Biology subjects

Loda, M.

Publications and source records attributed to Loda, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Stage-Specific Regulation of DNA Damage Repair by the Circadian Regulator, CRY1, in Prostate Cancer

Circadian dysregulation is increasingly linked to prostate cancer (PCa) progression, yet its role in directing DNA damage response (DDR) pathway selection remains poorly understood. Here, we identify circadian cryptochrome 1 (CRY1), a core circadian regulator, as a stage-specific determinant of DDR dependencies. Integrated transcriptomic and CRISPR-based analyses reveal that CRY1 promotes non-homologous end joining (NHEJ) and base excision repair (BER)-associated programs in hormone-sensitive disease (HTS), while driving a switch toward homologous recombination (HR) dependency in castration-resistant prostate cancer (CRPC). Mechanistically, CRY1 couples proliferative signaling to genome maintenance, enabling tumor cells to tolerate genotoxic stress and sustain progression. Notably, loss of CRY1 exposes distinct, context-dependent DDR vulnerabilities, revealing repair plasticity as a targetable actionable feature of disease evolution. These findings position CRY1 as a central regulator of DDR rewiring and support CRY1-directed combination strategies with DDR inhibitors as a rationale to delay or prevent progression to advanced, treatment-resistant PCa.

cancer biology↗

bioRxiv2026

Suppression of de novo lipogenesis and dietary PUFA supplementation inhibit prostate cancer progression

Prostate cancer progression is characterized by dysregulated lipid metabolism, with activation of fatty acid synthase (FASN), the rate-limiting step in de novo lipogenesis (DNL), resulting in significant accumulation of saturated lipids. Here, we show that pharmacologic FASN inhibition creates a metabolic state that increases reliance on exogenous polyunsaturated fatty acids (PUFAs). Inhibition of FASN profoundly alters membrane phospholipid composition, driving compensatory incorporation of PUFAs into membrane phospholipids, thus increasing susceptibility to lipid peroxidation and oxidative damage. Combined FASN inhibition and PUFA exposure induce mitochondrial hyperpolarization and enhance lipid peroxidation in both hormone-sensitive and castration-resistant prostate cancer models, resulting in increased reactive oxygen species production, ferroptosis, as well as apoptosis. Marked inhibition of growth in castration-resistant human and murine prostate cancer organoids is achieved ex vivo. In genetically engineered, DNL-reliant Hi-Myc mice, a diet enriched in PUFAs significantly inhibited invasive carcinoma compared to a saturated fat-enriched diet. Thus, environmental PUFAs modulate and enhance the therapeutic efficacy of FASN-targeted strategies. These findings set the stage for pharmacologic and dietary intervention in prostate cancer patients.

cancer biology↗

bioRxiv2025

TMPO promotes cellular dissemination and metastasis in circulating tumor cells

Metastasis--the process by which cancer cells spread beyond the primary tumor to distant organs--accounts for the vast majority of cancer-related deaths. To elucidate mechanisms underlying dissemination and metastasis in prostate cancer, we have investigated circulating tumor cells (CTCs) obtained from genetically engineered mouse models (GEMMs). The phenotypic and molecular properties of the CTCs, and organoids derived from these CTCs, closely model the tumor and metastatic phenotypes of their parental GEMMs. Moreover, organoids derived from individual CTCs exhibit molecular and morphological heterogeneity that is associated with distinct metabolic states as well as differences in human prostate cancer outcome. Using computational systems analyses, we have identified TMPO, encoding the nuclear membrane protein lamina-associated polypeptide 2 (Lap2), as a key driver of this heterogeneity. TMPO activity is upregulated in advanced human prostate tumors, metastases, and CTCs, and is associated with adverse clinical outcome. Our findings indicate that TMPO promotes dissemination and metastasis in vivo by enhancing survival in conditions of metabolic stress, and reveal a novel mechanistic link between CTC heterogeneity, stress adaptation, and metastatic potential.

cell biology↗

bioRxiv2025

Multi-omics integration and batch correction using a modality-agnostic deep learning framework

State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patterns not captured by manual annotations, and despite being modality-agnostic performs on par with specialized state-of-the-art tools. MIMAs flexibility and scalability make it a powerful tool for multimodal data analysis. MIMA provides a foundation for AI-based, multi-omics augmented digital pathology frameworks, offering new opportunities for improved patient stratification and precision medicine through the comprehensive integration of high-dimensional molecular data and histopathological imaging.

bioinformatics↗

bioRxiv2025

The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs

The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular- resolution reference of healthy adult human tissues across multiple organ systems. Integrating spatial transcriptomics, proteomics, and histological features across over 15 million cells from more than 100 donors, SAHA maps conserved and organ-specific cellular niches in gastrointestinal and immune tissues. High-resolution profiling using CosMx SMI, 10x Xenium, RNAscope, GeoMx DSP, and single-nucleus RNA-seq reveals spatially organized cell states, rare adaptive immune populations, and tissue-specific cell-cell interactions and ligand-receptor pairs. Comparative analyses with colorectal cancer and inflammatory bowel disease demonstrate the power of SAHA to detect disease-associated spatial disruptions, including crypt dedifferentiation, perineural invasion, and therapy-resistant immune remodeling. All data are openly accessible through a FAIR-compliant interactive portal to support exploration, benchmarking, and machine learning model training. Through SAHA, we provide a foundational framework for spatial diagnostics and next-generation precision medicine grounded in a comprehensive human tissue atlas, enabling the development of context-aware models that simulate tissue behavior, decode complex pathologies, and accelerate therapeutic innovation at unprecedented scale.

systems biology↗

bioRxiv2025

KODAMA enables self-guided weakly supervised learning in spatial transcriptomics

Spatial transcriptomics provides researchers with a powerful tool to investigate gene expression patterns within their native positional context in tissues, revealing intricate cellular relationships. However, analyzing spatial transcriptomics data presents unique challenges due to their high dimensionality and complexity. Several competitive tools have emerged, each aiming to integrate spatial dependencies into their workflow. Yet, the overall performance of these approaches remains constrained by limitations in prediction accuracy and compatibility with specific data types. Here, we introduce the third version of the KODAMA algorithm, specifically tailored for spatial transcriptomics analysis. This upgraded version incorporates a novel approach that effectively reduces data dimensionality while preserving spatial information. At its core, the KODAMA algorithm employs parallel iterations that enforce spatial constraints throughout an embedded clustering process. Our method provides the flexibility to simultaneously analyze multiple samples, streamlining analysis workflows. Additionally, it extends beyond traditional 2D analysis to accommodate multidimensional datasets, including 3D spatial information. KODAMA seamlessly integrates into various pipelines, such as Seurat and Giotto. Extensive evaluations of KODAMA on 10x Visium, Visium HD, and image-based 3D datasets demonstrate its high accuracy in spatial domain prediction. Comparative analyses against alternative dimensionality reduction techniques and spatial analysis tools consistently validate and highlight KODAMAs superior performance in unraveling the spatial organization of cellular components across both single and integrated tissue samples.

bioinformatics↗

bioRxiv2024

Cis-vaccenic acid is a key product of stearoyl-CoA desaturase 1 and a critical oncogenic factor in prostate cancer

Altered monounsaturated fatty acid (MUFA) metabolism is a hallmark of oncogenic transformation. Recently, the MUFA cis-vaccenic acid (cVA) was identified as a putative regulator of prostate cancer cell viability. cVA production requires the activity of stearoyl CoA desaturase 1 (SCD1), an enzyme frequently dysregulated in cancer, but the role of cVA in regulating cancer cell phenotypes has not been extensively explored, compared with the more commonly studied product and structural isomer of cVA, oleic acid. Here, we utilised SCD1 inhibition to study the effects of cVA in prostate cancer cells. We found that cVA consistently rescues reductions in cell viability due to SCD1 inhibition and promotes cell growth under normal conditions, thereby identifying cVA as an important and previously unrecognised product of SCD1 in prostate cancer. More broadly, we demonstrate that individual MUFA species exert a diverse range of influence on oncogenic phenotypes, highlighting a need to more precisely characterise the lipidome of cancer cells to understand the molecular pathology of the disease.

cell biology↗

bioRxiv2024

Canonical AREs are tumor suppressive regulatory elements in the prostate

The androgen receptor (AR) is the central determinant of prostate tissue identity and differentiation, controlling normal, growth-suppressive prostate-specific gene expression1. It is also a key driver of prostate tumorigenesis, becoming "hijacked" to drive oncogenic transcription2-5. However, the regulatory elements determining the execution of the growth suppressive AR transcriptional program, and whether this can be reactivated in prostate cancer (PCa) cells remains unclear. Canonical androgen response element (ARE) motifs are the classic DNA binding element for AR6. Here, we used a genome-wide strategy to modulate regulatory elements containing AREs to define distinct AR transcriptional programs. We find that activation of these AREs is specifically associated with differentiation and growth suppressive transcription, and this can be reactivated to cause death in AR+ PCa cells. In contrast, repression of AREs is well tolerated by PCa cells, but deleterious to normal prostate cells. Finally, gene expression signatures driven by ARE activity are associated with improved prognosis and luminal phenotypes in human PCa patients. This study demonstrates that canonical AREs are responsible for a normal, growth-suppressive, lineage-specific transcriptional program, that this can be reengaged in PCa cells for potential therapeutic benefit, and genes controlled by this mechanism are clinically relevant in human PCa patients.

cancer biology↗

bioRxiv2023

Integration of Multiple Spatial-Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

cancer biology↗

bioRxiv2023

NSD2 maintains lineage plasticity and castration-resistance in neuroendocrine prostate cancer

The clinical use of potent androgen receptor (AR) inhibitors has promoted the emergence of novel subtypes of metastatic castration-resistant prostate cancer (mCRPC), including neuroendocrine prostate cancer (CRPC-NE), which is highly aggressive and lethal1. These mCRPC subtypes display increased lineage plasticity and often lack AR expression2-5. Here we show that neuroendocrine differentiation and castration-resistance in CRPC-NE are maintained by the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2)6, which catalyzes histone H3 lysine 36 dimethylation (H3K36me2). We find that organoid lines established from genetically-engineered mice7 recapitulate key features of human CRPC-NE, and can display transdifferentiation to neuroendocrine states in culture. CRPC-NE organoids express elevated levels of NSD2 and H3K36me2 marks, but relatively low levels of H3K27me3, consistent with antagonism of EZH2 activity by H3K36me2. Human CRPC-NE but not primary NEPC tumors expresses high levels of NSD2, consistent with a key role for NSD2 in lineage plasticity, and high NSD2 expression in mCRPC correlates with poor survival outcomes. Notably, CRISPR/Cas9 targeting of NSD2 or expression of a dominant-negative oncohistone H3.3K36M mutant results in loss of neuroendocrine phenotypes and restores responsiveness to the AR inhibitor enzalutamide in mouse and human CRPC-NE organoids and grafts. Our findings indicate that NSD2 inhibition can reverse lineage plasticity and castration-resistance, and provide a potential new therapeutic target for CRPC-NE.

cancer biology↗

bioRxiv2023

Suppression of tumor cell lactate-generating signaling pathways eradicates murine PTEN/p53-deficient aggressive-variant prostate cancer via macrophage phagocytosis

PurposePTEN loss-of-function/PI3K pathway hyperactivation occurs in [~]50% of metastatic, castrate-resistant prostate cancer patients, resulting in poor therapeutic outcomes and resistance to immune checkpoint inhibitors across multiple malignancies. Our prior studies in prostate-specific PTEN/p53-deleted genetically engineered mice (Pb-Cre;PTENfl/flTrp53fl/fl GEM) with aggressive-variant prostate cancer (AVPC) demonstrated feedback Wnt/{beta}-catenin signaling activation in 40% mice resistant to androgen deprivation therapy (ADT)/PI3K inhibitor (PI3Ki)/PD-1 antibody (aPD-1) combination, resulting in restoration of lactate cross-talk between tumor-cells and tumor-associated macrophages (TAM), histone lactylation (H3K18lac) and phagocytic suppression within TAM. Here, we targeted immunometabolic mechanism(s) of resistance to ADT/PI3Ki/aPD-1 combination, with the goal of durable tumor control in PTEN/p53-deficient PC. Experimental designPb-Cre;PTENfl/flTrp53fl/fl GEM were treated with either ADT (degarelix), PI3Ki (copanlisib), aPD-1, MEK inhibitor (trametinib) or Porcupine inhibitor (LGK 974) as single agents or their combinations. MRI was used to monitor tumor kinetics and immune/proteomic profiling/ex vivo co-culture mechanistic studies were performed on prostate tumors or established GEM-derived cell lines. ResultsWe tested whether Wnt/{beta}-catenin pathway inhibition with LGK 974 addition to degarelix/copanlisib/aPD-1 therapy enhances tumor control in GEM, and observed de novo resistance due to feedback activation of MEK signaling. Based on our observation that degarelix/aPD-1 treatment resulted in partial inhibition of MEK signaling, we substituted trametinib for degarelix/aPD-1 treatment, and observed a durable tumor growth control of PI3Ki/MEKi/PORCNi in 100% mice via H3K18lac suppression and complete TAM activation within TME. ConclusionsAbrogation of lactate-mediated cross-talk between cancer cells and TAM results in durable ADT-independent tumor control in PTEN/p53-deficient AVPC, and warrants further investigation in clinical trials. STATEMENT OF TRANSLATIONAL RELEVANCEPTEN loss-of-function occurs in [~]50% of mCRPC patients, and associated with poor prognosis, and immune checkpoint inhibitor resistance across multiple malignancies. Our prior studies have demonstrated that ADT/PI3Ki/PD-1 triplet combination therapy controls PTEN/p53-deficient PC in 60% of mice via enhancement of TAM phagocytosis. Here, we discovered that resistance to ADT/PI3K/PD-1 therapy occurred via restoration of lactate production via feedback Wnt/MEK signaling following treatment with PI3Ki, resulting in inhibition of TAM phagocytosis. Critically, co-targeting of PI3K/MEK/Wnt signaling pathways using an intermittent dosing schedule of corresponding targeted agents resulted in complete tumor control and significantly prolonged survival without significant long-term toxicity. Collectively, our findings provide "proof-of-concept" that targeting lactate as a macrophage phagocytic checkpoint controls growth of murine PTEN/p53-deficient PC and warrant further investigation in AVPC clinical trials.

cancer biology↗

bioRxiv2023

Distinct mesenchymal cell states mediate prostate cancer progression

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

cancer biology↗

bioRxiv2023

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of heterogeneity on genomic signature scoring

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx(R), Decipher(R), Prostadiag(R)) using a step-wise approach increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses. Patient summaryProstate cancers are very variable, including within a single tumour. Current genetic scoring systems, which are sometimes used to make decisions for how to treat patients with prostate cancer, are based on sampling methods which do not reflect these variations. We found, using state-of-the-art spatial genetic technology to simulate accurate assessment of variation in biopsies, that the current approaches miss important details which could negatively impact clinical decisions. Take home messageVirtual biopsies from spatial transcriptomic analysis of a whole prostate reveal that current genomic risk scores potentially deliver misleading results as they are based on bulk analysis of prostate biopsies and ignore tumour heterogeneity.

genomics↗

bioRxiv2022

Using Attention-based Deep Learning to Predict ERG:TMPRSS2 Fusion Status in Prostate Cancer from Whole Slide Images

Prostate cancer (PCa) is associated with several genetic alterations which play an important role in the disease heterogeneity and clinical outcome including gene fusion between TMPRSS2 and members of the ETS family of transcription factors specially ERG. The expanding wealth of pathology whole slide images (WSIs) and the increasing adoption of deep learning (DL) approaches offer a unique opportunity for pathologists to streamline the detection of ERG:TMPRSS2 fusion status. Here, we used two large cohorts of digitized H&E-stained slides from radical prostatectomy specimens to train and evaluate a DL system capable of detecting the ERG fusion status and also detecting tissue regions of high diagnostic and prognostic relevance. Slides from the PCa TCGA dataset were split into training (n=318), validation (n=59), and testing sets (n=59) with the training and validation sets being used for training the model and optimizing its hyperparameters, respectively while the testing set was used for evaluating the performance. Additionally, we used an internal testing cohort consisting of 314 WSIs for independent assessment of the models performance. The ERG prediction model achieved an Area Under the Receiver Operating Characteristic curve (AUC) of 0.72 and 0.73 in the TCGA testing set and the internal testing cohort, respectively. In addition to slide-level classification, we also identified highly attended patches for the cases predicted as either ERG-positive or negative which had distinct morphological features associated with ERG status. We subsequently characterized the cellular composition of these patches using HoVer-Net model trained on the PanNuke dataset to segment and classify the nuclei into five main categories. Notably, a high ratio of neoplastic cells in the highly-attended regions was significantly associated with shorter overall and progression-free survival while high ratios of immune, stromal and stromal to neoplastic cells were all associated with longer overall and metastases-free survival. Our work highlights the utility of deploying deep learning systems on digitized histopathology slides to predict key molecular alteration in cancer together with their associated morphological features which would streamline the diagnostic process.

pathology↗

bioRxiv2022

Adipose triglyceride lipase is regulated by CAMKK2-AMPK signaling and drives advanced prostate cancer

Lipid metabolism plays a central role in prostate cancer. To date, the major focus on prostate cancer lipid metabolism has centered on de novo lipogenesis and lipid uptake with little consideration for how cancer cells access these lipids once they are created or taken up and stored. Patient-derived phosphoproteomics identified adipose triglyceride lipase (ATGL), a previously suspected tumor suppressor, as a CAMKK2-AMPK signaling target that, conversely, promotes castration-resistant prostate cancer (CRPC) progression. Phosphorylation of ATGL increased its lipase activity, cancer cell proliferation, migration, and invasion. Shotgun lipidomics and mass spectrometry imaging demonstrated ATGLs profound regulation of lipid metabolism in vitro and in vivo, remodeling membrane composition. Inhibition of ATGL induced metabolic plasticity, causing a glycolytic shift that could be exploited therapeutically by co-targeting both metabolic pathways. Together, these data nominate ATGL and intracellular lipolysis as potential therapeutic targets for the treatment of CRPC and provide insights for future combination therapies.

cancer biology↗

bioRxiv2022

Multi-omics biomarkers aid prostate cancer prognostication

Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the "gold standard" to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature 1: EGLN3, succinate, trans-4-hydroxyprolin; and signature 2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.

systems biology↗

bioRxiv2021

Compromised CDK12 activity causes dependency on the non-essential spliceosome components

Prostate cancer (PC) is the most common cancer in men and after development of the castration-resistant PC (CRPC), there are no curative treatment options. Inactivating mutations in cyclin-dependent kinase 12 (CDK12) define an aggressive sub-type of CRPC. We hypothesized that compromised CDK12 activity leads to significant rewiring of the CRPC cells, and that this rewiring results in actionable synthetic lethal interactions. MethodsWe used combinatorial lethal screening, ChIP-seq data, RNA-seq data, global alternative splicing analysis, and comprehensive mass spectrometry (MS) profiling to understand how the compromised CDK12 activity rewires the CRPC cells. In addition, we used DepMap-, PC- and CRPC-datasets as a strategy to identify factors that are selectively required by the CDK12-mutant cells. ResultsWe show that inhibition of O-GlcNAc transferase (OGT) and CDK12 induces cancer cell-selective growth-defect. OGT catalyzes all nucleocytoplasmic O-GlcNAcylation, and we use unbiased MS-profiling to show that the short-term CDK12 inhibition induces hyper-O-GlcNAcylation of the spliceosome-machinery in PC and CRPC cells. Integration of DepMap- and a small scale-drug screen data reveled that depletion of CDK12 activity causes addiction to non-essential spliceosome components (CLK1/4 and SRPK1). CDK12-mutant tumors overexpress CLK1/4 and SRPK1. Finally, we show that the genomes of the CDK12-mutant tumors have lower DNA methylation, and that CDK12 inhibition induces the expression of the genes marked by DNA methylation. ConclusionsCompromised CDK12 activity rewires DNA methylation, transcription and splicing, and this rewiring renders the affected cells addicted on the non-essential spliceosome components. We propose that inactivation of CDK12 is a biomarker for sensitivity against inhibitors of the non-essential spliceosome components just entering the clinical trials. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/470703v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a128e8org.highwire.dtl.DTLVardef@84a70dorg.highwire.dtl.DTLVardef@f37b10org.highwire.dtl.DTLVardef@1145f1b_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Long Noncoding RNA RROL Provides Chromatin Scaffold for MYC-WDR82 Interaction to Impact Lipid Metabolism and Tumor Cell Growth in Multiple Myeloma

Long noncoding RNAs (lncRNA) can drive the tumorigenesis and be susceptible to therapeutic intervention. To define the landscape of therapeutically actionable lncRNA dependencies in multiple myeloma (MM), we coupled our extensive lncRNA transcriptomic profile with lncRNA targeted CRISPR interference viability screen and identified RNA Regulator of Lipogenesis (RROL) as a leading lncRNA dependency in MM. RROL shares its origin with the microRNA locus MIR17HG, however supports the proliferation and survival of MM cells in a microRNA- and DROSHA- independent manner. We found that RROL provides a chromatin scaffold for the functional interaction between c-MYC and WDR82 to promote the regulation of the lipogenic pathways via the transcriptional control of the rate-limiting enzyme ACC1 in MM cells. Inhibition of RROL with clinically applicable antisense molecules disrupts its transcriptional and functional activities causing potent anti-tumor effects both in vitro and in vivo in two pre-clinical animal models. This study establishes lncRNA RROL as a therapeutically actionable dependency with a unique mechanism of action in support of myeloma cell growth.

cancer biology↗