About this Research Topic
Cancer research has undergone radical changes over the last few years. The issue today is no longer the amount of basic and clinical information available, but how to handle it. System biology is the latest in a series of techniques driven by technological advances that have provided us with a suite of “omics”. However, despite this continuous progress, prostate cancer remains a major public health problems throughout the world.
This is largely due to the fact that the tumoral mass cannot be identified using current imaging techniques. Prostate cancer can only be diagnosed on the basis of increased prostate-specific antigen levels associated with a low accurancy of the biopsy fragments and the well-known subjectivity of a pathologist’s interpretation. This has led to many patients being over-treated, under-treated or simply inappropriately treated, and allowed the progression of the disease. The current classification of very low-, low-, intermediate- and high-risk disease is only accurate in the case of patients with high-risk disease. Furthermore, other factors such as early detection, population aging and better treatment have contributed to increasing the prevalence of prostate cancer, thus fuelling a need for the continuous monitoring of prevalence indicators in order to identify needs, plan the allocation of resources, and improve healthcare programmes for cancer survivors.
Despite the technical advantages offered by robotic systems and other techniques, the diagnostic process requires further improvement. It is now widely accepted that prostate cancer encompasses various pathological entities and a wide range of clinical behaviours, and is underpinned by a complex array of genetic alterations that affect supra-molecular processes. It is this genetic and phenotypical variability that primarily determines the progression of prostate cancer and its response to therapy. Furthermore, the asynchrony and self-progression of cancer cell populations suggests that the extent to which each neoplastic cell shares the properties of a natural cell differs in terms of time and space. Individual cells from a clonal cell population may respond differently (or not at all) to the same stimulus. Prostate cancer consists of distinct subpopulations of cancer cells, each with its own characteristic sensitivity to a given therapeutic agent. Cancer therapies can therefore be seen as filters that remove the sensitive subpopulations, but allow insensitive subpopulations to escape.
The aim of this Research Topic is to review the molecular, cellular, clinical, epidemiological and imaging findings that are fundamental for identifying the patients who really need to undergo primary treatment. Furthermore, viewing prostate cancer as a system that is dynamically complex in time and space might reveals more about its underlying behavioural characteristics. The combined efforts of urologists, pathologists, biologists, radiotherapists and mathematicians can contribute much towards improving our understanding of the complexity of cancer, and such a multidisciplinary approach will help to clarify existing concepts, categorise current knowledge, and suggest alternative approaches to the discovery of biomarkers and predictive values that urgently need to be translated into clinical practice.
This is largely due to the fact that the tumoral mass cannot be identified using current imaging techniques. Prostate cancer can only be diagnosed on the basis of increased prostate-specific antigen levels associated with a low accurancy of the biopsy fragments and the well-known subjectivity of a pathologist’s interpretation. This has led to many patients being over-treated, under-treated or simply inappropriately treated, and allowed the progression of the disease. The current classification of very low-, low-, intermediate- and high-risk disease is only accurate in the case of patients with high-risk disease. Furthermore, other factors such as early detection, population aging and better treatment have contributed to increasing the prevalence of prostate cancer, thus fuelling a need for the continuous monitoring of prevalence indicators in order to identify needs, plan the allocation of resources, and improve healthcare programmes for cancer survivors.
Despite the technical advantages offered by robotic systems and other techniques, the diagnostic process requires further improvement. It is now widely accepted that prostate cancer encompasses various pathological entities and a wide range of clinical behaviours, and is underpinned by a complex array of genetic alterations that affect supra-molecular processes. It is this genetic and phenotypical variability that primarily determines the progression of prostate cancer and its response to therapy. Furthermore, the asynchrony and self-progression of cancer cell populations suggests that the extent to which each neoplastic cell shares the properties of a natural cell differs in terms of time and space. Individual cells from a clonal cell population may respond differently (or not at all) to the same stimulus. Prostate cancer consists of distinct subpopulations of cancer cells, each with its own characteristic sensitivity to a given therapeutic agent. Cancer therapies can therefore be seen as filters that remove the sensitive subpopulations, but allow insensitive subpopulations to escape.
The aim of this Research Topic is to review the molecular, cellular, clinical, epidemiological and imaging findings that are fundamental for identifying the patients who really need to undergo primary treatment. Furthermore, viewing prostate cancer as a system that is dynamically complex in time and space might reveals more about its underlying behavioural characteristics. The combined efforts of urologists, pathologists, biologists, radiotherapists and mathematicians can contribute much towards improving our understanding of the complexity of cancer, and such a multidisciplinary approach will help to clarify existing concepts, categorise current knowledge, and suggest alternative approaches to the discovery of biomarkers and predictive values that urgently need to be translated into clinical practice.
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