Towards targeted combinatorial therapy design for the treatment of castration-resistant prostate cancer

Abstract

Background: Prostate cancer is one of the most prevalent cancers in males in the United States and amongst the leading causes of cancer related deaths. A particularly virulent form of this disease is castration-resistant prostate cancer (CRPC), where patients no longer respond to medical or surgical castration. CRPC is a complex, multifaceted and heterogeneous malady with limited standard treatment options. Results: The growth and progression of prostate cancer is a complicated process that involves multiple pathways. The signaling network comprising the integral constituents of the signature pathways involved in the development and progression of prostate cancer is modeled as a combinatorial circuit. The failures in the gene regulatory network that lead to cancer are abstracted as faults in the equivalent circuit and the Boolean circuit model is then used to design therapies tailored to counteract the effect of each molecular abnormality and to propose potentially efficacious combinatorial therapy regimens. Furthermore, stochastic computational modeling is utilized to identify potentially vulnerable components in the network that may serve as viable candidates for drug development. Conclusion:The results presented herein can aid in the design of scientifically well-grounded targeted therapies that can be employed for the treatment of prostate cancer patients.

A comprehensive mechanistic basis of prostate cancer advancement & its personalized implementation-bridging the gap: present state and future prospect

“…Basically, current therapeutic inconsistencies for CRPC can be explained by several other critical factors which not only increases the complexities of these diseases but also are centrally responsible for observed ineffectiveness of available treatment processes during late stage of CRPC progression. Fundamentally, CRPC progression can be characterized by its extreme levels of inter-tumor & intra-tumor heterogeneity [21][22][23], remarkable variability's in clinical behavior along with mechanistic complexities and presence of redundancies in tumor cell signaling pathways driven by the key functional interplay of multiple oncogenes [8,[50][51][52][53]. In fact, these prostate cancer patient specific intra-tumor heterogeneities also provides the essential challenges for attaining desired clinical effectiveness during metastatic prostate cancer treatment [54,55].…”

“…But, in most of the cases current therapeutic strategy which is mainly based on targeting either androgen signaling pathways and cell proliferation wing or immune checkpoint blockade, cannot effectively cover the pathogenic complexities and signaling redundancies observed in CRPC [10,16,33]. Actually, the development of castration resistance state is markedly triggered by orchestration of several critical factors including AR dependent and independent pathways, interactions between AR signaling and alternative survival pathways, neuroendocrine transdifferentiation, prostate cancer stem cell, autophagy, tumor hypoxia, microenvironment, immune-suppression and selective pressure provided by ADT [8,53,53,125]. Similarly, prostate cancer bone metastasis which adversely effects more than 90% of advanced prostate cancer patient is also an extremely pathologically complex phenomenon that is quite difficult to treat effectively with a single molecularly targeted agent like Denosumab or Zoledronic acid [126,127].…”

Drug discovery and computational strategies in the multitarget drugs era

“…Prostate cancer (PC) is the most common diagnosis of cancer among men and the second leading cause of cancer death. Several forms of treatment for the local tumor have been widely used (Arshad et al, 2017). However, when invasion occurs to other organs, conventional treatment is not always effective.…”

Targeting Oncogenic Mutations in Colorectal Cancer using Cryptotanshinone

“…The key goal of these modeling techniques in cancer is to provide a basis for designing chemotherapy regimes and to facilitate qualitative prognosis about the dynamic evolution of the disease. Various techniques such as differential equations [8], boolean networks [9]- [11], probabilistic boolean networks [12], and bayesian networks [13]- [15] have had some success along these lines.…”