Biology subjects

Fenyo, D.

Publications and source records attributed to Fenyo, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Specific Aneuploidies Predict Immune Evasion and Poor Immunotherapy Response in Melanoma

Melanoma is one of the leading cancer types treated with immune checkpoint blockade (ICB), yet a substantial proportion of patients fail to respond. While tumor mutational burden and PD-L1 expression are established ICB biomarkers, they leave large gaps in predictive accuracy. Somatic copy number alterations (SCNAs) are pervasive in melanoma but their role in shaping the tumor immune microenvironment (TME) and predicting immunotherapy outcomes has been insufficiently characterized. Here we present KaryoTME, an integrated computational framework that systematically links SCNAs to immune phenotypes using genomic, transcriptomic, and clinical data from over 15,000 patients. Applying this framework to skin melanoma (SKCM) within a pan-cancer context, we identify arm-level chromosome 1q gain and 9p loss as the most prominent SCNA events associated with an immune-cold tumor microenvironment. These alterations act through distinct mechanisms: 9p loss preferentially depletes NK and CD8+ T cells, whereas 1q gain is more strongly associated with reduced anti-tumor immune cell infiltration. At the focal level, regions 1q21 and 1q42 show the strongest immune-suppressive associations in melanoma. Applying the TUSON-Immune algorithm, we predict candidate Tumor immune Suppressor Genes (TiSG) and immune Oncogenes (iOG) within these chromosomal regions, revealing enrichment for pathways including IFN signaling, JAK/STAT pathway, and immune-suppressive cytokine secretion. Critically, 1q gain emerged as a strong and independent predictor of poor survival following anti-PD-1/PD-L1 therapy across two independent clinical cohorts: the MSK-IMPACT cohort (p = 0.018, N = 77) and a large real-world Caris Life Sciences dataset (HR = 1.2, p = 0.002, N = 1,167). Multivariate analysis confirmed that 1q gain predicts poor outcomes independently of CD8+ T-cell infiltration, B-cell infiltration, tumor mutational burden, and PD-L1 status. These findings establish chromosome 1q gain as a compelling biomarker of immunotherapy resistance in melanoma and highlight aneuploidies as underappreciated drivers of immune evasion in this disease.

cancer biology↗

bioRxiv2026

Pan-cancer proteogenomic interrogation of the Ubiquitin Proteasome System

Somatic mutations rewire the ubiquitin-proteasome system (UPS) to support tumor growth, but the proteome-wide consequences of cancer-driver alterations on UPS composition remain incompletely understood. Using harmonized proteogenomic data from up to 11 CPTAC cohorts, we performed an integrated pan-cancer analysis of UPS protein dysregulation, prognostic associations, and mutation-driven remodeling. We show that mRNA poorly predicts UPS protein abundance, that a defined set of E3 ligases is recurrently dysregulated across cancers, and that somatic mutations (most strikingly TP53 loss) produce coherent UPS protein-quantitative trait locus (pQTL) signatures. Two case studies (UBR5 and TRIM28) illustrate orthogonal modes of UPS rewiring: a mutation-driven axis in which TP53-mutant tumors elevate UBR5 to support replication stress tolerance, and a lineage-driven axis in which TRIM28 engages tissue-restricted regulatory networks with opposing prognostic effects in glioblastoma versus head and neck cancer. Each axis exposes context-specific therapeutic vulnerabilities, including sensitivity to DNA damage response inhibitors (UBR5-high) and lineage-specific drug responses (TRIM28-high). Together, these analyses define a mechanistic framework for how cancer-driver mutations reshape proteostasis through the UPS and nominate mutation- and lineage-defined dependencies for precision degrader therapy. The harmonized pan-tissue atlas and the UbiDash interactive resource that underpin parts of this analysis are reported in our companion paper1. HighlightsO_LIProteome-centric profiling reveals UPS dysregulation invisible at the transcript level across human cancers C_LIO_LIPan-cancer differential expression identifies recurrently dysregulated E3 ligases with prognostic value C_LIO_LIGenome-wide pQTL analysis uncovers mutation-driven UPS remodeling, with TP53 loss as a dominant driver C_LIO_LIUBR5 and TRIM28 define orthogonal mutation- and lineage-driven axes of UPS rewiring with distinct therapeutic vulnerabilities C_LI

systems biology↗

bioRxiv2026

Genome writing and Targeted Delivery of the NKX6-3/ANK1 gene cluster and its Type 2 Diabetes GWAS Variants to Human iPSCs

Genome-wide association studies (GWAS) identified over 600 loci containing single-nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D), most of which reside in non-coding regions. Among the set of T2D SNPs, linking causal genome variants to disease risk experimentally has remained a challenge; however, advances in synthetic mammalian genome writing techniques now enable the delivery of multiple haplotypes to human induced pluripotent stem cells (hiPSCs) to create a series of isogenic cell lines that can be differentiated and phenotyped in vitro. Here, to begin efforts in dissecting a T2D GWAS locus, we engineered an NKX6-3/ANK1 gene cluster knockout hiPSC line and introduced a landing pad facilitating the delivery of synthetic haplotype payloads. We built four haplotypes, including several that are not observed in nature, containing risk SNPs spanning the NKX6-3/ANK1 gene cluster using a method called "variant Switching Auxotrophic markers for Integration" (vSwAP-In), and integrated them precisely into hiPSCs. NKX6-3/ANK1 deletion blocked pancreatic progenitor and skeletal muscle differentiation, suggesting that NKX6-3 and ANK1 are required for early pancreatic and skeletal muscle development, and perhaps related to the existence of two nonoverlapping sets of SNPs in linkage disequilibrium that associate with the expression of the two adjacent genes. When NKX6-3/ANK1 T2D "Risk" haplotypes were reintroduced, skeletal muscle and pancreatic progenitor differentiation capabilities were restored. ANK1 expression was elevated in the ANK1 Risk and All-Risk haplotypes compared to the NKX6-3 Risk and Non-Risk haplotypes, establishing a functional experimental platform to examine risk SNP clusters in their native contexts. Overall, this work establishes a platform for the dissection of GWAS loci using synthetic haplotype genomics in hiPSCs. Significance StatementGenome-wide association studies have been used to identify disease-associated SNPs; however, most SNPs lie in non-coding regions, making functional experimentation difficult to perform. Using vSwAP-In, a yeast-based DNA variant-building method, and mSwAP-In, a mammalian genome engineering approach, we establish a platform for functional GWAS dissection in hiPSCs. This platform allows us to build DNA harboring virtually any combination of disease-risk SNPs, allowing for functional characterization of SNPs without the limitations of linkage disequilibrium. We demonstrate this approach using a Type 2 diabetes GWAS gene cluster, NKX6-3/ANK1.

genetics↗

bioRxiv2025

Pan-cancer analysis reveals context-dependent roles of LINE-1 ORF1p in immune regulation and copy number alterations

L1 comprises 17% of the human genome, with 50-150 full-length sequences capable of retrotransposition. Although largely inactive in normal somatic tissues, tumorigenesis leads to L1 derepression and overexpression of its RNA binding chaperone protein, ORF1p. A potential cancer biomarker, ORF1p expression is a hallmark of multiple cancers and an early event in precursor lesions. Our study provides a comprehensive pan-cancer analysis of ORF1p using CPTAC proteogenomic data, revealing a dichotomous role in modulating immune responses. Integrated analyses and supervised learning models divide ORF1p-high tumors into two groups: (1) high ORF1p associates with immunosuppression, reduced interferon signaling and diminished immune cell infiltration (HNSCC and LSCC), and (2) ORF1p-high tumors associate with immune activation (UCEC). Linear regression models reveal that cancer-specific aneuploidies may underlie this immune dichotomy, highlighting the prognostic significance of ORF1p and informing new strategies to leverage detection of plasma-circulating ORF1p to enhance immunotherapeutic efficacy in different tumor contexts.

genomics↗

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

SimSpace: a comprehensive in-silico spatial omics data simulation framework

Spatial omics technologies provide rich measurements of molecular states in their native tissue context, but the development and benchmarking of computational methods remain constrained by limited access to datasets with known ground truth. We present SimSpace, a flexible framework for simulating spatial omics data for method development, stress-testing, and controlled in silico experiments. SimSpace uses a hierarchical generative model that couples tissue-scale spatial organization, local cell-type interactions, and modular omics-profile generation. Its Markov random field-based spatial model enables controllable simulation of niche structure, cell-type co-localization, phenotype-dependent density, and spatially mediated molecular interactions. SimSpace supports both reference-free simulations under user-defined generative assumptions and reference-guided simulations that calibrate spatial parameters to real datasets while generating new spatial realizations. Across Xenium, MERFISH, and CODEX examples, SimSpace reproduced multiple evaluated spatial statistics, preserved key domain-level structure in reference-guided settings, and supported benchmarking of cell-type deconvolution and spatially variable gene detection methods. We further show that inferred interaction parameters can be perturbed to generate interpretable counterfactual spatial proteomics scenarios. SimSpace is implemented as an open-source Python package with reproducible workflows for rigorous benchmarking and methodological development in spatial omics.

bioinformatics↗

bioRxiv2025

A shotgun approach for highly multiplexed mammalian metabolic engineering

Mammalian metabolic engineering is critical to advancing basic biology, bioproduction, and cell therapy. However, as pathway complexity increases, so does the size of both the combinatorial design space and the DNA constructs required, rendering unbiased screens intractable. To address this, we developed Shotgun Genetic Engineering (SGE), a scalable approach that exploits the ease of delivering many barcoded small constructs--rather than a single large one--into mammalian cells. This allows each cell to serve as an independent experiment, carrying a unique synthetic metabolic pathway that explores combinations of gene content, stoichiometry, and organellar localization. Functional pathways are identified by sequencing barcodes from cells exhibiting the desired phenotype. Using SGE, we screened millions of pathway combinations to engineer essential amino acid biosynthesis in two mammalian cell lines (CHO and Jurkat), achieving near-wild-type growth in valine-free medium and, for the first time, enabling isoleucine prototrophy in CHO cells. Successful solutions favored mitochondrial localization and required integration of 23-52 kb of synthetic DNA--lengths that are impractical to screen by conventional methods. The resulting datasets are compatible with machine learning frameworks, positioning SGE as a powerful platform for decoding and engineering complex biosynthetic traits in mammalian systems.

synthetic biology↗

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

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making. O_FIG O_LINKSMALLFIG WIDTH=164 HEIGHT=200 SRC="FIGDIR/small/592032v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@754668org.highwire.dtl.DTLVardef@1616263org.highwire.dtl.DTLVardef@827009org.highwire.dtl.DTLVardef@1899ba7_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Machine Learning Classification of 53BP1 Foci

Background53BP1 foci are reflective of DNA double-strand break formation and have been used as radiation markers. Manual focus counting, while prone to bias and time constraints, remains the most accurate mode of detecting 53BP1 foci. Several studies have pursued automated focus detection to replace manual methods. Deep learning, spatial 3D images, and segmentation techniques are main components of the highest performing automated methods. While these approaches have achieved promising results regarding accurate focus detection and cell classification, they are not compatible with time-sensitive large-scale applications due to their demand for long run times, advanced microscopy, and computational resources. Further, segmentation of overlapping foci in 2D images has the potential to represent focus morphologies inaccurately. ResultsTo overcome these limitations, we developed a novel method to classify 2D fluorescence microscopy images of 53BP1 foci. Our approach consisted of three key features: (1) general 53BP1 focus classes, (2) varied parameter space composed of properties from individual foci and their respective Fourier transform, and (3) widely-available machine learning classifiers. We identified four main focus classes, which consisted of blurred foci and three levels of overlapping foci. Our parameter space for the training focus library, composed of foci formed by fluorescently-tagged BP1-2, showed a wide correlation range between variables which was validated using a publicly-available library of immunostained 53BP1 foci. Random forest achieved one of the highest and most stable performances for binary and multiclass problems, followed by a support vector machine and k-nearest neighbors. Specific metrics impacted the classification of blurred and low overlap foci for both train and test sets. ConclusionsOur method classified 53BP1 foci across separate fluorescent markers, resolutions, and damage-inducing methods, using off-the-shelf machine learning classifiers, a diverse parameter space, and well-defined focus classes.

bioinformatics↗

bioRxiv2024

Identifying Ferroptosis Inducers, HDAC, and RTK Inhibitor Sensitivity in Melanoma Subtypes through Unbiased Drug Target Prediction

The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here we mine large scale MM proteogenomic data integrating it with MM cell line dependency screen, and drug sensitivity data to identify druggable targets and forecast treatment efficacy and resistance. Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC and AKT1, across five distinct MM subtypes. These proteins serve as potential drug targets applicable to one or multiple MM subtypes. By analyzing transcriptomic data from 48 publicly accessible melanoma cell lines sourced from Achilles and CRISPR dependency screens, we forecasted 162 potentially targetable genes. We also identified genetic resistance in 260 genes across at least one melanoma subtype. In addition, we employed publicly available compound sensitivity data (Cancer Therapeutics Response Portal, CTRPv2) on the cell lines to assess the correlation of compound effectiveness within each subtype. We have identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype. Remarkably, employing this unbiased approach, we have uncovered compounds targeting ferroptosis, that demonstrate a striking 30x fold difference in sensitivity among different subtypes. This implies that the proteogenomic classification of melanoma has the potential to predict sensitivity to ferroptosis compounds. Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy. Highlights(1) Proteogenomic subtype classification can define the landscape of genetic dependencies in melanoma (2) Nine proteins from molecular subtypes were identified as potential drug targets for specified MM patients (3) 20 compounds identified that show potential effectiveness in at least one melanoma subtype (4) Proteogenomics can predict specific ferroptosis inducers, HDAC, and RTK Inhibitor sensitivity in melanoma subtypes O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/579424v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@73dd77org.highwire.dtl.DTLVardef@d0cb8eorg.highwire.dtl.DTLVardef@1e4117forg.highwire.dtl.DTLVardef@38b727_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cancer biology↗

bioRxiv2024

D-type cyclins regulate DNA mismatch repair in the G1 and S phases of the cell cycle, maintaining genome stability

The large majority of oxidative DNA lesions occurring in the G1 phase of the cell cycle are repaired by base excision repair (BER) rather than mismatch repair (MMR) to avoid long resections that can lead to genomic instability and cell death. However, the molecular mechanisms dictating pathway choice between MMR and BER have remained unknown. Here, we show that, during G1, D-type cyclins are recruited to sites of oxidative DNA damage in a PCNA- and p21-dependent manner. D-type cyclins shield p21 from its two ubiquitin ligases CRL1SKP2 and CRL4CDT2 in a CDK4/6-independent manner. In turn, p21 competes through its PCNA-interacting protein degron with MMR components for their binding to PCNA. This inhibits MMR while not affecting BER. At the G1/S transition, the CRL4AMBRA1-dependent degradation of D-type cyclins renders p21 susceptible to proteolysis. These timely degradation events allow the proper binding of MMR proteins to PCNA, enabling the repair of DNA replication errors. Persistent expression of cyclin D1 during S-phase increases the mutational burden and promotes microsatellite instability. Thus, the expression of D-type cyclins inhibits MMR in G1, whereas their degradation is necessary for proper MMR function in S. One-Sentence SummaryTo maintain genome stability, D-type cyclins limit mismatch repair (MMR) in G1, whereas their degradation is necessary for proper MMR function in S phase.

cell biology↗

bioRxiv2023

Histopathology-assisted proteogenomics provides foundations for stratification of melanoma metastases

Here we describe the histopathology-driven proteogenomic landscape of 142 treatment-naive metastatic melanoma samples. We identified five proteomic subtypes that integrate the immune and stroma microenvironment components, and associate with clinical and histopathological parameters, providing foundations for an in-depth molecular classification of melanoma. Our study shows that BRAF V600 mutated melanomas display heterogeneous biology, where the presence of an oncogene-induced senescence-like phenotype improves patient survival. Therefore, we propose a mortality-risk-based stratification, which may contribute to a more personalized approach to patient treatment. We also found a strong association between tumor microenvironment composition, disease progression, and patient outcome supported by single-cell omic signatures that point to straightforward histopathological connective tissue-to-tumor ratio assessment for better informed medical decisions. A melanoma-associated signature of single amino acid variants (SAAV) responsible for remodeling the extracellular matrix was uncovered together with SAAV-derived neoantigen candidates as targets of anti-tumor immune responses. Overall, this study offers comprehensive stratifications of melanoma metastases that may help develop tailored strategies for diagnosing and treating the disease.

cancer biology↗

bioRxiv2023

Engineered transcription-associated Cas9 targeting in eukaryotic cells

DNA targeting Class 2 CRISPR-Cas effector nucleases, including the well-studied Cas9 proteins, evolved protospacer-adjacent motif (PAM) and guide RNA interactions that sequentially license their binding and cleavage activities at protospacer target sites. Both interactions are nucleic acid sequence specific but function constitutively; thus, they provide intrinsic spatial control over DNA targeting activities but naturally lack temporal control. Here we show that engineered Cas9 fusion proteins which bind to nascent RNAs near a protospacer can facilitate spatiotemporal coupling between transcription and DNA targeting at that protospacer: Transcription-associated Cas9 Targeting (TraCT). Engineered TraCT is enabled in eukaryotic yeast or human cells when suboptimal PAM interactions limit basal activity and when one or more nascent RNA substrates are still tethered to the actively transcribed target DNA in cis. Using yeast, we further show that this phenomenon can be applied for selective editing at one of two identical targets in distinct gene loci, or, in diploid allelic loci that are differentially transcribed. Our work demonstrates that temporal control over Cas9s targeting activity at specific DNA sites may be engineered without modifying Cas9s core domains and guide RNA components or their expression levels. More broadly, it establishes co-transcriptional RNA binding as a cis-acting mechanism that can conditionally stimulate CRISPR-Cas DNA targeting in eukaryotic cells.

bioengineering↗

bioRxiv2023

The expression profile and tumorigenic mechanisms of CD97 (ADGRE5) in glioblastoma render it a targetable vulnerability

Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Adhesion G protein-coupled receptors (aGPCRs) have attracted interest for their functional role in gliomagenesis and their potential as treatment targets. To identify therapeutically targetable opportunities among aGPCR family members in unbiased fashion, we analyzed expression levels of all aGPCRs in GBM and non-neoplastic brain tissue. Using bulk and single cell transcriptomic and proteomic data, we show that CD97 (ADGRE5), an aGPCR previously implicated in GBM pathogenesis, is the most promising aGPCR target in GBM, by virtue of its abundance in all GBM tumors and its de novo expression profile in GBM compared to normal brain tissue and neural progenitors. CD97 knockdown or knockout significantly reduces the tumor initiation capacity of patient-derived GBM cultures (PDGC) in vitro and in vivo. Transcriptomic and metabolomic data from PDGCs suggest that CD97 promotes glycolytic metabolism. The oncogenic and metabolic effects of CD97 are mediated by the MAPK pathway. Activation of MAPK signaling depends on phosphorylation of the cytosolic C-terminus of CD97 and recruitment of {beta}-arrestin. Using single-cell RNA-sequencing and biochemical assays, we demonstrate that THY1/CD90 is the most likely CD97 ligand in GBM. Lastly, we show that targeting of PDGCs with an anti-CD97 antibody-drug conjugate in vitro selectively kills tumor cells but not human astrocytes or neural stem cells. Our studies identify CD97 as an important regulator of tumor metabolism in GBM, elucidate mechanisms of receptor activation and signaling, and provide strong scientific rationale for developing biologics to target it for therapeutic purposes.

cancer biology↗

bioRxiv2023

GEMspa: a Napari plugin for analysis of single particle tracking data

The cellular environment is crowded with macromolecules and far from thermodynamic equilibrium. This active, crowded environment influences biochemical reactions and the formation of cellular structures such as membraneless organelles. These physical properties can change during normal physiology and in disease states such as neurodegenerative diseases and cancer, impacting cell behavior and function. Therefore, it is crucial to develop methods to characterize these properties. Microrheology is the inference of physical properties from the motion of tracer particles embedded within a material. This technique requires single particle tracking (SPT) and analysis of these tracks through the framework of soft-condensed matter physics. Analysis of SPT data can be challenging due to the lack of comprehensive user-friendly software tools. To address this, we introduce GEMspa, a software package implemented as a plugin for the open source image analysis platform, napari. GEMspa provides a GUI for a commonly used localization/tracking algorithm (via Trackpy), and a suite of methods to extract basic parameters describing particle motion. This platform aims to streamline the workflow of data analysis steps and allow researchers to visualize and optimize parameters for high-quality results, thereby making microrheology accessible to non-expert scientists.

biophysics↗

bioRxiv2023

In vivo metabolomics identifies CD38 as an emergent vulnerability in LKB1-mutant lung cancer

LKB1/STK11 is a serine/threonine kinase that plays a major role in controlling cell metabolism, resulting in potential therapeutic vulnerabilities in LKB1-mutant cancers. Here, we identify the NAD+ degrading ectoenzyme, CD38, as a new target in LKB1-mutant NSCLC. Metabolic profiling of genetically engineered mouse models (GEMMs) revealed that LKB1 mutant lung cancers have a striking increase in ADP-ribose, a breakdown product of the critical redox co-factor, NAD+. Surprisingly, compared with other genetic subsets, murine and human LKB1-mutant NSCLC show marked overexpression of the NAD+-catabolizing ectoenzyme, CD38 on the surface of tumor cells. Loss of LKB1 or inactivation of Salt-Inducible Kinases (SIKs)--key downstream effectors of LKB1-- induces CD38 transcription induction via a CREB binding site in the CD38 promoter. Treatment with the FDA-approved anti-CD38 antibody, daratumumab, inhibited growth of LKB1-mutant NSCLC xenografts. Together, these results reveal CD38 as a promising therapeutic target in patients with LKB1 mutant lung cancer. SIGNIFICANCELoss-of-function mutations in the LKB1 tumor suppressor of lung adenocarcinoma patients and are associated with resistance to current treatments. Our study identified CD38 as a potential therapeutic target that is highly overexpressed in this specific subtype of cancer, associated with a shift in NAD homeostasis.

cancer biology↗