Biology subjects

Brugarolas, J.

Publications and source records attributed to Brugarolas, J..

10 recordsLinked to original sources

bioRxiv2026

A single chromosome 3p break initiates clear cell renal cell carcinoma evolution

Clear cell renal cell carcinoma (ccRCC) is initiated by chromosome 3p loss, yet chromosome losses impose a profound fitness burden on normal cells. How renal epithelial cells tolerate this deleterious aneuploidy during early tumorigenesis remains unclear. Analysis of 949 ccRCC genomes reveals two major classes of chromosome 3p alterations: simple deletions and complex rearrangements surrounding a terminal breakpoint - a pattern we term breakpoint-confined chromothripsis. We modeled both alterations in non-transformed human renal proximal tubule epithelial cells by introducing a single DNA double-strand break on chromosome 3p. Despite an initial fitness disadvantage, chromosome 3p loss drives adaptive genomic evolution that recapitulates recurrent ccRCC-associated aneuploidies, including 5q gain and 14q loss. These alterations alleviate the fitness constraints of 3p loss and promote metabolic reprogramming, clonal expansion, and malignant transformation, producing tumors with features of ccRCC. Thus, a single chromosome break initiates the evolutionary trajectory of ccRCC by creating a fitness bottleneck that selects for recurrent aneuploidies.

cancer biology↗

bioRxiv2026

Clear cell renal cell carcinoma consensus transcriptomic programs reveal converging trajectories towards aggressive disease

Clear cell renal cell carcinoma (ccRCC) is characterized by a branching genomic trajectory in which early biallelic VHL inactivation splits into PBRM1- and BAP1-mutant lineages. However, driver mutations alone do not account for the molecular and phenotypic heterogeneity. To dissect this heterogeneity, we developed a non-negative matrix factorization (NMF)-based gene expression analytical framework for identifying recurrent transcriptional programs across factorization dimensionalities and across datasets. We applied it to three curated datasets (IMmotion151, n = 823; JAVELIN Renal 101, n = 726; TCGA, n = 614) to define 17 consensus transcriptomic programs (CTPs). Mapping CTPs onto single-cell RNAseq (scRNAseq) of human ccRCC tumors and patient-derived tumorgraft models linked these programs to their cellular sources, distinguishing RCC-intrinsic, tumor-cell-extrinsic, and mixed programs. RCC-intrinsic CTPs associated with canonical drivers, including VHL (R1), PBRM1 (R2), BAP1 (R4), PTEN/TSC1 (R3), TFE3/TFEB fusions (R5), NF2 (R6), and CDKN2A/TP53 (MP-Prolif). Additional CTPs captured tumor microenvironment (TME) composition (TME-Tcell, TME-Myelo, TME-Endo, TME-Stroma) and biological processes active across multiple cellular compartments, including proliferation, Y-chromosome-linked expression in male tumors, ciliary biology, and translation. Trajectory inference methods revealed PBRM1-like and BAP1-like branches that converged on a shared aggressive late transcriptomic stage (TS) associated with higher nuclear grade, additional driver alterations, myeloid/stromal infiltration, and poor clinical outcomes. Spatial transcriptomics (and multiregional sequencing) of paired conventional ccRCC and sarcomatoid regions linked TS advancement with morphological progression and clonal evolution. After adjusting for stage, grade, and BAP1/PBRM1 status, TS remained independently prognostic. Furthermore, our data suggest that immune checkpoint inhibitor combinations are particularly beneficial for MP-Prolif and not R1 utilizing specimens. In summary, we present an atlas of recurring transcriptomic programs in RCC and an ontological framework bridging genotype, tumor-cell-intrinsic gene expression, and microenvironment remodeling, with implications for risk stratification and treatment selection in ccRCC.

Cancer Biology↗

bioRxiv2026

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets

Immune checkpoint inhibitors (ICI) are central to the treatment of metastatic clear cell renal cell carcinoma (ccRCC), yet only a subset of patients derive durable benefit, and clinically deployable predictive biomarkers remain an unmet need. RNA-based T-effector signatures capture cytotoxic immune biology and have been associated with ICI response in clinical trial cohorts; however, their clinical implementation is limited by the marked spatial heterogeneity of ccRCC, as well as cost, long turnaround time, sample quality requirements, and limited accessibility. Here, we developed a visually interpretable deep learning (DL) model that predicts a T-cell-enriched immune score directly from hematoxylin and eosin (H&E)-stained whole-slide images. To overcome the inability of H&E morphology alone to distinguish lymphocyte subsets, we trained the model using multimodal spatial supervision from CD8, PAX8, and ERG IHC, which respectively identified cytotoxic T-cell-rich regions, tumor cells, and endothelial cells, thereby constraining immune predictions to relevant tumor microenvironmental niches. The resulting H&E DL Immune score was validated by pathologist review, comparison with held-out CD8 IHC annotations, and independent datasets. The H&E DL Immune score correlated with T-effector RNA scores across independent institutional and IMmotion150 clinical trial cohorts (spearman correlations of 0.726; p=5.90x10-15 and 0.706; p=4.04x10-19). As a proof of principle, the score was used to characterize associations with key biological features across large cohorts, including sarcomatoid differentiation, BAP1 and PBRM1 mutation status, and additional transcriptomic signatures. In IMmotion150 clinical trial cohort, a median-dichotomized H&E DL Immune score, similar to RNA-based T-effector score, was significantly associated with clinical benefit from atezulumab therapy. In contemporary institutional cohorts of patients treated with frontline ipilimumab plus nivolumab or in initial 3 lines of nivolumab monotherapy, patients in the top quartile of H&E DL Immune score had significantly longer progression-free survival. Collectively, these findings support a scalable and interpretable H&E-based biomarker that captures T-effector biology and can help identify patients with ccRCC more likely to benefit from ICIs.

bioinformatics↗

bioRxiv2026

Differential Transcript Usage Reveals Isoform-Level Remodeling of Tumor Biology in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by transcriptional reprogramming driven by hypoxia signaling, metabolic rewiring, and immune modulation. While gene-level analyses have defined key features of ccRCC biology, they do not capture isoform-level variation arising from alternative splicing. Differential transcript usage (DTU) represents an additional regulatory layer that may influence protein function, pathway activity, and clinical outcomes, yet its role in ccRCC biology and prognosis remains incompletely understood. We assessed differential expression in 127 ccRCC tumors and 33 normal-adjacent tissues from the Dartmouth Cancer Center cohort, with external validation in 94 CPTAC tumors, adjusting for cell-type proportions. DTU was identified using DRIMSeq/stageR, followed by limmavoom modeling with clinical and tumor microenvironment covariates. Transcript-based consensus clustering defined tumor subgroups, and Cox proportional hazards modeling integrated transcript-level features with clinical variables. In tumor versus normal comparisons, 1,170 transcripts exhibited significant differential usage, mapping to canonical ccRCC pathways with distinct patterns across functional and non-functional transcript classes. Consensus clustering based on transcript us-age identified two subgroups with distinct angiogenic profiles and significant survival differences. Cluster-level analysis revealed DTU in genes involved in cytoskeletal organization (ACTB), immune processes (B2M), extracellular matrix organization (FN1, APLP2), and iron metabolism (FTH1) with protein domain alterations, including the loss of actin-associated domains in ACTB and immunoglobulin-like domains in B2M. Prognostic modeling identified twelve transcripts consistently retained across bootstraps, improving risk stratification over clinical variables alone. External validation confirmed overlapping prognostic transcripts, including FGFR1 and NUCB1. Isoform-level features may serve as biomarkers and therapeutic targets in ccRCC. Statement of significanceTranscript-level analysis uncovers potential regulatory pathways in ccRCC missed by gene-level approaches, revealing isoform-specific alterations that define survival sub-groups and offer potential biomarkers and therapeutic targets.

cancer biology↗

bioRxiv2024

An ultraconserved snoRNA-like element in long noncoding RNA CRNDE promotes ribosome biogenesis and cell proliferation

Cancer cells frequently upregulate ribosome production to support tumorigenesis. While small nucleolar RNAs (snoRNAs) are critical for ribosome biogenesis, the roles of other classes of noncoding RNAs in this process remain largely unknown. Here we performed CRISPRi screens to identify essential long noncoding RNAs (lncRNAs) in renal cell carcinoma (RCC) cells. This revealed that an alternatively-spliced isoform of lncRNA Colorectal Neoplasia Differentially Expressed containing an ultraconserved element (UCE), referred to as CRNDEUCE, is required for RCC cell proliferation. CRNDEUCE localizes to the nucleolus and promotes 60S ribosomal subunit biogenesis. The UCE of CRNDE functions as an unprocessed C/D box snoRNA that directly interacts with ribosomal RNA precursors. This facilitates delivery of eIF6, a key 60S biogenesis factor, which binds to CRNDEUCE through a sequence element adjacent to the UCE. These findings highlight the functional versatility of snoRNA sequences and expand the known mechanisms through which noncoding RNAs orchestrate ribosome biogenesis.

molecular biology↗

bioRxiv2024

Glycosaminoglycan-mediated lipoprotein uptake protects cancer cells from ferroptosis

Lipids are essential for tumours because of their structural, energetic, and signaling roles. While many cancer cells upregulate lipid synthesis, growing evidence suggests that tumours simultaneously intensify the uptake of circulating lipids carried by lipoproteins. Which mechanisms promote the uptake of extracellular lipids, and how this pool of lipids contributes to cancer progression, are poorly understood. Here, using functional genetic screens, we find that lipoprotein uptake confers resistance to lipid peroxidation and ferroptotic cell death. Lipoprotein supplementation robustly inhibits ferroptosis across numerous cancer types. Mechanistically, cancer cells take up lipoproteins through a pathway dependent on sulfated glycosaminoglycans (GAGs) linked to cell-surface proteoglycans. Tumour GAGs are a major determinant of the uptake of both low and high density lipoproteins. Impairment of glycosaminoglycan synthesis or acute degradation of surface GAGs decreases the uptake of lipoproteins, sensitizes cells to ferroptosis and reduces tumour growth in mice. We also find that human clear cell renal cell carcinomas, a distinctively lipid-rich tumour type, display elevated levels of lipoprotein-derived antioxidants and the GAG chondroitin sulfate than non-malignant human kidney. Altogether, our work identifies lipoprotein uptake as an essential anti-ferroptotic mechanism for cancer cells to overcome lipid oxidative stress in vivo, and reveals GAG biosynthesis as an unexpected mediator of this process.

cancer biology↗

bioRxiv2022

In-vivo characterization of glutamine metabolism identifies therapeutic targets in clear cell renal cell carcinoma

Targeting metabolic vulnerabilities has been proposed as a therapeutic strategy in renal cell carcinoma (RCC). Here, we analyzed metabolism in patient-derived xenografts (tumorgrafts) from diverse forms of RCC. Tumorgrafts from VHL-mutant clear cell RCC (ccRCC) retained metabolic features of human ccRCC and engage in oxidative and reductive glutamine metabolism. We used several approaches to suppress glutamine metabolism and test the effect on tumor growth. Genetic silencing of isocitrate dehydrogenase-1 or -2 impaired reductive labeling of TCA cycle intermediates and suppressed tumor growth. Glutaminase inhibition resulted in modest growth suppression and variable effects on glutamine metabolism in vivo. Infusions with [amide-15N]glutamine revealed persistent amidotransferase activity during glutaminase inhibition, and blocking these activities with the amidotransferase inhibitor JHU-083 also reduced tumor growth. We conclude that ccRCC tumorgrafts catabolize glutamine via multiple pathways, perhaps explaining why it has been challenging to achieve therapeutic responses in patients by inhibiting glutaminase. TeaserGlutamine fuels the TCA cycle and amidotransferase pathways in clear cell renal cell carcinoma.

cancer biology↗

bioRxiv2021

Oncogenic KRAS Requires Complete Loss of BAP1 Function for Development of Murine Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a primary biliary malignancy that harbors a dismal prognosis. Oncogenic mutations of KRAS and loss of function mutations of BRCA1-associated protein 1 (BAP1) have been identified as recurrent somatic alterations in ICC. However, an autochthonous genetically engineered mouse model of ICC that genocopies the co-occurrence of these mutations has never been developed. By crossing Albumin-Cre mice bearing conditional alleles of mutant Kras and/or floxed Bap1, Cre-mediated recombination within the liver was induced. Mice with hepatic expression of mutant KrasG12D alone (KA), bi-allelic loss of hepatic Bap1 (BhomoA), and heterozygous loss of Bap1 in conjunction with mutant KrasG12D expression (BhetKA) developed primary hepatocellular carcinoma (HCC), but no discernible ICC. In contrast, mice with homozygous loss of Bap1 in conjunction with mutant KrasG12D expression (BhomoKA) developed discrete foci of HCC and ICC. Further, the median survival of BhomoKA mice was significantly shorter at 24 weeks, when compared to median survival of [≥]40 weeks in BhetKA mice and approximately 50 weeks in BhomoA and KA mice (p <0.001). Microarray analysis performed on liver tissue from KA and BhomoKA mice identified differentially expressed genes in the setting of BAP1 loss and suggests that deregulation of ferroptosis might be one mechanism by which loss of BAP1 cooperates with oncogenic Ras in hepato-biliary carcinogenesis. Our autochthonous model provides an in vivo platform to further study this lethal class of neoplasm.

cell biology↗

bioRxiv2020

Tumor Neoantigenicity Assessment with CSiN Score Incorporates Clonality and Immunogenicity to Predict Immunotherapy Outcomes

Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical mediators of host immune response and immunotherapy treatment efficacy. Current studies of neoantigens almost entirely focus on total neoantigen load, which simplistically treats all neoantigens equally. Besides, neoantigen loads have been linked with treatment response and prognosis only in some studies, but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to characterize the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike simple neoantigen loads, CSiN incorporates the effect of both clonality and MHC-binding affinity of neoantigens when characterizing patient neoantigen profiles. By exploiting the clinical responses in 501 treated patients (mostly by checkpoint inhibitors) and the overall survival of 1,978 baseline patients, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in melanoma, lung cancer, and kidney cancer patients. CSiN substantially outperforms prior genetics-based prediction methods of responsiveness. Overall, our work fulfilled an important gap in current research involving neoantigens. One Sentence SummaryThe quality of tumor neoantigens predicts response to immunotherapy

bioinformatics↗

bioRxiv2019

SCINA: Semi-Supervised Analysis of Single Cells in silico

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. We reversed this paradigm and developed SCINA, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation-maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. Notably, SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out mouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

bioinformatics↗