Biology subjects

Bass, A. J.

Publications and source records attributed to Bass, A. J..

9 recordsLinked to original sources

bioRxiv2026

Genomic foundation models do not yet boost common variant discovery beyond standard functional annotations

Genomic foundation models (GFMs) use large-scale AI to model genetic sequences and their influence on molecular phenotypes. While GFMs have shown promise in rare variant analysis, their utility for common variant discovery remains untested. Using the surrogate functional false discovery rate framework for functionally informed GWAS discovery, we compared standard functional annotations (baselineLD) with annotations derived from three leading GFMs: the sequence-to-function models AlphaGenome and Borzoi, and the genomic language model GPN-Star. Across 27 UK Biobank traits, GPN-Star decreased locus discovery relative to a standard GWAS, whereas AlphaGenome and Borzoi increased discovery but by less than baselineLD (medians 1.8%, 3.8%, and 5.1%). When we combined the GFM and baselineLD annotations, there was no increase over baselineLD alone (median changes -0.1% to 0.2%), suggesting that AlphaGenome and Borzoi mostly capture information already in baselineLD. Thus, GFMs may require larger corpora or trait-specific feature engineering to improve discovery beyond standard functional annotations.

genomics↗

bioRxiv2025

PKMYT1 inhibition induces DNA damage and synergizes with immune checkpoint blockade in CCNE1-amplified gastroesophageal adenocarcinoma

PurposeCCNE1 amplification, found in approximately 10% of gastroesophageal adenocarcinoma (GEA), drives chromosomal instability and is associated with an immune-cold tumor microenvironment. Recent studies suggest that PKMYT1 inhibition is synthetic lethal in CCNE1-amplified cancers; however, its role in GEA is largely uncharacterized. We investigated the therapeutic activity of PKMYT1 inhibition in CCNE1-amplified GEA and its potential combination with immunotherapy. Experimental DesignWe evaluated lunresertib (RP-6306), a selective first-in-class PKMYT1 inhibitor, in CCNE1-amplified and wild-type GEA cell lines to define its effects on cell viability, DNA damage, and gene expression changes. To investigate the role of PKMYT1 blockade on immune microenvironment modulation, we used a syngeneic murine model of CCNE1;Trp53-/- GEA xenografts treated with lunresertib, anti-PD-1, or the combination. ResultsLunresertib was selectively cytotoxic in CCNE1-amplified GEA cell lines in cell viability and clonogenic assays. Mechanistically, treatment with lunresertib induced DNA damage, evidence by increased {gamma}H2AX expression and DNA micronuclei formation, and activated pro-inflammatory signaling pathways by gene set enrichment analysis. In a CCNE1-amplified GEA murine model, PKMYT1 inhibition promoted T cell infiltration, reduced myeloid cells, and synergized with anti-PD-1 therapy to induce tumor regression. ConclusionsOur results suggest that CCNE1 amplification is a potentially actionable target in GEA and support the development of combination therapeutic strategies utilizing PKMYT1 inhibition and PD-1 blockade. Translational RelevanceGastroesophageal adenocarcinoma (GEA) is an aggressive malignancy with poor prognosis and few actionable molecular alterations. CCNE1 amplification is present in approximately 10% of patients with GEA and represents a potential therapeutic vulnerability through synthetic lethality with PKMYT1 inhibition. We demonstrate that targeting CCNE1-amplified GEA through PKMYT1 inhibition with lunresertib is selectively cytotoxic, induces DNA damage, and activates inflammatory signaling pathways. In a syngeneic murine model of CCNE1-amplified gastric cancer, we further identified that PKMYT1 inhibition induced T cell infiltration and tumor microenvironment remodeling, and synergized with anti-PD-1 therapy to drive tumor regression. Together, these results support further development of PKMYT1 inhibition in combination with PD-1 blockade for CCNE1-amplified GEA.

cancer biology↗

bioRxiv2024

A powerful framework for differential co-expression analysis of general risk factors

Differential co-expression analysis (DCA) aims to identify genes in a pathway whose shared expression depends on a risk factor. While DCA provides insights into the biological activity of diseases, existing methods are limited to categorical risk factors and/or suffer from bias due to batch and variance-specific effects. We propose a new framework, Kernel-based Differential Co-expression Analysis (KDCA), that harnesses correlation patterns between genes in a pathway to detect differential co-expression arising from general (i.e., continuous, discrete, or categorical) risk factors. Using various simulated pathway architectures, we find that KDCA accounts for common sources of bias to control the type I error rate while substantially increasing the power compared to the standard eigengene approach. We then applied KDCA to The Cancer Genome Atlas thyroid data set and found several differentially co-expressed pathways by age of diagnosis and BRAF mutation status that were undetected by the eigengene method. Collectively, our results demonstrate that KDCA is a powerful testing framework that expands DCA applications in expression studies.

bioinformatics↗

bioRxiv2024

SCAMPI: A scalable statistical framework for genome-wide interaction testing harnessing cross-trait correlations

Family-based heritability estimates of complex traits are often considerably larger than their single-nucleotide polymorphism (SNP) heritability estimates. This discrepancy may be due to non-additive effects of genetic variation, including variation that interacts with other genes or environmental factors to influence the trait. Variance-based procedures provide a computationally efficient strategy to screen for SNPs with potential interaction effects without requiring the specification of the interacting variable. While valuable, such variance-based tests consider only a single trait and ignore likely pleiotropy among related traits that, if present, could improve power to detect such interaction effects. To fill this gap, we propose SCAMPI (Scalable Cauchy Aggregate test using Multiple Phenotypes to test Interactions), which screens for variants with interaction effects across multiple traits. SCAMPI is motivated by the observation that SNPs with pleiotropic interaction effects induce genotypic differences in the patterns of correlation among traits. By studying such patterns across genotype categories among multiple traits, we show that SCAMPI has improved performance over traditional univariate variance-based methods. Like those traditional variance-based tests, SCAMPI permits the screening of interaction effects without requiring the specification of the interaction variable and is further computationally scalable to biobank data. We employed SCAMPI to screen for interacting SNPs associated with four lipid-related traits in the UK Biobank and identified multiple gene regions missed by existing univariate variance-based tests. SCAMPI is implemented in software for public use.

genetics↗

bioRxiv2023

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This missing heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

genetics↗

bioRxiv2020

Selective vulnerability of aneuploid human cancer cells to inhibition of the spindle assembly checkpoint

Selective targeting of aneuploid cells is an attractive strategy for cancer treatment. Here, we mapped the aneuploidy landscapes of ~1,000 human cancer cell lines and classified them by their degree of aneuploidy. Next, we performed a comprehensive analysis of large-scale genetic and chemical perturbation screens, in order to compare the cellular vulnerabilities between near-diploid and highly-aneuploid cancer cells. We identified and validated an increased sensitivity of aneuploid cancer cells to genetic perturbation of core components of the spindle assembly checkpoint (SAC), which ensures the proper segregation of chromosomes during mitosis. Surprisingly, we also found highly-aneuploid cancer cells to be less sensitive to short-term exposures to multiple inhibitors of the SAC regulator TTK. To resolve this paradox and to uncover its mechanistic basis, we established isogenic systems of near-diploid cells and their aneuploid derivatives. Using both genetic and chemical inhibition of BUB1B, MAD2 and TTK, we found that the cellular response to SAC inhibition depended on the duration of the assay, as aneuploid cancer cells became increasingly more sensitive to SAC inhibition over time. The increased ability of aneuploid cells to slip from mitotic arrest and to keep dividing in the presence of SAC inhibition was coupled to aberrant spindle geometry and dynamics. This resulted in a higher prevalence of mitotic defects, such as multipolar spindles, micronuclei formation and failed cytokinesis. Therefore, although aneuploid cancer cells can overcome SAC inhibition more readily than diploid cells, the proliferation of the resultant aberrant cells is jeopardized. At the molecular level, analysis of spindle proteins identified a specific mitotic kinesin, KIF18A, whose levels were drastically reduced in aneuploid cancer cells. Aneuploid cancer cells were particularly vulnerable to KIF18A depletion, and KIF18A overexpression restored the sensitivity of aneuploid cancer cells to SAC inhibition. In summary, we identified an increased vulnerability of aneuploid cancer cells to SAC inhibition and explored its cellular and molecular underpinnings. Our results reveal a novel synthetic lethal interaction between aneuploidy and the SAC, which may have direct therapeutic relevance for the clinical application of SAC inhibitors.

cancer biology↗

bioRxiv2019

Determining sufficient sequencing depth in RNA-Seq differential expression studies

RNA-Seq studies require a sufficient read depth to detect biologically important genes. Sequencing below this threshold will reduce statistical power while sequencing above will provide only marginal improvements in power and incur unnecessary sequencing costs. Although existing methodologies can help assess whether there is sufficient read depth, they are unable to guide how many additional reads should be sequenced to reach this threshold. We provide a new method called superSeq that models the relationship between statistical power and read depth. We apply the superSeq framework to 393 RNA-Seq experiments (1,021 total contrasts) in the Expression Atlas and find the model accurately predicts the increase in statistical power gained by increasing the read depth. Based on our analysis, we find that most published studies (> 70%) are undersequenced, i.e., their statistical power can be improved by increasing the sequencing read depth. In addition, the extent of saturation is highly dependent on statistical methodology: only 9.5%, 29.5%, and 26.6% of contrasts are saturated when using DESeq2, edgeR, and limma, respectively. Finally, we also find that there is no clear minimum per-transcript read depth to guarantee saturation for an entire technology. Therefore, our framework not only delineates key differences among methods and their impact on determining saturation, but will also be needed even as technology improves and the read depth of experiments increases. Researchers can thus use superSeq to calculate the read depth to achieve required statistical power while avoiding unnecessary sequencing costs.

genomics↗

bioRxiv2019

The optimal discovery procedure for significance analysis of general gene expression studies

Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the optimal discovery procedure (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here we extend this work to the analysis of complex study designs and RNA sequencing studies. We then apply our extended framework to a static RNA sequencing study, a longitudinal and an independent sampling time-series study, and an independent sampling dose-response study. We find that our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus the extended ODP enables a superior significance analysis of genomic studies. The algorithm is implemented in our freely available R package called edge.

bioinformatics↗

bioRxiv2018

WRN Helicase is a Synthetic Lethal Target in Microsatellite Unstable Cancers

Summary paragraphSynthetic lethality, an interaction whereby the co-occurrence of two or more genetic events lead to cell death but one event alone does not, can be exploited to develop novel cancer therapeutics1. DNA repair processes represent attractive synthetic lethal targets since many cancers exhibit an impaired DNA repair pathway, which can lead these cells to become dependent on specific repair proteins2. The success of poly (ADP-ribose) polymerase 1 (PARP-1) inhibitors in homologous recombination-deficient cancers highlights the potential of this approach in clinical oncology3,4. Hypothesizing that other DNA repair defects would give rise to alternative synthetic lethal relationships, we asked if there are specific dependencies in cancers with microsatellite instability (MSI), which results from impaired DNA mismatch repair (MMR). Here we analyzed data from large-scale CRISPR/Cas9 knockout and RNA interference (RNAi) silencing screens and found that the RecQ DNA helicase WRN was selectively essential in MSI cell lines, yet dispensable in microsatellite stable (MSS) cell lines. WRN depletion induced double-strand DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models specifically required the helicase activity, but not the exonuclease activity of WRN. These findings expose WRN as a synthetic lethal vulnerability and promising drug target in MSI cancers.

cancer biology↗