Biology subjects

Boland, G.

Publications and source records attributed to Boland, G..

13 recordsLinked to original sources

bioRxiv2026

Genomic classification and immune profiling define mucosal melanoma

Mucosal melanoma is a rare, lethal melanoma subtype with distinct genetics and limited benefit from immunotherapy. To define features associated with mucosal melanoma clinical outcomes, we assembled a large, multimodally profiled cohort spanning primary and metastatic disease. Compared with cutaneous melanoma, primary mucosal melanoma showed greater genomic heterogeneity, ploidy and aneuploidy, and a more dedifferentiated, proliferative transcriptional state. Analysis of genomic sequencing of 339 patients with mucosal melanoma identified four mutually exclusive genomic subtypes: CDK4/MDM2/CCND1, SF3B1-mutated, TP53/ATRX, and mucosal melanoma Triple WT, associated with distinct anatomic sites but similar outcomes. In the tumor microenvironment, we observed reduced T cell infiltration and increased CD163+ tumor-associated macrophages in mucosal melanoma relative to cutaneous melanoma. In matched longitudinal samples, CD163+ tumor-associated macrophages increased with disease progression and immunotherapy resistance. CD163+ tumor-associated macrophages engaged non-regulatory T cells through GAL9-TIM-3 interactions, nominating TIM-3 blockade as a therapeutic strategy for mucosal melanoma. These findings define the distinct aggressive biology of mucosal melanoma and highlight potential therapeutic targets and mechanisms of immune resistance.

cancer biology↗

bioRxiv2026

Attomolar fecal cytokine profiling reveals gut immune dynamics and disease states

The gut modulates systemic health, influencing immune, neurological, and cardiovascular processes. While fecal sequencing of microbial nucleic acids provides a non-invasive view of microbial composition, sensitive measurement of host-derived signals in stool remains limited. Here we introduce DIGEST (Digital Immunoassay for Gut-Environment Single-molecule Targets), an ultrasensitive digital immunoassay that quantifies proteins in fecal extracts to attomolar levels. In mice, longitudinal profiling during a high-fat diet perturbation revealed coordinated host cytokine responses that occurred within 24 hours, with sustained elevation after diet withdrawal, enabling non-invasive tracking of within-subject immune dynamics. Application of DIGEST to quantify a panel of host inflammatory cytokines in patients with inflammatory bowel disease distinguished active ulcerative colitis from quiescent disease and non-IBD controls (AUC=0.98). In advanced melanoma patients receiving PD-1 blockade, pretreatment fecal IL-23 concentrations discriminated responders from non-responders with an AUC of 0.87. Together, these results establish DIGEST as a generalizable platform for sensitive, non-invasive quantification of host protein activity at the gut interface, with broad applications in basic science discovery, disease surveillance, and therapy response prediction.

systems biology↗

bioRxiv2025

YAP1 drives aggressive and therapy resistant state in melanoma through reprogramming the chromatin and regulating immune evasive programs

Despite promising initial results in targeting the RAF-MEK-ERK cascade, resistance to BRAF/MEK inhibitors remains a critical challenge in nearly 50% of melanoma patients. Our study demonstrates that robust YAP1 activation in metastatic melanoma correlates with poor survival and drives transcriptional programs linked to therapeutic resistance. Mechanistically, YAP1 predominantly remodels the chromatin landscape in resistant tumors by partnering with BRD4 and TEAD, creating a permissive transcriptional state that sustains oncogenic signaling. Clinical validation in biopsies from resistant melanoma confirms elevated expression of YAP1 target genes. Furthermore, pharmacological inhibition of BRD4 or TEAD reduces YAP1-driven transcription and reactivates antitumor immunity programs. TEAD specific inhibitors (and not verteporfin which is a highly non-specific inhibitor) synergize with immune checkpoint blockade in in vivo model system by promoting increased CD8 T cell infiltration and prolonged survival in the melanoma mouse model. Collectively, these findings reveal a chromatin-centric vulnerability in BRAF/MEK inhibitor-resistant melanoma and propose TEAD specific inhibitors as a promising dual strategy to overcome resistance and reinvigorate the immune response, offering a novel therapeutic avenue for patients. Resistance to BRAF and MEK inhibitors remains a major obstacle in the treatment of melanoma. Here, we show that elevated YAP1 activity in metastatic melanoma is associated with poor patient survival and drives transcriptional programs that promote therapeutic resistance. Mechanistically, YAP1 cooperates with BRD4 and TEAD to reprogram the chromatin landscape, establishing an oncogenic transcriptional state that sustains resistance. Analysis of patient-derived melanoma biopsies confirms increased expression of YAP1 target genes in resistant tumors. Targeting this epigenetic circuitry using pharmacological inhibitors of BRD4 or TEAD effectively suppresses YAP1-driven transcriptional output and restores antitumor immune activity, as evidenced by enhanced CD8 T cell infiltration and prolonged survival in resistant melanoma models. These effects are further supported by transcriptomic and chromatin accessibility analyses, which reveal reduced expression of immune-suppressive and proliferation-associated gene networks upon TEAD inhibition. Collectively, our findings identify a chromatin-centric mechanism underlying resistance to MAPK-targeted therapy and nominate TEAD inhibition as a promising dual-action strategy to both overcome resistance and re-engage antitumor immunity. This work offers a compelling therapeutic avenue for patients with drug-resistant melanoma.

cancer biology↗

bioRxiv2025

Distinct malignant cell states and myeloid glutamate signaling associated with aggressive pancreatic neuroendocrine tumors

Pancreatic neuroendocrine tumors (PNET) are rare malignancies of the endocrine pancreas with diverse clinical outcomes. While some PNETs are indolent, others are aggressive and metastasize quickly. However, clinically-relevant molecular stratification for PNET to predict outcomes and guide therapeutic decision-making is limited. Thus, there is an urgent need to understand the molecular heterogeneity of PNETs to refine prognostication and discover novel therapeutic vulnerabilities. We performed single-nucleus RNA sequencing on resected primary and metastatic PNETs (n = 20), including two PNETs with neoadjuvant treatment. We inferred gene expression programs (GEPs) of malignant and non-malignant cells and investigated associations with clinical outcomes. Next, we inferred interactions in the tumor microenvironment (TME) and performed transwell assays for functional validation. Finally, we explored genomic and transcriptomic evolution in a unique case study of an untreated primary PNET with two asynchronous hepatic metastases. A malignant GEP enriched for neural/synaptic signaling genes was associated with worse overall survival, broad chromosomal loss of heterozygosity, and alternative lengthening of telomeres. Another malignant GEP enriched for VEGF signaling increased throughout metastatic progression in our case study. We found that macrophage-derived glutamate drives polarization towards an immunosuppressive phenotype and activates the MAPK/ERK pathway in malignant cells to increase migratory capacity. This study provides a detailed single-nucleus transcriptomic classification of malignant, stromal, and immune cell types and states in PNETs, their interactions in the TME, and associations with clinical outcomes. The refined molecular taxonomy of PNET may guide the development of more efficacious biomarkers and therapeutic strategies.

cancer biology↗

bioRxiv2024

DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response

Despite their potential, current precision oncology approaches benefit only a small fraction of patients due to their limited focus on actionable genomic alterations. To expand its applicability, we propose DeepVul, a multi-task transformer-based model designed to predict gene essentiality and drug response from cancer transcriptome data. DeepVul aligns gene expressions, gene perturbations, and drug perturbations into a latent space, enabling simultaneous and accurate prediction of cancer cell vulnerabilities to numerous genes and drugs. Benchmarking against existing precision oncology approaches revealed that Deep-Vul not only matches but also complements oncogene-defined precision methods. Through interpretability analyses, DeepVul identifies underlying mechanisms of treatment response and resistance, as demonstrated with BRAF vulnerability prediction. By leveraging whole-genome transcriptome data, DeepVul enhances the clinical actionability of precision oncology, aiding in the identification of optimal treatments across a broader range of cancer patients. DeepVul is publicly available at https://github.com/alaaj27/DeepVul.git.

bioinformatics↗

bioRxiv2024

A unique interplay of access and selection shapes peritoneal metastasis evolution in colorectal cancer

Whether metastasis in humans can be accomplished by most primary tumor cells or requires the evolution of a specialized trait remains an open question. To evaluate whether metastases are founded by non-random subsets of primary tumor lineages requires extensive, difficult-to-implement sampling. We have realized an unusually dense multi-region sampling scheme in a cohort of 26 colorectal cancer patients with peritoneal metastases, reconstructing the evolutionary history of on average 28.8 tissue samples per patient with a microsatellite-based fingerprinting assay. To assess metastatic randomness, we evaluate inter- and intra-metastatic heterogeneity relative to the primary tumor and find that peritoneal metastases are more heterogeneous than liver metastases but less diverse than locoregional metastases. Metachronous peritoneal metastases exposed to systemic chemotherapy show significantly higher inter-lesion diversity than synchronous, untreated metastases. Projection of peritoneal metastasis origins onto a spatial map of the primary tumor reveals that they often originate at the deep-invading edge, in contrast to liver and lymph node metastases which exhibit no such preference. Furthermore, peritoneal metastases typically do not share a common subclonal origin with distant metastases in more remote organs. Synthesizing these insights into an evolutionary portrait of peritoneal metastases, we conclude that the peritoneal-metastatic process imposes milder selective pressures onto disseminating cancer cells than the liver-metastatic process. Peritoneal metastases unique evolutionary features have potential implications for staging and treatment.

genetics↗

bioRxiv2024

Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing, along with spatial profiling. We identified tumor microenvironmental features previously described to associate with therapy response. We identified five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which were associated with differential epigenetic plasticity and clinical outcomes, and for which we inferred candidate transcription factor regulons. Furthermore, we revealed diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predicted their significant interactions with microenvironmental cell types. We validated our findings in three external single-cell RNA-seq and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response.

cancer biology↗

bioRxiv2022

Whole genome error-corrected sequencing for sensitive circulating tumor DNA cancer monitoring

Circulating cell-free DNA (ccfDNA) sequencing for low-burden cancer monitoring is limited by sparsity of circulating tumor DNA (ctDNA), the abundance of genomic material within a plasma sample, and pre-analytical error rates due to library preparation, and sequencing errors. Sequencing costs have historically favored the development of deep targeted sequencing approaches for overcoming sparsity in ctDNA detection, but these techniques are limited by the abundance of ccfDNA in samples, which imposes a ceiling on the maximal depth of coverage in targeted panels. Whole genome sequencing (WGS) is an orthogonal approach to ctDNA detection that can overcome the low abundance of ccfDNA by supplanting sequencing depth with breadth, integrating signal across the entire tumor mutation landscape. However, the higher cost of WGS limits the practical depth of coverage and hinders broad adoption. Lower sequencing costs may thus allow for enhanced ctDNA cancer monitoring via WGS. We therefore applied emerging lower-cost WGS (Ultima Genomics, 1USD/Gb) to plasma samples at [~]120x coverage. Copy number and single nucleotide variation profiles were comparable between matched Ultima and Illumina datasets, and the deeper WGS coverage enabled ctDNA detection at the parts per million range. We further harnessed these lower sequencing costs to implement duplex error-corrected sequencing at the scale of the entire genome, demonstrating a [~]1,500x decrease in errors in the plasma of patient-derived xenograft mouse models, and error rates of [~]10-7 in patient plasma samples. We leveraged this highly de-noised plasma WGS to undertake cancer monitoring in the more challenging context of resectable melanoma without matched tumor sequencing. In this context, duplex-corrected WGS allowed us to harness known mutational signature patterns for disease monitoring without matched tumors, paving the way for de novo cancer monitoring.

genomics↗

bioRxiv2022

Enhancer Reprogramming in Melanoma Immune Checkpoint Therapy Resistance

Immune checkpoint blockade (ICB) therapy has improved long-term survival for patients with advanced melanoma. However, there is critical need to identify potential biomarkers of response and actionable strategies to improve response rates. Through generation and analysis of 148 chromatin modification maps for 36 melanoma samples from patients treated with anti-PD- 1, we identified significant enrichment of active enhancer states in non-responders at baseline. Analysis of an independent cohort of 20 samples identified a set of 437 enhancers that predicted response to anti-PD-1 therapy (Area Under the Curve of 0.8417). The activated non-responder enhancers marked a group of key regulators of several pathways in melanoma cells (including c- MET, TGF{beta}, EMT and AKT) that are known to mediate resistance to ICB therapy and several checkpoint receptors in T cells. Epigenetic editing experiments implicated involvement of c-MET enhancers in the modulation of immune response. Finally, inhibition of enhancers and repression of these pathways using bromodomain inhibitors along with anti-PD-1 therapy significantly decreased melanoma tumor burden and increased T-cell infiltration. Together, these findings identify a potential enhancer-based biomarker of resistance to anti-PD-1 and suggest enhancer blockade in combination with ICB as a potential strategy to improve responses.

cancer biology↗

bioRxiv2022

Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE uses deep learning and a ctDNA-specific feature space to increase SNV signal to noise enrichment in WGS by 300X compared to our previous noise suppression platform MRDetect. MRD-EDGE also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1Gb to 200Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal to noise enrichment in MRD-EDGE enables de novo mutation calling in melanoma without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition.

genomics↗

bioRxiv2021

Altered interactions between circulating and tissue-resident CD8 T cells with the colonic mucosa define colitis associated with immune checkpoint inhibitors

Therapeutic blockade of co-inhibitory immune receptors PD-1 and CTLA-4 has revolutionized oncology, but treatments are limited by immune-related adverse events (IRAEs). IRAE Colitis (irColitis) is the most common, severe IRAE affecting up to 25% of patients on dual PD-1 and CTLA-4 inhibition. Here, we present a systems biology approach to define the cell populations and transcriptional programs driving irColitis. We collected paired colon mucosal biopsy and blood specimens from 13 patients with irColitis, 8 healthy individuals, and 8 controls on immune checkpoint inhibitors (ICIs), and analyzed them with single-cell/nuclei RNA sequencing with paired TCR and BCR sequencing, multispectral fluorescence microscopy, and secreted factor analysis (Luminex). We profiled 299,407 cells from tissue and blood and identified 105 cell subsets that revealed significant tissue remodeling in active disease. Colon mucosal immune populations were dominated by tissue-resident memory (TRM) ITGAE-expressing CD8 T cells representing a phenotypic spectrum defined by gene programs associated with T cell activation, cytotoxicity, cycling, and exhaustion. CD8 TRM and effector CD4 T cells upregulated type 17 immune programs (IL17A, IL26) and Tfh-like programs (CXCL13, PDCD1). We also identified for the first time an increased abundance of two KLRG1 and ITGB2-expressing CD8 T cell populations with circulatory cell markers, including a GZMK TRM-like population and a CX3CR1 population that is predicted to be intravascular. These two populations were more abundant in irColitis patients treated with dual PD-1/CTLA-4 inhibition than those receiving anti-PD-1 monotherapy. They also had significant TCR sharing with PBMCs, suggesting a circulatory origin. In irColitis we observed significant epithelial turnover marked by fewer LGR5-expressing stem cells, more transit amplifying cells, and upregulation of apoptotic and DNA-sensing programs such as the cGAS-STING pathway. Mature epithelial cells with top crypt genes upregulated interferon-stimulated pathways, CD274 (PD-L1), anti-microbial genes, and MHC-class II genes, and downregulated aquaporin and solute-carrier gene families, likely contributing to epithelial cell damage and absorptive dysfunction. Mesenchymal remodeling was defined by increased endothelial cells, both in irColitis patients and specifically in patients on dual PD-1/CTLA-4 blockade. Cell-cell communication analysis identified putative receptor-ligand pairs that recruit CD8 T cells from blood to inflamed endothelium and positive feedback loops such as the CXCR3 chemokine system that retain cells in tissue. This study highlights the cellular and molecular drivers underlying irColitis and provides new insights into the role of CTLA-4 and PD-1 signaling in maintaining CD8 TRM homeostasis, regulating CD8 T recruitment from blood, and promoting epithelial-immune crosstalk critical to gastrointestinal immune tolerance and intestinal barrier function.

immunology↗

bioRxiv2019

Genome-wide CRISPR screens identify essential cell growth mediators in BRAF-inhibitor resistant melanoma

BRAF is a serine-threonine kinase that harbors activating mutations in [~]7% of human malignancies and [~]60% of melanomas. Despite initial clinical responses to BRAF inhibitors (BRAFi), patients frequently develop drug resistance. To identify candidate therapeutic targets for BRAFi-resistant melanoma, we conducted CRISPR screens in melanoma cells harboring an activating BRAF mutation that had also acquired resistance to BRAFi. The screens identified pathways and genes critical for BRAFi resistance in melanoma cells. To investigate the mechanisms and pathways enabling resistance to BRAFi in melanomas, we integrated expression data, ATAC-seq, and CRISPR screen results. We identified the JUN family of transcription factors and the ETS family transcription factor ETV5 as key regulators of CDK6 that enabled resistance to BRAFi in melanoma cells. Our findings reveal genes whose loss of function conferred resistance to a selective BRAF inhibitor, providing new insight into signaling pathways that contribute to acquired resistance in melanoma.

cancer biology↗

bioRxiv2018

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here we perform a genome-wide prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

cancer biology↗