Pickard, L. | bioRxiv Science

bioRxiv2025

Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed globally each year, and a 5-year survival rate of less than 20%. A lack of therapeutic options personalized to individual patient genetics, and the targeted therapies that exist quickly succumbing to resistance, leads to high variation in survival. Patient stratification combined with greater personalisation of therapies have the potential to improve outcomes, however, the wide variation in mutations found in NSCLC adenocarcinoma patients mean that experimentally determining suitable treatment combinations is time-consuming and expensive. Here we present an in silico model encompassing tumour intrinsic key oncogenic signalling pathways, including EGFR, AKT, JAK/STAT and WNT for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Using this model, we simulate diverse genetic profiles and test over 10,000 therapeutic combinations to identify optimal strategies to overcome resistance mechanisms specific to genetic profiles and p53 status. Our in silico model reproduces drug additivity experiments, predicts radio-sensitising genes validated in a CRISPR screen and identifies 53BP1 as a potential drug target that improves the therapeutic window during radiotherapy, as well as potential to use ATM inhibitors to overcome p53 loss-of-function driven radiotherapy resistance. We further use the in silico model to identify a 19-gene signature to stratify patients most likely to benefit from radiotherapy and validated this using TCGA data. These results further demonstrate the utility of in silico mechanistic modelling and present a bespoke computational resource for large-scale screening of personalised therapies applied to NSCLC.

cancer biology↗

bioRxiv2024

Mutations in the IgG B cell receptor associated with class-switched B cell lymphomas

Immunoglobulin class-switching from IgM to IgG enhances B cell receptor (BCR) signalling1,2 and promotes germinal centre (GC) B cell responses to antigens3,4. In contrast, non-Hodgkin lymphomas derived from GC B cells typically avoid IgG BCR expression and retain the unswitched IgM BCR, suggesting that the IgG BCR may protect B cells from malignant transformation5,6. However, the mechanism of this phenomenon and its significance for the pathogenicity of IgG-expressing lymphomas remains unclear. Here, we report that IgG-positive follicular lymphoma (FL) and the related EZB subset of diffuse large B cell lymphoma (DLBCL) acquire mutations in the IgG heavy chain, disrupting its unique intracellular tail. Enforced class switching of IgM-expressing EZB DLBCL cell lines to IgG reduces BCR surface levels, signalling via phosphoinositide-3 kinase (PI3K), levels of MYC, cell proliferation and in vivo growth. Inhibiting GSK3, a target of BCR-PI3K signalling, or stimulating the BCR rescues IgG+ cell proliferation. In contrast, IgG tail-truncating mutations enhance BCR surface expression, intracellular signalling and competitive growth. These findings suggest that the expansion of IgG-switched GC-like B lymphoma cells is limited by low tonic PI3K activity of the wild-type IgG BCR, but a subset of these cancers acquires mutations of the IgG intracellular tail that reverse this effect, promoting the oncogenicity of their BCRs. The presence of IgG tail mutations underscores the importance of isotype-specific BCR signalling in the pathogenesis of FL and EZB DLBCL and can potentially inform therapeutic targeting with BCR signalling inhibitors or antibody-drug conjugates.

immunology↗