Molecular classifications of prostate cancer: basis for individualized risk stratification and precision therapy

Figures & data

Table 1. Details of the eight molecular classifications for prostate cancer, including molecular subtypes, methods, and clinical relevance.

Classifications	PCa Subtypes and proportion	Model establish methodology	Clinical relevance	Authors	Journal	Issuing year
Tomlins classification	m-ERG+ (45%) m-ETS+ (9%) m-ETS+ (9%) m-SPINK1+ (8%) TripleNeg (38%)	Random Forrest supervised model for ERG + model Unsupervised outlier analysis for ETV1+, ETV4+, ETV5+, FLI1+ or SPINK1+ model	TripleNeg and m-SPINK1+ relate to high preoperative PSA and Gleason score m-ERG + exhibits high extraprostatic extension probability m-ETS + exhibits the highest probability of having seminal vesicle invasion (SVI) non-ERG + might represent anterior tumours Poor prognostic prediction value	Tomlins et al.	European Urology	2015
TCGA classification	ERG (46%) ETV1 (8%) ETV4 (4%) FLI1 (1%) SPOP (11%) FOXA1 (3%) IDH1 (1%) Others (26%)	Pathology evaluation for 333 tumours Unsupervised clustering Integrative clustering using iCluster algorithms	SPOP + and FOXA1+ subtypes exhibit the highest AR score, and the score of ERG + subtype is variable Mutation of SPOP enhances the susceptibility to ADT Unrevealed prognostic value	The Cancer Genome Atlas Program Office	Cell	2015
14-pathway classification	PCS1 (6%) PCS2 (47%) PCS3 (47%)	Calculate z-score to quantify pathway-activation, Non-negative matrix factorization clustering to identify the optimal numbers of cluster Construct 14-pathway classification via a naïve Bayes machine learning algorithm A random forest machine learning algorithm for a diagnostic gene panel	PCS1 represents a high-risk phenotype even in a low Gleason score setting The most aggressive subtype among PCS has not yet been agreed upon, while more evidence tends toward PCS1	You et al.	Cancer research	2016
PAM50 classification	Luminal A (34.3%) Luminal B (28.5%) Basal-like (37.1%)	PAM50 algorithm	Luminal B subtype presents the poorest phenotype, has the highest levels of PSA and GS, and the probabilities of extraseminal invasion and lymph node invasion Patients in luminal B are potential responder to ADT	Zhao et al.	JAMA oncology	2017
Bone-metastatic classification	MetA (71%) MetB (17%) MetC (12%)	Whole genome expression analysis Unsupervised cluster analysis to identify cluster numbers Latent structures discriminant analysis to establish the classification	MetB has the shortest PCa-related specific survival and lowest level of serum PSA MetA subtype can dedifferentiate into the more aggressive MetB subtype by altering AR activity; Morphological features of primary PCa would continue to the bone metastasis stage	Thysell et al.	Molecular oncology	2019
Epithelial-defined classification	Luminal A (30.0%) Luminal B (26.0%) Non-luminal C (14.7%) Non-luminal D (2%) Mixed (25.0%)	Consensus Clustering Deconvolution by CIBERSORT	93% of the localized PCa are luminal subtypes, 85% of early-onset PCa belong to the luminal A subtype, and 88% of mCRPC consist of the non-luminal C subtype Non-luminal C and D correlate to aggressive variant PCa PCa with a high PSA/PAP ratio cannot benefit from docetaxel	Han et al.	Prostate Cancer and Prostatic Diseases	2021
Immune-related classification	Immune-activated (14.9%) Immune-suppressed (25.5%) Nonimmune (59.6%)	Non-negative matrix factorization ssGSEA	Immune-suppressed subgroup has a higher T stage and poor prognosis Monotherapy with anti-PD-1 is available for immune-activated phenotype	Meng et al.	Molecular oncology	2020
Multi-omics classification	PMOC1 (47.9%) PMOC2 (28.2%) PMOC3 (35.3%)	Ten cluster algorithms in MOVICS R package PAM50, clustering prediction index, Gaps-statistics analysis	PMOC2 represents the poorest phenotype and correlates to the highest PSA, Gleason score and T stage PMOC can refine the PAM50 subtype.	Meng et al.	Clinical and translational medicine	2021

Figure 1. Schematic representation of the eight prostate cancer classifications.

Figure 2. The subtypes with high activation of androgen receptor, and their prognostic features, sensitivity to androgen-deprivation therapy (ADT), and molecular features.