Biology subjects

Atkinson, D.

Publications and source records attributed to Atkinson, D..

10 recordsLinked to original sources

bioRxiv2026

Proteomic Profiling Identifies CLDN3 as a Tumor-Selective Therapeutic Target in Small Cell Lung Cancer

Small cell lung cancer (SCLC) remains a highly lethal disease with limited targetable surface antigens beyond delta-like ligand 3 (DLL3). We sought to systematically identify and validate tumor-selective cell-surface targets in relapsed SCLC. To this end, we developed an integrated proteogenomic pipeline combining single-cell RNA sequencing, combinatorial optimization, mass spectrometry-based proteomics, and immunohistochemistry to systematically map the SCLC surfaceome of 49 tumors across 25 patients with relapsed SCLC. Using this approach we found claudin-3 (CLDN3) to be a consistently expressed and tumor-selective antigen, with broader coverage than DLL3, which is the current clinical benchmark. CLDN3 was highly expressed across various treatment states, while maintaining low expression in most nonmalignant tissues. Functional validation using a novel CLDN3-specific monoclonal antibody (ABN501) demonstrated NK cell-mediated cytotoxicity in vitro and tumor regression in vivo, as well as a favorable safety profile. These findings support clinical development of CLDN3-directed therapies and demonstrate the utility of integrated proteogenomic approaches for antigen discovery in solid tumors.

cancer biology↗

bioRxiv2026

Genome-wide classification of tumor-derived reads from bulk long-read sequencing

DNA extracted from tissue samples typically derives from a complex mixture of cell types. Without single cell analysis, it has been generally impossible to determine the cell type of origin for most molecules. One clear example of this is in the complex milieu of a human neoplasm. Here, we develop ROCIT (https://github.com/tobybaker/rocit), a transformer-based model to classify the tumor or non-tumor origin of individual reads from bulk tumor samples sequenced with long-read whole genome sequencing. Using somatic mutations to derive training data, ROCIT uses read-level methylation patterns to accurately classify reads from any-where in the genome without requiring the adjacent normal tissue or the explicit identification of tumor differentially methylated regions. We apply ROCIT to a cohort of prostate and ovarian tumors and demonstrate high classification accuracy across the entire genome. We then demonstrate the potential of ROCIT predictions to improve somatic variant calling. ROCIT represents a major step forward in the analysis of bulk tumors with long-reads, enabling the accurate and sensitive identification of reads with specific cell types of origin genome-wide.

bioinformatics↗

bioRxiv2025

HDAC inhibition unlocks tumor plasticity and enhances immunotherapy response in Myc- Driven Small Cell Lung Cancer

Small Cell Lung Cancer (SCLC) is a highly aggressive malignancy, accounting for approximately 15% of all lung cancer cases. Characterized by low immunogenicity, SCLC may utilize epigenetic mechanisms to evade immune detection. Here, we demonstrate that entinostat, a class I histone deacetylase inhibitor (HDACi) upregulates immune-related genes in human SCLC cells. In vivo, we confirmed entinostat treatment increased expression of immunecheckpoint ligands and antigen presentation machinery in Myc-driven tumors in a Rb1/Trp53/MycT58A (RPM) SCLC mouse model, while shifting tumors from a neuroendocrine(NE)-high to a NE-low phenotype. Notably, combining entinostat with anti-PD-1 immunotherapy significantly enhances T-cell infiltration, suppresses tumor growth, and prolongs survival in RPM allograft models. These findings underscore the potential of entinostat to reprogram the immunological landscape and NE status of SCLC, enhance immune checkpoint blockade efficacy, and improve therapeutic outcomes.

cancer biology↗

bioRxiv2024

Microenvironment Shapes Cell State, Plasticity, and Heterogeneity of Small Cell Lung Cancer

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intra-tumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the tumor microenvironment (TME) exhibits substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAF) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Together, our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLCs adaptable nature, opening possibilities for re-programming the intercellular communications that shape SCLC tumor states.

cancer biology↗

bioRxiv2022

ATR inhibition augments the efficacy of lurbinectedin in small cell lung cancer

Abstract/SummarySmall cell lung cancer (SCLC) is the most lethal type of lung cancer. Specifically, MYC-driven non-neuroendocrine SCLC are particularly resistant to standard therapies. Lurbinectedin was recently approved for the treatment of relapsed SCLC, but combinatorial approaches are needed to increase the depth and duration of responses to lurbinectedin. Using high-throughput screens, we found inhibitors of ataxia telangiectasia-mutated and rad3-related (ATR) as the most effective agents for augmenting lurbinectedin efficacy. First-in-class ATR inhibitor berzosertib synergized with lurbinectedin in multiple SCLC cell lines, organoid and in-vivo models. Mechanistically, ATR inhibition abrogated S-phase arrest induced by lurbinectedin and forced cell-cycle progression causing mitotic catastrophe and cell death. High CDKN1A/p21 expression was associated with decreased synergy due to G1 arrest, while increased levels of ERCC5/XPG were predictive of increased combination efficacy. Importantly, MYC-driven non-neuroendocrine tumors which were resistant to first-line therapies displayed decreased CDKN1A/p21 expression and increased ERCC5/XPG indicating they were primed for response to lurbinectedin-berzosertib combination. The combination is being assessed in a clinical trial NCT04802174. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/520783v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@19acf72org.highwire.dtl.DTLVardef@a9703forg.highwire.dtl.DTLVardef@86592org.highwire.dtl.DTLVardef@1448372_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Female fruit flies cannot protect stored sperm from high temperature damage

Recently, it has been demonstrated that heat-induced male sterility is likely to shape population persistence as climate change progresses. However, an under-explored possibility is that females may be able to successfully store and preserve sperm at temperatures that sterilise males, which could ameliorate the impact of male infertility on populations. Here, we test whether females from two fruit fly species can protect stored sperm from a high temperature stress. We find that sperm carried by female Drosophila virilis are almost completely sterilised by high temperatures, whereas sperm carried by female Zaprionus indianus show only slightly reduced fertility. Heat-shocked D. virilis females can recover fertility when allowed to remate, suggesting that the delivered heat-shock is destroying stored sperm and not directly damaging females in this species. The temperatures required to reduce fertility of mated females are substantially lower than the temperatures required to destroy mature sperm in males, suggesting that females are worse than males at protecting mature sperm. This suggests that female sperm storage is unlikely to ameliorate the impacts of high temperature fertility losses in males, and instead exacerbates fertility costs of high temperatures, representing an important determinant of population persistence during climate change.

physiology↗

bioRxiv2021

Termites have developed wider thermal limits to cope with environmental conditions in savannas

The most diverse and abundant family of termites, the Termitidae, evolved in warm, wet African tropical forests. Since then, they have colonised grassy biomes such as savannas. These environments have more extreme temperatures than tropical forests, and greater temporal fluctuations (both annually and diurnally) that are challenging for soft-bodied ectotherms. We propose that that a likely mechanism that facilitated the expansion from forest to savanna was the widening of physiological limits of savanna termite species in order to cope with more extreme environmental conditions. We sampled termites directly from mound structures across an environmental gradient in Ghana, and recorded the thermal tolerance of individual termites, both critical thermal maximum (CTmax) and critical thermal minimum (CTmin). We estimated colony thermal tolerance by taking an average of each tested individual, and modelled these data against several environmental factors (canopy cover above the mound, rainfall and temperature). We found that savanna termite species had significantly higher CTmax values, and significantly lower CTmin values, than forest species. In addition, areas with high canopy cover were significantly associated with low CTmax values, and areas with higher average daily rainfall were significantly associated with higher CTmin values. Our results suggest that the widening of thermal tolerances has occurred in savanna termite species, probably in response to the more extreme temperatures found in those environments.

physiology↗

bioRxiv2020

Quantifying multivariate genotype-by-environment interactions, evolutionary potential and its context-dependence in natural populations of the water flea, Daphnia magna.

Genotype-by-environment interactions (G x E) underpin the evolution of plastic responses in natural populations. Theory assumes that G x E interactions exist but empirical evidence from natural populations is equivocal and difficult to interpret because G x E interactions are normally univariate plastic responses to a single environmental gradient. We compared multivariate plastic responses of 43 Daphnia magna clones from the same population in a factorial experiment that crossed temperature and food environments. Multivariate plastic responses explained more than 30% of the total phenotypic variation in each environment. G x E interactions were detected in most environment combinations irrespective of the methodology used. However, the nature of G x E interactions was context-dependent and led to environment-specific differences in additive genetic variation (G-matrices). Clones that deviated from the population average plastic response were not the same in each environmental context and there was no difference in whether clones varied in the nature (phenotypic integration) or magnitude of their plastic response in different environments. Plastic responses to food were aligned with additive genetic variation (gmax) at both temperatures, whereas plastic responses to temperature were not aligned with additive genetic variation (gmax) in either food environment. These results suggest that fundamental differences may exist in the potential for our population to evolve novel responses to food versus temperature changes, and challenges past interpretations of thermal adaptation based on univariate studies.

evolutionary biology↗

bioRxiv2020

"Select and retrieve via direct upsampling" network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design

PurposeWe introduce "Select and retrieve via direct upsampling" network (SARDU-Net), a data-driven framework for model-free quantitative MRI (qMRI) protocol design, and demonstrate it on in vivo brain and prostate diffusion-relaxation imaging (DRI). MethodsSARDU-Net selects subsets of informative measurements within lengthy pilot scans, without the requirement to identify tissue parameters for which to optimise for. The algorithm consists of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using deep neural networks, which are trained jointly end-to-end. We demonstrate the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on 3 healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing the reproducibility of the procedure and testing sub-protocols for their potential to inform multi-contrast analyses via T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) modelling. ResultsIn both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations. The sub-protocols enable multi-contrast modelling for which they were not optimised explicitly, providing robust T1-SMDT and HM-MRI maps and goodness-of-fit in the top 5% against extensive sub-protocol comparisons. ConclusionsSARDU-Net gives new opportunities to identify economical but informative qMRI protocols from a subset of the pilot scans that can be used for acquisition-time-sensitive applications. The simple architecture makes the algorithm easy to train when exhaustive searches are intractable, and applicable to a variety of anatomical contexts.

bioengineering↗

bioRxiv2017

Quantitation of brain tumour microstructure response to Temozolomide therapy using non-invasive VERDICT MRI

There has been slow progress in the development of new therapeutic strategies for treating brain tumours, partly because assessment of treatment response is difficult and largely reliant on simple bi-dimensional measurements of MRI contrast-enhancing regions. Hence, there is a clinical need to develop improved imaging techniques for monitoring treatment response. In this study, we evaluate VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) MRI in mouse glioblastomas for the quantification of tumour microstructure and assessment of response to Temozolomide (TMZ) chemotherapy, and, we investigate the feasibility of applying VERDICT MRI in a range of human gliomas. VERDICT MRI detected response to TMZ earlier than structural and apparent diffusion coefficient (ADC) measurements. A significant reduction in the cell radius parameter was detected three days earlier than ADC and six days earlier than structural MRI. Histological analysis showed the same trend as VERDICT of decreased intracellular volume fraction in the TMZ-treated mice. Vascular volume fraction was not altered by TMZ, which was consistent with optical projection tomography measurements. In patients, glioblastoma compartmental volume fractions showed good agreement with mouse glioblastoma parameters. The VERDICT parameters varied across the human gliomas, with raised intracellular volume fraction in the oligodendrogliomas and elevated cell radius in both low-grade tumours subtypes. In conclusion, our results suggest that VERDICT MRI is more sensitive at detecting TMZ response than structural or ADC measurements. In patients, VERDICT is feasible within clinical scan times, and performed best at characterising glioblastoma. Further optimisation should improve assessment of different glioma subtypes.

biophysics↗