Biology subjects

Ochoa, A.

Publications and source records attributed to Ochoa, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatial context of cellular heterogeneity in triple-negative breast cancer reveals actionable subtypes

Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous disease with limited targeted treatment options. Leveraging bulk and spatial transcriptomics and cellular mapping of breast tissue from a large patient cohort, we identified three reproducible TNBC ecosystem subtypes that encode distinct spatial tumor-microenvironment architectures and clinically relevant treatment-response phenotype. Our analyses define: (1) an immune-exhausted subtype associated with immune checkpoint therapy (ICT); (2) a highly fibrotic/hypoxic subtype; and (3) a metabolically reprogrammed subtype characterized by nuclear-receptor and fatty-acid signaling. Spatial profiling further revealed subtype-specific organization of epithelial, immune, and stromal programs across tumor centers and invasive edges. Multiplexed imaging independently supported the contrasting immune-infiltrated and stromal-excluded architectures of the immune-engaged and fibrotic subtypes. The unique markers associated with the subtypes also provide the basis for subtyping and candidate therapeutic vulnerabilities. Our study provides a robust framework for personalized medicine, highlighting that targeting specific spatio-cellular architectures is critical to improving clinical outcomes.

cancer biology↗

bioRxiv2026

Estimating the correlation of exchangeable variables in assortative mating

In studies of assortative mating, similarity between variables measured in parents is often quantified using correlation. The order of the parents within any given pair can be arbitrary in these applications, but common correlation estimators are not robust to reordering within pairs. These unordered variable pairs are exchangeable, since the joint distributions of both orders are equal, and a given order is biased if the one variable has a lower expectation than the other. In this work, we characterize the effect of order bias on Pearson correlation estimates assuming exchangeable variables, and develop a new unbiased estimator, CorSym, that does not depend on order within each pair. Exchangeable variables have equal marginal distributions for both variables, a property accounted for by CorSym. In contrast, standard correlation estimators assume the two variables have different distributions, so biased orders skew the underlying mean, variance and covariance estimates. We show, through theory and simulations, how order bias often results in upwardly biased Pearson correlation estimates. Simulations confirm CorSym is unbiased, and validate its estimated confidence intervals. Using real admixed trios (parents and a child) from 1000 Genomes, we first demonstrate that the global ancestry of fathers and mothers are consistent with exchangeability, using both Kolmogorov-Smirnov tests and a Binomial test for order bias. However, ANCESTOR, which estimates parental global ancestry from a child's local ancestry, produces significant order biases in its output that result in substantial Pearson biases, which CorSym overcomes. Compared to ancestry proportions calculated directly on the parents, ANCESTOR also overestimates parent ancestry divergence and experiences another estimation artifact. Overall, CorSym solves an important estimation bias likely to be encountered in the study of assortative mating, providing unbiased and deterministic estimates that do not depend on the arbitrary order of the data.

genetics↗

bioRxiv2026

Spatial Landscape of Pregnancy-Associated Triple Negative Breast Cancer and Mammary Gland Involution

Pregnancy-associated triple negative breast cancer (PA-TNBC) is one of the highest-risk breast cancers, marked by an aggressive phenotype that lacks targeted treatment options. Studies have shown that post-lactational mammary gland involution plays a role in this increased risk. To delineate the underlying mechanisms, our study characterized the transcriptional state of the epithelia and surrounding microenvironment in women with PA-TNBC, comparing those diagnosed pre-involution (PRE) and post-involution (POST, <3 years after delivery). Spatial transcriptomics using the GeoMx Digital Spatial Profiler was performed on treatment-naive PA-TNBC tissues from 33 women (10 PRE, 23 POST). Regions of interest were segmented with pan-cytokeratin staining. We found that the most prominent transcriptional differences between PRE and POST epithelia occurred in the adjacent non-invasive regions and during the transition into invasive TNBC. POST non-invasive epithelia uniquely showed inflammatory and developmental pathway activation, while the transition into TNBC involved increased chromatin remodeling and cell migration pathways. Further, the tumor microenvironment (TME) in POST showed the highest proportion of immune cells and the highest prevalence of tumor- and immune exhaustion-associated cell states. Finally, a pseudotime analysis of POST transcriptional dynamics found that women diagnosed 1-2 years after delivery exhibited the strongest evidence for inflammatory signaling across the tissue. Our results highlight biological mechanisms distinguishing PRE and POST PA-TNBC across tissue regions and cell types. We emphasize the importance of early detection of malignant molecular signatures in morphologically normal epithelium in post-involution women and suggest that targeting the TME may improve treatment efficacy in post-involution PA-TNBC.

cancer biology↗

bioRxiv2025

A novel subtyping method for TNBC with implications for prognosis and therapy

The biological heterogeneity of triple-negative breast cancer (TNBC) poses significant challenges for diagnosis, prognosis, and treatment. While prior TNBC subtype classifications exist, they are not widely used clinically. Here, we aimed to subtype TNBC based on transcriptomic profiles using cell type and state heterogeneity in tumor tissue from 250 pre-treatment women (127 African-American and 123 European-American). We identified three major subtypes and three distinct groups exhibiting unique cell-type composition and mechanisms: Subtype-1 immune signaling/T-cell response; Subtype-2 pro-fibrotic and immune desert; Subtype-3 fatty acid and nuclear receptor signaling. Subtype-1 showed potential responsiveness to immunotherapy, while Subtypes-2 and 3 suggested alternative therapeutic targets. In Subtype-3, which contained a patient group with high ESR1, (but not high ER protein expression) we identified putative mutations in the gene that are unique to these patients. This framework provides a path toward personalized TNBC treatment and is accessible through a user-friendly RShiny application for clinical use.

bioinformatics↗

bioRxiv2025

Kinship estimation bias carries over to heritability estimation bias using variance components

Heritability is a fundamental parameter of diseases and other traits, quantifying the contribution of genetics to that trait. Kinship matrices, also known as Genetic Relatedness Matrices or "GRMs", are required for heritability estimation with variance components models. However, the most common "standard" kinship estimator employed by GCTA and other approaches, can be severely biased in structured populations. In this study, we characterize heritability estimation biases in GCTA due to kinship estimation biases under population structure. For the standard ratio-of-means (ROM) kinship estimator, we derive a closed-form expression for heritability bias given by the mean kinship value and the true heritability. The standard mean-of-ratios (MOR) estimator is the most widely used in practice, and exhibits more severe bias than ROM due to upweighing low-frequency variants. Using simulation studies with admixture and family structures, as well as simulated traits from 1000 Genomes genotypes, we find that only Popkin, which is the only unbiased population kinship estimator, produces unbiased heritability estimates in structured settings. Pedigree-only estimates have upward heritability biases when there is population structure. Finally, we analyze three structured datasets with real phenotypes--the San Antonio Family Study, the Hispanic Community Health Study / Study of Latinos, and a multiethnic Nephrotic Syndrome cohort. The standard MOR estimator can produce both downward and upward heritability biases depending on population structure and variant frequency spectrum, compared to the other two estimators. Overall, common kinship estimators result in heritability estimation biases when applied to structured populations, a challenge that Popkin successfully overcomes.

genetics↗

bioRxiv2025

Genetic association meta-analysis is susceptible to confounding by between-study cryptic relatedness

Meta-analysis of Genome-Wide Association Studies (GWAS) has important advantages, but it assumes that studies are independent, which does not hold when there is relatedness between studies. As a motivating example, recent work suggested applying sex-stratified meta-analysis to correct for participation bias, without considering that men and women from the same population will be highly related. Our theory demonstrates how cryptic relatedness results in correlated test statistics between studies, inflating meta-analysis. We characterize the effects of different between-study relatedness scenarios, particularly population structure and recent family relatedness, on meta-analysis type I error control and power. We simulated data with (1) no family relatedness between subpopulations, (2) family relatedness within subpopulations, (3) family relatedness across subpopulations, and (4) single population with family relatedness. We run joint and meta-analyses on simulations using both binary and quantitative traits. In scenarios with family relatedness, sex-stratified meta-analysis exhibits severe inflation and lower AUC compared to joint and subpopulation meta-analyses. Remarkably, genomic control succeeds in correcting inflation in these cases, but does not alter calibrated power. Analysis of real datasets confirms severe inflation for sex-stratified meta-analysis in family studies, but a negligible effect for population studies with up to 10,000 individuals. Our theoretical framework demonstrates that the inflation factor increases as the sample size increases. We recommend against meta-analyzing studies that share the same populations, which increases the risk of inflation due to cryptic relatedness between studies.

genetics↗

bioRxiv2025

Spatially distinct cellular and molecular landscapes define prognosis in triple negative breast cancer

BackgroundTriple-negative breast cancer is a prevalent breast cancer subtype with the lowest 5-year survival. Several factors contribute to its treatment response, but the inherent molecular and cellular tumor heterogeneity are increasingly acknowledged as crucial determinants. MethodsSpatial transcriptomic profiling was performed on FFPE tissues from a retrospective, treatment-naive group of women with differential prognoses (17 with >15 years survival-good prognosis (GPx) and 15 with <3 years survival-poor prognosis (PPx)) using GeoMX(R) Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin and analyzed for tumor and stromal components, probed using GeoMx human whole transcriptome atlas (WTA) panel. Data quality control, normalization, and differential analysis was performed in R using GeomxTools and linear mixed models. Additional analyses including cell-type deconvolution, spatial entropy, functional enrichment, TF-target / ligand-receptor analysis and convolution neural networks were employed to identify significant gene signatures contributing to differential prognosis. ResultsHere we report on the spatial and molecular heterogeneity underlying differential prognosis. We observe that the state of the epithelia and its microenvironment (TME) are transcriptionally distinct between the two groups. Invasive epithelia in GPx show a significant increase in immune transcripts with the TME exhibiting increased immune cell presence (via IF), while in PPx they are more metabolically and translationally active, with the TME being more mesenchymal/fibrotic. Specifically, pre-cancerous epithelia in PPx display a prescience of aggressiveness as evidenced by increased EMT-signaling. We identify distinct epithelial gene signatures for PPx and GPx, that can, with high accuracy, classify samples at the time of diagnosis and likely inform therapy. ConclusionsTo the best of our knowledge, this is the first study to leverage spatial transcriptomics for an in-depth delineation of the cellular and molecular underpinnings of differential prognosis in TNBC. Our study highlights the potential of spatial transcriptomics to not only uncover the molecular drivers of differential prognosis in TNBC but also to pave the way for precision diagnostics and tailored therapeutic strategies, transforming the clinical landscape for this aggressive breast cancer subtype.

cancer biology↗

bioRxiv2025

Multi-lineage transcriptional and cell communication signatures define pathways in individuals at-risk for developing rheumatoid arthritis that initiate and perpetuate disease

Rheumatoid arthritis (RA) is a systemic autoimmune disease arising from loss of tolerance and autoantibody development in at-risk individuals. Targeted therapies yield variable responses and current preventive strategies delay but do not stop disease onset. We propose that a set of transcription factors (TFs) and their downstream pathways regulate inflammatory cell communication networks in at-risk populations and RA. These networks enable multiple pathogenic cell types and mediators and could account for variable responses to targeted agents. To test this hypothesis, we identified anti-citrullinated protein antibody (ACPA)-positive at-risk individuals, patients with early and established RA and healthy controls. Single cell chromatin accessibility and transcriptomic profiles from blood mononuclear r cells were integrated and identified share pathogenic mechanisms, especially SUMOylation, RUNX2, YAP1, NOTCH3, and {beta}-Catenin Pathways. Surprisingly, this signature was found in multiple cell types. Individualized gene expression patterns were then confirmed in RA synovium. Cell communication analysis revealed that multiple lineages can deliver a core set of pro-inflammatory mediators to receiver cells. Longitudinal analysis showed that the signature cell types evolve in individual at-risk participants. Cell-type-specific signature pathways could contribute to the differing clinical responses to targeted therapies. This study describes how a common clinical phenotype could arise from multiple pathogenic mechanisms.

systems biology↗

bioRxiv2023

Whole-genome sequencing confirms multiple species of Galapagos giant tortoises

Galapagos giant tortoises are endemic to the Galapagos Archipelago, where they are found in isolated populations. While these populations are widely considered distinguishable in morphology, behavior, and genetics, the recent divergence of these taxa has made their status as species controversial. Here, we apply multispecies coalescent methods for species delimitation to whole genome resequencing data from 38 tortoises across all 13 extant taxa to assess support for delimiting these taxa as species. In contrast to previous studies based solely on divergence time, we find strong evidence to reject the hypothesis that all Galapagos giant tortoises belong to a single species. Instead, a conservative interpretation of model-based and divergence-based results indicates that these taxa form a species complex consisting of a minimum of 9 species, with some analyses supporting as many as 13 species. There is mixed support for the species status of taxa living on the same island, with some methods delimiting them as separate species and others suggesting multiple populations of a single species per island. These results make clear that Galapagos giant tortoise taxa represent different stages in the process of speciation, with some taxa further along in that evolutionary process than others. A better understanding of the more complex parts of that process is urgently needed, given the threatened status of Galapagos giant tortoises. Lay SummarySpecies delimitation is a challenging problem in evolutionary biology, but one that is central to the field. Distinguishing species can affect conservation management practices, from conservation status assessments to strategies for breeding programs. More fundamentally, understanding species boundaries affects our ability to assess biodiversity and to study evolutionary processes. The Galapagos Archipelago presents several radiations of closely related taxa that inspired Charles Darwin to develop his theory of evolution by natural selection and later led to foundational case studies in speciation. The Galapagos giant tortoises were one such inspiration. Nearly two centuries later, there is still an ongoing debate about the taxonomic status of these tortoises, with opinions on their status ranging from barely differentiated populations to separate species. Here, we present the first genomic species delimitation of Galapagos giant tortoises and provide convincing evidence that this group is a complex consisting of between 9 and 13 species. These results provide valuable guidance to conservation stakeholders in the Galapagos, while also adding an important case study to the delimitation of island species.

evolutionary biology↗

bioRxiv2023

m6A methylated long noncoding RNA regulates proinflammatory response emerging as novel target for IBD

Cytokine mediated sustained inflammation increases the risk to develop different complex chronic inflammatory diseases, such as inflammatory bowel disease (IBD). Recent studies highlighted the involvement of inflammation associated gene variants in m6A methylation. Moreover, long noncoding RNAs (lncRNAs) participate in the pathogenesis of inflammatory disorders and their function can be influenced by differential methylation. Here we describe the functional implication of LOC339803 lncRNA in the development of IBD. We found that allele-specific m6A methylation affects YTHDC1 mediated protein binding affinity. LOC339803-YTHDC1 interaction dictates chromatin localization of LOC339803 ultimately inducing IL1B and contributing to the development of intestinal inflammation. Our findings were confirmed using human intestinal biopsy samples from IBD and controls. Overall, our results support LOC339803 lncRNA as an important mediator of intestinal inflammation, presenting this lncRNA as a potential novel therapeutic target for the treatment of IBD.

molecular biology↗

bioRxiv2022

Genetic association models are robust to common population kinship estimation biases

Common genetic association models for structured populations, including Principal Component Analysis (PCA) and Linear Mixed-effects Models (LMM), model the correlation structure between individuals using population kinship matrices, also known as Genetic Relatedness Matrices or "GRMs". However, the most common kinship estimators can have severe biases that were only recently determined. Here we characterize the effect of these kinship biases on genetic association. We employ a large simulated admixed family and genotypes from the 1000 Genomes Project, both with simulated traits, to evaluate key kinship estimators. Remarkably, we find practically invariant association statistics for kinship matrices of different bias types (matching all other features). We then prove using statistical theory and linear algebra that LMM association tests are invariant to these kinship biases, and PCA approximately so. Our proof shows that the intercept and relatedness effect coefficients compensate for the kinship bias, an argument that extends to generalized linear models. As a corollary, association testing is also invariant to changing the reference ancestral population of the kinship matrix. Lastly, we observed that all kinship estimators, except for popkin ROM, can give improper non-positive semidefinite matrices, which can be problematic although some LMMs handle them surprisingly well, and condition numbers can be used to choose kinship estimators. Overall, we find that existing association studies are robust to kinship estimation bias, and our calculations may help improve association methods by taking advantage of this unexpected robustness, as well as help determine the effects of kinship bias in related problems.

genetics↗

bioRxiv2022

Limitations of principal components in quantitative genetic association models for human studies

Principal Component Analysis (PCA) and the Linear Mixed-effects Model (LMM), sometimes in combination, are the most common genetic association models. Previous PCA-LMM comparisons give mixed results, unclear guidance, and have several limitations, including not varying the number of principal components (PCs), simulating simple population structures, and inconsistent use of real data and power evaluations. We evaluate PCA and LMM both varying number of PCs in realistic genotype and complex trait simulations including admixed families, subpopulation trees, and real multiethnic human datasets with simulated traits. We find that LMM without PCs usually performs best, with the largest effects in family simulations and real human datasets and traits without environment effects. Poor PCA performance on human datasets is driven by large numbers of distant relatives more than the smaller number of closer relatives. While PCA was known to fail on family data, we report strong effects of family relatedness in genetically diverse human datasets, not avoided by pruning close relatives. Environment effects driven by geography and ethnicity are better modeled with LMM including those labels instead of PCs. This work better characterizes the severe limitations of PCA compared to LMM in modeling the complex relatedness structures of multiethnic human data for association studies.

genetics↗

bioRxiv2020

Detecting regulatory elements in high-throughput reporter assays

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure genome-wide regulatory element activity across the human genome. The assays, however, also present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq signals. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local signal correlations.

bioinformatics↗

bioRxiv2019

Testing the effectiveness of principal components in adjusting for relatedness in genetic association studies

Modern genetic association studies require modeling population structure and family relatedness in order to calculate correct statistics. Principal Components Analysis (PCA) is one of the most common approaches for modeling this population structure, but nowadays the Linear Mixed-Effects Model (LMM) is believed by many to be a superior model. Remarkably, previous comparisons have been limited by testing PCA without varying the number of principal components (PCs), by simulating unrealistically simple population structures, and by not always measuring both type-I error control and predictive power. In this work, we thoroughly evaluate PCA with varying number of PCs alongside LMM in various realistic scenarios, including admixture together with family structure, measuring both null p-value uniformity and the area under the precision-recall curves. We find that PCA performs as well as LMM when enough PCs are used and the sample size is large, and find a remarkable robustness to extreme number of PCs. However, we notice decreased performance for PCA relative to LMM when sample sizes are small and when there is family structure, although LMM performance is highly variable. Altogether, our work suggests that PCA is a favorable approach for association studies when sample sizes are large and no close relatives exist in the data, and a hybrid approach of LMM with PCs may be the best of both worlds.

genetics↗

bioRxiv2019

New kinship and FST estimates reveal higher levels of differentiation in the global human population

Kinship coefficients and FST, which measure genetic relatedness and the overall population structure, respectively, have important biomedical applications. However, existing estimators are only accurate under restrictive conditions that most natural population structures do not satisfy. We recently derived new kinship and FST estimators for arbitrary population structures [1, 2]. Our estimates on human datasets reveal a complex population structure driven by founder effects due to dispersal from Africa and admixture. Notably, our new approach estimates larger FST values of 26% for native worldwide human populations and 23% for admixed Hispanic individuals, whereas the existing approach estimates 9.8% and 2.6%, respectively. While previous work correctly measured FST between subpopulation pairs, our generalized FST measures genetic distances among all individuals and their most recent common ancestor (MRCA) population, revealing that genetic differentiation is greater than previously appreciated. This analysis demonstrates that estimating kinship and FST under more realistic assumptions is important for modern population genetic analysis.

genetics↗

bioRxiv2017

Enabling cross-study analysis of RNA-Sequencing data

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at https://github.com/mskcc/RNAseqDB).

bioinformatics↗

bioRxiv2016

Domain prediction with probabilistic directional context

MotivationProtein domain prediction is one of the most powerful approaches for sequence-based function prediction. While domain instances are typically predicted independently of each other, newer approaches have demonstrated improved performance by rewarding domain pairs that frequently co-occur within sequences. However, most of these approaches have ignored the order in which domains preferentially co-occur and have also not modeled domain co-occurrence probabilistically.\n\nResultsWe introduce a probabilistic approach for domain prediction that models \"directional\" domain context. Our method is the first to score all domain pairs within a sequence while taking their order into account, even for non-sequential domains. We show that our approach extends a previous Markov model-based approach to additionally score all pairwise terms, and that it can be interpreted within the context of Markov random fields. We formulate our underlying combinatorial optimization problem as an integer linear program, and demonstrate that it can be solved quickly in practice. Finally, we perform extensive evaluation of domain context methods and demonstrate that incorporating context increases the number of domain predictions by [~]15%, with our approach dPUC2 (Domain Prediction Using Context) outperforming all competing approaches.\n\nAvailabilitydPUC2 is available at http://github.com/alexviiia/dpuc2.

bioinformatics↗

bioRxiv2016

FST and kinship for arbitrary population structures I: Generalized definitions

FST is a fundamental measure of genetic differentiation and population structure, currently defined for subdivided populations. FST in practice typically assumes independent, non-overlapping subpopulations, which all split simultaneously from their last common ancestral population so that genetic drift in each subpopulation is probabilistically independent of the other subpopulations. We introduce a generalized FST definition for arbitrary population structures, where individuals may be related in arbitrary ways, allowing for arbitrary probabilistic dependence among individuals. Our definitions are built on identity-by-descent (IBD) probabilities that relate individuals through inbreeding and kinship coefficients. We generalize FST as the mean inbreeding coefficient of the individuals local populations relative to their last common ancestral population. We show that the generalized definition agrees with Wrights original and the independent subpopulation definitions as special cases. We define a novel coancestry model based on \"individual-specific allele frequencies\" and prove that its parameters correspond to probabilistic kinship coefficients. Lastly, we extend the Pritchard-Stephens-Donnelly admixture model in the context of our coancestry model and calculate its FST. To motivate this work, we include a summary of analyses we have carried out in follow-up papers, where our new approach has been applied to simulations and global human data, showcasing the complexity of human population structure, demonstrating our success in estimating kinship and FST, and the shortcomings of existing approaches. The probabilistic framework we introduce here provides a theoretical foundation that extends FST in terms of inbreeding and kinship coefficients to arbitrary population structures, paving the way for new estimators and novel analyses.\n\nNote: This article is Part I of two-part manuscripts. We refer to these in the text as Part I and Part II, respectively.\n\nPart I: Alejandro Ochoa and John D. Storey. \"FST and kinship for arbitrary population structures I: Generalized definitions\". bioRxiv (10.1101/083915) (2019). https://doi.org/10.1101/083915. First published 2016-10-27.\n\nPart II: Alejandro Ochoa and John D. Storey. \"FST and kinship for arbitrary population structures II: Method of moments estimators\". bioRxiv (10.1101/083923) (2019). https://doi.org/10.1101/083923. First published 2016-10-27.

genetics↗