Chia, Nicholas

Abstract Disclosure: A. Zhang: None. J. Kapoor: None. C. Deveney: None. A.G. Cope: None. M.M. Walther-Antonio: None. N. Chia: None. C.W. Pattanayak: None. M.J. Tetel: None. The vaginal microbiome (VM) consists of the microorganisms and their metabolites residing in the vaginal tract and is critical for maintaining female reproductive health. The Lactobacillus species predominate most VMs and produce lactic acid, which has been cited as a key protector of vaginal health. Alterations in the finely balanced vaginal microbiome can cause infections, such as bacterial vaginosis, and may contribute to the development of endometrial and ovarian cancer. While it is well-established that stress and the gut microbiome impact each other via the gut-brain axis, the influence of stress, hormones, and lifestyle factors on the VM composition remains poorly understood. We investigated the relationship between stress, hormones (menstruation, hormonal contraception), lifestyle factors (diet, exercise, sleep), and the VM composition. In this 10-week study, healthy, young female volunteers (n=62) provided daily vaginal and fecal swabs, weekly stress ratings, daily data on sleep and activity (via FitBit) and diet information. High-throughput 16S rRNA sequencing was used to identify the vaginal microbial composition of each sample. Types of hormonal birth control were none (n=30), local progestin (n=9), systemic progestin-only (n=7), or systemic estrogens and progestin combined (n=16). Consistent with our previous study, vaginal microbial diversity, as measured by the Shannon index, varied by type of hormonal birth control, with participants not on hormonal birth control having the highest average diversity. There was a positive relationship between average stress and vaginal microbial diversity, with preliminary results suggesting that this relationship depends on hormonal birth control status. Participants who menstruated during the 10-week study had a higher vaginal microbial diversity than those who did not. We are currently analyzing which microbes contribute to these differences in vaginal microbial diversity. Microbiota and fungal data from the daily vaginal and fecal swab samples will be analyzed to determine how stress, hormones and/or lifestyle factors relate to crosstalk between the vaginal and gut microbiomes. These findings may provide long-term, time-longitudinal insights into how these two important microbiomes are involved in important aspects of women’s health. Presentation: Sunday, July 13, 2025

Abstract In this work, we seek to characterize the learned feature space of cancer drug response prediction models in a drug-blind setting and quantify the limits of their generalizability. Drug-blind prediction failure describes the inability of models to predict cell line response to drugs in the test set that are not present during training. The experiments performed in this study utilize a two-arm multilayer perceptron model where embeddings for drugs and cell-lines are calculated separately and then concatenated to predict response. Drug structure is represented by Morgan fingerprint while cell lines utilize gene expression values. We first examine the connection between learned drug features and cell lines by permuting responses within both cell lines and drugs during model training. When permuting response values within cell lines, model performance was entirely depleted, but permutation of responses within drugs resulted in only a 10-15% decrease in performance. This displays the bulk of model performance is due to learned distributions of response for each drug. We also determine the impact of dataset size on drug-blind performance. Increasing the cell line examples per drug did not improve performance, but drug-blind performance exhibited higher variance than mixed set testing. From these experiments, we hypothesized that drug-blind performance was a function of the set of drugs in the training set. We trained a set of 208 models with varying training set and constant test set and fit an elastic net model using drugs in the training set as features and performance of each model as the target. We were able to accurately predict the drug-blind performance of a model, measured in Pearson correlation, based on the drugs in the training set with a mean absolute error of 0.049. We then trained a set of 1641 models where all drugs in the dataset were present in the test set but only 50% of unique drugs were present during training. Hierarchical clustering on the coefficients of an elastic net model fit to each unique drug’s performance across all models shows drugs cluster into groups that correspond to specific mechanisms of action. By examining the relationship between drug embeddings during model training, we see that decreasing validation loss corresponds to reinforcement of mechanistic relationships of drugs not captured by raw Morgan fingerprints. Finally, we show that training on a dataset confined to a single mechanism of action significantly improves overall mixed set performance on those drugs against training on the entire set of drugs. In this study, we identify that drug-blind performance in current large pharmacogenomic datasets is limited to generalization within drug mechanism of action classes. Therefore, global drug-blind prediction benchmarking is a poor indicator of model generalization as the data itself creates these limits, not model architecture. As more data is collected on novel anti-cancer compounds, we hope that the results presented here create a foundation on which to measure progress in cancer drug generalization. Citation Format: William G. Herbert, Paul A. Jensen, Nicholas Chia, Marina RS. Walther-Antonio. Monotherapy cancer drug-blind response prediction is limited to intraclass generalization [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A005.

Abstract DNMT3A and TET2 are epigenetic regulator genes commonly mutated in age-related clonal hematopoiesis (CH). Despite having opposed epigenetic functions, these mutations are associated with increased all-cause mortality and a low risk for progression to hematologic neoplasms. Although individual impacts on the epigenome have been described using different model systems, the phenotypic complexity in humans remains to be elucidated. Here, we make use of a natural inflammatory response occurring during coronavirus disease 2019 (COVID-19), to understand the association of these mutations with inflammatory morbidity (acute respiratory distress syndrome [ARDS]) and mortality. We demonstrate the age-independent, negative impact of DNMT3A mutant (DNMT3Amt) CH on COVID-19–related ARDS and mortality. Using single-cell proteogenomics we show that DNMT3A mutations involve myeloid and lymphoid lineage cells. Using single-cell multiomics sequencing, we identify cell-specific gene expression changes associated with DNMT3A mutations, along with significant epigenomic deregulation affecting enhancer accessibility, resulting in overexpression of interleukin-32 (IL-32), a proinflammatory cytokine that can result in inflammasome activation in monocytes and macrophages. Finally, we show with single-cell resolution that the loss of function of DNMT3A is directly associated with increased chromatin accessibility in mutant cells. Hence, we demonstrate the negative prognostic impact of DNMT3Amt CH on COVID-19–related ARDS and mortality. DNMT3Amt CH in the context of COVID-19, was associated with inflammatory transcriptional priming, resulting in overexpression of IL32. This overexpression was secondary to increased chromatic accessibility, specific to DNMT3Amt CH cells. DNMT3Amt CH can thus serve as a potential biomarker for adverse outcomes in COVID-19.

Abstract Background DNMT3A and TET2 are epigenetic regulator genes commonly mutated in age-related clonal hematopoiesis (CH). Despite having opposing epigenetic functions, these mutations are associated with increased all-cause mortality and a low risk for progression to hematological neoplasms. While individual impacts on the epigenome have been described using different model systems, the phenotypic complexity in humans remains to be elucidated. Results Here we make use of a natural inflammatory response occurring during coronavirus disease 2019 (COVID-19), to understand the association of these mutations with inflammatory morbidity and mortality. We demonstrate the age-independent, negative impact of DNMT3A mutant CH on COVID-19-related cytokine release severity and mortality. Using single cell proteogenomics we show that DNMT3A mutations involve myeloid and lymphoid cells. Using single cell multiomics sequencing, we identify cell-specific gene expression changes associated with DNMT3A mutations, along with significant epigenomic deregulation affecting enhancer accessibility, resulting in overexpression of IL32, a proinflammatory cytokine that can result in inflammasome activation in monocytes and macrophages. Finally, we show with single cell resolution that the loss of function of DNMT3A is directly associated with increased chromatin accessibility in mutant cells. Conclusions We demonstrate the negative prognostic impact of DNMT3Amt CH on COVID-19 related inflammatory morbidity and mortality. DNMT3Amt CH involves myeloid and lymphoid cells and in the context of COVID-19, was associated with inflammatory transcriptional priming, resulting in overexpression of IL32. This overexpression was secondary to increased chromatic accessibility, specific to DNMT3Amt CH cells. DNMT3Amt CH can serve as a potential biomarker for adverse inflammatory outcomes.

Abstract Given the morbidity and mortality associated with colorectal cancer, novel biomarkers are clearly warranted, especially for early detection, particularly for advanced adenomas. An earlier 1317-plex aptamer-based proteomic screen of stool proteins revealed 92 proteins to be significantly elevated in CRC stool, with the most discriminatory proteins being MMP9, haptoglobin, myeloperoxidase, fibrinogen, and adiponectin (PMID: 34117903). Results from a recent 2000-plex proteomic screen of CRC stools are detailed below, while a 11,000-plex aptamer based proteomic screen is in progress. Comprehensive proteomic screening revealed 116 proteins to be differentially expressed in CRC stool compared to control stool, with 45 being elevated 2-fold or higher. 37 of these proteins were ELISA validated in 3 independent cohorts. Stool MMP-8, MMP-9, Hemoglobin, PGRP-S, Haptoglobin, Myeloperoxidase and Fibrinogen emerged as being most discriminatory for distinguishing CRC from healthy controls (AUC: 0.91-0.95), while Stool Fibrinogen, MMP-9, Hemoglobin, MMP-8, and PGRP-S best distinguished advanced adenoma from healthy control (HC) stool, with stool Fibrinogen topping the list with an ROC AUC value of 0.86, as detailed in Table 1. Functional pathway analysis revealed a significant over-representation of pathways related to anti-oxidant activity, integrin/receptor binding, cytokines, blood coagulation, and lipoprotein biosynthesis in patients with CRC compared to HC. Nuclear Factor IC (NFIC) and IKZF2 were identified as key regulators of the molecular cascades over-represented in CRC. Stool Fibrinogen, MMP-8, MMP-9, PGRP-S, Haptoglobin, and Myeloperoxidase emerge as promising biomarkers for distinguishing CRC/advanced adenomas/healthy stools, outperforming Hemoglobin. Thus, comprehensive proteomics and independent ELISA validation have uncovered novel stool biomarkers for identifying CRC and advanced adenomas, that warrant further head to head comparison against current diagnostic tests. Citation Format: Chandra Mohan, Kamala Vanarsa, Jessica Castillo, Robert Bresalier, Nicholas Chia. 11,000-plex aptamer based proteomics of colorectal cancer stools for the discovery of novel disease biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1845.

The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.

Abstract Background: COVID-19 causes significant morbidity and mortality, albeit with considerable heterogeneity among affected individuals. It remains unclear which host factors determine disease severity and survival. Given the propensity of clonal hematopoiesis (CH) to promote inflammation in healthy individuals, we investigated its effect on COVID-19 outcomes. Methods: We performed a multi-omics interrogation of the genome, epigenome, transcriptome, and proteome of peripheral blood mononuclear cells from COVID-19 patients (n=227). We obtained clinical data, laboratory studies, and survival outcomes. We determined CH status and TET2-related DNA methylation. We performed single-cell proteogenomics to understand clonal composition in relation to cell phenotype. We interrogated single-cell gene expression in isolation and in conjunction with DNA accessibility. We integrated these multi-omics data to understand the effect of CH on clonal composition, gene expression, methylation of cis-regulatory elements, and lineage commitment in COVID-19 patients. We performed shRNA knockdowns to validate the effect of one candidate transcription factor in myeloid cell lines. Results: The presence of CH was strongly associated with COVID-19 severity and all-cause mortality, independent of age (HR 3.48, 95% CI 1.45-8.36, p=0.005). Differential methylation of promoters and enhancers was prevalent in TET2-mutant, but not DNMT3A-mutant CH. TET2-mutant CH was associated with enhanced classical/intermediate monocytosis and single-cell proteogenomics confirmed an enrichment of TET2 mutations in these cell types. We identified cell-type specific gene expression changes associated with TET2 mutations in 102,072 single cells (n=34). Single-cell RNA-seq confirmed the skewing of hematopoiesis towards classical and intermediate monocytes and demonstrated the downregulation of EGR1 (a transcription factor important for monocyte differentiation) along with up-regulation of the lncRNA MALAT1 in monocytes. Combined scRNA-/scATAC-seq in 43,160 single cells (n=18) confirmed the skewing of hematopoiesis and up-regulation of MALAT1 in monocytes along with decreased accessibility of EGR1 motifs in known cis-regulatory elements. Using myeloid cell lines for functional validation, shRNA knockdowns of EGR1 confirmed the up-regulation of MALAT1 (in comparison to wildtype controls). Conclusions: CH is an independent prognostic factor in COVID-19 and skews hematopoiesis towards monocytosis. TET2-mutant CH is characterized by differential methylation and accessibility of enhancers binding myeloid transcriptions factors including EGR1. The ensuing loss of EGR1 expression in monocytes causes MALAT1 overexpression, a factor known to promote monocyte differentiation and inflammation. These data provide a mechanistic insight to the adverse prognostic impact of CH in COVID-19. Citation Format: Moritz Binder, Terra L. Lasho, Wazim Mohammed Ismail, Nana A. Ben-Crentsil, Jenna A. Fernandez, Minsuk Kim, Susan M. Geyer, Amelia Mazzone, Christy M. Finke, Abhishek A. Mangaonkar, Jeong-Heon Lee, Kwan Hyun Kim, Vernadette A. Simon, Fariborz Rakhshan Rohakthar, Amik Munankarmy, Susan M. Schwager, Jonathan J. Harrington, Melissa R. Snyder, Nathalie M. Droin, Eric Solary, Keith D. Robertson, Eric D. Wieben, Eric Padron, Nicholas Chia, Alexandre Gaspar-Maia, Mrinal M. Patnaik. Enhancer deregulation inTET2-mutant clonal hematopoiesis is associated with increased COVID-19 severity and mortality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 806.

Abstract Pre and post-licensure studies have supported the safety of the intranasally administered live, attenuated influenza vaccine (LAIV). The objective of this study was to measure changes to the nasal microbiome in subjects who received LAIV. We hypothesized that administration would result in only a transient alteration of the nasal microbiome. To test this hypothesis, we swabbed the anterior nares just before vaccination (Day 0), and on Day 7 and Day 28. We performed next-generation sequencing on amplified 16s rRNA genes. There was a significant increase in alpha diversity (Observed, CHAO, and ACE) from Days 0 to 7 (p = 0.02, 0.00, 0.01; respectively) and Days 0 to 28 (p = 0.05, 0.03, 0.05; respectively). When measuring differences in specific observed taxanomic units (OTUs), we identified a significant increase in Flavobacteriaceae and Enterobacteriaceae after vaccination. These data suggest that LAIV may increase the ability of these less abundant OTUs to establish a community niche and, therefore, could be clinically relevant. We have begun to investigate the second objective of this study, which is to correlate microbiome composition with mucosal immunity to influenza virus. To our knowledge, this is the first study to investigate LAIV-induced alterations to the human microbiome. These data may lead to a better understanding of the influences of the host microbiome on the response to vaccine, and perhaps infection.

Whole-genome sequencing (WGS) is rapidly replacing traditional typing methods for the investigation of infectious disease outbreaks. Additionally, WGS data are being used to predict phenotypic antimicrobial susceptibility. Acinetobacter baumannii , which is often multidrug-resistant, is a significant culprit in outbreaks in health care settings.