Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.12.05.570063

Signalling-state dependent drug-tolerance in head and neck squamous cell carcinoma.

Karjosukarso, D. W.·Dini, A.·Wingens, L. J. A.·Liu, R.·Joosten, L. A. B.·Bussink, J.·Mulder, K. W.
View original source ↗Export metadata ↓

Abstract

Intratumor heterogeneity negatively impacts therapeutic response and patient prognosis. Besides the established role of genetic heterogeneity, non-genetic mechanisms of persistence to drug treatment are emerging. Here, we characterise cells selected for their persistence to control, epidermal growth factor inhibition (EGFRi), radiation and combined treatment from low passage head and neck squamous cell carcinoma (HNSCC) cultures. Using a panel of 70 (phospho-)specific DNA-conjugated antibodies we measured activities of 8 signalling pathways, self-renewal, differentiation, DNA damage and cell-cycle, in conjunction with the transcriptional output in single cells, using our RNA and Immuno-Detection (RAID) technology. Six recurrent transcriptional programs reflecting processes including proliferation, differentiation and metabolic activity, as well as protein-based signalling-states, were associated with drug persistence, while copy number variation inference indicated involvement of non-genetic tolerance mechanisms. Projecting RNA velocity onto the antibody-derived signalling-states suggested a key role for integrin-mediated focal-adhesion signalling in drug-persistence in our cell system. Using machine-learning we derived a core transcriptional signature connected to adhesion-based drug-persistence, which was predictive of poor prognosis in a TGCA HNSCC cohort (hazard-ratio 1.87, p<10-5). Furthermore, functional analyses confirmed that cells expressing high levels of integrin alpha-6 (ITGA6) were tolerant to EGFRi treatment, and that forcing cells out of this cell-state through transient targeted inhibition of Focal Adhesion Kinase activity re-instated EGFRi sensitivity in drug persistent cells. Taken together, our single-cell multi-omics analysis identified an actionable adhesion-signalling mediated cell-state driving drug tolerance in HNSCC.

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Karjosukarso, D. W., Dini, A., Wingens, L. J. A., Liu, R., Joosten, L. A. B., Bussink, J., Mulder, K. W.. 2023-12-07. Signalling-state dependent drug-tolerance in head and neck squamous cell carcinoma.. https://doi.org/10.1101/2023.12.05.570063

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Large language models complement established workflows for mechanistic model construction and refinement in systems biology

Mechanistic models provide a framework for describing, understanding, and predicting biological processes, but their development requires an interdisciplinary team with expertise in biology, mathematics, and computation. Here we present Octomod, a multi-agent workflow based on large language models (LLMs) that integrates biological knowledge retrieval, model construction, parameter estimation, and hypothesis testing. Built on established software tools, including AMICI and pyPESTO, and community standards, including SBML and PEtab, Octomod supports model development and refinement through natural-language interaction with a user. The agents' proposals must pass defined checks and the user's decision before they are used. Every input, model, check, and decision is recorded, so that model building remains transparent and reproducible. We applied Octomod to four problems. Three used published data and differ in model size and in how much the user guided the run: Octomod constructed a model that matched a published model without user guidance, supported the interactive refinement of a gene-regulatory model, and built models of cell-cell communication that explained the responses of five immune cell types to different stimuli. In the fourth application, on newly generated and unpublished lipidomics data, Octomod helped to determine which processes the measurements could inform, identify unresolved label fluxes, and reduce a supplied model to those processes. Together, these results show how LLMs can strengthen and complement established workflows for constructing and refining biologically informed models.

systems biology↗
bioRxiv2026

A Mathematical Model Connecting Appetite-Driven Food Intake to Metabolic Changes across Diet and GLP-1RA Treatments in Mice

Mouse models are widely used to study obesity and the effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on appetite-driven meal responses and eating behaviours. However, connecting these behavioural changes to disease prevention remains limited. One reason is that eating behaviour is often summarised as daily or weekly energy intake, obscuring meal-level dynamics. Here we present a mathematical model of mouse appetite-driven food intake across diets and treatment with the GLP-1RAs semaglutide and exenatide. The model jointly described three studies and passed both qualitative assessments and a statistical {chi}2 test (disagreement<threshold, 348<1111). Predictive performance was subsequently validated using four pre-existing studies and one new study. Finally, the model was used to simulate mouse populations under dietary and GLP-1RA interventions, with predictions showing qualitative agreement with the literature. This work aids preclinical drug discovery, supports mouse study design, and improves understanding of how individual meals accumulate into long-term physiological changes.

systems biology↗
bioRxiv2026

Generalized inference of noise-corrected state variability in single cells

Single-cell omics methods operate at their detection limit and are less conducive to technical controls than mini-bulk approaches, which combine cells of the same type before profiling. To integrate mini-bulk samples with paired split-pool controls more holistically, we built stochprofIC, an algorithm that considers data, noise, and covariates in a distributional-regression framework. We vet stochprofIC with synthetic datasets and compare against an adjusted-variance alternative using mini-bulk data in mouse brains and human breast cancers. The noise-batch corrections provided by stochprofIC enable inference of the single-cell populations comprising the mini-bulk, which are validated repeatedly by RNA FISH in different contexts. We use stochprofIC outputs as input to a systems model of nucleocytoplasmic transport and distinguish heterogeneous trafficking states. Pharmacologic inhibition of a MAP3K11-high state inhibits apoptosis in primary breast-cancer organoids, supporting relevance to phenotypes. By deconvolving changes in location and scale, stochprofIC supports mini-bulking when high-sensitivity and reliability profiles are important.

systems biology↗