From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Abstract

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

“…PC is phenotypically and molecularly very heterogeneous [ 11 , 12 ], and presents therefore severe challenges for diagnosis and treatment. The unambiguous identification of distinct metabolic signatures for different PC subtypes by us here, provides promising opportunities for diagnostic tools which identify the molecular processes driving PC development to enable a reliable stratification into patient-tailored therapies [ 16 , 56 , 57 ]. With respect to PC heterogeneity, we characterized the metabolic differences between molecular PC subtypes based on the combined Ki67/PSA immunoreactivity score; a useful marker for tumor aggressiveness and providing prognostic information independent to the Gleason and ISUP grading.…”

“…Importantly, the Ki67/PSA immunoreactivity score of diagnostic tumor biopsies seems to allow prediction of patient prognosis and bone metastatic subtype [ 14 , 15 ]. Together, those various subtypes provide valuable insights into prostate tumor heterogeneity and mechanisms involved in tumor progression [ 16 , 17 ], but validated classifiers are still absent in the clinical environment to enable tailored subtype-specific therapies. Moreover, there is also only scarce knowledge about the underlying molecular mechanisms and biochemical pathways driving various PC subtypes.…”

Preclinical Anticipation of On- and Off-Target Resistance Mechanisms to Anti-Cancer Drugs: A Systematic Review

“…Mutations leading to the loss of inhibitor binding or even to the switch to activator properties have thereby been found. This has, for instance, been reported for the AR in prostate cancer [35,36]. A randomly mutated plasmid library expressing the AR co-transfected with a reporter gene assay was successfully used to identify amino acids leading to resistance to the AR inhibitor enzalutamide following selection by fluorescence-activated cell sorting (FACS) [37].…”

Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia

“…Analysis of biofluids by mass spectrometry or NMR-based ‘omics techniques is a well-developed method in identifying markers for diagnosis and/or prognosis of various conditions, including PCa [ 7 , 10 , 11 ]. Recent studies have explored a variety of different multi-omics approaches to diagnosing PCa [ 12 , 13 ]; matrices including blood, urine, tissue and others have all been investigated in this way [ 14 ]. Such analyses frequently use machine-learning algorithms to process large high-dimensional datasets, but in many cases there is a risk that these analyses will introduce bias by training the models on idealised cohorts with clear distinctions between cases and healthy controls [ 15 ].…”