Computational modeling of pancreatic cancer patients receiving FOLFIRINOX and gemcitabine-based therapies identifies optimum intervention strategies

Selection of patients with pancreatic adenocarcinoma who may benefit from radiotherapy

Despite combination chemotherapy demonstrating a positive effect on survival, the clinical outcomes of pancreatic adenocarcinoma (PDAC) remain poor. Radiotherapy was previously a component of the curative treatment of PDAC. Advances in imaging and computer sciences have enabled the prescription of higher dosage of radiation focused on tumours with minimal toxicity to normal tissue. However, the role of radiotherapy has not been established in the curative treatment of localized PDAC because of the conflicting results from large prospective trials. Most studies have demonstrated improved locoregional control but no survival benefit from additional chemoradiotherapy (CRT) in addition to chemotherapy for resectable, borderline or locally advanced PDAC. The improved locoregional control enabled by CRT does not cause extended survival because of rapid distant progression in a significant proportion of patients with PDAC. Several single-institute studies of prescribing intensive chemotherapy with modern ablative radiotherapy for locally advanced PDAC have demonstrated extended survival with an acceptable safety profile. In an analysis after long-term follow-up, the PREOPANC study demonstrated a survival benefit from neoadjuvant gemcitabine-based CRT in resected PDAC relative to upfront surgery followed by adjuvant gemcitabine only. These observations indicated that the role of radiotherapy in PDAC should be evaluated in a subgroup of patients without rapid distant progression because systemic therapy for PDAC remains underdeveloped. We reviewed critical imaging, tissue, liquid and clinical biomarkers to differentiate the heterogeneous biologic spectra of patients with PDAC to identify those who may benefit the most from local radiotherapy. Exclusion of patients with localised PDAC who develop distant progression in a short time and undergo extended upfront chemotherapy for over 4 months may enable the identification of a survival benefit of local radiotherapy. Though promising, the effectiveness of biomarkers must be validated in a multi-institutional prospective study of patients with PDAC receiving CRT or not receiving CRT.

A Quantitative Paradigm for Decision-Making in Precision Oncology

The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Beyond Deterministic Models in Drug Discovery and Development

The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.

Mathematical Modeling to Address Challenges in Pancreatic Cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.