Biology subjects

Ceccarelli, M.

Publications and source records attributed to Ceccarelli, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Graph-based modeling of multiparametric MRI deciphers molecular states of high-grade glioma invasion with prognostic implications

AbstractThe infiltrative, non-enhancing margin of IDH wildtype high grade glioma (IDHwt HGG) harbors distinct molecular programs that drive invasion and therapeutic resistance, yet remains largely unevaluable by conventional tissue sampling approaches and by conventional imaging. Here we show that this invasive architecture is encoded within multiparametric MRI (mpMRI) feature relationships and can be decoded using a graph-based framework trained on multiregional image-localized biopsies. Across 134 spatially matched biopsy-imaging pairs from 35 patients with primary IDHwt HGG (29 glioblastomas (GBM) and 6 non-glioblastoma HGGs), unsupervised graph community detection identifies two imaging-defined clusters that localize to invasive tumor regions without molecular supervision. Transcriptomic profiling associates these clusters with neuronal (NEU) and glycolytic-plurimetabolic (GPM) molecular programs. Building on this framework, a graph convolutional network (GCN) accurately predicts NEU and GPM transcriptional states in independent training and validation cohorts and significantly outperforms conventional convolutional neural networks. Applied to whole-tumor mpMRI volumes, the trained GCN generates spatially resolved probability maps that quantify the distribution and relative burden of NEU and GPM programs across both MRI contrast-enhancing and non-enhancing invasive regions. These imaging-derived molecular maps stratify patients by overall survival. Increased GPM burden is associated with poorer survival, consistent with the aggressive behavior associated with mesenchymal-like transcriptional programs in IDHwt HGG. In contrast, increased NEU burden is associated with improved survival, identifying a previously unrecognized imaging-derived prognostic biomarker that was not detected by biopsy-based molecular classification alone. Together, these findings establish a graph-based imaging framework for spatially resolved molecular classification of invasive IDHwt HGG and demonstrate that whole-tumor molecular state architecture carries prognostic information beyond conventional tissue sampling.

cancer biology↗

bioRxiv2026

Targeting an RNA Editor to Impede H3K27M+ Pediatric Gliomas

Diffuse midline glioma (DMG) is a lethal pediatric brain tumor with no curative therapies. Immune checkpoint blockade (ICB) has shown limited efficacy in DMG, largely due to poor T cell infiltration, low immune checkpoint (IC) expression, and a low tumor mutational burden. Here, we identify adenosine deaminase acting on RNA (ADAR), an RNA-editing enzyme that suppresses endogenous dsRNA sensing, as a key mediator of immune evasion in H3K27M-mutant DMG. ADAR is significantly overexpressed in H3K27M tumors relative to wild-type high-grade gliomas, and its depletion selectively suppresses proliferation in patient-derived DMG cells. The H3K27M mutation was found to synergize with ADAR loss to increase retroelement expression, activate type I interferon signaling, and induce immune checkpoint expression. We further identify all-trans retinoic acid (ATRA) as a pharmacologic inducer of ADAR degradation. At clinically relevant doses, ATRA phenocopies ADAR depletion, enhancing antiviral and interferon responses while increasing tumor immunogenicity. In orthotopic immunocompetent DMG models, ATRA enhances CD8+ T cell infiltration and synergizes with ICB and irradiation to improve survival.

cancer biology↗

bioRxiv2026

Pharmacologic DPP-4 inhibition promotes CD8⁺ T cell metabolic fitness to enhance anti-tumor activity

Metabolic dysfunction is a hallmark of CD8+ T cell exhaustion in the tumor microenvironment. Thus, there is growing interest in developing strategies that enhance anti-tumor functions of CD8+ T cells via metabolic reprogramming. Here, we identify dipeptidyl peptidase 4 (DPP-4) as a previously unknown regulator of CD8+ T cell function and metabolism. We discovered that DPP-4 is upregulated in exhausted CD8+ T cells. Pharmacological inhibition of DPP-4 with the FDA-approved anti-diabetic drug sitagliptin transcriptionally and metabolically reprogrammed CD8+ T cells, increasing spare mitochondrial respiratory capacity, proliferation, cytotoxic mediator production, and antigen-specific cancer cell killing capability. The functional effects of sitagliptin were dependent on upregulation of glutamate decarboxylase 1 (GAD1), an enzyme that feeds glutamate into the tricarboxylic acid (TCA) cycle, highlighting a new role for GAD1 in CD8+ T cell respiration and proliferation. We found that systemic inhibition of DPP-4 in preclinical mouse glioblastoma (GBM) models prolongs survival in a CD8+ T cell-dependent manner, and retrospective clinical cohort analysis revealed better outcomes in GBM patients using DPP-4 inhibitors. Importantly, preconditioning of Chimeric Antigen Receptor (CAR) T-cells with DPP-4 inhibition enhanced their cytotoxicity, persistence, and therapeutic efficacy in pediatric GBM. Together, our findings provide mechanistic and biological rationale for repurposing readily accessible DPP-4 inhibitors to enhance anti-tumor CD8+ T cell responses.

immunology↗

bioRxiv2025

Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection

Liquid biopsy offers a minimally invasive opportunity to detect and monitor cancers through analysis of cell-free DNA (cfDNA). However, current approaches face challenges of limited sensitivity at low tumor fractions, technical variability, and poor generalization across cohorts. Tumor-informed targeted methods offer high specificity but suffer from low sensitivity due to random sampling, tumor evolution and adaptation (including resistance mechanisms), and other sources of heterogeneity. Conversely, tumor-naive genome-wide methods can increase sensitivity but often sacrifice specificity, particularly at low tumor fractions. We developed Fragmentomics Analysis for Tumor Evaluation with AI (Fate-AI), a multimodal framework that integrates fragmentomic and methylation-derived features from low-pass whole-genome sequencing (LPWGS) and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). It employs a knowledge-informed strategy to select recurrently altered genomic regions and tissue-specific methylation loci to combine the advantages of tumor-naive approaches with the specificity of tumor-informed approaches. This approach derives robust per-sample normalized features that mitigate batch effects and enhance cross-cohort reproducibility. We evaluated Fate-AI on a total of 1,219 plasma samples spanning ten cancer types and healthy controls from multiple laboratories and sequencing centers, including 432 newly profiled cases (280 with both cfMeDIP-seq and LPWGS) together with 787 samples from four independent public datasets. Fate-AI achieved superior sensitivity and specificity compared to state-of-the-art methods, detecting tumor-derived signals at fractions as low as 10-5 in experimental dilutions. Fate-AI scores correlated with disease stage and tracked longitudinal progression, anticipating relapse months before clinical progression. Furthermore, Fate-AI enabled tissue-of-origin classification, with AUCs ranging from 0.84 to 0.97 across six cancer types. Collectively, our results demonstrate that Fate-AI provides a sensitive, generalizable, and clinically actionable platform for early detection, minimal residual disease monitoring, and tissue-of-origin classification, supporting its potential as a liquid biopsy framework in precision oncology.

cancer biology↗

bioRxiv2025

Transposable Elements and Homotypic Niches Drive Immune Dynamics and Resistance in Melanoma Epigenetic-based immunotherapy

Melanoma plasticity drives immune evasion and therapy resistance through dynamic cell-state transitions beyond genetic alterations. Epigenetic remodeling critically influences such processes, yet its role in reshaping the tumor ecosystem under therapeutic pressure remains unresolved. Here, we profiled longitudinal biopsies from melanoma patients treated in the phase Ib NIBIT-M4 epi-immunotherapy clinical trial (NCT02608437), testing the combination of a DNMT1 inhibitor with anti-CTLA4 using single-cell multiome and high-resolution spatial transcriptomics. Integrated analyses resolved seven malignant meta-programs, including a rare Wnt/{beta}-catenin-driven melanocytic state and a de-differentiated neural crest-like state enriched in non-responders. Spatial modeling revealed that homotypic clustering stabilizes resistant programs, with neural crest-like cells forming compact, centrally localized niches, whereas Wnt/{beta}-catenin subpopulations displayed a bimodal architecture, either cohesive clusters sustained by adhesion or dispersed, transcriptionally plastic cells. Responders exhibited progressive enrichment of an antigen presentation/interferon program and coordinated remodeling of the tumor microenvironment with T and B cell expansion, whereas tumors from non-responder patients maintained stable composition of neural crest-like clusters. Epigenetic therapy reactivated transposable elements, providing both regulatory signals that prime innate immunity within microenvironment and generating antigens that drive immunoediting and immunogenicity of Antigen presentation/interferon cell states in responders. Finally, NFATC2 emerged as a master regulator of neural crests-like transcriptional phenotypes and promoter of resistance to therapeutic interventions in melanoma patients. NFATC2 perturbation was able to shift tumor cells towards more differentiated and immunogenic states. These findings reveal how epigenetic-based immunotherapy reshapes melanoma ecosystems, provide mechanistic insights into how multiple transcriptional programs promote tumor plasticity and resistance to both combinatorial therapies and immune checkpoint blockade, identify spatial clustering as a principle stabilizing resistant niches, and highlight {beta}-catenin and NFATC2 as actionable vulnerabilities to overcome resistance.

genomics↗

bioRxiv2025

PRESTIGE-ST: Patch Resolution and Encoder STrategies for Inference of Gene Expression from Spatial Transcriptomics

Spatial Transcriptomics (ST) integrates histology with spatially resolved gene expression, offering rich insights into tissue architecture and function. However, its clinical and large-scale deployment is hindered by high costs, technical complexity, and limited accessibility. To address this, computational pathology methods have emerged to predict gene expression directly from histology images, typically framing the task as a multi-output regression problem mapping image patches to gene expression profiles. While several Convolutional Neural Network (CNN) models have been proposed, little is known about how performance is influenced by (a) the number of trainable parameters and (b) the patch size used for prediction. Moreover, existing studies rely primarily on quantitative metrics and overlook biological relevance of predictions. In this study, we systematically evaluated multiple convolution based models (including a Vision Transformer (ViT) model) with different patch sizes on the Xenium based Autoimmune Machine Learning Challenge (AMLC) dataset. We assessed model performance on both globally expressed genes and subsets enriched for immune or disease associated pathways. Our findings reveal that compact CNNs trained on larger patches outperform deeper models, offering superior accuracy in predicting gene expression, especially for biologically important genes. These insights provide practical guidance for designing efficient and biologically meaningful models in the emerging field of image-based gene expression prediction. CCS CONCEPTSComputing methodologies [->] Computer vision; Neural networks; * Applied computing [->] Computational genomics; Imaging.

pathology↗

bioRxiv2025

Integrated multi-omics profiling reveals the role of the DNA methylation landscape in shaping biological heterogeneity and clinical behaviour of metastatic melanoma

The biological and clinical relevance of the DNA methylation landscape in metastatic melanoma (MM) remains underexplored. In a retrospective cohort of 191 MM lesions from 165 AJCC Stage III and IV patients (EPICA cohort) we identified four tumor subsets (i.e. DEMethylated, LOW, INTermediate and CIMP) with progressively increasing levels of DNA methylation. These findings were validated in the TCGA MM. In EPICA, patients with LOW methylation tumors exhibited a significantly longer survival and a lower progression rate to more advanced AJCC stages, compared to patients with CIMP tumors. Furthermore, in an independent adjuvant immune checkpoint blockade MM cohort, patients with DEM/LOW pre-therapy lesions showed significantly longer relapse-free survival compared to those with INT/CIMP lesions. RNA-seq data analysis revealed that LOW and CIMP EPICA tumors showed opposite activation of master molecules influencing prognostic target genes, and differential expression of immunotherapy response and melanoma differentiation signatures. Compared to CIMP tumors, LOW lesions showed enrichment for pre-exhausted and exhausted T cells and more frequent retention of HLA Class I antigens. The differentiation and immune-related transcriptional features associated with LOW vs CIMP lesions were tumor-intrinsic programs retained in-vitro by melanoma cell lines. Consistently, treatment of differentiated melanoma cell lines with a DNMT inhibitor induced global DNA de-methylation, promoted de-differentiation and upregulated viral mimicry and IFNG predictive signatures of immunotherapy response. These findings underscore the role of DNA methylation in driving MM biological and clinical heterogeneity and support exploration of methylome targeting strategies for precision immunotherapy in melanoma.

cancer biology↗

bioRxiv2024

Targeting ZNF638 activates antiviral immune responses and potentiates immune checkpoint inhibition in glioblastoma

Viral mimicry refers to the activation of innate anti-viral immune responses due to the induction of endogenous retroelement (RE) expression. Viral mimicry has been previously described to augment anti-tumor immune responses and sensitize solid tumors to immunotherapy including colorectal cancer, melanoma, and clear renal cell carcinoma. Here, we found that targeting a novel, master epigenetic regulator, Zinc Finger Protein 638 (ZNF638), induces viral mimicry in glioblastoma (GBM) preclinical models and potentiates immune checkpoint inhibition (ICI). ZNF638 recruits the HUSH complex, which precipitates repressive H3K9me3 marks on endogenous REs. In GBM, ZNF638 is associated with marked locoregional immunosuppressive transcriptional signatures, reduced endogenous RE expression and poor immune cell infiltration (CD8+ T-cells, dendritic cells). ZNF638 knockdown decreased H3K9-trimethylation, increased cytosolic dsRNA and activated intracellular dsRNA-signaling cascades (RIG-I, MDA5 and IRF3). Furthermore, ZNF638 knockdown upregulated antiviral immune programs and significantly increased PD-L1 immune checkpoint expression in patient-derived GBM neurospheres and diverse murine models. Importantly, targeting ZNF638 sensitized mice to ICI in syngeneic murine orthotopic models through innate interferon signaling. This response was recapitulated in recurrent GBM (rGBM) samples with radiographic responses to checkpoint inhibition with widely increased expression of dsRNA, PD-L1 and perivascular CD8 cell infiltration, suggesting dsRNA-signaling may mediate response to immunotherapy. Finally, we showed that low ZNF638 expression was a biomarker of clinical response to ICI and improved survival in rGBM patients and melanoma patients. Our findings suggest that ZNF638 could serve as a target to potentiate immunotherapy in gliomas.

cancer biology↗

bioRxiv2024

The oncometabolite D-2-hydroxyglutarate promotes DNA hypermethylation at lineage-specific enhancers controlling microglial activation in IDHmut gliomas

Tumor-associated microglia and macrophages (TAMs), the most abundant myeloid populations in gliomas, shape immune responses through transcriptional programs influenced by the tumor microenvironment. Although these programs differ according to tumor IDH status, the underlying epigenetic mechanisms remain poorly understood. Here, we uncover widespread DNA hypermethylation in the myeloid compartment of IDH-mutant gliomas, predominantly at distal enhancers enriched for motifs of core microglial transcription factors (TFs). This remodeled enhancer landscape strongly correlated with reduced activity of TF regulons and coordinated repression of immunomodulatory programs that normally support microglial activation. Using primary human microglia, we show that prolonged exposure to the oncometabolite D-2-hydroxyglutarate (D-2HG) reduces TET activity and increases 5mC/5hmC ratios near TF-binding motifs within enhancers affected ex vivo. Consistent with these epigenetic alterations, D-2HG-treated microglia exhibited transcriptional signatures compatible with blunted proinflammatory responses, whereas pharmacological inhibition of mutant IDH in patients partially restored microglial immune reactivity. Altogether, our findings reveal a chronic D-2HG-driven epigenetic priming mechanism that promotes a hyporesponsive microglial state, providing a rationale for the immunologically cold phenotype of IDH-mutant gliomas and offering insight into how IDH-targeted therapies may reshape microglial immune responses.

cancer biology↗

bioRxiv2024

DNA methylation status classifies pleural mesothelioma cells according to their immune profile: implication for precision epigenetic therapy

Backgroundco-targeting of immune checkpoint inhibitors (ICI) CTLA-4 and PD-1 has recently become the new first-line standard of care therapy of pleural mesothelioma (PM) patients, with a significant improvement of overall survival over conventional chemotherapy. The analysis by tumor histotype demonstrated a greater efficacy of ICI therapy in non-epithelioid (non-E) vs epithelioid (E) PM; although some E PM patients also benefit from treatment. This evidence suggests that molecular tumor features, beyond histotype, could be relevant to improve the efficacy of ICI therapy in PM. Among these, tumor DNA methylation emerges as a promising factor to explore, due to its potential role in driving the immune phenotype of cancer cells. Thus, we utilized a panel of cultured PM cells of different histotype, to provide preclinical evidence supporting the role of the tumor methylation landscape and of its pharmacologic modulation, to prospectively improve the efficacy of ICI therapy of PM patients. Methodsthe methylome profile (EPIC array) of distinct E (#5) and non-E (#9) PM cell lines was analyzed, followed by integrated analysis with their associated transcriptomic profile (Clariom S array), before and after in vitro treatment with the DNA hypomethylating agent (DHA) guadecitabine. The most variable methylated probes were selected to calculate the methylation score (CIMP index) for each cell line at baseline. Genes that were differentially expressed and methylated were then selected for gene ontology analysis. Resultsthe CIMP index stratified PM cell lines in two distinct classes, CIMP (hyper-methylated; #7) and LOW (hypo-methylated; #7), regardless of their E or non-E histotype. Integrated analyses of methylome and transcriptome data revealed that CIMP PM cells had a substantial number of hyper-methylated, silenced genes, which negatively impacted their immune phenotype compared to LOW PM cells. Treatment with DHA reverted the methylation-driven immune-compromised profile of CIMP PM cells and enhanced the constitutive immune-favorable profile of LOW PM cells. Conclusionthe study highlighted the relevance of DNA methylation in shaping the constitutive immune classification of PM cells, that is independent from their histological subtypes. The identified role of DHA in shifting the phenotype of PM cells towards an immune-favorable state supports its role in clinical trials of precision epigenetic therapy combined with ICI.

cancer biology↗

bioRxiv2024

γ-aminobutyric acid receptor B signaling drives glioblastoma in females in an immune-dependent manner

Sex differences in immune responses impact cancer outcomes and treatment response, including in glioblastoma (GBM). However, host factors underlying sex specific immune-cancer interactions are poorly understood. Here, we identify the neurotransmitter {gamma}-aminobutyric acid (GABA) as a driver of GBM-promoting immune response in females. We demonstrated that GABA receptor B (GABBR) signaling enhances L-Arginine metabolism and nitric oxide synthase 2 (NOS2) expression in female granulocytic myeloid-derived suppressor cells (gMDSCs). GABBR agonist and GABA analog promoted GBM growth in females in an immune-dependent manner, while GABBR inhibition reduces gMDSC NOS2 production and extends survival only in females. Furthermore, female GBM patients have enriched GABA transcriptional signatures compared to males, and the use of GABA analogs in GBM patients is associated with worse short-term outcomes only in females. Collectively, these results highlight that GABA modulates anti-tumor immune response in a sex-specific manner, supporting future assessment of GABA pathway inhibitors as part of immunotherapy approaches.

cancer biology↗

bioRxiv2023

Robust Feature Selection strategy detects a panel of microRNAs as putative diagnostic biomarkers in Breast Cancer

MicroRNAs represent a comprehensive class of short, single-stranded, non-coding RNA transcripts able to interfere with the translation of their targets or degrade them. Since these molecules are dysregulated in several malignancies, they represent reliable biomarkers in various contexts. In this study, the application of several Feature Selection methods uncovers a panel of 20 microRNAs, of which hsa-mir-337, hsa-mir-378c, and hsa-mir-483 are still poorly investigated in the context of Breast Cancer. This signature is capable of discriminating between healthy and tumoral samples, showing better classification performance when compared with differentially expressed microRNAs. Furthermore, a network-based centrality analysis on the gene targets of these transcripts highlighted CDC25, TPX2, KIF18B, CDCA3, TGFBR2, CAV1, TNS1, and LHFPL6 as key dysregulated genes. This study provides in silico hypotheses as new insights for future in vivo or in vitro studies uncovering the role of these putative diagnostic biomarkers in Breast Cancer.

bioinformatics↗

bioRxiv2023

Evolutionary adaptation from hydrolytic to oxygenolytic catalysis

Protein fold adaptation to novel enzymatic reactions is a fundamental evolutionary process. Cofactor-independent oxygenases degrading N-heteroaromatic substrates belong to the /{beta}-hydrolase (ABH) fold superfamily that typically does not catalyze oxygenation reactions. Here, we have integrated crystallographic analyses at normoxic and hyperoxic conditions with molecular dynamics and quantum mechanical calculations to investigate its prototypic 1-H-3-hydroxy-4-oxoquinaldine 2,4-dioxygenase (HOD) member. O2 localization to the "oxyanion hole", where catalysis occurs, is an unfavorable event and the direct competition between dioxygen and water for this site is modulated by the "nucleophilic elbow" residue. A hydrophobic pocket that overlaps with the organic substrate binding site can act as a proximal dioxygen reservoir. Freeze-trap pressurization allowed to determine the structure of the ternary complex with a substrate analogue and O2 bound at the oxyanion hole. Theoretical calculations reveal that O2 orientation is coupled to the charge of the bound organic ligand. When 1-H-3-hydroxy-4-oxoquinaldine is uncharged, O2 binds with its molecular axis along the ligands C2-C4 direction in full agreement with the crystal structure. Substrate activation triggered by deprotonation of its 3-OH group by the His-Asp dyad, rotates O2 by approximately 60 degrees. This geometry maximizes the charge-transfer between the substrate and O2 thus weakening the double bond of the latter. Electron density transfer to the O2({pi}*) orbital promotes the formation of the peroxide intermediate via intersystem crossing that is rate-determining. Our work provides a detailed picture of how evolution has repurposed the ABH-fold architecture and its simple catalytic machinery to accomplish metal-independent oxygenation. SignificanceMany of the current O2-dependent enzymes have evolved from classes that existed prior to the switch from a reducing to an oxidative atmosphere and whose original functions are unrelated to dioxygen chemistry. A group of bacterial dioxygenases belong to the /{beta}-hydrolase (ABH) fold superfamily that typically does not catalyze oxygenation reactions. These enzymes degrade their N-heteroaromatic substrates in a cofactor-independent manner relying only on the simple nucleophile-histidine-acid ABH-fold catalytic toolbox. In this work we show how O2 localizes at the catalytic site by taking advantage of multiple strategies that minimize the strong competition by water, the co-substrate in the ancestral hydrolytic enzyme. We also show that substrate activation by the His-Asp catalytic dyad leads a ligand-O2 complex that maximizes the electron transfer from the organic substrate to O2, thus promoting intersystem crossing and circumventing the spin-forbiddeness of the reaction. Overall, our work explains how evolution has repurposed the ABH-fold architecture and its simple catalytic machinery to accomplish spin-restricted metal-independent oxygenation.

biochemistry↗

bioRxiv2023

MOViDA: Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model

Drug discovery is a challenging task, characterized by a protracted period of time between initial development and market release, with a high rate of attrition at each stage. Computational virtual screening, powered by machine learning algorithms, has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between features learned by these algorithms can be challenging to decipher. We have devised a neural network model for the prediction of drug sensitivity, which employs a biologically-informed visible neural network (VNN), enabling a greater level of interpretability. The trained model can be scrutinized to investigate the biological pathways that play a fundamental role in prediction, as well as the chemical properties of drugs that influence sensitivity. The model leverages multi-omics data obtained from diverse tumor tissue sources and molecular descriptors that encode drug properties. We have extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the often imbalanced nature of publicly available drug screening datasets, our model demonstrates superior performance compared to state-of-the-art visible machine learning algorithms.

bioinformatics↗

bioRxiv2023

Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution is a reliable way to disentangle the diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA-seq) rather than protein levels in tumor cells. While gene expression is less expensive to measure, protein levels provide a more accurate view of immune markers. To facilitate the development as well as improve the reproducibility and reusability of multi-omic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic data sets. Decomprolute incorporates the large-scale multiomic data sets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. The platform consists of modular architecture and it comes with well-defined input and output formats at each module. As a result, it is robust and extendable easily with additional algorithms or analyses. The platform is available for access and use at http://pnnl-compbio.github.io/decomprolute. MotivationTo provide a comprehensive platform for algorithm developers and researchers to benchmark and run tumor deconvolution algorithms on multiomic data.

cancer biology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

bioinformatics↗

bioRxiv2022

Landscape of immune-related signatures induced by targeting of different epigenetic regulators in melanoma: implications for immunotherapy

BackgroundInnovative cancer immunotherapy approaches aim at combining immune checkpoint inhibitors with other immunomodulatory agents. Epigenetic regulators can control immune-related genes, therefore targeting them with specific inhibitors may be a potential way forward. Here we identified immune-related signatures induced by four classes of epigenetic drugs in human melanoma cells to define the most promising agent and to understand its biological activity in-vitro, in-vivo and in clinical samples. MethodsHuman melanoma cell lines were characterized for mutational and differentiation profile and treated with inhibitors of DNA methyltransferases (guadecitabine), histone deacetylases (givinostat), bromodomain and extraterminal domain proteins (JQ1 and OTX-015) and enhancer of zeste homolog 2 (GSK126). Drug-specific gene signatures were identified by Clariom S and Nanostring platforms. Modulation of 14 proteins was determined by quantitative western blot. Ingenuity Pathway Analysis (IPA) identified Upstream Regulator (UR) molecules explaining changes in gene expression and biological activity of drugs. Gene set enrichment and IPA were used to test modulation of guadecitabine-specific gene and UR signatures, respectively, in on-treatment tumor biopsies from melanoma patients enrolled in the Phase Ib NIBIT-M4 Guadecitabine + Ipilimumab Trial. ResultsDrug-specific gene and UR signatures were identified for each of the four inhibitors. Immune-related genes were frequently upregulated by guadecitabine, to a lesser extent by givinostat, but downregulated by JQ1 and OTX-015. GSK126 was the least active drug. Treatment of melanoma cells with combination of two epigenetic drugs revealed a dominant effect of guadecitabine and JQ1 on immune-related gene modulation. Drug-specific modulatory profiles were confirmed at the protein level. The guadecitabine-specific UR signature was characterized by activated molecules of the TLR, NF-kB, and IFN innate immunity pathways and was induced in drug-treated melanoma, mesothelioma, hepatocarcinoma cell lines and human melanoma xenografts. Most of the guadecitabine-specific signature genes (n>160) were upregulated in on-treatment tumor biopsies from NIBIT-M4 trial. Progressive activation of guadecitabine UR signature molecules was observed in on-treatment tumor biopsies from responding compared to non-responding patients. ConclusionsGuadecitabine was the most promising immunomodulatory agent among those investigated. This DNA methyltransferases inhibitor emerged as a strong inducer of innate immunity pathways, supporting the rationale for its use in combinatorial immunotherapy approaches.

immunology↗