Biology subjects

Banerji, U.

Publications and source records attributed to Banerji, U..

5 recordsLinked to original sources

bioRxiv2026

Cellular uptake of folate-olaparib conjugates via folate receptor-mediated endocytosis: Potential for selective delivery of DNA damage response inhibitors into tumour cells

The folate receptor (FR) is overexpressed in a range of human tumours including ovarian cancer cells. We propose that the overexpression of the FR on the surface of ovarian tumour cells could be exploited for the selective delivery of a DNA damage response inhibitor (DDRi) in the form of an intact folate drug conjugate (FDC). This approach would improve the therapeutic index of the parent DDRi facilitating combination studies of the DDRi-based FDC with DNA damaging chemotherapy. FR-mediated cellular uptake of the proposed folate drug conjugates is requisite for FDC selective delivery into tumours. In this study, we synthesised a series of olaparib-based folate conjugates that maintained the biochemical PARP1 inhibition associated with olaparib and showed binding affinity for the folate receptor. Significantly, we identified compounds 10b and 11 that selectively enter FR overexpressing tumour cells via folate receptor-mediated endocytosis in their intact form and engage with their target as demonstrated by the potent inhibition of PARylation (KB cells, PARylation IC50 = 5.7 and 3.9 nM; respectively).

biochemistry↗

bioRxiv2026

Inference of Cancer Drug Cross Resistance Using Only Single-Drug Exposure Data

Combining drugs may prevent resistance in cancer treatment. However, the evolution of resistance to more than one drug simultaneously - cross resistance - remains a major obstacle to durable clinical response. Here, we show that we can identify the presence and strength of cross resistance using lineage tracing data from cell populations exposed only to each drug independently, removing the need for complex combinatorial treatment experiments. Simulations where the ground truth was known showed that the method accurately recovered the true underlying resistance dynamics. We applied the framework to breast cancer, lung cancer and lymphoma datasets, quantifying cross resistance across diverse therapy types. Our predictions align with biological expectations and reveal that even drugs with shared putative targets can exhibit variable cross resistance across pre-clinical models. Finally, we used inferred cross resistance strengths to evaluate treatment strategies, predicting that high cross resistance contributes to the limited efficacy of switching between CDK4/6 inhibitors. By enabling the identification of cross resistance without requiring multi-drug exposure experiments, our framework provides a method for prioritising drug combinations and screening for novel resistance-minimising drug targets.

cancer biology↗

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↗

bioRxiv2019

Exploiting evolutionary herding to control drug resistance in cancer

Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called evolutionary herding that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 108-109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.

cancer biology↗

bioRxiv2019

The off-target kinase landscape of clinical PARP inhibitors

Four PARP inhibitors have been approved by the FDA as cancer therapeutics, yet there are several clinical settings where no strong rationale exists to inform clinicians on which to choose in terms of clinical effectiveness and toxicity. The four drugs have largely similar PARP family inhibition profiles, but several differences at the molecular and clinical level have been reported that remain poorly understood. We previously hypothesized that PARP inhibitors could have an inherent capacity to inhibit kinases off-target. Here, we characterise the off-target kinase landscape of four FDA-approved PARP drugs. We demonstrate that all four PARP inhibitors have a unique polypharmacological profile across the kinome. Niraparib and rucaparib inhibit DYRK1s, CDK16 and PIM3 at clinically achievable submicromolar concentrations. These represent the most potently inhibited off-targets of PARP inhibitors identified to date and should be investigated further to clarify their potential implications for efficacy and safety in the clinic, including use of PARP inhibitors in combination with other agents, including immunotherapy.

pharmacology and toxicology↗