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Abstract Clinical trials are costly and time-intensive endeavors, with a high rate of drug candidate failures. Moreover, the standard approaches often evaluate drugs under a limited number of protocols. In oncology, where multiple treatment protocols can yield divergent outcomes, addressing this issue is crucial. Here, we present a computational framework that simulates clinical trials through a combination of mathematical and statistical models. This approach offers a means to explore diverse treatment protocols efficiently and identify optimal strategies for oncological drug administration. We developed a computational framework with a stochastic mathematical model as its core, capable of simulating virtual clinical trials closely recapitulating the clinical scenarios. Testing our framework on the landmark SOLO-1 clinical trial investigating Poly-ADP-Ribose Polymerase maintenance treatment in high-grade serous ovarian cancer, we demonstrate that managing toxicity through treatment interruptions or dose reductions does not compromise treatment’s clinical benefits. Additionally, we provide evidence suggesting that further reduction of hematological toxicity could significantly improve the clinical outcomes. The value of this computational framework lies in its ability to expedite the exploration of new treatment protocols, delivering critical insights pivotal to shaping the landscape of upcoming clinical trials. more...

Abstract Background The majority of genes in the human genome is present in two copies but the expression levels of both alleles is not equal. Allelic imbalance is an aspect of gene expression relevant not only in the context of genetic variation, but also to understand the pathophysiology of genes implicated in genetic disorders, in particular, dominant genetic diseases where patients possess one normal and one mutant allele. Polyglutamine (polyQ) diseases are caused by the expansion of CAG trinucleotide tracts within specific genes. Spinocerebellar ataxia type 3 (SCA3) and Huntington’s disease (HD) patients harbor one normal and one mutant allele that differ in the length of CAG tracts. However, assessing the expression level of individual alleles is challenging due to the presence of abundant CAG repeats in the human transcriptome, which make difficult the design of allele-specific methods, as well as of therapeutic strategies to selectively engage CAG sequences in mutant transcripts. Results To precisely quantify expression in an allele-specific manner, we used SNP variants that are linked to either normal or CAG expanded alleles of the ataxin-3 (ATXN3) and huntingtin (HTT) genes in selected patient-derived cell lines. We applied a SNP-based quantitative droplet digital PCR (ddPCR) protocol for precise determination of the levels of transcripts in cellular and mouse models. For HD, we showed that the process of cell differentiation can affect the ratio between endogenous alleles of HTT mRNA. Additionally, we reported changes in the absolute number of the ATXN3 and HTT transcripts per cell during neuronal differentiation. We also implemented our assay to reliably monitor, in an allele-specific manner, the silencing efficiency of mRNA-targeting therapeutic approaches for HD. Finally, using the humanized Hu128/21 HD mouse model, we showed that the ratio of normal and mutant HTT transgene expression in brain slightly changes with the age of mice. Conclusions Using allele-specific ddPCR assays, we observed differences in allele expression levels in the context of SCA3 and HD. Our allele-selective approach is a reliable and quantitative method to analyze low abundant transcripts and is performed with high accuracy and reproducibility. Therefore, the use of this approach can significantly improve understanding of allele-related mechanisms, e.g., related with mRNA processing that may be affected in polyQ diseases. more...

We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004-2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers. more...

Spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease (MJD), is autosomal-dominant neurodegenerative disease caused by an expansion of polyglutamine-encoding CAG repeats in the ATXN3 gene. Here we established IBCHi002-A induced pluripotent stem cells (iPSCs) line generated from SCA3 patient fibroblasts by using non-integrative Sendai-virus delivery system of four reprogramming factors. This cellular model provides a valid platform for study SCA3 pathogenesis and potential therapies for this so far incurable disease. more...

Dentatorubral–pallidoluysian atrophy (DRPLA) is an incurable autosomal dominant disease caused by an expansion of a CAG repeats in ATN1 gene encoding atrophin 1 protein. Here we report the generation of IBCHi001-A, an induced pluripotent stem cell (iPSC) line derived from DRPLA patient fibroblasts using non-integrative reprogramming technology with OCT4, SOX2, cMYC and KLF4 reprogramming factors. The pluripotency of iPSC was confirmed by immunocytochemistry and PCR for pluripotency markers and by the ability to form three germ layers in vitro. The established iPSC line offers a useful resource to study the pathogenesis of DPRLA. more...