Biology subjects

Dela Cruz, F.

Publications and source records attributed to Dela Cruz, F..

6 recordsLinked to original sources

bioRxiv2026

SMARCAL1 is a candidate therapeutic target for ALT-positive tumors

A significant subset of tumors, including over 50% of osteosarcomas--an aggressive bone malignancy affecting children, adolescents, and young adults--relies on alternative lengthening of telomeres (ALT), a telomerase-independent, DNA repair-based mechanism for telomere elongation. The overall 5-year survival rate for osteosarcoma patients is [~]65%, underlying the need to develop novel targeted therapies. Through the Cancer Dependency Map, we identify SMARCAL1, a DNA translocase previously shown to remodel stalled replication forks, as a top selective dependency factor in telomerase-negative tumors. Using a panel of ALT-positive and ALT-negative cancer cell lines, as well as osteosarcoma patient-derived xenograft cells, we confirm that ALT-positive cells are uniquely sensitive to the loss of SMARCAL1, whose depletion exacerbates ALT-dependent phenotypes and telomeric DNA damage. Notably, we demonstrate that suppressing ALT abrogates their dependency on SMARCAL1. Mechanistically, we show that SMARCAL1 loss leads to telomeric ssDNA accumulation in ALT-positive cells, dependent in part on DNA repriming mediated by the DNA primase/polymerase PRIMPOL. Moreover, SMARCAL1s ssDNA annealing activity counteracts DNA unwinding by the BLM helicase, limiting telomeric ssDNA accumulation and DNA damage in ALT-positive cells. Importantly, SMARCAL1 depletion induces senescence in ALT-positive cancer cells, rendering them susceptible to treatment with senolytic agents. Together, these findings establish SMARCAL1 as a key regulator of ALT metabolism and highlight SMARCAL1 as a promising therapeutic target for ALT-positive tumors.

cancer biology↗

bioRxiv2022

Clonal evolution during metastatic spread in high-risk neuroblastoma

High-risk neuroblastoma is generally metastatic and often lethal. Using genomic profiling of 470 sequential and spatially separated samples from 283 patients, we characterize subtype-specific genetic evolutionary trajectories from diagnosis, through progression and end-stage metastatic disease. Clonal tracing timed disease initiation to embryogenesis. Continuous acquisition of structural variants at disease defining loci (MYCN, TERT, MDM2-CDK4) followed by convergent evolution of mutations targeting shared pathways emerged as the predominant feature of progression. At diagnosis metastatic clones were already established at distant sites where they could stay dormant, only to cause relapses years later and spread via metastasis-to-metastasis and polyclonal seeding after therapy.

genomics↗

bioRxiv2022

Dynamic Network Curvature Analysis of RNA-Seq Data in Sarcoma

In this work, we utilized network features of cancer gene interactomes to cluster pediatric sarcoma tumors and identify candidate therapeutic targets in an unsupervised manner. RNA-Seq data were mapped to protein-level interactomes to construct weighted networks for mathematical analysis. We employed a geometric approach centered on a discrete notion of curvature, which provides a measure of the functional association between genes in the context of their connectivity. Specifically, we adopted a recently proposed dynamic extension of graph curvature to extract features of the non-Euclidean, multiscale structure of genomic networks. We propose a hierarchical clustering approach to reveal preferential gene clustering according to their geometric cooperation which captured the characteristic EWSR1-FLI1 fusion in Ewing sarcoma. We also performed in silico edge perturbations to assess systemic response to simulated interventions quantified by changes in curvature. These results demonstrate that geometric network-based features can be useful for identifying non-trivial gene associations in an agnostic manner.

bioinformatics↗

bioRxiv2019

Single-cell based elucidation of molecularly-distinct glioblastoma states and drug sensitivity

Glioblastoma heterogeneity and plasticity remain controversial, with proposed subtypes representing the average of highly heterogeneous admixtures of independent transcriptional states. Single-cell, protein-activity-based analysis allowed full quantification of >6,000 regulatory and signaling proteins, thus providing a previously unattainable single-cell characterization level. This helped identify four novel, molecularly distinct subtypes that successfully harmonize across multiple GBM datasets, including previously published bulk and single-cell profiles and single cell profiles from seven orthotopic PDX models, representative of prior subtype diversity. GBM is thus characterized by the plastic coexistence of single cells in two mutually-exclusive developmental lineages, with additional stratification provided by their proliferative potential. Consistently, all previous subtypes could be recapitulated by single-cell mixtures drawn from newly identified states. Critically, drug sensitivity was predicted and validated as highly state-dependent, both in single-cell assays from patient-derived explants and in PDX models, suggesting that successful treatment requires combinations of multiple drugs targeting these distinct tumor states.\n\nSignificanceWe propose a new, 4-subtype GBM classification, which harmonizes across bulk and single-cell datasets. Single-cell mixtures from these subtypes effectively recapitulate all prior classifications, suggesting that the latter are a byproduct of GBM heterogeneity. Finally, we predict single-cell level activity of three clinically-relevant drugs, and validate them in patient-derived explant.

systems biology↗

bioRxiv2017

Therapeutic targeting of PGBD5-induced DNA repair dependency in pediatric solid tumors

Despite intense efforts, the cure rates of childhood and adult solid tumors are not satisfactory. Resistance to intensive chemotherapy is common, and targets for molecular therapies are largely undefined. We have now found that the majority of childhood solid tumors, including rhabdoid tumors, neuroblastoma, medulloblastoma and Ewing sarcoma, express an active DNA transposase PGBD5 that can promote site-specific genomic rearrangements in human cells. Using functional genetic approaches, we found that mouse and human cells deficient in non-homologous end joining (NHEJ) DNA repair cannot tolerate the expression of PGBD5. In a chemical screen of DNA damage signaling inhibitors, we identified AZD6738 as a specific sensitizer of PGBD5-dependent DNA damage and apoptosis. We found that expression of PGBD5, but not its nuclease activity-deficient mutant, was sufficient to induce hypersensitivity to AZD6738. Depletion of endogenous PGBD5 conferred resistance to AZD6738 in human tumor cells. PGBD5-expressing tumor cells accumulated unrepaired DNA damage in response to AZD6738 treatment, and underwent apoptosis in both dividing and G1 phase cells in the absence of immediate DNA replication stress. Accordingly, AZD6738 exhibited nanomolar potency against the majority of neuroblastoma, medulloblastoma, Ewing sarcoma and rhabdoid tumor cells tested, while sparing non-transformed human and mouse embryonic fibroblasts in vitro. Finally, treatment with AZD6738 induced apoptosis and regression of human neuroblastoma and medulloblastoma tumors engrafted in immunodeficient mice in vivo. This effect was potentiated by combined treatment with cisplatin, including significant anti-tumor activity against patient-derived primary neuroblastoma xenografts. These findings delineate a therapeutically actionable synthetic dependency induced in PGBD5-expressing solid tumors.

cancer biology↗

bioRxiv2017

Pediatric Sarcoma Data Forms a Unique Cluster Measured via the Earth Mover’s Distance

In this note, we combined pediatric sarcoma data from Columbia University with adult sarcoma data collected from TCGA, in order to see if one can automatically discern a unique pediatric cluster in the combined data set. Using a novel clustering pipeline based on optimal transport theory, this turned out to be the case. The overall methodology may find uses for the classification of data from other biological networking problems.

cancer biology↗