Biology subjects

Vazquez, F.

Publications and source records attributed to Vazquez, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Synergistic targeting of EP300/CBP and EYA co-activators collapses the rhabdomyosarcoma core regulatory circuit

Rhabdomyosarcoma (RMS) is a multi-subtype, high-risk pediatric sarcoma with a low mutational burden. The mutations found in RMS often alter genes involved in transcriptional control. Approaches to target dysregulated RMS transcription have remained elusive. Here, we develop a novel approach to target RMS transcription comprising simultaneous targeting of two distinctly acting transcriptional co-activators. We discover a common identity-controlling pan-RMS core regulatory circuit (CRC) composed of oncogenic and lineage-specific myogenic master transcription factors (mTFs). Using a super-enhancer-based reporter screen, we identify the EP300/CBP inhibitor A485 as a potent inhibitor of the pan-RMS CRC, though with efficacy-limiting toxicities. To enhance efficacy, we identify the mTF-binding co-activator EYA2 as a co-factor of this pan-RMS CRC and exploit a new second-generation EYA1/2 inhibitor, LG1-34, to disrupt its function. Combined co-activator inhibition inactivates the CRC and synergistically reduces RMS growth. This strategy dually targets CRC-associated co-activators to cooperatively suppress the RMS transcriptome and enforce cell death.

cancer biology↗

bioRxiv2026

Dissecting context-dependent cancer vulnerabilities using Perturb-seq

Background CRISPR-mediated viability assays in diverse cancer cell lines have informed cancer biology and precision medicine, but cell fitness is not the only cancer-relevant phenotype. Gene expression profiling provides insight into cellular stress, inflammation, and differential state, while still identifying activation of cell-death pathways. Perturb-seq allows scalable functional genomics screening of expression phenotypes at single-cell resolution, however existing datasets cover only a small number of work-horse cell lines. Results We produced a proof-of-concept Perturb-seq dataset targeting 100 genes in 16 diverse cancer cell lines. In the process, we established methods to address single-cell technical artifacts, identified Cas9-mediated chromosomal aberrations and assessed screen quality. Even with a limited library, we observed common signatures of deleting essential genes as well as context-specific responses based on intrinsic genomic properties of the models. For example, we inferred a previously undescribed relationship between dependence on the ER-golgi transport gene immediate early response 3 interacting protein 1 (IER3IP1) and oxidative stress, demonstrating the potential of integrated Perturb-seq for hypothesis generation. Conclusions We established a framework for building a comprehensive map of post-perturbational transcriptional phenotypes using parallel Perturb-seq experiments across multiple cell lines. We demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes - suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.

cancer biology↗

bioRxiv2026

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

Alternative lengthening of telomeres (ALT) is a telomerase-independent pathway used by aggressive cancers to maintain their replicative immortality. Because ALT is absent from normal human cells, it is an appealing target for cancer therapy, but the lack of ability to determine ALT status at scale has hindered discovery. Here, we developed ALTitude, a machine learning method from a tabular foundation model that infers ALT from cell line whole genome sequencing data, without need for paired germline analysis. We deployed ALTitude across the DepMap, doubling the number of known ALT+ cancer models. Systematic integration of ALTitude with CRISPR-Cas9 screens yielded the selective dependency on SMARCAL1 in ALT+ cell lines, where we show it stabilizes the ALT phenotype. Acute depletion of SMARCAL1 leads to G2/M arrest, mitotic catastrophe, and cell death. These data provide a valuable resource for studying ALT-related genomic features and present SMARCAL1 as a therapeutic target for ALT+ malignancies.

cancer biology↗

bioRxiv2025

Beyond Arrhenius: Nonlinear and negative temperature scaling of biological rates from multi-step mechanisms

Temperature shapes all biological processes, particularly during the early development of ectothermic organisms. A widely used framework for describing temperature dependence is the Arrhenius equation, which predicts an exponential increase in rates with temperature. However, biological rates often deviate from this prediction when measured across broader temperature ranges. While negative apparent activation energies are often attributed to protein denaturation, this cannot explain similar behavior observed at temperatures where enzymes remain stable. These broader scaling patterns remain mechanistically unexplained. Here we present a general Markov chain framework for modeling biological timing as cascades of reversible, temperature-dependent steps. Applying this model to 121 published datasets spanning diverse species, biological timescales, and temperature ranges, we find that a consistent three-zone scaling pattern emerges: Arrhenius-like behavior at low and high temperatures, and a quadratic exponential regime at intermediate temperatures. We show that this pattern arises naturally from differences in activation energies across steps in the network. The quadratic exponential regime is an emergent feature of averaging across many steps and is robust to variation across network realizations. In contrast, Arrhenius-like scaling at the extremes tends to be more variable and originates from smaller sub-networks. Apparent negative activation energies can emerge naturally from the dynamics of multi-step networks, even in the absence of protein denaturation. Our framework provides a unified mechanistic explanation for diverse temperature-scaling behaviors in biology and may help predict how developmental and physiological processes respond to environmental change. Although we focus on development, the model is broadly applicable to biological systems governed by multi-step reaction networks.

systems biology↗

bioRxiv2025

Controlling False Discovery in CRISPR Screens

Excluding false positives is critical for interpreting CRISPR screens. Here, we introduce a new Chronos module for estimating false discovery rates for identifying knockouts that cause loss of viability or have differential viability effects in different conditions. We introduce a rigorous benchmarking framework using real CRISPR data. We show with multiple real datasets that existing methods such as MAGeCK are miscalibrated and can generate uncontrolled numbers of false positives even after multiple hypothesis correction. Only Chronos correctly controls false discovery for all tested tasks. Additionally, Chronoss estimates are well-calibrated, allowing users to accurately specify the acceptable false discovery rate.

bioinformatics↗

bioRxiv2024

Generation of a biliary tract cancer cell line atlas reveals molecular subtypes and therapeutic targets

Biliary tract cancers (BTCs) are a group of deadly malignancies encompassing intrahepatic and extrahepatic cholangiocarcinoma, gallbladder carcinoma, and ampullary carcinoma. Here, we present the integrative analysis of 63 BTC cell lines via multi-omics clustering and genome- scale CRISPR screens, providing a platform to illuminate BTC biology and inform therapeutic development. We identify dependencies broadly enriched in BTC compared to other cancers as well as dependencies selective to the anatomic subtypes. Notably, cholangiocarcinoma cell lines are stratified into distinct lineage subtypes based on biliary or dual biliary/hepatocyte marker signatures, associated with dependency on specific lineage survival factors. Transcriptional analysis of patient specimens demonstrates the prognostic significance of these lineage subtypes. Additionally, we delineate strategies to enhance targeted therapies or to overcome resistance in cell lines with key driver gene mutations. Furthermore, clustering based on dependencies and proteomics data elucidates unexpected functional relationships, including a BTC subgroup with partial squamous differentiation. Thus, this cell line atlas reveals potential therapeutic targets in molecularly defined BTCs, unveils biologically distinct disease subtypes, and offers a vital resource for BTC research.

cancer biology↗

bioRxiv2024

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

cancer biology↗

bioRxiv2023

RPL22 is a tumor suppressor in MSI-high cancers and a key splicing regulator of MDM4

Microsatellite instability high (MSI-H) tumors are malignant tumors that, despite harboring a high mutational burden, often have intact TP53. One of the most frequent mutations in MSI-H tumors is a frameshift mutation in RPL22, a ribosomal protein. Here, we identified RPL22 as a modulator of MDM4 splicing through an alternative splicing switch in exon 6. RPL22 loss increases MDM4 exon 6 inclusion, cell proliferation, and augments resistance to the MDM inhibitor Nutlin-3a. RPL22 represses expression of its paralog, RPL22L1, by mediating the splicing of a cryptic exon corresponding to a truncated transcript. Therefore, damaging mutations in RPL22 drive oncogenic MDM4 induction and reveal a common splicing circuit in MSI-H tumors that may inform therapeutic targeting of the MDM4-p53 axis and oncogenic RPL22L1 induction.

cancer biology↗

bioRxiv2023

Human aneuploid cells depend on the RAF/MEK/ERK pathway for overcoming increased DNA damage

Aneuploidy is a hallmark of human cancer, yet the cellular mechanisms that allow cells to cope with aneuploidy-induced cellular stresses remain largely unknown. Such coping mechanisms may present cellular vulnerabilities that can be harnessed for targeting cancer cells. Here, we induced aneuploidy in non-transformed RPE1-hTERT cells and derived multiple stable clones with various degrees of chromosome imbalances. We performed an unbiased genomic profiling of 6 isogenic clones, using whole-exome and RNA sequencing. We then functionally interrogated their cellular dependency landscapes, using genome-wide CRISPR/Cas9 screens and large-scale drug screens. We found that aneuploid clones activated the DNA damage response (DDR), and were consequently more resistant to further DNA damage induction. Interestingly, aneuploid cells also exhibited elevated RAF/MEK/ERK pathway activity, and were more sensitive to several clinically-relevant drugs targeting this pathway, and in particular to genetic and chemical CRAF inhibition. CRAF activity was functionally linked to the resistance to DNA damage induction, as CRAF inhibition sensitized aneuploid cells to DNA damage-inducing chemotherapies. The association between aneuploidy, RAF/MEK/ERK signaling, and DDR was independent of p53. The increased activity and dependency of aneuploid cells on the RAF/MEK/ERK pathway was validated in another isogenic aneuploid system, and across hundreds of human cancer cell lines, confirming their relevance to human cancer. Overall, our study provides a comprehensive resource for genetically-matched karyotypically-stable cells of various aneuploidy states, and reveals a novel therapeutically-relevant cellular dependency of aneuploid cells.

cancer biology↗

bioRxiv2023

Increased RNA and protein degradation is required for counteracting transcriptional burden and proteotoxic stress in human aneuploid cells

Aneuploidy, an abnormal chromosome composition, results in a stoichiometric imbalance of protein complexes, which jeopardizes the fitness of aneuploid cells. Aneuploid cells thus need to compensate for the imbalanced DNA levels by regulating their RNA and protein levels, a phenomenon known as dosage compensation. However, the molecular mechanisms involved in dosage compensation in human cells - and whether they can be targeted to selectively kill aneuploid cancer cells - remain unknown. Here, we addressed this question via molecular dissection of multiple diploid vs. aneuploid cell models. Using genomic and functional profiling of a novel isogenic system of RPE1-hTERT cells with various degrees of aneuploidy, we found that aneuploid cells cope with both transcriptional burden and proteotoxic stress. At the mRNA level, aneuploid cells increased RNA synthesis, but concomitantly elevated several RNA degradation pathways, in particular the nonsense-mediated decay (NMD) and the microRNA-mediated mRNA silencing pathways. Consequently, aneuploid cells were more sensitive to the genetic or chemical perturbation of several key components of these RNA degradation pathways. At the protein level, aneuploid cells experienced proteotoxic stress, resulting in reduced translation and increased protein degradation, rendering them more sensitive to proteasome inhibition. These findings were recapitulated across hundreds of human cancer cell lines and primary tumors, confirming that both non-transformed and transformed cells alter their RNA and protein metabolism in order to adapt to the aneuploid state. Our results reveal that aneuploid cells are dependent on the over- or under-activation of several nodes along the gene expression process, identifying these pathways as clinically-actionable vulnerabilities of aneuploid cells.

cancer biology↗

bioRxiv2022

Mapping the landscape of genetic dependencies in chordoma

Identifying the spectrum of genes required for cancer cell survival can reveal essential cancer circuitry and therapeutic targets, but such a map remains incomplete for many cancer types. We applied genome-scale CRISPR-Cas9 loss-of-function screens to map the landscape of selectively essential genes in chordoma, a bone cancer with few validated targets. This approach confirmed a known chordoma dependency, TBXT (T; brachyury), and identified a range of additional dependencies, including PTPN11, ADAR, PRKRA, LUC7L2, SRRM2, SLC2A1, SLC7A5, FANCM, and THAP1. CDK6, SOX9, and EGFR, genes previously implicated in chordoma biology, were also recovered. We found genomic and transcriptomic features that predict specific dependencies, including interferon-stimulated gene expression, which correlates with ADAR dependence and is elevated in chordoma. Validating the therapeutic relevance of dependencies, small-molecule inhibitors of SHP2, encoded by PTPN11, had potent preclinical efficacy against chordoma. Our results generate an emerging map of chordoma dependencies to enable biological and therapeutic hypotheses.

cancer biology↗

bioRxiv2022

Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal

Hundreds of genome-wide loss-of-function screens have been performed, as part of efforts such as The Cancer Dependency Map, to create a catalog of genetic dependencies in a diverse set of cancer contexts. In recent years, large-scale screening efforts have shifted perturbation technology from RNAi to CRISPR-Cas9, due to the superior efficacy and specificity of CRISPR-Cas9-mediated approaches. However, questions remain about the extent to which partial suppression of gene targets could result in selective dependency across cell lines, potentially revealing a larger set of targetable cancer vulnerabilities than can be identified using CRISPR knockout alone. Here, we use CRISPR-Cas9 and RNAi screening data for more than 400 shared cell lines to represent knockout and partial suppression genetic perturbation modalities and evaluate the utility of each for therapeutic target discovery and the inference of gene function. We find that CRISPR screens identify more dependencies, and yield more accurate predictive models and co-dependency relationships overall. However, RNAi outperforms CRISPR in identifying associations (omics, drug, co-dependencies) with genes that are common dependencies for most cell lines (pan-dependencies). As pan-dependencies occur frequently in the CRISPR dataset (~2,000 genes), using results from both RNAi and CRISPR analyses facilitates the discovery of predictive models and associated co-dependencies for a wider range of gene targets than could be detected using either dataset alone. These findings can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

cancer biology↗

bioRxiv2022

A Mesenchymal Tumor Cell State Confers Increased Dependency on the BCL-XL Anti-apoptotic Protein in Kidney Cancer

Genome-wide genetic screens have identified cellular dependencies in many cancers. Using the Broad Institutes Achilles shRNA screening dataset, we mined for targetable dependencies by cell lineage. Our studies identified a strong dependency on BCL2L1, which encodes the BCL-XL anti-apoptotic protein, in a subset of kidney cancer cells. Genetic and pharmacological inactivation of BCL-XL, but not the related anti-apoptotic proteins BCL-2, led to fitness defects in renal cancer cells, and also sensitized them to chemotherapeutics. Neither BCL-XL levels (absolute or normalized to BCL-2) nor the status of the VHL gene, which is frequently mutated in kidney cancer, predicted BCL-XL dependence. Transcriptional profiling, however, identified a BCL-XL dependency mRNA signature, which included elevated mesenchymal gene expression in BCL-XL dependent cells. Promoting mesenchymal transition increased BCL-XL dependence; whereas, conversion to a more differentiated state overcame BCL-XL dependence in kidney cancer cells. The BCL-XL dependency mRNA signature was observed in almost a third of human clear cell Renal Cell Carcinomas (ccRCCs), which were also associated with worse clinical outcomes. Finally, an orally bioavailable BCL-XL inhibitor, A-1331852, showed anti-tumor efficacy in vivo. Altogether, our studies uncovered an unexpected link between cancer cell state and dependence on the anti-apoptotic BCL-XL protein and justify further testing on BCL-XL blockade as a potential way to target a clinically aggressive subset of human kidney cancers. One Sentence SummaryCell state, but not pVHL and/or HIF status, defines the dependency of kidney cancer cells on the BCL-XL anti-apoptotic protein.

cancer biology↗

bioRxiv2021

Spatial effects in parasite induced marine diseases of immobile hosts

Marine infectious diseases are more prevalent in recent times due to climate change and other anthropogenic pressures, posing a substantial threat to marine ecosystems and the conservation of their biodiversity. An important subset of marine organisms are sessile, for which the most common mechanism for disease transmission is direct contact with waterborne parasites. Only recently, some deterministic compartmental models have been proposed to describe this kind of epidemics, being these models based on non-spatial descriptions where space is homogenised and parasite mobility is not explicitly accounted for. However, in realistic situations, epidemic transmission is conditioned by the spatial distribution of hosts and the parasites mobility patterns. Thus, the interplay between these factors is expected to have a crucial effect in the evolution of the epidemic, so calling for a explicit description of space. In this work we develop a spatially-explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission, performing extensive numerical simulations and analytical approximations. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal evolution of the epidemic are assessed. We show that larger values of pathogen mobility have two main implications: more severe epidemics, as the number of infections increases, and shorter time-scales to extinction. Moreover, an analytical expression for the basic reproduction number of the spatial model, [Formula], is derived as function of the non-spatial counterpart, R0, which characterises a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system. This allows to determine a phase diagram for the epidemic model as function of the parasite mobility and the basic reproduction number of the non-spatial model.

ecology↗

bioRxiv2021

Chronos: a CRISPR cell population dynamics model

CRISPR loss of function screens are a powerful tool to interrogate cancer biology but are known to exhibit a number of biases and artifacts that can confound the results, such as DNA cutting toxicity, incomplete phenotype penetrance and screen quality bias. Computational methods that more faithfully model the CRISPR biological experiment could more effectively extract the biology of interest than typical current methods. Here we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of the dynamics of cell proliferation after CRISPR gene knockout. Chronos is able to exploit longitudinal CRISPR data for improved inference. Additionally, it accounts for multiple sources of bias and can effectively share information across screens when jointly analyzing large datasets such as Project Achilles and Score. We show that Chronos outperforms competing methods across a range of performance metrics in multiple types of experiments.

bioinformatics↗

bioRxiv2020

Species exclusion and coexistence in a noisy voter model with a competition-colonization tradeoff

We introduce an asymmetric noisy voter model to study the joint effect of immigration and a competition-dispersal tradeoff in the dynamics of two species competing for space on a one-dimensional lattice. Individuals of one species can invade a nearest-neighbor site in the lattice, while individuals of the other species are able to invade sites at any distance but are less competitive locally, i.e., they establish with a probability g[≤] 1. The model also accounts for immigration, modeled as an external noise that may spontaneously replace an individual at a lattice site by another individual of the other species. This combination of mechanisms gives rise to a rich variety of outcomes for species competition, including exclusion of either species, mono-stable coexistence of both species at different population proportions, and bi-stable coexistence with proportions of populations that depend on the initial condition. Remarkably, in the bi-stable phase, the system undergoes a discontinuous transition as the intensity of immigration overcomes a threshold, leading to an irreversible loop dynamics that may cause the loss of the species with shorter dispersal range.

ecology↗

bioRxiv2020

Phosphate dysregulation via the XPR1:KIDINS220 protein complex is a therapeutic vulnerability in ovarian cancer

Clinical outcomes for patients with ovarian and uterine cancers have not improved greatly in the past twenty years. To identify ovarian and uterine cancer vulnerabilities, we analyzed genome-scale CRISPR/ Cas9 loss-of-function screens across 739 human cancer cell lines. We found that many ovarian cancer cell lines overexpress the phosphate importer SLC34A2, which renders them sensitive to loss of the phosphate exporter XPR1. We extensively validated the XPR1 vulnerability in cancer cell lines and found that the XPR1 dependency was retained in vivo. Overexpression of SLC34A2 is frequently observed in tumor samples and is regulated by PAX8 -a transcription factor required for ovarian cancer survival. XPR1 overexpression and copy number amplifications are also frequently observed. Mechanistically, SLC34A2 overexpression and impaired phosphate efflux leads to the accumulation of intracellular phosphate and cell death. We further show that proper localization and phosphate efflux by XPR1 requires a novel binding partner, KIDINS220. Loss of either XPR1 or KIDINS220 results in acidic vacuolar structures which precede cell death. These data point to the XPR1:KIDINS220 complex - and phosphate dysregulation more broadly -as a therapeutic vulnerability in ovarian cancer.

cancer biology↗

bioRxiv2020

Epigenetic transgenerational effects on RNAi in Arabidopsis

Epigenetic states and even certain environmental responses in nematodes, mammals and seed plants can be inherited over successive generations. RNAi in plants is an epigenetic form of RNA degradation that is normally reset early in development. In contrast, competence, i.e., the susceptibility of a transgene for RNAi, is believed to be genetically determined and faithfully transmitted over many generations. Unexpectedly, we found that the same yellow fluorescent protein (YFP) transgene in a monogenic, homozygous line of Arabidopsis exhibited two epigenetic states of RNAi: type I, which is initiated in rosette leaves and is stable in later developmental stages, and, type II, which is initiated after floral induction and is unstable. The incidence of type I plants descended from type I parents decreased over three successive generations of self-fertilized plants. By the third generation, a substantial fraction of plants from type I parents exhibited either the type II phenotype or remained high-YFP expressing throughout sporophytic development, indicating that silencing had been completely lost. Similarly, plants descended from type II parents showed a progressive shift from the type II phenotype and a complete loss of silencing. These results show that the silencing types (or states) of parental plants have significant influence on both silencing determination and silencing types (or states) of their offspring, however, this influence fades through several generations of self; as regardless of the types of the parental lines, competence for RNA silencing tends to loss after generations. Thus, we propose that RNA silencing in plants can be the target of transgenerational effects. While the underlying mechanisms are still unknown, our findings in the Arabidopsis RNA silencing system complement the current understanding of heritable RNAi in C. elegans, that this phenomenon is likely a common mechanism in Eukaryote systems.

plant biology↗