Biology subjects

Tsirigos, A.

Publications and source records attributed to Tsirigos, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Vision-Language Models for Image-Based Dietary Assessment: A Benchmark of Accuracy, Cost, and Prompt Strategies Across Ten Models

BackgroundDietary assessment is the cornerstone of clinical management and research studies evaluating diet and health. Traditional methods such as food diaries and 24-hour recalls can be burdensome, prone to recall bias, and difficult to adhere to. Image-based dietary assessment using vision-language models (VLMs) offers a potential solution. ObjectiveOur goal was to benchmark state-of-the-art VLMs for automated food recognition, weight estimation, and calorie estimation using Googles Nutrition5k dataset. MethodsWe evaluated 3,229 food images using ten approaches: proprietary VLMs (Gemini 2.0 Flash, 2.5 Flash, 3.0 Flash, and 3.1 Flash Lite; GPT- 4o, GPT-4o-mini, and GPT-5 Mini; and Claude Haiku 4.5), an open-source VLM (Qwen2-VL-7B), and a commercial food recognition API (FatSecret). We assessed calorie and weight estimation using Lins Concordance Correlation Coefficient (CCC) and component detection using Jaccard similarity. ResultsGemini 3.0 Flash achieved the best calorie estimation (CCC 0.767, MAE 80.7 kcal), while Gemini 3.1 Flash Lite offered very comparable accuracy (CCC 0.754) with the highest ingredient recognition (Jaccard 0.655) at the lowest cost among top-performing models ($0.59/1K images). Among earlier-generation models, Gemini 2.0 Flash remained competitive (CCC 0.742, Jaccard 0.621) at a fraction of the cost ($0.10/1K images). A human validation study in which four annotators reviewed 440 images revealed systematic omissions in the original Nutrition5k labels. After correction, the extrapolated ingredient-overlap score for Gemini 2.0 Flash increased from 0.62 to an estimated 0.82, suggesting that raw Jaccard scores substantially underestimate true model performance. ConclusionsCurrent VLMs can perform automated dietary assessment with reasonable accuracy from single overhead photographs. Our results inform model selection for dietary assessment applications and highlight remaining challenges in calorie estimation and component detection for complex, multi-item meals.

bioinformatics↗

bioRxiv2026

Multicenter self-supervised computational pathology identifies prognostic histomorphological phenotypes in colorectal cancer

H&E whole-slide images capture prognostic information encoded in tumor morphology and the surrounding microenvironment, but these signals remain difficult to extract and interpret at scale. Here, we developed a self-supervised computational pathology framework to predict disease-free survival in colorectal cancer and link model-derived risk to interpretable histomorphology and spatial tumor biology. Using a multicenter developmental cohort spanning colorectal adenomas and invasive colorectal cancer, we trained HPL-PanColon, a self-supervised representation model, to extract tile-level embeddings and identify recurrent histomorphological phenotype clusters across the adenoma-carcinoma spectrum. Compared with general-purpose pathology foundation models, HPL-PanColon yielded representations with reduced institution- and dataset-specific batch effects. We then applied HPL-PanColon to a global survival cohort of 1,024 colorectal cancer patients in a leave-one-institution-out framework, using tile embeddings to train an attention-based survival model and derive the Colon Histomorphology Prognostic Score (CHiPS). CHiPS stratified patients by disease-free survival and provided complementary prognostic information to a UICC TNM-informed clinicopathological model, increasing the c-index from 0.683 to 0.706. Integrating model attention with phenotype assignments traced CHiPS-associated risk to pathologist-recognizable tissue patterns, with high-risk regions enriched for desmoplastic, stromal, and fibroinflammatory morphologies and low-risk regions reflecting tumor-rich epithelial glandular patterns. Spatial transcriptomic analysis further linked high-risk morphologies to fibroblastic, perivascular, myofibroblastic, and immune-reactive tumor microenvironment programs, while low-risk morphologies mapped to epithelial and tumor-enriched regions. These findings establish a scalable framework for interpretable histology-based prognosis and spatial biological discovery in colorectal cancer.

pathology↗

bioRxiv2025

TASC: A transcriptome-driven machine learning classifier to explore molecular heterogeneity and relapse-associated programs in T-cell Acute Lymphoblastic Leukemia

T-cell acute lymphoblastic leukemia is a biologically heterogeneous malignancy characterized by diverse transcriptional and genomic alterations. Recent studies have defined a set of recurrent molecular subtypes associated with distinct differentiation stages and clinical outcomes. However, no unified framework currently exists for assigning these subtypes in a standardized and accessible manner. Existing approaches often rely on mutation or fusion detection and may overlook broader transcriptional programs. The lack of a comprehensive, transcriptome-based classification tool has hampered the use of subtype-specific insights in both research and clinical settings. Here, we present a machine learning-based classifier trained on transcriptomic data to predict previously defined multi-omic subtypes of T-cell acute lymphoblastic leukemia. The model accurately assigns subtype identity across patient samples and cell lines, and provides a practical tool for standardized molecular stratification, supporting future integration into diagnostic and translational workflows, as demonstrated by its ability to reveal subtype-specific patterns of relapse.

bioinformatics↗

bioRxiv2025

High-throughput in silico screen uncovers key regulators of 3D genome architecture

The vertebrate genome is spatially organized into topologically associating domains (TADs), primarily via cohesin-mediated loop extrusion which typically halts at convergent CTCF binding sites to establish domain boundaries. However, despite the essential roles of CTCF and cohesin in establishing TADs, a long-standing paradox persists: CTCF and cohesin binding sites dramatically outnumber observed TAD boundaries, suggesting the existence of undiscovered architectural factors. To identify such missing factors, we conducted high-resolution in silico screens using C.Origami, a multi-modal AI model for predicting chromatin interactions. Remarkably, we identified ZNF654 and JMJD6 as novel factors uniquely defining TAD boundaries. Experimental validation confirmed that ZNF654, an uncharacterized vertebrate-specific zinc-finger protein, interacts with CTCF to form an architectural protein complex that demarcates chromatin domains. Genetic knockout of ZNF654 weakens TAD boundary strength without influencing other CTCF or cohesin binding sites. JMJD6, a deeply conserved jmjC-family dioxygenase, marks the anchors of the strongest chromatin stripes at both TAD boundaries and enhancer-promoter sites, while deleting JMJD6 weakens or diminishes such interaction signature. These results revealed the long-sought factors that uniquely mark TAD boundary and chromatin interaction anchors which, together with CTCF and cohesin, demarcate chromatin domains during 3D genome organization. Last, the evolutionary trajectory of ZNF654 and JMJD6 offers key insight into the evolutionary origins of 3D genome organization across metazoan species.

genomics↗

bioRxiv2025

Multiomic characterization, early detection, and therapeutic targeting of myeloid sarcoma

Myeloid sarcoma, an aggressive extramedullary subtype of acute myeloid leukemia (AML), occurs in [~]10% of patients, and has not yet been included in large-scale genomic studies. The critical biological changes that drive tumor evolution are unknown, its detection in asymptomatic patients remains a clinical challenge, and treatment options are limited as patients are often excluded from clinical trials, rendering it a neglected disease entity. Based on comprehensive multi-omic profiling, we demonstrate that myeloid sarcoma evolves from medullary AML with distinct sitespecific clonal evolution patterns. Additionally, we show that circulating tumor DNA sequencing can serve as a non-invasive method for molecular profiling of myeloid sarcoma, offering a novel avenue in molecular diagnostics. We characterize unique transcriptional profiles of myeloid sarcoma, reflecting immune evasion and adaptation to an extramedullary microenvironment. We provide evidence for a key role of RAS pathway activation and demonstrate in murine models of myeloid sarcoma that RAS inhibition effectively reduces tumor burden. Overall, our data highlight key differences between medullary AML and myeloid sarcoma including universal molecular evolution and RAS pathway activation as hallmarks of the disease and nominate RAS inhibition as a promising therapeutic strategy for patients with myeloid sarcoma.

cancer biology↗

bioRxiv2025

A developmentally regulated long-range enhancer-promoter contact mediates human neural development

SOX2 is a core pluripotency factor in human embryonic stem cells (hESCs), but upon differentiation to the three germ layers, its expression is preserved selectively in neuroectoderm. The mechanisms regulating SOX2 transcription in distinct developmental stages remain incompletely understood. Here, we demonstrate that a distant enhancer 550 kb from the human SOX2 locus is selectively activated in neural stem cells (NSCs) and establishes long-range contact with the SOX2 gene. CRISPR-Cas9 excision of the enhancer has no effect in hESCs but reduces SOX2 transcription in NSCs and impairs neuroectodermal differentiation and forebrain specification in teratomas and cerebral organoids. CRISPR excision of a CTCF recognition motif adjacent to the enhancer does not affect enhancer activation in neuroectoderm but reduces chromatin looping and SOX2 transcription to partially reproduce phenotypes seen with enhancer deletion. Our findings indicate that the development of the human nervous system depends on a developmentally regulated long-range contact between a distant enhancer and the SOX2 locus.

developmental biology↗

bioRxiv2025

Islands of Signal and Transcriptomic Sequencing: A Foundation Model for Mutation and Lineage Prediction based on DNAMethylation and RNA-seq

DNA methylation and RNA-seq provide complementary views of oncogenic state, but their high dimensionality complicates robust modeling. We develop a pancancer, multiomic foundation model that jointly encodes CpG-island DNA methylation and gene expression from TCGA, TARGET, CPTAC-3, and HCMI. Probe-level methylation is aggregated into CpG-island features, and RNA-seq is reduced to high-variance genes, yielding compact inputs for modality-specific MLP encoders. A BERT-like transformer with masked reconstruction and cross-modal prediction objectives learns a shared embedding space that supports missing-modality inputs. We evaluate the learned representations in two zero-shot settings: (i) cancer-type classification using a linear probe on frozen embeddings, and (ii) mutation prediction for 214 genes using a shallow MLP. The model achieves high performance for many tumor types and gene-cancer pairs without encoder finetuning. Pathway-level analyses show that hallmark oncogenic and immune programs appear as smooth gradients in the embedding space, indicating that the model captures biologically meaningful structure. These results demonstrate that combining CpG-island grouping with multiomic foundation pre-training yields compact, informative embeddings for mutation and lineage inference across cancers.

bioinformatics↗

bioRxiv2025

Cross-species analysis identifies genotype-driven vulnerabilities in lung adenocarcinoma

While three major genetic alteration subsets, characterized by mutations in STK11, KRAS, and EGFR, are seminal in driving tumorigenesis in LUAD, their distinct effects on tumor cells and the tumor microenvironment are not fully understood. Here, we map critical oncogenic subset-specific vulnerabilities by identifying conserved cell-type-specific reprogrammings between human and mouse LUAD. Through harmonized scRNA-seq analysis of 57 human and 18 mouse specimens, we unveil that genetic alterations impose genotype-specific immune imprints on the tumor microenvironment: KRAS is associated with a transitional immune state, whereas STK11 and EGFR mutations define discrete and contrasting immune phenotypes. We find that STK11-mutant tumors exhibit complement and interferon-rich immune microenvironments while EGFR-mutant tumors harbor a naive T cell-rich phenotype accompanied by a global HLA downregulation, stress-responsive alveolar macrophages marked by MARCO. In the epithelial compartments, cross-species analysis reveals metabolic dependencies, including OGDH in EGFR- and PDE4D in STK11-mutant cells. Statement of SignificanceThis study delineates oncogenotype-specific molecular and immune imprints in lung adenocarcinoma, revealing STK11-, KRAS-, and EGFR-mutated tumors as transcriptionally and immunologically discrete ecosystems. By mapping epithelial vulnerabilities alongside immune imprints, we unveil genotype-imposed dependencies that inform the rational development of precision therapies with direct translational relevance.

cancer biology↗

bioRxiv2025

Non-segmented unsupervised learning of multispectral whole slide images for robust analysis of tissue repair and regeneration

Analyzing whole tissue architecture remains challenging due to the inherent complexity of multicellular organization, variable morphology, and the limitations of conventional segmentation-based image analysis. Traditional approaches often rely on partial sampling or nuclear/cytoplasmic boundaries, which risk introducing bias and fail to capture the contextual interplay of diverse tissue compartments. To overcome these barriers, we developed a segmentation free framework for analyzing multispectral whole slide images (WSIs). By tiling WSIs into fixed sized regions and extracting quantitative tile features, we applied unsupervised machine learning to systematically reveal patterns of tissue organization at scale. This approach preserved spatial context without the need for cell-level delineation, recapitulating expected compartments such as epidermis, adipose, and scab, while also revealing subtle but coherent substructures within stromal and granulation regions. Applied to murine wound healing, the method distinguished wild type from diabetic repair dynamics without prior labels, uncovering both gross and nuanced differences in tissue composition. Together, this work establishes a robust, unbiased strategy for whole-tissue analysis that circumvents the limitations of segmentation, leverages unsupervised learning for discovery, and advances the study of tissue repair and regenerative pathology.

cell biology↗

bioRxiv2025

Multimodal learning decodes the global binding landscape of chromatin-associated proteins

The identity of a cell is governed by regulatory proteins binding to the genome to control gene expression. Mapping these genome-wide binding events across thousands of proteins and cell types is essential for understanding development and disease at scale, yet has remained a major experimental and computational barrier. Here we present Chromnitron, a multimodal foundation model that learns the rules of protein-DNA binding from protein sequence, DNA sequence, and context-specific chromatin states. Unlike prior single-task and multi-task learning approaches, Chromnitron implements a multimodal learning framework that accurately predicts the binding landscape for proteins and cell types not seen during training. Using Chromnitron, we discovered and experimentally validated new protein regulators of T cell exhaustion. Chromnitron also uncovered previously uncharacterized dynamic shifts in the binding landscape of regulatory proteins during neurogenesis. This marks a critical step toward a predictive model of interpretable gene regulatory programs across cell types, enabling rapid discovery of regulatory circuits and identification of new therapeutic targets.

systems biology↗

bioRxiv2025

Hi-C for genome-wide detection of enhancer-hijacking rearrangements in routine lymphoid cancer biopsies

Standard techniques for detecting genomic rearrangements in formalin-fixed paraffin-embedded (FFPE) biopsies have important limitations. We performed FFPE-compatible Hi-C on 44 clinical biopsies comprising large B-cell lymphomas (n=18), plasma cell neoplasms (n=14), and other diverse lymphoid cancers, identifying consistent topological differences between malignant B cell and plasma cell states. Hi-C detected expected oncogene rearrangements at high concordance with fluorescence in-situ hybridization (FISH) and supported enhancer-hijacking in recurrent rearrangements of BCL2, CCND1, and MYC, plus unanticipated variants involving homologous loci. Hi-C identified unanticipated non-coding rearrangements involving PD-1 ligand genes and other loci of potential therapeutic relevance, distinguished between functionally divergent classes of BCL6 rearrangements, and provided topological information supporting the interpretation of atypical MYC rearrangements. In biopsies lacking MYC-activating rearrangements, Hi-C revealed differential interactions with functionally-validated disease-specific native MYC locus enhancers. FFPE-compatible Hi-C detects oncogene rearrangements and their topological consequences at genome-wide scale, finding clinically-relevant drivers that are missed by standard approaches.

genomics↗

bioRxiv2025

A multi-scale segmentation-free self-supervised AI model to characterize the heterogeneity of the brain tumor microenvironment

Brain tumors affect about 1 million people in the U.S., with aggressive types like glioblastoma having very low survival rates due to complex tumor biology and the protective blood-brain barrier. Current treatments are limited in effectiveness, and our understanding of brain tumor biology remains incomplete. High dimensional multiplexed imaging has enabled us to better understand the tumor microenvironment (TME); however, analyses typically rely on cell segmentation, which is error-prone, may discard useful context outside the cell boundary, and neglects complex tissue-wide features. To address this limitation, we developed a segmentation-free, self-supervised representation learning framework that enables us to train directly on multiplexed images using masked image modeling. We used this approach to analyze 389 imaging mass cytometry images from 185 brain tumor patients. To study tissue-wide features, we first trained our model on 64x64 micron tiles capturing neighborhoods of 10-20 cells, which we termed local tumor microenvironments (LTMEs). To further characterize these LTMEs, we trained our model on 16x16 micron tiles centered on individual cells in our dataset, so that each tile captures a single cell and its surrounding area, which we termed single-cell microenvironments (SCMEs). This multi-scale, self-supervised approach enables a detailed analysis of the heterogeneity within the brain TME, examining single cells in their spatial context. In addition to validating known findings, we identified a novel LTME in GBM patients, composed primarily of tumor cells and a few B and T cells, which strongly correlated with increased survival. By analyzing these B cells with our SCME model, we found they were distinct from other GBM B cells, and higher concentrations of these B cells were linked to improved survival. In conclusion, our study introduces a multi-scale, segmentation-free, self-supervised machine learning model that provides unprecedented insights into brain TMEs, enabling discovery of previously unrecognized cell interactions and spatial features that are predictive of patient survival.

cancer biology↗

bioRxiv2025

Clonal Heterogeneity in Human Pancreatic Ductal Adenocarcinoma and Its Impact on Tumor Progression

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer characterized by profound desmoplasia and cellular heterogeneity, which cannot be fully resolved using traditional bulk sequencing approaches. To understand the contribution of this heterogeneity to PDAC biology, we analyzed a large cohort of primary human PDAC samples (n = 62), profiling 443,451 single cells and 53,236 spatial transcriptomic spots using a combined single-cell RNA sequencing and spatial transcriptomics approach. Our analysis revealed significant intratumoral heterogeneity, with multiple genetically distinct neoplastic clones co-existing within individual tumors. These clones exhibited diverse transcriptional states and subtype profiles, challenging the traditional binary classification of PDAC into basal and classical subtypes; instead, our findings support a transcriptional continuum influenced by clonal evolution and spatial organization. Additionally, these clones each interacted uniquely with surrounding cell types in the tumor microenvironment. Phylogenetic analysis uncovered a rare but consistent classical-to-basal clonal transition associated with MYC amplification and immune response depletion, which were validated experimentally, suggesting a mechanism driving the emergence of a more aggressive basal clonal phenotype. Spatial analyses further revealed dispersed clones enriched for epithelial-to-mesenchymal transition (EMT) activity and immune suppression, correlating with metastatic potential and colonization of lymph node niches. These dispersed clones tended to transition toward a basal phenotype, contributing to disease progression. Our findings highlight the critical role of clonal diversity, transcriptional plasticity, and TME interactions in shaping human PDAC biology. This work provides new insights into the molecular and spatial heterogeneity of PDAC and offers potential avenues for therapeutic intervention targeting clonal evolution and the mechanisms driving metastasis.

cancer biology↗

bioRxiv2025

Regulating white blood cell activity through the novel Universal Receptive System

The understanding of the mechanisms that control key features of immune cells in various disease contexts remains limited, and few techniques are available for manipulating immune cells. Thus, discovering novel strategies for regulating immune cells is essential for gaining insight into their roles in health and disease. In this study, we investigated the potential of the recently described Universal Receptive System to regulate human immune cell functions. This was achieved for the first time by specifically targeting newly discovered surface-bound DNA and RNA-based receptors on leukocytes and generating "Leukocyte-Tells." This approach upregulated numerous genes related to immune cell signaling, migration, endocytosis, and phagocytosis pathways. The antimicrobial and anticancer activities of Leukocyte-Tells exceeded the activity of control leukocytes in vitro. In some settings, such as in antibiofilm experiments, the Leukocyte-Tells showed up to 1,000,000-fold higher activities than control leukocytes. Our findings reveal, for the first time, that the Universal Receptive System can orchestrate fundamental properties of immune cells, including enhanced antimicrobial and anti-tumor activities. This novel approach offers a new avenue for understanding the biology and regulation of white blood cells.

immunology↗

bioRxiv2024

The Universal Receptive System acts as a novel regulator in the production of antimicrobial and anticancer bioactive compounds by white blood cells

Despite recent advances, the regulation of anticancer and antimicrobial bioactive compound (AABC) production by leukocytes remains poorly understood. Here, we demonstrate that inactivation of the DNA- and RNA-based Teazeled receptors of the Universal Receptive System in human leukocytes generated so called "Leukocyte-Tells," which showed enhanced AABC production. Comprehensive analysis of the AABCs produced by Leukocyte-Tells based on LC/MS identified 707 unique or differentially produced peptide or non-peptide metabolites. Functional testing demonstrated that many of these metabolites exhibited increased antibacterial, antifungal, and anticancer activities. The AABCs produced by the Leukocyte-Tells were effective against different multidrug-resistant clinical isolates of fungi and gram-positive and gram-negative bacteria (including their biofilms), as well as various cancer cell lines, with >100,000-fold activity than AABCs derived from control leukocytes. Notably, the AABCs produced by the Leukocyte-Tells exhibited greater stability under adverse environmental conditions. Our findings highlight the important role of the Universal Receptive System in regulating AABC production through a process named here as cell genome memory management, offering new insights into immune functions and suggesting potential therapeutic applications. SummaryThe Universal Receptive System acts as a novel regulator of biosynthetic activity in leukocytes. Modulating the leukocyte Universal Receptive System by inactivating Teazeled receptors triggers the production of new compounds not observed in naive cells. We refer to these TezR-modified cells as "Leukocyte-Tells." Leukocytes produce unique metabolites with strong anticancer and antimicrobial activities. Reproducibility in generating leukocytes from the blood of different donors suggests that the observed alterations in cell activity were preprogrammed.

molecular biology↗

bioRxiv2024

Universal Receptive System as a novel regulator of transcriptomic activity of Staphylococcus aureus

Our previous studies revealed the existence of a Universal Receptive System that regulates interactions between cells and their environment. This system is composed of DNA- and RNA-based Teazeled receptors (TezRs) found on the surface of prokaryotic and eukaryotic cells, as well as integrases and recombinases.. In the current study, we aimed to provide further insight into the regulatory role of TezR and its loss in Staphylococcus aureus gene transcription. To this end, transcriptomic analysis of S. aureus MSSA VT209 was performed following the destruction of TezRs. Bacterial RNA samples were extracted from nuclease-treated and untreated S. aureus MSSA VT209. After destruction of the DNA-based-, RNA-, or combined DNA- and RNA-based TezRs of S. aureus, 103, 150, and 93 genes were significantly differently expressed, respectively. The analysis revealed differential clustering of gene expression following the loss of different TezRs, highlighting individual cellular responses following the loss of DNA- and RNA-based TezRs. KEGG pathway gene enrichment analysis revealed that the most upregulated pathways following TezR inactivation included those related to energy metabolism, cell wall metabolism, and secretion systems. Some of the genetic pathways were related to the inhibition of biofilm formation and increased antibiotic resistance, and we confirmed this at the phenotypic level using in vitro studies. The results of this study add another line of evidence that the Universal Receptive System plays an important role in cell regulation, including cell responses to the environmental factors of clinically important pathogens, and that nucleic acid-based TezRs are functionally active parts of the extrabiome.

microbiology↗

bioRxiv2024

Characterization of tumor heterogeneity through segmentation-free representation learning

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

cancer biology↗

bioRxiv2024

CRISPR-inhibition screen for lncRNAs linked to melanoma growth and metastasis

Melanoma being one of the most common and deadliest skin cancers, has been rising since the past decade. Patients at advanced stages of the disease have very poor prognoses, as opposed to at the earlier stages. Nowadays the standard-of-care of advanced melanoma is resection followed by immune checkpoint inhibition based immunotherapy. However, a substantial proportion of patients either do not respond or develop resistances. This underscores a need for novel approaches and therapeutic targets as well as a better understanding of the mechanisms of melanoma pathogenesis. Long non-coding RNAs (lncRNAs) comprise a poorly characterized class of functional players and promising targets in promoting malignancy. Certain lncRNAs have been identified to play integral roles in melanoma progression and drug resistances, however systematic screens to uncover novel functional lncRNAs are scarce. Here, we profile differentially expressed lncRNAs in patient derived short-term metastatic cultures and BRAF-MEK-inhibition resistant cells. We conduct a focused growth-related CRISPR-inhibition screen of overexpressed lncRNAs, validate and functionally characterize lncRNA hits with respect to cellular growth, invasive capacities and apoptosis in vitro as well as the transcriptomic impact of our lead candidate the novel lncRNA XLOC_030781. In sum, we extend the current knowledge of ncRNAs and their potential relevance on melanoma. SignificancePreviously considered as transcriptional noise, lncRNAs have emerged as novel players in regulating many cellular aspects in health and disease including melanoma. However, the number and as well as the extent of functional significance of most lncRNAs remains elusive. We provide a comprehensive strategy to identify functionally relevant lncRNAs in melanoma by combining expression profiling with CRISPR-inhibition growths screens lowering the experimental effort. We also provide a larger resource of differentially expressed lncRNAs with potential implications in melanoma growth and invasion. Our results broaden the characterized of lncRNAs as potential targets for future therapeutic applications.

cancer biology↗