Biology subjects

Luna, A.

Publications and source records attributed to Luna, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Dynamic Schlafen 11 (SLFN11) expression revealed by single-cell RNA sequencing of clonal human cancer cells

Schlafen 11 (SLFN11) is a key determinant of sensitivity to DNA-damaging therapies in multiple cancers including leukemia, Ewing sarcoma, head and neck cancers, mesothelioma and small cell lung cancer (SCLC). Here, utilizing immunofluorescence microscopy and single-cell RNA sequencing we characterized SLFN11 expression across eight different human cancer cell lines and found cell-to-cell heterogenous expression both at the protein and RNA levels. Gene set enrichment and pathway analyses show that SLFN11 is coexpressed with a distinct stress-response state and enhanced genome maintenance programs while being inversely correlated with protein translation pathways. Publicly available single-cell RNA-sequencing datasets of human SCLC tumors confirmed the heterogeneous distribution of SLFN11 expression and confirmed its association with replication-related transcriptional programs. These findings indicate that heterogeneous SLFN11 expression reflects dynamic regulation at the single-cell level. They also underscore the limitations of bulk proteomic and transcriptome profiling and point to a potential reservoir of intrinsic chemotherapy resistance.

cancer biology↗

bioRxiv2025

Striatal pathways oppositely shift cortical activity along the decision axis

The cortex and basal ganglia are organized into multiple parallel loops that serve motor, limbic, and cognitive functions. The classic model of cortico-basal ganglia interactions posits that within each loop, the direct pathway of the basal ganglia activates the cortex and the indirect pathway inhibits it1-3. While this model has found support in the motor domain4,5, whether opponent control by the two pathways extends to the cognitive domain remains unknown. Here, we record from anterior cingulate cortex (ACC) and dorsomedial striatum (DMS) while inhibiting direct or indirect pathway neurons in DMS, as mice perform an accumulation-of-evidence task6-10. Inconsistent with the classic model, the manipulations do not produce opponent changes in overall ACC activity. Instead, the pathways exert opponent influence over a subpopulation of ACC neurons that encode accumulated sensory evidence, the task-relevant decision variable. The direction of the modulation depends on a neurons tuning to ipsilateral versus contralateral evidence, such that the two pathways generate opponent shifts in coding specifically along the decision axis. Thus, our results uncover unexpected specificity in the effects of basal ganglia pathways on the cortex, with the two pathways of the DMS exerting opponent control not on overall activity but on coding of the relevant task variable. This functional specificity may extend to other basal ganglia loops to support different aspects of adaptive behavior, with the pathways serving a general role in selecting and shifting cortical representations to subserve circuit-specific functions.

neuroscience↗

bioRxiv2025

textToKnowledgeGraph: Generation of Molecular Interaction Knowledge Graphs Using Large Language Models for Exploration in Cytoscape

MotivationKnowledge graphs (KGs) are powerful tools for structuring and analyzing biological information due to their ability to represent data and improve queries across heterogeneous datasets. However, constructing KGs from unstructured literature remains challenging due to the cost and expertise required for manual curation. Prior works have explored text-mining techniques to automate this process, but have limitations that impact their ability to capture complex relationships fully. Traditional text-mining methods struggle with understanding context across sentences. Additionally, these methods lack expert-level background knowledge, making it difficult to infer relationships that require awareness of concepts indirectly described in the text. Large Language Models (LLMs) present an opportunity to overcome these challenges. LLMs are trained on diverse literature, equipping them with contextual knowledge that enables more accurate extraction of information. ResultsWe present textToKnowledgeGraph, an artificial intelligence tool using LLMs to extract interactions from individual publications directly in Biological Expression Language (BEL). BEL was chosen for its compact and detailed representation of biological relationships, allowing for structured and computationally accessible encoding. This work makes several contributions. 1. Development of the open-source Python textToKnowledgeGraph package (pypi.org/project/texttoknowledgegraph) for BEL extraction from scientific articles, usable from the command line and within other projects, 2. An interactive application within Cytoscape Web to simplify extraction and exploration, 3. A dataset of extractions that have been both computationally and manually reviewed to support future fine-tuning efforts. Availabilityhttps://github.com/ndexbio/llm-text-to-knowledge-graph

bioinformatics↗

bioRxiv2025

SCLC-TumorMiner: A Directly Accessible Genomics Resource for Precision Oncology: Big Data for Small Cells

Small cell lung cancer (SCLC) is among the most aggressive malignancies. Unlike many other cancers, it is not represented in The Cancer Genome Atlas, and available datasets are fragmented across institutions, disease stages, and treatment settings. RNA sequencing provides a powerful and cost-effective approach, but the high dimensionality of transcriptomic data and the heterogeneity of patient cohorts pose significant challenges. To address such challenges, we developed SCLC TumorMiner (https://discover.nci.nih.gov/SclcTumorMinerCDB/), which includes 50 tumor samples from relapse patients at the National Cancer Institute (NCI) and 154 samples from untreated patients at the University of Cologne and Tongji University. SCLC TumorMiner enables molecular classification, genomic pathway analyses, risk stratification, identification of predictive cell-surface biomarkers such as DLL3 or TROP2, and drug-response biomarkers such as SLFN11. SCLC TumorMiner illustrates profound differences between untreated and relapse patient samples. Additionally, "MyPatient", one of SCLC TumorMiners modules, is presented as a medical assistant application prototype.

cancer biology↗

bioRxiv2025

Design of combination therapeutics from protein response to drugs in ovarian cancer cells

High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecologic malignancy, and novel treatment approaches are needed. Here, we used unbiased quantitative protein mass spectrometry to assess the cellular response profile to drug perturbations in ovarian cancer cells for the rational design of potential combination therapies. Analysis of the perturbation profiles revealed proteins responding across several drug perturbations (called frequently responsive below) as well as drug-specific protein responses. The frequently responsive proteins included proteins that reflected general drug resistance mechanisms, such as changes in drug efflux pumps. Network analysis of drug-specific protein responses revealed known and potential novel markers of resistance, which were used to rationalize the design of anti-resistance drug pairs. We experimentally tested the anti-proliferative effects of 12 of the proposed drug combinations in 6 HGSOC cell lines. While response typically varies across different cell lines, 10 of the 12 combinations tested have either an additive or synergistic CI index in at least one cell line and may therefore be plausible candidates for overcoming or preventing resistance to single agents. Serendipitously, we observed an unexpectedly strong 0.05-0.11 micromolar response to GPX4 inhibitors as single agents in the OVCAR-4 cell line. We propose several drug combinations as potential therapeutic candidates in ovarian cancer, as well as GPX4 inhibitors as single agents. HighlightsO_LI5000-protein response to 7 drugs in an ovarian cancer cell line profiled by protein mass spectrometry C_LIO_LINetwork analysis suggested potential pathways of drug resistance inferred from response profiles C_LIO_LIDemonstration of a general method for profiling adaptive response to therapeutic interventions with implications for the development of anti-resistance therapy C_LIO_LIPlausible anti-resistance drug combinations tested for antiproliferative effect in up to 6 ovarian cancer cell lines C_LIO_LIDrug combinations with additive or synergistic antiproliferative effects are plausible pre-clinical candidates for overcoming or preventing resistance to single agents C_LIO_LISeveral combinations were synergistic in some cell lines (with PARPi, MEKi, and SRCi) C_LIO_LIWe observed 0.05-0.11 micromolar response to GPX4 inhibitors as single agents in the OVCAR-4 cell line C_LI

cancer biology↗

bioRxiv2025

Aberrant formation of long-range projections across different neurodevelopmental disorders converges on molecular and cellular nexuses

Establishing long-range connections during human brain development is an intricate multi-step process disturbed in many neurodevelopmental disorders (NDDs). The aberrant formation of these connections is caused by mutations in a plethora of different genes with distinct molecular functions, triggering the question of whether there are common key downstream mediators at which different pathologies are converging. We employed brain organoids to model early human brain developmental aspects of Coffin-Siris-like 9, Opitz BBB/G, and Pitt-Hopkins syndromes. These NDDs are caused by mutations in SOX11, MID1, and TCF4 respectively, and are characterized by a multitude of distinct symptoms yet share alterations in long-range projections as a common feature. Here, we uncover that mutations in all three genes phenotypically converge, showing impaired neurite extension with increased tortuosity and decreased growth speed resulting in shorter beelines. Moreover, the mutant neurites exhibit a decrease in growth persistence providing a conceptual framework explaining why long- but not short-range connections are affected. Correlating with the converging cellular phenotype, molecular characterization revealed a striking convergence on signaling pathways implicated in the interaction of neurites with their extracellular environment. In-silico modeling and perturbation of neurite outgrowth suggest that altered neurite-extracellular environment interactions are sufficient to recapitulate the mutant phenotypes but also facilitate the prediction of specific parameters causing disturbed neurite growth in mutant neurons.

developmental biology↗

bioRxiv2025

Bimodal genomic approach predicting Semaphorin 7A (SEMA7A) as prognostic biomarker in adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy with high mortality and poor prognosis. To elucidate the genetic underpinnings of ACCs, we have analyzed the transcriptome data of 112 ACC tumor samples from patients enrolled in the TCGA and NCI. Among 72 bimodally expressed genes stratifying patients into prognostic groups, we focused on SEMA7A, as it encodes a glycosylphosphatidylinositol-anchored membrane glycoprotein (Semaphorin 7a) regulating integrin-mediated signaling, cell migration and immune responses. We find that high SEMA7A gene expression is associated with poor prognosis (hazard ratio = 4.27; p-value < 0.001). In hormone-producing ACCs, SEMA7A expression is elevated and positively correlated with genes driving steroidogenesis, aldosterone and cortisol synthesis, including CYP17A1, CYP11A1, INHA, DLK1, NR5A1 and MC2R. Correlation analyses show that SEMA7A is co-expressed with the integrin-{beta}1, FAK (focal adhesion kinase) and MAPK/ERK (mitogen-activated protein kinase/extracellular signal regulated kinases) signaling pathways. Immunohistochemistry (IHC) staining demonstrates the feasibility of evaluating SEMA7A in ACC tissues and shows significant correlation between gene expression (RNA-Seq) and protein expression (IHC). These findings suggest SEMA7A as a candidate for further research in ACC biology, a candidate for cancer therapy, as well as a potential prognosis biomarker for ACC patients. Translational relevanceAdrenocortical cancer (ACC) remains a challenging disease primarily due to the scarcity of reliable biomarkers for predicting patient outcomes and informing innovative therapeutic strategies, as well as its rarity, which restricts the scope of clinical trials. In our study, we performed RNAseq and IHC analyses of ACC samples sourced from The Cancer Genome Atlas (TCGA), tissue microarray slide, and National Cancer Institute (NCI) cancer patient samples. Our findings indicate that a substantial proportion of ACC tumors exhibit expression of SEMA7A, a glycoprotein involved in Semaphorin cell surface signaling. Notably, elevated levels of SEMA7A were identified as a poor prognostic biomarker and were associated with activation of the integrin-ERK-MAPK kinase signaling pathways. These results suggest that ACC tumors with high SEMA7A expression should be considered at elevated risk, and SEMA7A may serve as a potential target for immunotherapeutic strategies, including antibody-drug conjugates, T-cell engagers, and/or small molecule inhibitors targeting the MAPK pathway.

cancer biology↗

bioRxiv2024

Ethanol alters mechanosensory habituation in C. elegans by way of the BK potassium channel through a novel mechanism

In this research, we investigated how alcohol modulates the simplest form of learning, habituation, in Caenorhabditis elegans. We used our high throughput Multi-Worm Tracker to conduct a large scale study of more than 21,000 wild-type worms to assess the effects of different doses of alcohol on habituation of the well-characterized tap withdrawal response. We found that the effect of alcohol on habituation of the reversal response to repeated mechanosensory stimuli (taps) differed depending on the component of the reversal response assessed. Furthermore, we discovered that alcohol shifted the dominant response to tap from a backward reversal to a brief forward movement. Because the large conductance potassium (BK) channel has been shown to be important for alcohols effects on behaviour in a variety of organisms, including C. elegans, we investigated whether the C. elegans BK channel ortholog, SLO-1, mediated the effects of alcohol on habituation. We tested several different strains of worms with mutations in slo-1 along with wild-type controls; null mutations in slo-1 made animals resistant to alcohol induced changes in learning. However, a mutation in the putative ethanol binding site on SLO-1 did not disrupt ethanols impact on habituation. Finally, by degrading SLO-1 in different parts of the nervous system we found that SLO-1s function in ethanols impact on habituation is likely distributed throughout the neural circuit that responds to tap. Based on these results, our main conclusions are 1) ethanol is not a general facilitator or inhibitor of habituation but rather a complex modulator, 2) SLO-1 is required for ethanols effect on habituation, 3) ethanol is interacting (directly or indirectly) with SLO-1 through a novel unidentified mechanism to influence response plasticity.

neuroscience↗

bioRxiv2024

Deficiency of mitophagy mediator Parkin in aortic smooth muscle cells exacerbates abdominal aortic aneurysm

Abdominal aortic aneurysms (AAAs) are a degenerative aortic disease and associated with hallmarks of aging, such as mitophagy. Despite this, the exact associations among mitophagy, aging, and AAA progression remain unknown. In our study, gene expression analysis of human AAA tissue revealed downregulation of mitophagy pathways, mitochondrial structure, and function-related proteins. Human proteomic analyses identified decreased levels of mitophagy mediators PINK1 and Parkin. Aged mice and, separately, a murine AAA model showed reduced mitophagy in aortic vascular smooth muscle cells (VSMCs) and PINK1 and Parkin expression. Parkin knockdown in VSMCs aggravated AAA dilation in murine models, with elevated mitochondrial ROS and impaired mitochondrial function. Importantly, inhibiting USP30, an antagonist of the PINK1/Parkin pathway, increased mitophagy in VSMCs, improved mitochondrial function, and reduced AAA incidence and growth. Our study elucidates a critical mechanism that proposes AAAs as an age-associated disease with altered mitophagy, introducing new potential therapeutic approaches.

molecular biology↗

bioRxiv2022

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

BackgroundThe ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers. MethodsWe built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers. FindingsWe built and maintain a web server COVIDpro (https://www.guomics.com/covidPro/) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80. InterpretationCOVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients. FundingNational Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504) Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAlthough an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the diseases mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets. Added value of this studyWe integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples. Implications of all the available evidenceCOVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

systems biology↗

bioRxiv2022

scPerturb: Information Resource for Harmonized Single-Cell Perturbation Data

Recent biotechnological advances led to growing numbers of single-cell perturbation studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, and data filtering. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. In addition, we introduce E-statistics for perturbation effect quantification and significance testing, and demonstrate E-distance as a general distance measure for single cell data. Using these datasets, we illustrate the application of E-statistics for quantifying perturbation similarity and efficacy. The data and a package for computing E-statistics is publicly available at scperturb.org. This work provides an information resource and guide for researchers working with single-cell perturbation data, highlights conceptual considerations for new experiments, and makes concrete recommendations for optimal cell counts and read depth.

systems biology↗

bioRxiv2022

Proteomic Dynamics of Breast Cancers Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We quantified the proteomes of 76 human BC cell lines using data independent acquisition (DIA) based proteomics, identifying 6091 proteins. We then established a 24-protein panel distinguishing TNBC from other BC types. Integrating prior multi-omics datasets with the present proteomic results to predict the sensitivity of 90 drugs, we found that proteomics data improved drug sensitivity predictions. The sensitivity of the 90 drugs was mainly associated with cell cytoskeleton, signal transduction and mitochondrial function. We next profiled the proteome changes of nine cell lines (five TNBC cell lines, four non-TNBC cell lines) perturbated by EGFR/AKT/mTOR inhibitors. In the TNBC cell lines, metabolism pathways were dysregulated after EGFR/mTOR inhibitors treatment, while RNA modification and cell cycle pathways were dysregulated after AKT inhibitor treatment. Our study presents a systematic multi-omics and in-depth analysis of the proteome of BC cells. This work aims to aid in prioritization of potential therapeutic targets for TNBC as well as to provide insight into adaptive drug resistance in TNBC.

systems biology↗

bioRxiv2021

Mapping the functional interactions at the tumor-immune checkpoint interface

It is challenging to identify the tumor-immune system interactions that modulate immune states and immunotherapy responses due to the prohibitively complex space of possible interactions. Our statistical analysis framework, ImogiMap quantifies tumor-immune interactions based on their synergistic co-associations with immune-associated phenotypes. ImogiMap-based analyses recapitulated known interactions modulating immunotherapy responses and nominated the CD86/CD70 axis as an immunotherapy target that overlaps with IFNG overexpression and patient survival in endometrial carcinoma.

bioinformatics↗

bioRxiv2021

Targeting Adaptation to Cancer Treatment by Drug Combinations

Adaptation of tumors to therapeutic interventions contributes to dismal long-term patient outcomes. Adaptation to therapy involves co-action of functionally related proteins that together activate cell survival programs and compensate for the therapeutic impact. Oncogenic dependencies to such adaptive events, however, can generate new therapeutic vulnerabilities that can be targeted with drug combinations. The precision medicine approaches in which targeted drugs are matched to pre-existing genomic aberrations fail to address the adaptive responses and resulting vulnerabilities. Here, we provide the mathematical formulation, implementation and validation of the TargetScore method. The TargetScore identifies collective adaptive responses to targeted interventions as concurrent changes of phospho-proteins that are connected within a signaling network. Based on the adaptive responses, the method predicts drug-induced vulnerabilities. Using TargetScore, we inferred the adaptive responses with short-term (i.e., days) stress and long-term (i.e., months) acquired resistance to inhibitors of anti-apoptotic mediators, MCL1 and BCL2. With experiments guided by the predictions, we identified synergistic interactions between inhibitors of PARP, SHP2, and MCL1 in breast cancer cells. TargetScore is readily applicable to existing precision oncology efforts by matching targeted drug combinations to emerging molecular signatures under therapeutic stress.

systems biology↗

bioRxiv2021

Capturing scientific knowledge in computable form

Technological advances in computing provide major opportunities to accelerate scientific discovery. The wide availability of structured knowledge would allow us to take full advantage of these by enabling efficient human-computer interaction. Traditionally, biological knowledge is captured in publications and knowledge bases, however, the information in articles is not directly accessible to computers, and knowledge bases are constrained by finite resources available for manual curation. To accelerate knowledge capture and communication and to keep pace with the rapid growth of scientific reports, we developed the Biofactoid (biofactoid.org) software suite, which crowdsources structured knowledge in articles from authors. Biofactoid is a web-based system that lets scientists draw a network of interactions between genes, their products, and chemical compounds and employs smart-automation to translate user input into a structured language using the expressive power of a formal ontology. The resulting data is shared via public information resources, enabling author-curated knowledge to be appreciated in the context of all existing computable knowledge. Authors of recently published papers across a range of journals have already contributed their pathway information, much of which is novel and extends existing pathway databases into new biological areas. We envision the adoption of Biofactoid for crowdsourced curation by scientists and publishers as part of an ecosystem of tools that accelerate scientific communication and discovery. AvailabilityBiofactoid server at https://biofactoid.org

bioinformatics↗

bioRxiv2020

Precision combination therapies from recurrent oncogenic co-alterations

Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Discovery of effective combination therapies is challenging due to the complexity of the biomolecular landscape of drug responses. Here, we developed the method REFLECT (REcurrent Features Leveraged for Combination Therapies), which integrates machine learning and cancer informatics algorithms. The method maps recurrent co-alteration signatures from multi-omic data across patient cohorts to combination therapies. Using the REFLECT framework, we generated a precision therapy resource matching 2,201 drug combinations to co-alteration signatures across 201 cohorts stratified from 10,392 patients and 33 cancer types. We validated that REFLECT-predicted combinations introduce significantly higher therapeutic benefit through analysis of independent data from comprehensive drug screens. In patient cohorts with immunotherapy response markers, HER2 activation and DNA repair aberrations, we identified therapeutically actionable co-alteration signatures shared across patient sub-cohorts. REFLECT provides a framework to design combination therapies tailored to patient cohorts in data-driven clinical trials.

cancer biology↗

bioRxiv2020

NetBoxR: Automated Discovery of Biological Process Modules by Network Analysis in R

SummaryLarge-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have accumulated a variety of high throughput sequencing and molecular profiling data, but it is still challenging to identify potentially causal genetic mutations in cancer as well as in other diseases in an automated fashion. We developed the NetBoxR package written in the R programming language, that makes use of the NetBox algorithm to identify candidate cancer-related processes. The algorithm makes use of a networkbased approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of functionally curated gene sets. A key aspect of this approach is its ability to combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. Availability and implementationThe NetBoxR package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html Contactlium2@mskcc.org; aluna@jimmy.harvard.edu; sander.research@gmail.com Supplementary informationNone

bioinformatics↗

bioRxiv2020

SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures

Model systems are necessary to understand the biology of SCLC and develop new therapies against this recalcitrant disease. Here we provide the first online resource, CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) incorporating 118 individual SCLC cell lines and extensive omics and drug sensitivity datasets, including high resolution methylome performed for the purpose of the current study. We demonstrate the reproducibility of the cell lines and genomic data across the CCLE, GDSC, CTRP, NCI and UTSW datasets. We validate the SCLC classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and show transcription networks connecting each them with their downstream and upstream regulators as well as with the NOTCH and HIPPO pathways and the MYC genes (MYC, MYCL1 and MYCN). We find that each of the 4 subsets express specific surface markers for antibody-targeted therapies. The SCLC-Y cell lines differ from the other subsets by expressing the NOTCH pathway and the antigen-presenting machinery (APM), and responding to mTOR and AKT inhibitors. Our analyses suggest the potential value of NOTCH activators, YAP1 inhibitors and immune checkpoint inhibitors in SCLC-Y tumors that can now be independently validated. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/980623v2_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@10930e7org.highwire.dtl.DTLVardef@699d9corg.highwire.dtl.DTLVardef@1ea773corg.highwire.dtl.DTLVardef@3a1589_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISCLC-CellMiner provides the most extensive SCLC resource in terms of number of cell lines (118 cell lines), extensive omics data (exome, microarray, RNA-seq, copy number, methylomes and microRNA) and drug sensitivity testing. C_LIO_LIWe find evidence of distinct epigenetic profile of SCLC cell lines (global hypomethylation and histone gene methylation), which is consistent with their plasticity. C_LIO_LITranscriptome analyses demonstrate the coherent transcriptional networks associated with the 4 main genomic subgroups (NEUROD1, ASCL1, POU2F3 & YAP1 = NAPY classification) and their connection with the NOTCH and HIPPO signaling pathways. C_LIO_LISCLC-CellMiner provides a conceptual framework for the selection of therapies for SCLC in a personalized fashion allowing putative biomarkers according molecular classifications and molecular characteristics. C_LIO_LISCLC-Y cell lines differ from the other cancer cell lines; their transcriptome resemble NSCLC cell lines. YAP1 cell lines while being the most resistant to standard of care treatments (etoposide, cisplatin and topotecan) respond to mTOR and AKT inhibitors and present native immune predisposition suggesting sensitivity to immune checkpoint inhibitors. C_LI

cancer biology↗