Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Abstract

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available, that precludes the generation of largescale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Two heads are better than one: current landscape of integrating QSP and machine learning

“…These methods pragmatically focus on semi-quantitative data that are typically used to investigate biological decision pathways and have developed alongside refinements in the generation of experimental data. As experimental perturbations can improve our understanding of regulatory pathways [28], rich mutliplexed data are being coupled with ML-based model structure generation to identify therapeutic approaches, for example, to control cell fate [29] and to identify personalized cancer therapy [30]. These approaches offer the opportunity to build a QSP model supporting the full pipeline of activities starting with target identification, validation, and model refinement as questions become more focused later in clinical development.…”

Building patient‐specific models for receptor tyrosine kinase signaling networks

“…Mathematical models have helped to further identify drug-sensitive crosstalk that enables effective induction of apoptosis in cancer cells [104,105]. Eduati et al developed a logic-based extrinsic and intrinsic apoptosis model by integrating TNF and EGFR pathways and calibrating the parameters using drug treatment data derived in vitro and ex vivo, and extended it to generate a patient-specific model [106]. Using patient biopsy data, this model explained the cause of interpatient variability in pancreatic cancer resulting from dissimilar 3-kinase [phosphoinositide 3-kinase (PI3K)]-AKT activity.…”

Personalized signaling models for personalized treatments

“…Therefore, experimental technologies that perform perturbation screenings with large throughput from small amounts of material can be really helpful. Microfluidic platforms to analyze low amounts of patient material are promising technologies in this regard and have already been used to generate data for patient‐specific models (Eduati et al , ). However, they also have limitations, including the number of available readouts and the fact that by suspending the cells the tissue structure and interactions are lost.…”