Biology subjects

Barkley, D.

Publications and source records attributed to Barkley, D..

4 recordsLinked to original sources

bioRxiv2023

Desmosome mutations impact the tumor microenvironment to promote melanoma proliferation

Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma where desmosomes are mutated in >70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations in desmosome genes associates with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics and protein immunofluorescence suggests that these expression decreases occur in keratinocytes in the microenvironment rather than in primary melanoma cells. In further support of a microenvironmental origin, we find that desmosome gene knockdown in keratinocytes yields markedly increased proliferation of adjacent melanoma cells in keratinocyte/melanoma co-cultures. Similar increases in melanoma proliferation are observed in media preconditioned by desmosome-deficient keratinocytes. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanoma cells for neoplastic transformation.

systems biology↗

bioRxiv2022

Drug-induced adaptation along a resistance continuum in cancer cells

Advancements in rational drug design over the past decades have consistently produced new cancer therapies, but such treatments are inevitably countered through an adaptive process that fosters therapy resistance. Malignant cells achieve drug resistance through intrinsic and acquired mechanisms, rooted in genetic and non-genetic determinants. In particular, recent work has highlighted the role of intrinsic cellular heterogeneity in the emergence of transient drug-tolerant persister cells that survive drug treatment, as well as non-genetically driven cell plasticity toward stable resistance. However, these models do not account for the role of dose and treatment duration as extrinsic forces in eliciting cancer cell adaptation. Here, we show that these two components together drive the resistance of ovarian cancer cells to targeted therapy along a trajectory of cellular adaptation, that we denote the resistance continuum. We report that gradual dose exposure and prolonged treatment promote a continuous increase in fitness, and show that this process is mediated by evolving transcriptional, epigenetic and genetic changes that promote multiple cell state transitions. The resistance continuum is underpinned by the assembly of gene expression programs and epigenetically reinforced stress response regulation. Using both in vivo and in vitro models, we found that this process involves widespread reprogramming of cell survival pathways, including interferon response, lineage reprogramming, metabolic rewiring and oxidative stress regulation. Together, the resistance continuum reveals the dynamic nature of cellular adaptation, and carries implications for cancer therapies, as initial exposure to lower doses primes cells over time for increased resistance to higher doses. Beyond cancer, such continuous adaptation exposes a basic aspect of cellular plasticity, which may also be deployed in other biological systems such as development, immune response and host-pathogen interactions.

cancer biology↗

bioRxiv2021

Recurrence of cancer cell states across diverse tumors and their interactions with the microenvironment

While genetic tumor heterogeneity has long been recognized, recent work has revealed significant variation among cancer cells at the epigenetic and transcriptional levels. Profiling tumors at the single-cell level in individual cancer types has shown that transcriptional heterogeneity is organized into cancer cell states, implying that diverse cell states may represent stable and functional units with complementary roles in tumor maintenance and progression. However, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Furthermore, the role of cancer cell states in tumor progression and their specific interactions with cells of the tumor microenvironment remain to be elucidated. Here, we perform a pan-cancer single-cell RNA-Seq analysis across 15 cancer types and identify a catalog of 16 gene modules whose expression defines recurrent cancer cell states, including stress, interferon response, epithelial-mesenchymal transition, metal response, basal and ciliated. Using mouse models, we find that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Moreover, spatial transcriptomic analysis further links the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance, and metastasis.

cancer biology↗

bioRxiv2019

ENHANCE: Accurate denoising of single-cell RNA-Seq data

Single-cell RNA-Seq measurements are commonly affected by high levels of technical noise, posing challenges for data analysis and visualization. A diverse array of methods has been proposed to computationally remove noise by sharing information across similar cells or genes, however their respective accuracies have been difficult to establish. Here, we propose a simple denoising strategy based on principal component analysis (PCA). We show that while PCA performed on raw data is biased towards highly expressed genes, this bias can be mitigated with a cell aggregation step, allowing the recovery of denoised expression values for both highly and lowly expressed genes. We benchmark our resulting ENHANCE algorithm and three previously described methods on simulated data that closely mimic real datasets, showing that ENHANCE provides the best overall denoising accuracy, recovering modules of co-expressed genes and cell subpopulations. Implementations of our algorithm are available at https://github.com/yanailab/enhance.

bioinformatics↗