Biology subjects

Nichol, D.

Publications and source records attributed to Nichol, D..

3 recordsLinked to original sources

bioRxiv2017

Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

Immune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and \"virtual CT\" images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.

cancer biology↗

bioRxiv2017

Collateral sensitivity is contingent on the repeatability of evolution

Antibiotic resistance represents a growing health crisis that necessitates the immediate discovery of novel treatment strategies. One such strategy is the identification of collateral sensitivities, wherein evolution under a first drug induces susceptibility to a second. Here, we report that sequential drug regimens derived from in vitro evolution experiments may have overstated therapeutic benefit, predicting a collaterally sensitive response where cross resistance ultimately occurs. We quantify the likelihood of this phenomenon by use of a mathematical model parametrised with combinatorially complete fitness landscapes for Escherichia coli. Through experimental evolution we then verify that a second drug can indeed stochastically exhibit either increased susceptibility or increased resistance when following a first. Genetic divergence is confirmed as the driver of this differential response through targeted and whole genome sequencing. Taken together, these results highlight that the success of evolutionarily-informed therapies is predicated on a rigorous probabilistic understanding of the contingencies that arise during the evolution of drug resistance.

evolutionary biology↗

bioRxiv2016

Harnessing the lymphocyte meta-phenotype to optimize adoptive cell therapy

There is an urgent need for reliable effective therapy for patients with metastatic sarcoma. Approaches that manipulate the immune system have shown promise for patients with advanced, widely disseminated malignancies. One of these approaches is adoptive cell therapy (ACT), where tumor-infiltrating lymphocytes (TIL) are isolated from the tumor, expanded ex vivo, and then transferred back to the patient. This approach has shown great promise in melanoma, leading to an objective response in approximately half of treated patients [14]. Standard protocols involve characterization of TIL populations with respect to adaptive CD4+ and CD8+ T-lymphocytes, but neglect the possible role of the innate lymphoid repertoire. Due to toxicity and the high cost associated with ACT, the IFN-{gamma} release assay is currently used as a proxy to identify suitable TIL isolates for ACT. Efforts in TIL-ACT for sarcoma, which are pre-clinical and pioneered at Moffitt Cancer Center, have shown that only a minority of the TIL cultures show tumor specific activity in ex vivo IFN-{gamma} assays. Surprisingly, internal melanoma trial data reveal a lack of correlation between IFN-{gamma} assay and clinical outcomes, highlighting the need for a more reliable proxy. We hypothesize the existence of a predictable TIL meta-phenotype that leads to optimal tumor response. Here, we describe preliminary efforts to integrate prospective and existing patient data with mathematical models to optimize the TIL meta-phenotype prior to re-injection.

cancer biology↗