Regulators of TRAIL resistance in normal and transformed cells

TRAIL is a member of the Tumor Necrosis Factor superfamily which was shown to be able to induce apoptotic cell death in a wide variety of transformed cells, leaving normal, healthy cells unharmed. However, approximately 50-60% of the cancer cells were shown to be resistant to the cytotoxic effect of TRAIL, a large number of chemotherapeutics are being studied to broaden the efficacy of TRAIL. However the effect of these co-treatments on non-transformed cells is unpredictable as at present we do not have a proper understanding on the regulation behind the resistance of normal cells to TRAIL. Preliminary works in our lab showed that in primary non-transformed cells TRAIL resistance is maintained by multiple anti-apoptotic proteins. In order to overcome the resistance toward TRAIL-induced apoptosis, the pathway has to be inhibited at two different stages by removal of anti-apoptotic proteins (cFLIP, Mcl-1, Bcl-2, Bcl-XL or XIAP). Using different transcription site searches we have identified the transcription factor Sp1 as a candidate for maintaining the expression levels of these anti-apoptotic proteins, which in turn is regulated by the activity of GSK3 and CDK1. In contrast to the observed redundancy in resistance mechanism to TRAIL seen in non-transformed cell lines, the majority of the cancer cell lines tend to rely on a single mechanism of resistance against TRAIL. This redundancy in TRAIL resistance in non-transformed cells indicate that there is a safe therapeutic window using TRAIL-based combination therapies, which targets a single, dominating resistance pathway. However knowledge of the mechanism of resistance should aid the choice of therapy. TRAIL also activates non-apoptotic/inflammatory signaling, such as the NF-kappaB pathway which in certain cases may drive the resistance to TRAIL. In order to examine the involvement of this non-apoptotic pathway in TRAIL signaling, a reliable inhibitor would be needed that is able to uncouple the death ligand-mediated canonical NF-kappaB activation from apoptosis signaling. Using computer-aided drug design we have performed several virtual screening methods to identify lead molecules that would be able to break up the interaction between TRADD and TRAF2 and thus selectively block death receptor induced NF-kappaB activation.2019-05-2

Dying to Survive—The p53 Paradox

The p53 tumour suppressor is best known for its canonical role as “guardian of the genome”, activating cell cycle arrest and DNA repair in response to DNA damage which, if irreparable or sustained, triggers activation of cell death. However, despite an enormous amount of work identifying the breadth of the gene regulatory networks activated directly and indirectly in response to p53 activation, how p53 activation results in different cell fates in response to different stress signals in homeostasis and in response to p53 activating anti-cancer treatments remains relatively poorly understood. This is likely due to the complex interaction between cell death mechanisms in which p53 has been activated, their neighbouring stressed or unstressed cells and the local stromal and immune microenvironment in which they reside. In this review, we evaluate our understanding of the burgeoning number of cell death pathways affected by p53 activation and how these may paradoxically suppress cell death to ensure tissue integrity and organismal survival. We also discuss how these functions may be advantageous to tumours that maintain wild-type p53, the understanding of which may provide novel opportunity to enhance treatment efficacy

surviveR: a flexible shiny application for patient survival analysis

Kaplan–Meier (KM) survival analyses based on complex patient categorization due to the burgeoning volumes of genomic, molecular and phenotypic data, are an increasingly important aspect of the biomedical researcher’s toolkit. Commercial statistics and graphing packages for such analyses are functionally limited, whereas open-source tools have a high barrier-to-entry in terms of understanding of methodologies and computational expertise. We developed surviveR to address this unmet need for a survival analysis tool that can enable users with limited computational expertise to conduct routine but complex analyses. surviveR is a cloud-based Shiny application, that addresses our identified unmet need for an easy-to-use web-based tool that can plot and analyse survival based datasets. Integrated customization options allows a user with limited computational expertise to easily filter patients to enable custom cohort generation, automatically calculate log-rank test and Cox hazard ratios. Continuous datasets can be integrated, such as RNA or protein expression measurements which can be then used as categories for survival plotting. We further demonstrate the utility through exemplifying its application to a clinically relevant colorectal cancer patient dataset. surviveR is a cloud-based web application available at https://generatr.qub.ac.uk/app/surviveR, that can be used by non-experts users to perform complex custom survival analysis.<br/