Quantitative modeling and analysis of drug screening data for personalized cancer medicine

Despite recent progress in the field of molecular medicine, the treatment and cure of complex diseases such as cancer remains a challenge. Development of resistance to first-line chemotherapy is a common cause of current anticancer treatment failure. To deal with this problem, the personalized medicine (PM) approach has been adapted toward more targeted cancer research and management. The PM approach is based on each patient s genetic, epigenetic and drug response profiling, which is used to design the best treatment option for the given patient. As the PM approach is increasingly being adopted in clinical practice, there is an urgent need for computational models and data mining methods that allow fast processing and analysis of the massive relevant profiling datasets. High-throughput drug screening enables systematic profiling of cellular responses to a wide collection of oncology compounds and their combinations, hence providing an unbiased strategy for personalized drug treatment selection. However, screening experiments with patient-derived cell samples often results in high-dimensional data matrices, with inherent sources of noise. This complicates many downstream analyses, such as the detection of differential drug activity or understanding the mechanisms behind drug sensitivity and resistance in a given patient. To meet these challenges, a computational pipeline for drug response profiling was developed in this thesis. The pipeline was based on a novel metric to quantify drug response, called the drug sensitivity score (DSS). Further, by combining the normalized drug response profile of each cancer sample with a global drug-target interaction network, a target addiction score (TAS) was developed to de-convolute the selective protein targets and obtain knowledge on their functional importance. Finally, delta scoring was developed to quantify drug combination effects and to address the problem of the clonal evolution of cancer, which often leads to resistance to mono therapies. This novel computational pipeline improves understanding of cancer development and translates compound activities into informed treatment choices for clinicians. As exemplified in two case studies of adult acute myeloid leukemia (AML) and adult granulosa cell tumor (AGCT), the models developed here have the potential to significantly contribute to the effective analysis of data from individual cancer patients and from pan-cancer cell line panels. Hence, these models will play a substantial role in future personalized cancer treatment strategies and the selection of effective treatment options for individual cancer patients.N

Identifying Candidate Biomarkers of Clinical Response to Ustekinumab in Psoriasis

The development and use of biologic drugs, including ustekinumab, secukinumab, adalimumab and newer anti-interleukin (IL)-23p19 biologics, have revolutionised treatment for the immune-mediated inflammatory skin disease, psoriasis. Ustekinumab targets the shared IL-12/IL-23p40 subunit, while anti-IL-23p19 biologics target the specific IL-23p19 subunit. Both forms of biologics prevent the binding of IL-23 to its receptor, inhibiting the translocation of Signal Transducer and Activator of Transcription 3 (STAT3) and downstream IL-17A expression. In turn, secukinumab blocks IL-17A and adalimumab blocks tumour necrosis factor (TNF). Although highly effective, clinical response is variable with up to 30% of patients failing to achieve a satisfactory clinical response, namely a 75% reduction in Psoriasis Area Severity Index (PASI75). Hence, there is a need to identify predictive biomarkers of response to biologics.Mechanistic biomarkers i.e., associated with the drug’s mechanism of action, are considered more accurate biomarkers. Moreover, their identification in the blood would prove advantageous as they would be easy-to-access and implement in clinical settings. Therefore, we hypothesized that biomarkers predictive of response to biologics targeting the IL-23/IL-17A axis could be identified among IL-23-responsive cells within peripheral blood mononuclear cells (PBMCs).As a first step, we developed an imaging-flow cytometry assay to quantify IL-23-induced STAT3 translocation in healthy volunteer PBMCs and identified mucosal-associated invariant T (MAIT) cells and CD8+CCR6+CD161+Vα7.2- T cells to be IL-23-responsive. Next, we identified the optimum IL-23 concentration to induce STAT3 translocation and established a threshold to reproducibly define positive translocation. Additionally, we confirmed the robustness of this assay by measuring STAT3 translocation in PBMCs over time with no fluctuations.Having optimised an experimental assay, we measured IL-23-induced STAT3 translocation in PBMCs of psoriasis patients before (baseline week (Wk)0) and during (Wk1, Wk4 and Wk12) ustekinumab therapy. Clinical response was assessed as PASI75 and residual disease at Wk12. Within MAIT cells, STAT3 translocation was significantly lower in PASI75 non-responders compared to responders at Wk0 (p&lt;0.05, FDR&lt;0.05) and highly predictive of PASI75 outcome in Receiver Operating Characteristic (ROC) curve analysis, yielding 87.5% specificity and 100% sensitivity.To investigate a clinically scalable alternative to STAT3 translocation as a predictive biomarker, we measured cytokine production in PBMCs of ustekinumab-treated psoriasis patients. The frequency of IL-17A-producing CD8+ T cells significantly decreased after Wk12 of ustekinumab treatment in PASI75 responders (p&lt;0.05), but not in non-responders. However, no differences with potential predictive value were identified at Wk0.After identifying IL-23-induced STAT3 translocation as a potential biomarker predictive of response to ustekinumab, we also assessed STAT3 translocation in psoriasis patients prior receiving treatment with adalimumab and secukinumab. We observed no difference in STAT3 translocation between adalimumab PASI75 non-responders and responders. However, STAT3 translocation was significantly lower (p&lt;0.05) in MAIT cells of secukinumab PASI75 non-responders than responders at Wk0. Next, we validated IL-23-induced STAT3 translocation as a predictive biomarker by applying the previously determined ROC cut-off to an independent ustekinumab-treated patient cohort.Finally, we investigated the ability of IL-23-induced STAT3 translocation to predict clinical response to newer anti-IL-23p19 biologics. PASI75 clinical response was accurately predicted in 8/10 patients undergoing treatment with anti-IL23p19 biologics, highlighting the predictive potential of this biomarker in the context of newer biologics targeting IL-23.Taken together, we have identified IL-23-induced STAT3 translocation in MAIT cells as a candidate predictive biomarker of response to biologics targeting the IL-23/IL-17 axis. Future work should involve refining the clinical feasibility of this biomarker by investigating STAT3 phosphorylation as an alternative. Finally, this biomarker has potential for further validation in clinical trials, with the aim to be utilised for patient stratification.<br/