Biology subjects

Tyson, D. R.

Publications and source records attributed to Tyson, D. R..

14 recordsLinked to original sources

bioRxiv2025

KLF4 promotes a KRT13+ hillock-like state in squamous lung cancer

Lung squamous cell carcinoma (LUSC) is a basal-like subtype of lung cancer with limited treatment options. While prior studies have identified tumour-propagating cell states in squamous tumours, the broader landscape of intra-tumoural heterogeneity within LUSC remains poorly understood. Here, we employ Sox2-driven mouse models, organoid cultures, and single-cell transcriptomic analyses to uncover previously unrecognized levels of cell fate diversity within LUSC. Specifically, we identify a KRT13+ hillock-like population of slower-dividing tumour cells characterized by immunomodulatory gene expression signatures. The tumour hillock-like state is conserved across multiple animal and human-derived models and is present in the majority of human LUSCs as well as head and neck and esophageal squamous tumours. Our findings shed light on the cellular origins of tumour hillock-like states: lung club cells give rise to tumours with luminal hillock-like populations, while basal-like tumour-propagating cells transition into basal hillock-like states, resembling lineage plasticity trajectories of the normal lung. Mechanistically, KLF4 promotes KRT13, a broadly conserved hillock-like state with enrichment of potential therapeutic targets, and resistance to platinum-based chemotherapy. Together, these results provide molecular insights into the lineage plasticity underlying intra-tumoural heterogeneity within LUSC, offering potential avenues for new therapeutic strategies.

cancer biology↗

bioRxiv2025

Intercellular signaling stabilizes single-cell level phenotypic transitions and accelerates the reestablishment of equilibrium of heterogeneous cancer cell populations

BackgroundCancer cells within tumors exhibit a wide range of phenotypic states driven by non-genetic mechanisms in addition to extensively studied genetic alterations. Conversions among cancer cell states can result in intratumoral heterogeneity which contributes to metastasis and development of drug resistance. However, mechanisms underlying the initiation and/or maintenance of such phenotypic plasticity are poorly understood. In particular, the role of intercellular communications in phenotypic plasticity remains elusive. MethodsIn this study, we employ a multiscale inference-based approach using single-cell RNA sequencing (scRNA-seq) data to explore how intercellular interactions influence phenotypic dynamics of cancer cells, particularly cancers undergoing epithelial-mesenchymal transition. In addition, we use mathematical models based on our data-driven findings to interrogate the roles of intercellular communications at the cell populations from the viewpoint of dynamical systems. ResultsOur inference approach reveals that signaling interactions between cancerous cells in small cell lung cancer (SCLC) result in the reinforcement of the phenotypic transition in single cells and the maintenance of population-level intratumoral heterogeneity. Additionally, we find a recurring signaling pattern across multiple types of cancer in which the mesenchymal-like subtypes utilize signals from other subtypes to support its new phenotype, further promoting the intratumoral heterogeneity. Our models show that inter-subtype communication both accelerates the development of heterogeneous tumor populations and confers robustness to their steady state phenotypic compositions. ConclusionsOur work highlights the critical role of intercellular signaling in sustaining intratumoral heterogeneity, and our approach of computational analysis of scRNA-seq data can infer inter- and intra-cellular signaling networks in a holistic manner.

systems biology↗

bioRxiv2024

Basal cell of origin resolves neuroendocrine-tuft lineage plasticity in cancer

Summary paragraphNeuroendocrine and tuft cells are rare, chemosensory epithelial lineages defined by expression of ASCL1 and POU2F3 transcription factors, respectively1,2. Neuroendocrine cancers, including small cell lung cancer (SCLC), frequently display tuft-like subsets, a feature linked to poor patient outcomes3-13. The mechanisms driving neuroendocrine-tuft tumour heterogeneity, and the origins of tuft-like cancers are unknown. Using multiple genetically-engineered animal models of SCLC, we demonstrate that a basal cell of origin (but not the accepted neuroendocrine origin) generates neuroendocrine-tuft-like tumours that highly recapitulate human SCLC. Single-cell clonal analyses of basal-derived SCLC further uncovers unexpected transcriptional states and lineage trajectories underlying neuroendocrine-tuft plasticity. Uniquely in basal cells, introduction of genetic alterations enriched in human tuft-like SCLC, including high MYC, PTEN loss, and ASCL1 suppression, cooperate to promote tuft-like tumours. Transcriptomics of 944 human SCLCs reveal a basal-like subset and a tuft-ionocyte-like state that altogether demonstrate remarkable conservation between cancer states and normal basal cell injury response mechanisms14-18. Together, these data suggest that the basal cell is a plausible origin for SCLC and other neuroendocrine-tuft cancers that can explain neuroendocrine-tuft heterogeneity--offering new insights for targeting lineage plasticity.

cancer biology↗

bioRxiv2024

DNA damage response signatures are associated with frontline chemotherapy response and routes of tumor evolution in extensive stage small cell lung cancer

IntroductionA hallmark of small cell lung cancer (SCLC) is its recalcitrance to therapy. While most SCLCs respond to frontline therapy, resistance inevitably develops. Identifying phenotypes potentiating chemoresistance and immune evasion is a crucial unmet need. Previous reports have linked upregulation of the DNA damage response (DDR) machinery to chemoresistance and immune evasion across cancers. However, it is unknown if SCLCs exhibit distinct DDR phenotypes. MethodsTo study SCLC DDR phenotypes, we developed a new DDR gene analysis method and applied it to SCLC clinical samples, in vitro, and in vivo model systems. We then investigated how DDR regulation is associated with SCLC biology, chemotherapy response, and tumor evolution following therapy. ResultsUsing multi-omic profiling, we demonstrate that SCLC tumors cluster into three DDR phenotypes with unique molecular features. Hallmarks of these DDR clusters include differential expression of DNA repair genes, increased replication stress, and heightened G2/M cell cycle arrest. SCLCs with elevated DDR phenotypes exhibit increased neuroendocrine features and decreased "inflamed" biomarkers, both within and across SCLC subtypes. Treatment naive DDR status identified SCLC patients with different responses to frontline chemotherapy. Tumors with initial DDR Intermediate and DDR High phenotypes demonstrated greater tendency for subtype switching and emergence of heterogeneous phenotypes following treatment. ConclusionsWe establish that SCLC can be classified into one of three distinct, clinically relevant DDR clusters. Our data demonstrates that DDR status plays a key role in shaping SCLC phenotypes, chemotherapy response, and patterns of tumor evolution. Future work targeting DDR specific phenotypes will be instrumental in improving patient outcomes.

cancer biology↗

bioRxiv2023

Purinergic calcium signaling drives drug tolerance through ERK reactivation in BRAF-mutant melanoma

Drug tolerance is a major cause of relapse after cancer treatment. In spite of intensive efforts1-9, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels), and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma, and points to actionable strategies to overcome it.

cancer biology↗

bioRxiv2023

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

bioinformatics↗

bioRxiv2022

Analysis of transcriptome datasets reveals involvement of epithelial-mesenchymal transition genes in small cell lung cancer phenotypic plasticity

Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intra-tumor heterogeneity plays critical roles in SCLC disease progression, metastasis and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)--a well-studied cellular process contributing to cancer invasiveness and resistance--using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a mesenchymal state (M1) that is distinct from the non-NE mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.

cancer biology↗

bioRxiv2022

A heterogeneous drug tolerant persister state in BRAF-mutant melanoma is characterized by ion channel dysregulation and susceptibility to ferroptosis

Drug-tolerant persisters (DTPs) represent a major obstacle to durable responses in targeted cancer therapy. DTPs are commonly described as distinct single-cell states that survive drug treatment via reversible, non-genetic mechanisms and drive tumor recurrence. Recent work demonstrates that multiple DTPs can coexist, reflecting diversity in lineage, signaling programs, or stress responses. However, each DTP is still generally viewed as a uniform cellular phenotype. Building on our prior work describing a population-level DTP termed "idling" [Paudel et al., Biophys. J. (2018) 114, 1499-1511], here we present evidence supporting a fundamentally different view: that DTPs are not single-cell states, but rather heterogeneous populations composed of multiple sub-states with distinct division and death rates that balance to produce near-zero net population growth. Using single-cell transcriptomics and lineage barcoding, we identify multiple phenotypic states within idling DTP populations, with reduced heterogeneity compared to untreated populations, and find that idling DTP cells emerge from nearly all lineages. Transcriptomic and functional analyses further reveal altered ion-channel activity in idling DTPs, which we confirm experimentally. Moreover, drug-response assays reveal increased susceptibility of idling DTPs to ferroptosis, a non-apoptotic form of regulated cell death, indicating the emergence of vulnerabilities associated with drug tolerance. Altogether, our results support a population-level view of tumor drug tolerance in which DTPs comprise stable collections of phenotypic states, shaped by treatment-defined phenotypic landscapes, which are potentially vulnerable to subsequent interventions. This perspective implies that eradicating DTPs will require a fundamental shift away from cell-type-centric strategies toward sequential treatments that progressively reduce phenotypic heterogeneity by modulating the molecular and cellular processes that establish the DTP landscape, an approach previously termed "targeted landscaping."

systems biology↗

bioRxiv2022

Real-time luminescence enables continuous drug-response analysis in adherent and suspension cell lines

The drug-induced proliferation (DIP) rate is a metric of in vitro drug response that avoids inherent biases in commonly used metrics such as 72h viability. However, DIP rate measurements rely on direct cell counting over time, a laborious task that is subject to numerous challenges, including the need to fluorescently label cells and automatically segment nuclei. Moreover, it is incredibly difficult to directly count cells and accurately measure DIP rates for cell populations in suspension. As an alternative, we use real-time luminescence measurements derived from the cellular activity of NAD(P)H oxidoreductase to efficiently estimate drug response in both adherent and suspension cell populations to a panel of known anticancer agents. For the adherent cell lines, we collect both luminescence reads and direct cell counts over time simultaneously to assess their congruency. Our results demonstrate that the proposed approach significantly speeds up data collection, avoids the need for cellular labels and image segmentation, and opens the door to significant advances in high-throughput screening of anticancer drugs.

molecular biology↗

bioRxiv2021

A community-based approach to image analysis of cells, tissues and tumors

Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. We convened a workshop to characterize these shared computational challenges and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).

bioinformatics↗

bioRxiv2021

Cancer Hallmarks Define a Continuum of Plastic Cell States between Small Cell Lung Cancer Archetypes

Small Cell Lung Cancer (SCLC) tumors are heterogeneous mixtures of transcriptional subtypes. Understanding subtype dynamics could be key to explaining the aggressive properties that make SCLC a recalcitrant cancer. Applying archetype analysis and evolutionary theory to bulk and single-cell transcriptomics, we show that SCLC cells reside within a cell-state continuum rather than in discrete subtype clusters. Gene expression signatures and ontologies indicate each vertex of the continuum corresponds to a functional phenotype optimized for a cancer hallmark task: three neuroendocrine archetypes specialize in proliferation/survival, inflammation and immune evasion, and two non-neuroendocrine archetypes in angiogenesis and metabolic dysregulation. Single cells can trade-off between these defined tasks to increase fitness and survival. SCLC cells can easily transition from specialists that optimize a single task to generalists that fall within the continuum, suggesting that phenotypic plasticity may be a mechanism by which SCLC cells become recalcitrant to treatment and adaptable to diverse microenvironments. We show that plasticity is uncoupled from the phenotype of single cells using a novel RNA-velocity-based metric, suggesting both specialist and generalist cells have the capability of becoming destabilized and transitioning to other phenotypes. We use network simulations to identify transcription factors such as MYC that promote plasticity and resistance to treatment. Our analysis pipeline is suitable to elucidate the role of phenotypic plasticity in any cancer type, and positions SCLC as a prime candidate for treatments that target plasticity.

systems biology↗

bioRxiv2020

ACDC: Automated Cell Detection and Counting for Time-lapse Fluorescence Microscopy

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7x speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

bioinformatics↗

bioRxiv2020

A unifying framework disentangles genetic, epigenetic, and stochastic sources of drug-response variability in an in vitro model of tumor heterogeneity

Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability of targeted therapy response is observed. Additional genetic mutations can only partially explain this variability, leading to consideration of non-genetic factors, such as "stem-like" and "mesenchymal" phenotypic states, as critical contributors to tumor relapse and resistance. Here, we show that both genetic and non-genetic factors contribute to targeted drug-response variability in an experimental tumor heterogeneity model based on multiple versions and clonal sublines of PC9, the archetypal EGFR-mutant non-small cell lung cancer cell line. We observe significant drug-response variability across PC9 cell line versions, among sublines, and within sublines. To disentangle genetic, epigenetic, and stochastic components underlying this variability, we adopt a theoretical framework whereby distinct genetic states give rise to multiple epigenetic "basins of attraction", across which cells can transition driven by stochastic factors such as gene expression noise and asymmetric cell division. Using mutational impact analysis, single-cell differential gene expression, and semantic similarity of gene ontology terms to connect genomics and transcriptomics, we establish a baseline of genetic differences explaining drug-response variability across PC9 cell line versions. In contrast, with the same approach, we conclude that in all but one of the clonal sublines, drug-response variability is due to epigenetic rather than genetic differences. Finally, using a clonal drug-response assay and stochastic simulations, we attribute drug-response variability within sublines to intracellular stochastic fluctuations and confirm that one subline likely contains a genetic resistance mutation that emerged in the absence of selective pressures. We propose that a theoretical framework deconvolving the complex interplay among genetic, epigenetic, and stochastic sources of intratumoral heterogeneity will lead to novel therapeutic strategies to combat tumor relapse and resistance.

systems biology↗

bioRxiv2019

Visualization and analysis of high-throughput in vitro dose-response datasets with Thunor

High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets. Thunor supports both end-point and time-based proliferation assays as input. It provides a simple, user-friendly interface with interactive plots and publication-quality images of cell proliferation time courses, dose-response curves, and derived dose-response metrics, e.g. IC50, including across datasets or grouped by tags. Tags are categorical labels for cell lines and drugs, used for aggregation, visualization, and statistical analysis, e.g. cell line mutation or drug class/target pathway. A graphical plate map tool is included to facilitate plate annotation with cell lines, drugs, and concentrations upon data upload. Datasets can be shared with other users via point-and-click access control. We demonstrate the utility of Thunor to examine and gain insight from two large drug response datasets: a large, publicly available cell viability database and an in-house, high-throughput proliferation rate dataset. Thunor is available from www.thunor.net.

pharmacology and toxicology↗