Biology subjects

Amador, C.

Publications and source records attributed to Amador, C..

11 recordsLinked to original sources

bioRxiv2026

Lymphoma response to chimeric antigen receptor T cell therapy is driven by endogenous tumor-reactive CD8⁺ T cells

CD19-directed chimeric antigen receptor T-cell (CAR-T) therapy induces durable remissions in a subset of patients with relapsed or refractory large B-cell lymphoma; however, most patients fail to respond or eventually relapse. By integrating whole-genome and transcriptome profiling of 54 patients with functional assays, syngeneic models, and mathematical modeling, we demonstrate that durable CAR-T efficacy requires cooperation between CAR-T cells and endogenous CD8 (eCD8) T cells recruited to join the attack on malignant cells. Tumors with biallelic loss of MHC class I or CD58 exhibited high genomic complexity, increased neoantigen burden, and resistance to CAR-T therapy. MHC-I-deficient lymphomas showed suboptimal response in immunocompetent hosts compared to wild type. Importantly, sustained responses in mice occurred only in the presence of eCD8 cells, whereas all CD8-depleted mice relapsed after initial CAR-T-mediated tumor regression. Patients and murine ELISPOT assays confirmed enhanced endogenous T-cell activation post-CAR-T. These findings reveal that CAR-T therapy functions not only as a cytotoxic modality but also as a catalyst for endogenous anti-tumor immunity.

cancer biology↗

bioRxiv2025

RHOA Loss of Function Impairs the IFNγ Response and Promotes CD19 Antigen Escape to Drive CAR-T Resistance in Diffuse Large B-cell Lymphoma

CD19-directed chimeric antigen receptor (CAR)-T cells are breakthrough therapies for aggressive B-cell lymphomas, but less than half of patients achieve durable responses. We previously showed through whole-genome sequencing of tumors from CAR-T-treated patients that deletions of RHOA (3p21.31) are enriched in cases progressing after treatment. RHOAs roles in resistance and pathogenesis are poorly defined, despite loss-of-function alterations that occur in [~]20% of newly diagnosed large B-cell lymphoma (LBCL) cases. We created RHOA-deficient LBCL systems and confirmed cell-intrinsic loss of response to CAR-19 in vitro and in vivo driven by CD19 downregulation. Impact on CD19, however, was variable and would not explain selection for RHOA deletion in newly diagnosed cases. We therefore created RHOA-deficient tumors in immunocompetent mice and found remarkable correlation with dysfunctional lymphoma microenvironment (LME) signatures in CAR-19 resistant patients. These LMEs are marked by a type 1-like immune infiltrate with terminally exhausted CD8 T cells, Th1-like CD4 cytotoxic lymphocytes (CTLs), and increased production of interferon gamma (IFN{gamma}). RHOA-deficient tumor cells themselves have significantly impaired IFN{gamma} responses, providing resistance to CD8 T cell clearance by way of diminished induction of major histocompatibility complex class I (MHC-I). These findings support a model that depletion of healthy effector populations by RHOA-deficient lymphoma is a key driver of immune dysfunction thwarting CAR-19 clinical responses. Overall, we describe for the first time how a single-gene alteration found recurrently in CAR-19-resistant LBCL contributes to treatment failures.

cancer biology↗

bioRxiv2023

Methodological Considerations When Using Polygenic Scores to Explore Parent-Offspring Genetic Nurturing Effects

BackgroundResearch has begun to explore the effects of parental genetic nurturing on offspring outcomes using polygenic scores (PGSs). However, there are concerns regarding potential biases due to confounding when mediating parental phenotypes are included. MethodsDepression, educational attainment and height PGSs were generated for 2680 biological parent-offspring trios using genome-wide association study (GWAS) meta-analysis summary statistics in a large population study: Generation Scotland. Regression and pathway models were estimated incorporating PGSs for both parents and offspring to explore direct (offspring PGS) and genetic nurturing (parental PGS) effects on psychological distress, educational attainment and height. Genetic nurturing via parental phenotypes were incorporated into the models. To explore sources of bias we conducted simulation analyses of 10,000 trios using combinations of PGS predictive accuracy and accounted variance. ResultsModels incorporating both offspring and parental PGSs suggested positive parental genetic nurturing effects on offspring educational attainment, but not psychological distress or height. In contrast, models additionally incorporating parental phenotypic information suggested positive parent phenotype mediated genetic nurturing effects were at play for all phenotypes explored as well as negative residual genetic nurturing effects for height. 10,000 parent-offspring trio effects (without genetic nurturing effects) were simulated. Simulations demonstrated that models incorporating parent and offspring PGSs resulted in genetic nurturing effects that were unbiased. However, adding parental phenotypes as mediating variables results in biased positive estimates of parent phenotype mediated genetic nurturing effects and negative estimates of residual genetic nurturing effects. Biased effects increased in magnitude as PGS accuracy and accounted variance decreased. These biases were only eliminated when PGSs were simulated to capture the entirety of trait genetic variance. ConclusionResults suggest that in the absence of PGSs that capture all genetic variance, parental phenotypes act as colliders in the same way as heritable environments. Relatively simple models combining parental and offspring PGSs can be used to detect genetic nurturing effects in complex traits. However, our findings suggest alternative methods should be utilised when aiming to identify mediating phenotypes and potentially modifiable parental nurturing effects.

genomics↗

bioRxiv2023

Phenome-wide analysis identifies parent-of-origin effects on the human methylome associated with changes in the rate of aging

Variation in the rate at which humans age may be rooted in early life events acting through genomic regions that are influenced by such events and subsequently are related to health phenotypes in later life. The parent-of-origin-effect (POE)-regulated methylome includes regions either enriched for genetically controlled imprinting effects (the typical type of POE) or atypical POE introduced by environmental effects associated with parents. This part of the methylome is heavily influenced by early life events, making it a potential route connecting early environmental exposures, the epigenome and the rate of aging. Here, we aim to test the association of POE-influenced methylation of CpG dinucleotides (POE-CpG sites) with early and later environmental exposures and subsequently with health-related phenotypes and adult aging phenotypes. We do this by performing phenome-wide association analyses of the POE-influenced methylome using a large family-based population cohort (GS:SFHS, Ndiscovery=5,087, Nreplication=4,450). At the single CpG level, 92 associations of POE-CpGs with phenotypic variation were identified and replicated. Most of the associations were contributed by POE-CpGs belonging to the atypical class and the most strongly enriched associations were with aging (DNAmTL acceleration), intelligence and parental (maternal) smoking exposure phenotypes. We further found that a proportion of the atypical-POE-CpGs formed co-methylation networks (modules) which are associated with these phenotypes, with one of the aging-associated modules displaying increased internal module connectivity (strength of methylation correlation across constituent CpGs) with age. Atypical POE-CpGs also displayed high levels of methylation heterogeneity and epigenetic drift (i.e. information loss with age) and a strong correlation with CpGs contained within epigenetic clocks. These results identified associations between the atypical-POE-influenced methylome and aging and provided new evidence for the "early development of origin" hypothesis for aging in humans.

genetics↗

bioRxiv2021

Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome

Backgroundparent-of-origin effects (POE) play important roles in development and complex disease and thus understanding their regulation and associated molecular and phenotypic variation are warranted. Previous studies have mainly focused on the detection of genomic regions or phenotypes regulated by POE. Understanding whether POE may be modified by environmental or genetic exposures is important for understanding of the source of POE-associated variation, but only a few case studies addressing these modifiable POE exist. Methodsin order to understand this high order of POE regulation, we screened 101 genetic and environmental factors such as "predicted mRNA expression levels" of DNA methylation/imprinting machinery genes and early/late lifestyle/environmental exposures. POE-mQTL-modifier interaction models were proposed to test the potential of these factors to modify POE at DNA methylation using data from Generation Scotland: The Scottish Family Health Study(N=2315). Resultsa set of vulnerable/modifiable POE-CpGs were identified (modifiable-POE-regulated CpGs, N=3). Four factors, "lifetime smoking status" and "predicted mRNA expression levels" of TET2, SIRT1 and KDM1A, were found to significantly modify the POE on the three CpGs in both discovery and replication datasets. Importantly, the POE on one of the CpGs were modified by both genetic and environmental factors. We further identified plasma protein and health-related phenotypes associated with the methylation level of one of the identified CpGs. Conclusionsthe modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation, motivating future comprehensive evaluation of the role of these modifiers in complex diseases. Key MessagesO_LIPrevious population studies showed that parent-origin-effects(POE) on human methylome can be widespread and affect health-related traits and diseases. C_LIO_LIWhether the POE remained stable throughout the life or can be modified by genetic or environmental factors were largely unknown. C_LIO_LIBy systematically screening 101 genetic and environmental factors in a large cohort(GS:SFHS) we provided the first population-level replicated evidence that those measuring lifestyle (smoking) and predicted expression of DNA methylation- or imprinting-machinery genes are amongst the factors that can modulate the POE of mQTLs for a set of CpG sites. C_LIO_LIWe found those modifiable-POE-regulated CpGs are also phenotypically relevant - one is associated with the plasma levels of CLEC4C and health-related phenotypes such as HDL levels. C_LIO_LIThe modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation and their potential phenotypic consequences. This also provided a paradigm for further studies to explore how environmental and genetic effects can be integrated at methylation level. C_LI

genetics↗

bioRxiv2020

Omics versus Questionnaires: Using methylation data to assess the environmental impact of smoking on obesity-related traits

Variation in obesity-related traits has a genetic basis with heritabilities between 40 and 70%. While the global obesity pandemic is usually associated with environmental changes related to lifestyle and socioeconomic changes, most genetic studies do not include all relevant environmental covariates, so genetic contribution to variation in obesity-related traits cannot be accurately assessed. Some studies have described interactions between a few individual genes linked to obesity and environmental variables but there is no agreement on their total contribution to differences between individuals. Here we compared self-reported smoking data and a methylation-based proxy to explore the effect of smoking and genome-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain. Our results indicate that exploiting omic measures can improve models for complex traits such as obesity and can be used as a substitute for, or jointly with, environmental records to better understand causes of disease.

genomics↗

bioRxiv2019

Assessment of DNA methylation differences between carriers of APOE ε4 and APOE ε2

BACKGROUNDThe Apolipoprotein E (APOE) {varepsilon}4 allele is the strongest genetic risk factor for late onset Alzheimers disease, while the {varepsilon}2 allele confers protection. Previous studies report differential DNA methylation of APOE between {varepsilon}4 and {varepsilon}2 carriers, but associations with epigenome-wide methylation have not previously been characterised. METHODSUsing the EPIC array, we investigated epigenome-wide differences in whole blood DNA methylation patterns between Alzheimers disease-free APOE {varepsilon}4 (n=2469) and {varepsilon}2 (n=1118) carriers from the two largest single-cohort DNA methylation samples profiled to date. Using a discovery, replication and meta-analysis study design, methylation differences were identified using epigenome-wide association analysis and differentially methylated region (DMR) approaches. Results were explored using pathway and methylation quantitative trait loci (meQTL) analyses. RESULTSWe obtained replicated evidence for DNA methylation differences in a ~169kb region, which encompasses part of APOE and several upstream genes. Meta-analytic approaches identified DNA methylation differences outside of APOE: differentially methylated positions were identified in DHCR24, LDLR and ABCG1 (2.59 x 10-100[≤]P[≤]2.44 x 10-8) and DMRs were identified in SREBF2 and LDLR (1.63 x 10-4[≤]P[≤]3.01 x 10-2). Pathway and meQTL analyses implicated lipid-related processes and high density lipoprotein cholesterol was identified as a partial mediator of the methylation differences in ABCG1 and DHCR24. CONCLUSIONSAPOE {varepsilon}4 vs. {varepsilon}2 carrier status is associated with epigenome-wide methylation differences in the blood. The loci identified are located in trans as well as cis to APOE and implicate genes involved in lipid homeostasis.

genetics↗

bioRxiv2019

Loss of FBXO9 enhances proteasome activity and promotes aggressiveness in acute myeloid leukemia

The hematopoietic system is maintained throughout life by hematopoietic stem cells that are capable of differentiating into all hematopoietic lineages. An intimate balance between self-renewal, differentiation, and quiescence is required to maintain hematopoiesis. Disruption of this balance can result in hematopoietic malignancy, including acute myeloid leukemia (AML). FBXO9, from the F-box E3 ubiquitin ligases, is down-regulated in patients with AML compared to normal bone marrow. FBXO9 is the substrate recognition component of the Skp1-Cullin-F-box (SCF)-type E3 ligase complex. FBXO9 is highly expressed in hematopoietic stem and progenitor populations, which contain the tumor-initiating population in AML. In AML patients, decrease in FBXO9 expression is most pronounced in patients with the inversion of chromosome 16 (inv(16)), a rearrangement that generates the transcription factor fusion gene, CBFB-MYH11. To study FBXO9 in malignant hematopoiesis, we generated a conditional knockout mouse model using a novel CRISPR/Cas9 strategy. Our data show that deletion of Fbxo9 in mice expressing Cbfb-MYH11 leads to markedly accelerated and aggressive leukemia development. In addition, we find loss of FBXO9 leads to increased proteasome expression and tumors are more sensitive to bortezomib suggesting that FBXO9 expression may predict patient response to bortezomib treatment.

cancer biology↗

bioRxiv2019

Proteome-by-phenome Mendelian Randomisation detects 38 proteins with causal roles in human diseases and traits

Target identification remains a crucial challenge in drug development. To enable unbiased detection of proteins and pathways that have a causal role in disease pathogenesis or progression, we propose proteome-by-phenome Mendelian Randomisation (P2MR). We first detected genetic variants associated with plasma concentration of 249 proteins. We then used 64 replicated variants in two-sample Mendelian Randomisation to quantify evidence of a causal role for each protein across 846 phenotypes: this yielded 509 robust protein-outcome links. P2MR provides substantial promise for drug target prioritisation. We provide confirmatory evidence for a causal role for the proteins encoded at multiple cardiovascular disease risk loci (FGF5, IL6R, LPL, LTA), and discovered that intestinal fatty acid binding protein (FABP2) contributes to disease pathogenesis. Additionally, we find and replicate evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia. Our results provide specific prediction of the effects of changes of plasma protein concentration on complex phenotypes in humans.

genomics↗

bioRxiv2017

Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

BackgroundStressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable traits that are correlated with genetic risk for MDD. In the current study, we sought to investigate the genetic and environmental contributions to SLEs in a large family-based sample, and quantify any genetic overlap with MDD and neuroticism.\n\nMethodsA subset of Generation Scotland: the Scottish Family Health Study, consisting of 9618 individuals comprise the present study. We estimated the heritability of SLEs using pedigree-based and molecular genetic data. The environment was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression we analysed the genetic overlap between MDD, neuroticism and SLEs.\n\nResultsPast 6-month life events were positively correlated with lifetime MDD status ({beta}=0.21, r2=1.1%, p=2.5 x 10-25) and neuroticism ({beta} =0.13, r2=1.9%, p=1.04 x 10-37). Common SNPs explained 8% of the variance in personal life events (those directly affecting the individual) (S.E.=0.03, p=9 x 10-4). A significant effect of couple environment accounted for 13% (S.E.=0.03, p=0.016) of variation in SLEs. PRS analyses found that individuals with higher PRS for MDD reported more SLEs ({beta} =0.05, r2=0.3%, p=3 x 10-5). LD score regression demonstrated genetic correlations between MDD and both SLEs (rG=0.33, S.E.=0.08) and neuroticism (rG=0.15, S.E.=0.07).\n\nConclusionsThese findings suggest that SLEs are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work should determine the causal direction and source of these associations.

genetics↗

bioRxiv2017

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using [~]20 000 individuals in the Generation Scotland family cohort genotyped for [~]700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that [~]50% of differences in intelligence, and [~]40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.

genetics↗