cohorts.
Clustering derived four subtypes: luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), and basal neuroendocrine (BN). LP and LD tumors both had higher androgen receptor activity. LP tumors also had a higher expression of cell proliferation genes, MYC activity, and characteristics of homologous recombination deficiency. BI tumors possessed significant interferon γactivity and immune infiltration on immunohistochemistry. BN tumors were characterized by lower androgen receptor activity expression, lower immune infiltration, and enrichment with neuroendocrine expression patterns. Patients with LD tumors had less aggressive tumor characteristics and the longest time to metastasis after surgery. Only patients with BI tumors derived benefit from radiotherapy after surgery in terms of time to metastasis (hazard ratio [HR], 0.09; 95% CI, 0.01-0.71; n = 855). In a phase 3 trial that randomized patients with metastatic PCa to androgen deprivation with or without docetaxel (n = 108), only patients with LP tumors derived survival benefit from docetaxel (HR, 0.21; 95% CI, 0.09-0.51).
With the use of expression profiles from over 100,000 tumors, a PSC was developed that identified four subtypes with distinct biological and clinical features.
Prostate cancer can behave in an indolent or aggressive manner and vary in how it responds to certain treatments. To differentiate prostate cancer on the basis of biological features, we developed a novel RNA signature by using data from over 100,000 prostate tumors-the largest data set of its kind. This signature can inform patients and physicians on tumor aggressiveness and susceptibilities to treatments to help personalize cancer management.
前列腺癌（PCa）是一种临床表现具有异质性的疾病。本研究旨在建立一种基于前列腺特异性生物学过程的表达谱亚分型模型。
对前瞻性采集的原发性前列腺肿瘤（训练组，n=32000；验证组，n=68547）的基因表达谱进行无监督机器学习，以创建一种基于基底细胞和腔细胞表达模式及其他与 PCa 生物学相关基因特征的前列腺亚分类器（PSC）。在另外五个临床队列中探索了亚类分子途径和临床特征。
聚类分析得到 4 种亚型：腔细胞分化型（LD）、腔细胞增殖型（LP）、基底免疫型（BI）和基底神经内分泌型（BN）。LP 和 LD 肿瘤均具有较高的雄激素受体活性。LP 肿瘤还表现出更高的细胞增殖基因表达、MYC 活性和同源重组缺陷的特征。BI 肿瘤的免疫组织化学染色具有显著的干扰素 γ 活性和免疫浸润。BN 肿瘤的雄激素受体活性表达较低，免疫浸润较少，神经内分泌表达模式富集。LD 肿瘤患者的肿瘤特征侵袭性较小，手术后转移时间最长。仅 BI 肿瘤患者在手术后接受放疗的转移时间（风险比 [HR]，0.09；95%置信区间，0.01-0.71；n=855）方面获益。在一项将转移性 PCa 患者随机分为雄激素剥夺加或不加多西他赛的 3 期临床试验（n=108）中，仅 LP 肿瘤患者从多西他赛治疗中获益（HR，0.21；95%置信区间，0.09-0.51）。
本研究使用了超过 10 万个肿瘤的表达谱，开发了一种 PSC，可识别出具有不同生物学和临床特征的 4 种亚型。
