Biology subjects

Aparicio, A.

Publications and source records attributed to Aparicio, A..

9 recordsLinked to original sources

bioRxiv2026

Regulatory mode and enhancer architecture determine limits of blood DNA methylation as a molecular proxy

Blood-based DNA methylation is widely used as a molecular surrogate for traits ranging from circulating protein and metabolite levels to biological aging, but whether the CpGs driving these models reflect genuine gene regulation or statistical convenience is unresolved. Training elastic-net models using the expression of 77 T cell genes across 333 matched T-cell enriched blood samples, we show that predictability is governed by regulatory mode: genes associated with differentiation and lineage-identity are strongly predictable, whereas dynamically regulated genes encoding cytokines and immune checkpoint ligands are not. The predictive CpGs are neither at the gene promoter nor at master-regulator binding sites; instead, the signal is carried by gene-specific sets of distal CpGs enriched within T-cell enhancer chromatin states distributed throughout the genome. Methylation-predicted gene expression validated against directly measured NanoString values in an independent 228-sample cancer cohort, remaining highly concordant for lineage genes but only weakly concordant for stimulus-responsive checkpoint ligands, confirming the lineage-versus-stimulus divide independently of the training cohort. In short, how far methylation can substitute for direct measurement depends on a genes regulatory biology rather than modeling choices; the on-gene control and chromatin-state audit introduced here offer a general way to test this for any methylation-based biomarker.

genomics↗

bioRxiv2026

Improve the Efficacy of B7-H3-Targeting Antibody-Drug Conjugate DS-7300a in TP53-deficient Tumors by Inducing Ferroptosis

Immune checkpoint B7-H3 is an emerging target for immunotherapy. DS-7300a is an advanced B7-H3-targeting antibody-drug conjugate (ADC) warheaded with the topoisomerase I inhibitor DXd. DS-7300a has demonstrated clinical activity, but molecular biomarkers to predict its therapeutic response remain elusive. TP53 is one of the most mutated tumor suppressor genes across cancers, and effective therapies are urgently needed for TP53-deficient cancers. Using prostate cancer (PCa) as a model system, we reported that DS-7300as anti-tumor efficacy is highly dependent on functional p53 in cancer cells, and TP53 defects confer resistance to DS-7300a. Mechanistically, we found that DS-7300a and its payload, DXd, induce DNA damage and activate the ATM/ATR/CHK signaling cascade, thereby stabilizing p53 and inducing a pro-apoptotic and senescence-associated transcriptome. In contrast, TP53-deficient cells fail to detect DXd-induced DNA damage, maintain a high proliferation rate, and exhibit low levels of apoptosis and senescence, thereby conferring resistance to DS-7300a. Ferroptosis is an iron-dependent form of regulated cell death triggered by lipid peroxidation, which is mechanistically and morphologically distinct from apoptosis. Interestingly, DS-7300a treatment elevates lipid peroxidation in TP53-deficient cancer cells and upregulates glutathione peroxidase 4 (GPX4), an antioxidant enzyme that mitigates lipid peroxidation. Using isogeneic xenograft models and a newly developed humanized B7-H3 PCa model, we demonstrated that inducing ferroptosis by pharmacological inhibition of GPX4 enhances DS-7300as efficacy in TP53-deficient tumors. Our studies demonstrate that TP53 status dictates anti-tumor responses to DS-7300a, and ferroptosis induction represents a promising therapeutic approach to overcome resistance to DS-7300a in malignancies harboring TP53 defects.

cancer biology↗

bioRxiv2024

Polyploid cancer cells reveal signatures of chemotherapy resistance

Therapeutic resistance in cancer significantly contributes to mortality, with many patients eventually experiencing recurrence after initial treatment responses. Recent studies have identified therapy-resistant large polyploid cancer cells in patient tissues, particularly in late-stage prostate cancer, linking them to advanced disease and relapse. Here, we analyzed bone marrow aspirates from 44 advanced prostate cancer patients and found the presence of circulating tumor cells with increased genomic content (CTC-IGC) was significantly associated with poorer progression- free survival. Single cell copy number profiling of CTC-IGC displayed clonal origins with typical CTCs, suggesting complete polyploidization. Induced polyploid cancer cells from PC3 and MDA-MB-231 cell lines treated with docetaxel or cisplatin were examined through single cell DNA sequencing, RNA sequencing, and protein immunofluorescence. Novel RNA and protein markers, including HOMER1, TNFRSF9, and LRP1, were identified as linked to chemotherapy resistance. These markers were also present in a subset of patient CTCs and associated with recurrence in public gene expression data. This study highlights the prognostic significance of large polyploid tumor cells, their role in chemotherapy resistance, and their expression of markers tied to cancer relapse, offering new potential avenues for therapeutic development.

cancer biology↗

bioRxiv2024

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome

Intra-tumor heterogeneity is characterized by a diverse population of tumor clones and subclones which are important drivers of tumor evolution and therapeutic response. However, accurate subclonal reconstruction at scale remains challenging. We developed a machine learning tool, CliPP, and surveyed 9,972 tumors from 32 cancer types. We found that high subclonal mutation load (sML), the fraction of subclonal single nucleotide variants (SNVs) to all SNVs in the coding region, was prognostic of survival (progression free survival or overall survival) in 18 cancer types. In 14 cancers with low to moderate tumor mutation burden (TMB), high sML was associated with better prognosis. In immunotherapy trials for 42 metastatic prostate cancer (mCRPC), high sML was predictive of favorable response to ipilimumab and associated with increased CD8+ T-cell infiltration and decreased macrophage population. A validation using 613 whole-genomes of esophageal adenocarcinoma confirms the favorable effect of high sML and the observed tumor-associated macrophage. Our study identifies sML as a key feature of cancer, suggesting a biphasic relationship between evolutionary dynamics and differential immune environments. Finally, sML may serve as an orthogonal approach to identify likely responders of immune checkpoint blockade in low to moderate TMB tumors.

genomics↗

bioRxiv2023

The DeMixSC deconvolution framework uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for deconvolving large cohorts of disease tissues, and potentially cancer.

bioinformatics↗

bioRxiv2023

Feasibility in MacArthur's Consumer-Resource Model

Finding the conditions that ensure the survival of species has occupied ecologists for decades. Theoretically, for mechanistic models such as MacArthurs consumer-resource model, most of the efforts have concentrated on proving the stability of an equilibrium assuming that it is feasible, but overlooking the conditions that ensure its feasibility. Here we address this gap by finding the range of conditions that lead to a feasible equilibrium of MacArthurs consumer-resource model and study how changes in the systems structural and parametric properties affect those ranges. We characterize the relationship between the loss of feasibility and the increase in complexity (measured by the systems richness and connectance) by a power law that can be extended to random competition matrices. Focusing on the pool of consumers, we find that while the feasibility of the entire system decreases with the size of the pool, the expected fraction of feasible consumers increases --safety in consumer numbers. Focusing on the pool of resources, we find that if resources grow linearly, the larger the pool of resources, the lower the feasibility of the system and the expected fraction of feasible consumers --danger in resource numbers. However, if resources grow logistically, this pattern is reversed with a sublinear increase in feasibility, as it has been previously reported in experimental work. This work provides testable predictions for consumer-resource systems and is a gateway to exploring feasibility in other mechanistic models.

ecology↗

bioRxiv2022

KDM4A promotes NEPC progression through regulation of MYC expression

Neuroendocrine prostate cancer (NEPC) represents one of the most lethal forms of prostate cancer (PCa) and lacks life-prolonging treatment. The incidence of NEPC is increased due to the widespread use of AR pathway inhibitors (ARPIs) in the treatment of non-metastatic CRPC and hormone-sensitive metastatic tumors. Here, we identified histone lysine demethylase KDM4A as a key player in NEPC progression and an effective therapeutic target. We found that KDM4A mRNA and protein are overexpressed in human and mouse NEPC compared to prostate adenocarcinoma. Knockdown (KD) or knockout (KO) of KDM4A in NEPC cell lines suppressed cancer cell growth in vitro and in vivo. Mechanistically, we found that KDM4A promotes NEPC progression, in part, through direct transcriptional regulation of MYC. We showed that MYC is hyper-activated in human and mouse NEPC. KDM4A KD led to suppression of MYC signaling. MYC KD or inhibition profoundly suppressed NEPC cell proliferation. Furthermore, a potent pan-KDM4 inhibitor QC6352 significantly reduced NEPC cell growth in vitro and in vivo. Taken together, we demonstrated that KDM4A is an important regulator of NEPC progression and targeting KDM4A may potentially be an effective therapeutic strategy for NEPC. SignificanceNeuroendocrine prostate cancer (NEPC) is a highly aggressive prostate cancer subtype that is resistant to potent androgen receptor pathway inhibitors (ARPIs) and currently lacks effective therapeutic options. Histone lysine demethylase KDM4A is an important epigenetic regulator of gene expression in development and cancer. In this study, we show that KDM4A is highly expressed in NEPC and is required for NEPC proliferation, anchorage-independent growth, and in vivo growth, which is in part mediated through the regulation of MYC expression. Importantly, we demonstrate that inhibition of KDM4A significantly impairs NEPC growth in preclinical models. Thus, our findings provide valuable insights into the molecular mechanisms underlying NEPC progression and offer a rationale for clinical trials with KDM4 inhibitor in NEPC patients.

cancer biology↗

bioRxiv2021

Transcriptional Inactivation of TP53 and the BMP Pathway Mediates Therapy-induced Dedifferentiation and Metastasis in Prostate Cancer

Unsupervised clustering and deconvolution analysis identifies a novel subtype of M-CRPC endowed with hybrid epithelial/mesenchymal (E/M) and luminal progenitor-like traits (Mesenchymal and Stem-like PC, MSPC). Analysis of patient datasets and mechanistic studies indicate that MSPC arises as a consequence of therapy-induced lineage plasticity. AR blockade instigates two separate and complementary processes: 1) transcriptional silencing of TP53 and hence acquisition of hybrid E/M and stem-like traits; and 2) inhibition of the BMP signaling, which promotes resistance to the pro-apoptotic and anti-proliferative effects of AR inhibition. The drug-tolerant prostate cancer cells generated through reprogramming are rescued by neuregulin and generate metastases in mice. Combined inhibition of HER2/3 and AR or mTORC1 exhibit efficacy in preclinical models of mixed ARPC/MSPC or MSPC, respectively. These results identify a novel subtype of M-CRPC, trace its origin to therapy-induced lineage plasticity, and reveal its dependency on HER2/3 signaling.

cancer biology↗

bioRxiv2018

Identifying sensor species to predict critical transitions in complex ecosystems

Ecosystems provide key services needed for human well-being, such as purifying air and regulating our health, many of which are disrupted when these ecosystems undergo a critical transition to undesired states. Detecting early-warning signals of these critical transitions remains challenging for complex ecosystems with a large number of species. Here we built a mathematical formalism to identify minimal sets of \"sensor species\" from which we can determine the state of a whole ecosystem, allowing us to predict a critical transition in an ecosystem by monitoring a minimal subset of its species. We rigorously prove that minimal sets of sensor species can be generically identified knowing only the structure of the ecological network underlying the ecosystem, regardless of its population dynamics. We numerically validated our formalism to predict critical transitions in large complex ecosystems, and then we applied it to experimental data of a critical transition in a lake food-web. Our results contribute to better monitoring complex ecosystems, especially those with poorly known population dynamics such as host-associated microbial communities.

ecology↗