Prostate cancer (PCa) is the second most common neoplasm in males and
in Western countries it is the 1 st cause of death in the 60-80 years age
group. In the PCa development and progression, the tumor
microenvironment (TME), consisting of immune and non-immune cells,
plays a pivotal role; due to the interaction between prostatic epithelial cells
and TME, surrounding stromal agents undergo complex changes linked to
the disease progression, metastasis, and resistance to conventional
therapies. Early diagnosis of clinically significant PCa (csPCa) could
positively change the natural history of the disease. To improve the
diagnostic algorithm the European Urological Guidelines strongly
recommend performing multiparametric MRI before prostate biopsy since
2020. However, prostate MRI results are based on a qualitative evaluation
of the images, and consequently, they are limited by a large inter-reader
variability. Despite the recent technological evolutions in the diagnostic
field, there still is a significant risk of up-staging and up-grading in the final
specimen. As opposed, advanced PCa management has a rather high socialhealth impact. In fact, the cost of early diagnosed csPCa treatment, with
radical prostatectomy, is approximately € 10,000-15,000 per patient while
the management of hormone refractory PCa requires about € 140,000 per
patient per year. Moreover, PCa treatments could be burdened by several
side-effects and in the last few years the research has been focused on
personalized surgery with the aim of tailored treatments for each patient.
In this field, the efficacy and usefulness of robot-assisted minimally invasive
surgery has now been demonstrated capable of combining oncological and
functional outcomes. The first objective of the study is the genetic
evaluation by Next Generation Sequencing of germline mutations
associated with an increased risk of PCa to select patients at higher risk of
disease progression. The second objective is the detailed characterization
of the TME to evaluate its potential role in the carcinogenesis, its
application in clinical practice for diagnosis and prognosis, and its potential
role in tailored therapies. The third objective is to identify new
methodologies and diagnostic pathways developing a fully automated
computer aided diagnosis system to detect and characterize PCa based on
its aggressiveness. A further objective is to identify therapeutic pathways,
capable of improving patient survival without altering their quality of life,
using real-time 3D virtual models. This multidisciplinary project is
considered to have a duration of 36 months with 200 patients enrolled per
year. The professional practitioners involved are pathologists, molecular
biologists, chemists, biochemists, geneticists, radiologists, radiotherapists,
and urologists