Biology subjects

Hawi, G.

Publications and source records attributed to Hawi, G..

3 recordsLinked to original sources

bioRxiv2025

Modelling Immune Dynamics in Locally Advanced MSI-H/dMMR Colorectal Cancer with Neoadjuvant Pembrolizumab Treatment: From Differential Equations to an Agent-Based Framework

Colorectal cancer (CRC) is the third most common malignancy worldwide, and accounts for approximately 10% of all cancers and an estimated 850,000 deaths annually. Within CRC, MSI-H/dMMR tumours are highly immunogenic due to their high mutational burden and neoantigen load, yet can evade immunosurveillance via PD-1/PD-L1-mediated signalling. Pembrolizumab, an anti-PD-1 antibody approved for unresectable or metastatic MSI-H/dMMR CRC, is emerging as a promising neoadjuvant option in the locally advanced setting, inducing rapid, deep and durable immune responses. In this work, we construct a minimal model of neoadjuvant pembrolizumab therapy in locally advanced MSI-H/dMMR CRC (laMCRC) using ordinary differential equations (ODEs), providing a highly extensible model that captures the main immune dynamics involved. On the other hand, agent-based models (ABMs) naturally capture stochasticity, interactions at an individual level, and discrete events that lie beyond the scope of differential-equation formulations. As such, we also convert our ODE model, with parameters calibrated to experimental data, to an ABM, preserving its dynamics while providing a flexible platform for future mechanistic investigation and modelling.

cancer biology↗

bioRxiv2025

Optimising Chemotherapy for Locally Advanced High-Grade Serous Ovarian Cancer via Delay-Differential Equations

Ovarian cancer is the deadliest gynaecological cancer and the fourth leading cause of cancer deaths in women. High-grade serous ovarian cancer (HGSOC) accounts for 75% of cases, and chemotherapy resistance and relapse occur in 85% of patients, leading to a 5-year survival of 45%. Currently, the literature lacks comprehensive immunobiological models of HGSOC, and developing such models could provide critical insights into the diseases underlying mechanisms and interactions within the tumour microenvironment. We address this by constructing an immunobiological model using delay differential equations and then optimise chemotherapy regimens to maximise efficacy, minimise toxicity, and improve treatment efficiency for first-line treatment. The model consists of two compartments, the tumour site and tumour-draining lymph node, with immune processes such as DC maturation, T cell priming and proliferation, and cytokine interactions modelled. Parameter values are estimated using experimental data from ovarian cancer tissue samples as well as the TCGA OV database. The results indicate that low-dose dose more frequent chemotherapy provides comparable results to the standard regimen with a lower toxicity, and alternative dosing strategies with rest weeks can allow patients to recover from the toxic side effects of chemotherapy.

cancer biology↗

bioRxiv2025

Optimisation of pembrolizumab therapy for de novo metastatic MSI-H/dMMR colorectal cancer using data-driven delay integro-differential equations

Colorectal cancer (CRC), the third most commonly diagnosed cancer worldwide, presents a growing public health concern, with 20% of new diagnoses involving de novo metastatic disease and up to 80% of these patients presenting with unresectable metastatic lesions. Microsatel-lite instability-high (MSI-H) CRC and deficient mismatch repair (dMMR) CRC constitute 15% of all CRC, and 4% of metastatic CRC, and, while less responsive to conventional chemotherapy, exhibit notable sensitivity to immunotherapy, especially programmed cell death protein 1 (PD-1) checkpoint inhibitors such as pembrolizumab. Despite this, there is a significant need to optimise immunotherapeutic regimens to maximise clinical efficacy and patient quality of life whilst minimising financial burden. In this work, we adapt our mechanistic model for locally advanced MSI-H/dMMR CRC to de novo metastatic MSI-H/dMMR CRC (dnmMCRC), deriving model parameters from pharmacokinetic, bioanalytical, and radiographic studies, as well as bulk RNA-sequencing data deconvolution from the TCGA COADREAD and GSE26571 datasets. We finally optimised treatment with pembrolizumab to balance efficacy, efficiency, and toxicity in dnmMCRC, comparing against currently FDA-approved regimens, analysing factors influencing treatment success and comparing immune dynamics to those in locally advanced disease.

cancer biology↗