Functional Genomics Research Articles

What is the role of microbial biotechnology and genetic engineering in medicine?

Microbial products are essential for developing various therapeutic agents, including antibiotics, anticancer drugs, vaccines, and therapeutic enzymes. Genetic engineering techniques, functional genomics, and synthetic biology unlock previously uncharacterized natural products. This review highlights major advances in microbial biotechnology, focusing on gene-based technologies for medical applications.

Target identification and validation in research

Target identification is a critical step in biomedical research because it lays the groundwork for the development of new therapies and drugs. Genetic research, including genome-wide association studies (GWAS), genomic sequencing, functional genomics, and data integration, is crucial for understanding disease genetics and potential treatment targets. Transcriptomics and proteomics give data on gene and protein expression, making it easier to identify targets in dysregulated diseases. Target identification is essential for drug discovery, precision medicine, lowering medication attrition, increasing therapeutic efficacy, and, eventually, transforming patient care and drug development. Target validation is a critical stage in drug development because it verifies that revealed molecular targets play a substantial role in disease progression and are therefore suitable for treatment. It employs a range of approaches, including genetic validation, pharmacological validation, and animal model validation. Target validation assures that discovered targets are physiologically relevant, druggable, and have a direct impact on disease processes, thereby reducing pharmaceutical attrition, promoting precision medicine, and hastening therapeutic development. Historically, target identification relied on limited knowledge, typically through candidate-based techniques based on assumptions or prior observations. Target validation experiments looked into how gene knockdown or RNA interference affected illness symptoms. Genomics, proteomics, and functional genomics have all made advances in recent years, as have high-throughput screening and data integration. CRISPR-based technologies and high-throughput sequencing have assisted in the validation of targets. Single-cell validation, machine learning and artificial intelligence, advanced in vitro models like organoids, and patient-derived models will all help to make future assessments of target relevance and treatment responses more precise and individualized. These developments have the potential to dramatically revolutionize research target identification and validation.

Identification of Alzheimer’s Disease Hub Genes Based on Improved HITS Algorithm

Alzheimer's disease is a severe, neurodegenerative condition that gradually breaks memories, thinking abilities, and the ability to carry out even the most basic tasks. The hub genes of AD were examined in this study. They understand how interactions between proteins and non-protein substances are crucial to understanding how proteins work. Network investigations of protein-protein interactions, in particular, help understand biological issues. This article offers a novel approach to identifying essential proteins using weighted PPI networks and Hyperlink-Induced Topic Search (HITS) algorithm. We discovered the top 10 hub genes linked to AD using a protein network analysis: AKT1, TGFB1, GRB2, NFKB1, PIK3CA, PIK3R1, TNF, IFNG, VEGFA, and TP53. It was discovered by gene enrichment that most gene activities might be categorized as vital to the plasma membrane, including engagement in signaling cascades, G-protein composite reliability activation, and cell contact. The prioritized genes were determined by the convergent functional genomics ranking AKT1, TGFB1, GRB2, NFKB1, PIK3CA, PIK3R1, TNF, IFNG, VEGFA, and TP53. To better understand AD pathophysiology and find new biomarkers or medication targets for AD treatment, these molecular pathways hub genes will be helpful.

DrivR-Base: a feature extraction toolkit for variant effect prediction model construction.

Recent advancements in sequencing technologies have led to the discovery of numerous variants in the human genome. However, understanding their precise roles in diseases remains challenging due to their complex functional mechanisms. Various methodologies have emerged to predict the pathogenic significance of these genetic variants. Typically, these methods employ an integrative approach, leveraging diverse data sources that provide important insights into genomic function. Despite the abundance of publicly available data sources and databases, the process of navigating, extracting, and pre-processing features for machine learning models can be highly challenging and time-consuming. Furthermore, researchers often invest substantial effort in feature extraction, only to later discover that these features lack informativeness. In this article, we introduce DrivR-Base, an innovative resource that efficiently extracts and integrates molecular information (features) related to single nucleotide variants. These features encompass information about the genomic positions and the associated protein positions of a variant. They are derived from a wide array of databases and tools, including structural properties obtained from AlphaFold, regulatory information sourced from ENCODE, and predicted variant consequences from Variant Effect Predictor. DrivR-Base is easily deployable via a Docker container to ensure reproducibility and ease of access across diverse computational environments. The resulting features can be used as input for machine learning models designed to predict the pathogenic impact of human genome variants in disease. Moreover, these feature sets have applications beyond this, including haploinsufficiency prediction and the development of drug repurposing tools. We describe the resource's development, practical applications, and potential for future expansion and enhancement. DrivR-Base source code is available at https://github.com/amyfrancis97/DrivR-Base.

Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the complexity of the sequence to regulatory function mapping (the cis-regulatory code), it has been suggested that the genome contains insufficient sequence variation to train models with suitable complexity. Data augmentation is a widely used approach to increase the data variation available for model training, however current data augmentation methods for genomic sequence data are limited. Inspired by the success of comparative genomics, we show that augmenting genomic sequences with evolutionarily related sequences from other species, which we term phylogenetic augmentation, improves the performance of deep learning models trained on regulatory genomic sequences to predict high-throughput functional assay measurements. Additionally, we show that phylogenetic augmentation can rescue model performance when the training set is down-sampled and permits deep learning on a real-world small dataset, demonstrating that this approach improves data efficiency. Overall, this data augmentation method represents a solution for improving model performance that is applicable to many supervised deep-learning problems in genomics. The open-source GitHub repository agduncan94/phylogenetic_augmentation_paper includes the code for rerunning the analyses here and recreating the figures.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Molecular mechanisms involved in alcohol craving, IRF3, and endoplasmic reticulum stress: a multi-omics study

Alcohol use disorder (AUD) is the most prevalent substance use disorder worldwide. Acamprosate and naltrexone are anti-craving drugs used in AUD pharmacotherapy. However, molecular mechanisms underlying their anti-craving effect remain unclear. This study utilized a patient-derived induced pluripotent stem cell (iPSC)-based model system and anti-craving drugs that are used to treat AUD as “molecular probes” to identify possible mechanisms associated with alcohol craving. We examined the pathophysiology of craving and anti-craving drugs by performing functional genomics studies using iPSC-derived astrocytes and next-generation sequencing. Specifically, RNA sequencing performed using peripheral blood mononuclear cells from AUD patients with extreme values for alcohol craving intensity prior to treatment showed that inflammation-related pathways were highly associated with alcohol cravings. We then performed a genome-wide assessment of chromatin accessibility and gene expression profiles of induced iPSC-derived astrocytes in response to ethanol or anti-craving drugs. Those experiments identified drug-dependent epigenomic signatures, with IRF3 as the most significantly enriched motif in chromatin accessible regions. Furthermore, the activation of IRF3 was associated with ethanol-induced endoplasmic reticulum (ER) stress which could be attenuated by anti-craving drugs, suggesting that ER stress attenuation might be a target for anti-craving agents. In conclusion, we found that craving intensity was associated with alcohol consumption and treatment outcomes. Our functional genomic studies suggest possible relationships among craving, ER stress, IRF3 and the actions of anti-craving drugs.

DiffGR: Detecting Differentially Interacting Genomic Regions from Hi-C Contact Maps

Abstract Recent advances in high-throughput chromosome conformation capture (Hi-C) techniques have allowed us to map genome-wide chromatin interactions and uncover higher-order chromatin structures, thereby shedding light on the principles of genome architecture and functions. However, statistical methods for detecting changes in large-scale chromatin organization such as topologically associating domains (TADs) are still lacking. We proposed a new statistical method, DiffGR, for detecting differentially interacting genomic regions at the TAD level between Hi-C contact maps. We utilized the stratum-adjusted correlation coefficient to measure similarity of local TAD regions. We then developed a nonparametric approach to identify statistically significant changes of genomic interacting regions. Through simulation studies, we demonstrated that DiffGR can robustly and effectively discover differential genomic regions under various conditions. Furthermore, we successfully revealed cell type-specific changes in genomic interacting regions in both human and mouse Hi-C datasets, and illustrated that DiffGR yielded consistent and advantageous results compared with state-of-the-art differential TAD detection methods. The DiffGR R package is published under the GNU General Public License (GPL) ≥ 2 license and is publicly available at https://github.com/wmalab/DiffGR.

Bioactivity assessment of organophosphate flame retardants via a dose-dependent yeast functional genomics approach

Organophosphate flame retardants (OPFRs) have been widely detected in multiple environment media and have many adverse effects with complex toxicity mechanisms. However, the early molecular responses to OPFRs have not been fully elucidated, thereby making it difficult to assess their risks accurately. In this work, we systematically explored the point of departure (POD) of biological pathways at genome-wide level perturbed by 14 OPFRs with three substituents (alkyl, halogen, and aryl) using a dose-dependent functional genomics approach in Saccharomyces cerevisiae at 24 h exposure. Firstly, our results demonstrated that the overall biological potency at gene level (PODDRG20) ranged from 0.013 to 35.079 μM for 14 OPFRs, especially the tributyl phosphate (TnBP) exhibited the strongest biological potency with the least PODDRG20. Secondly, we found that structural characteristics of carbon number and logKow were significantly negatively correlated with POD, and carbon number and logKow also significantly affected lipid metabolism associated processes. Thirdly, these early biological pathways of OPFRs toxification were found to be involved in lipid metabolism, oxidative stress, DNA damage, MAPK signaling pathway, and amino acid and carbohydrate metabolism, among which the lipid metabolism was the most sensitive molecular response perturbed by most OPFRs. More importantly, we identified one resistant mutant strain with knockout of ERG2 (YMR202W) gene participated in steroid biosynthesis pathway, which can serve as a key yeast strain of OPFRs toxification. Overall, our study demonstrated an effective platform for accurately assessing OPFRs risks and provided a basis for further green OPFRs development.

Abstract 4333: Identification of glucose-6-phosphate dehydrogenase dependency in IDH1 mutant glioma cells using functional genomics

Abstract Astrocytoma and oligodendroglioma are most commonly initiated by missense mutations at the arginine 132 codon of isocitrate dehydrogenase 1 (IDH1). Biochemically the mutant IDH1 protein acquires neomorphic activity: reductive NADPH-dependent catalysis of α-ketoglutarate (αKG) to (R)-2-hydroxyglutarate [(R)-2-HG]. (R)-2-HG interferes with the function of αKG-dependent epigenetic modifiers, resulting in extensive epigenetic remodeling that impairs cellular differentiation and promotes gliomagenesis. Strategies for selectively targeting IDH1-mutant cells offer promise for improved treatment over the standard modalities of surgery, chemotherapy and radiotherapy. Whole-genome CRISPR/Cas9 knockout screens were conducted to identify gene dependencies in isogenic IDH1-mutant and wild-type (WT) U-87 MG cells. Cells were transduced with the Brunello single guide RNA (sgRNA) library, grown for 21 days, then sgRNA distributions were assessed by amplicon sequencing. Gene dependencies were identified using CRISPRcleanR and BAGELR pipelines. We discovered that glucose-6-phosphate dehydrogenase (G6PD), a pentose phosphate pathway (PPP) enzyme, was needed for viability of IDH1-mutant cells, but was dispensable for U-87 MG WT cells. The PPP is the main producer of cytosolic NADPH that is required for de novo lipogenesis, maintenance of antioxidants and production of (R)-2-HG by mutant IDH1. Previous reports demonstrated that IDH1-mutant cells increase PPP flux to support these processes. We have validated this dependency using G6PDi-1, a cell-active inhibitor of G6PD. Growth inhibition (IC50) assays demonstrated that IDH1-mutant U-87 MG cells were threefold more sensitive to G6PDi-1 with IC50 of 23 μM and enhanced cell death (increased propidium iodide uptake), compared with WT U-87 MG cells with IC50 of 67 μM. G6PD knockout was then conducted to further validate this dependency. Nucleofection of IDH1 WT U-87 MG cells with G6PD multi guide RNA-Cas9 ribonucleoproteins resulted in highly efficient knockout of G6PD at day 5 (99-100%), which was maintained at day 21 (96-98%). In contrast, efficient G6PD knockout in IDH1-mutant U-87 MG cells at day 5 (98-100%) was lost following 21 days culture (0-5%), indicating death of almost all edited cells within the culture. Finally, Kaplan-Meier analysis of IDH1-mutant patients in the TCGA lower grade glioma dataset demonstrated that higher G6PD expression correlated with worse median overall survival (7.3 versus 9.5 years; log-rank p=0.01), but was not prognostic in the IDH1 WT TCGA glioblastoma multiforme dataset. These findings demonstrate that a cell line model of IDH1-mutant glioma is highly dependent on G6PD for survival. This observation builds on the recognized importance of the PPP in the metabolic biology of IDH1-mutant malignancy by establishing G6PD as a targetable dependency in this disease. Citation Format: Kim M. Paras, Pooja Patel, Paul Choi, William R. Wilson, Tet-Woo Lee, Stephen M. Jamieson, Dean C. Singleton. Identification of glucose-6-phosphate dehydrogenase dependency in IDH1 mutant glioma cells using functional genomics [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 4333.

Abstract 2945: Using pooled minimal genome-wide sgRNA libraries to drive oncology drug discovery

Abstract The emergence of CRISPR technologies has enabled rapid and precise gene editing opportunities. The Functional Genomics Centre (FGC) - a joint venture between Cancer Research UK’s (CRUK) drug discovery engine, Cancer Research Horizons, and AstraZeneca has been launched to better utilize and improve the CRISPR technology within cancer research. The FGC leverages the strengths of both CRUK’s and AstraZeneca’s world-class oncology research and drug discovery experience, with the potential to drive scientific innovation in how new drug targets are identified and validated, and uniquely democratize access to the latest cutting-edge research tools and expertise for many thousands of scientists and clinicians funded by CRUK. Here, we will showcase how the FGC is leading the development and refinement of functional genomics technologies. Through independent evaluation of publicly available CRISPR tools and leveraging this data we have designed optimized, minimal genome-wide CRISPR libraries. These libraries permit a reduction of the experimental scale of CRISPR screens by 50% and enable our Target Discovery platform to impact more projects, faster, at a lower cost and importantly - more sustainably. Citation Format: Sebastian Lukasiak, Alex Kalinka, Nikhil Gupta, Khalid Saeed, Angelos Papadopoulos, Ultan McDermott, Gregory J. Hannon, Douglas Ross-Thriepland, David Walter. Using pooled minimal genome-wide sgRNA libraries to drive oncology drug discovery [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 2945.

Abstract 3352: A CRISPR-enabled function genomics profiling platform in acute leukemia: Updates from a pilot study of Function (Fx) Heme utilizing fresh patient samples

Abstract Introduction: In this study we utilized a novel primary cell CRISPR/Cas9 profiling approach to identify gene dependencies in fresh acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL) patient samples. The primary aim of this prospective study was to demonstrate the feasibility of the CRISPR-based personalized functional genomic testing with fresh patient samples in the clinical setting. Methods: AML and ALL patient peripheral blood samples (10-16 mL) were obtained at time of initial diagnosis and/or at relapse, and if feasible post-treatment. Primary cells were transduced with lentivirus harboring Cas9 enzyme and a ‘drug targets’ sgRNA panel. Transduced cells were harvested at early (input) and Day 8 timepoints, and sgRNA distribution was assessed by amplicon sequencing of DNA barcodes. Changes in barcode abundance were quantitated and aggregated to calculate gene-level phenotype scores, enabling identification of gene dependencies. Results: 37 patients were included in the study, 23 with AML, 9 with Philadelphia (Ph) (+) ALL, 5 with Ph (-) ALL. The majority of patients had adverse or poor risk disease. The average age of patients was 52.6, range 18-84. 23 were male, 14 female; 20 identified as white, 17 as other. 20 patients were sampled at initial diagnosis, 12 were sampled at time of relapse and 5 at time of refractory disease. Serial samples were collected from 9 patients. 34 patients received treatment following sample collection. The majority of patients received intensive chemotherapy or a hypomethylating agent with venetoclax, however 15 patients received some form of targeted therapy. At the time of the abstract submission, Fx Heme results were available for 23 patients and among these, we identified potential treatment targets in 10 samples (43%). Importantly, 60% of the positive samples have a dependency on a gene clinically targetable by an approved leukemia drug. In addition, our profiling platform enabled the discovery of novel druggable dependencies in 100% of the positive samples. Conclusions: With this pilot study we demonstrated that utilizing small volume fresh patient samples to assess CRISPR-defined gene dependencies on targeted therapies in AML and ALL is feasible, timely, and results in clear Fx Heme dependency gene output. Our data indicates that CRISPR-based comprehensive functional profiling has the potential to add valuable information beyond traditional NGS methods to determine the optimal therapy for patients with AML and ALL. Future directions include translating further into the clinical setting and correlating Fx Heme dependence with response to targeted treatment in acute leukemia patients. Citation Format: Kaitlyn C. Dykes, Tuyet R. Tan, Karl Hodel, Kathleen Tran, Maci Meyers, Leonardo Hagmann, Michele Baltay, Peter Kilfeather, Yali Li, Christian Schmedt, Srinath Sampath, Srihari Sampath, Tiffany N. Tanaka, Stephane Corbel. A CRISPR-enabled function genomics profiling platform in acute leukemia: Updates from a pilot study of Function (Fx) Heme utilizing fresh patient samples [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 3352.

Abstract 3580: Understanding and overcoming innate and acquired resistance to type I and II RAF inhibitors in anaplastic thyroid cancer using translational functional genomics

Abstract Anaplastic thyroid cancer (ATC) is one of the most lethal human cancers, with some patients succumbing to the disease within weeks of diagnosis. Despite a subset of BRAFV600E mutant ATC patients responding to monomeric type I RAF inhibitor (RAFi) dabrafenib in combination with MEK inhibitor (MEKi) trametinib, almost all patients rapidly develop adaptive or acquired resistance. These patients, along with those who do not harbor the BRAFV600E alteration, have limited treatment options. To understand the mechanism of resistance to dabrafenib and trametinib, we utilized multi-region whole genome sequencing and single nuclei profiling of ATC patient tumors to unravel genomic, transcriptomic, and microenvironmental evolution during type I RAFi and MEKi therapy. Single-cell nuclei sequencing of matched primary and resistant ATC patient tumors identified transcriptomic reactivation of MAPK-pathway, along with immunosuppressive macrophage proliferation, underlie the development of acquired resistance. Our translation genomics led us to hypothesize that deeper inhibition of the MAPK-pathway can be efficacious in overcoming treatment resistance. Screening of a panel of type II RAFis revealed that ATC cell lines are exquisitely sensitive to the type II RAFi naporafenib. We further demonstrate that naporafenib in combination with MEKi trametinib can durably and robustly overcome both innate and acquired treatment resistance to dabrafenib and trametinib using ATC cell lines and patient-derived xenograft models, including in a PDX model from an ATC patient who developed acquired resistance to dabrafenib and trametinib. Finally, we describe a novel mechanism of acquired resistance to type II RAF inhibitor and MEK inhibitor through compensatory mutations in MAST1. Taken together, our work using translational and functional genomics have unraveled the differential mechanisms of treatment resistance to type I and type II RAFi in combination with trametinib, and rationalizes the clinical investigation of type II RAFi in the setting of thyroid cancer. Citation Format: Peter Zeng, Jalna Meens, John Barrett, Matthew J. Cecchini, Sarah B. Ryan, Nachuan Pan, Amir Karimi, Laura Jarycki, Alice E. Dawson, Mushfiq Shaikh, David Palma, Eric Winquist, Paul C. Boutros, Laurie Ailles, Anthony C. Nichols. Understanding and overcoming innate and acquired resistance to type I and II RAF inhibitors in anaplastic thyroid cancer using translational functional genomics [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 3580.

Abstract 950: Functional characterization of atypical somatic variants in the Gundersen Precision Oncology Cohort

Abstract The generation and widespread sharing of large somatic mutation datasets has revolutionized our understanding of genetic mechanisms driving cancer initiation and progression. For well-understood mutations, this has created the opportunity for individualized therapeutic intervention in a wide range of malignancies. Beyond these well-characterized mutations, there exists a wide range of alterations that remain poorly understood and which require some kind of functional genomics analysis to determine whether such patients are eligible for the targeted therapy used for more typical mutations in that gene, whether these alterations are passenger mutations that don’t affect throughput on the associated signaling pathway, or reflect another more nuanced phenomenon. Whether using ectopic overexpression in cell lines or generation of genetically modified mice, addressing this problem at scale using functional genomics approaches is challenging. A key limitation is the absence of the original tissue context in which that variant co-evolved with other genetic alterations as well as data on the clinical response of the patient to successive lines of therapy. This can be addressed, in part, by the banking of tissue from patient cohorts who have had comprehensive genomic profiling as part of their routine clinical care. The Gundersen Precision Oncology Cohort is a growing dataset of FFPE specimens and longitudinal clinical data from more than 750 patients who have had comprehensive cancer genome profiling using assays from a wide range of diagnostic companies. We use this resource for biomarker discovery and functional classification of many atypical cancer variants. Current subgroups include atypical point mutations in BRAF, ERBB2 and ERBB3, as well as fusions affecting BRAF, PIK3CA and ROS1. We present examples illustrating the value of this approach for classification of variants detected in a range of tumor types. Citation Format: Peter Feiszt, Paraic A. Kenny. Functional characterization of atypical somatic variants in the Gundersen Precision Oncology Cohort [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 950.

Abstract 1699: Defining the impact of aberrant transcription on the chromatin structure

Abstract Genome organization in humans is governed by two major mechanisms: loop extrusion by cohesin and CTCF and the spatial compartmentalization of the chromosomes. These folding mechanisms have been shown to regulate several genomic functions, including gene expression. However, how transcription might regulate the chromatin structure remains a subject of many ongoing investigations. In this study, we use the Integrator protein complex as a tool to understand the impact of aberrant transcription on the 3D structure of the genome. During transcription initiation, RNA Polymerase-II (Pol-II) often pauses proximally to the promoter before further elongating transcripts. The Integrator protein complex regulates this checkpoint by its endonuclease and phosphatase activity. This protein complex is essential in premature transcription termination in hundreds of protein-coding genes. It also facilitates the synthesis of non-coding (ncRNA), such as enhancer RNAs (eRNAs) and long non-coding RNAs (lncRNAs), which have been shown to support loop extrusion domains, and their absence may disrupt these domains. Using HiC sequencing, we found that depleting Integrator proteins can interfere with forming and maintaining loop domains. This study aims to be the first to decipher the relationship between chromosome structure and Integrator protein activity and address our very little knowledge of how misregulation of transcription termination may influence the folding of chromosomes. Citation Format: Naiju Thomas, Timothy E. Reznicek, Erez Lieberman Aiden, M. Jordan Rowley, Eric Wagner, Guy Nir. Defining the impact of aberrant transcription on the chromatin structure [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 1699.

OsRR26, a type-B response regulator, modulates salinity tolerance in rice via phytohormone-mediated ROS accumulation in roots and influencing reproductive development.

OsRR26 is a cytokinin-responsive response regulator that promotes phytohormone-mediated ROS accumulation in rice roots, regulates seedling growth, spikelet fertility, awn development, represses NADPH oxidases, and negatively affects salinity tolerance. Plant two-component systems (TCS) play a pivotal role in phytohormone signaling, stress responses, and circadian rhythm. However, a significant knowledge gap exists regarding TCS in rice. In this study, we utilized a functional genomics approach to elucidate the role of OsRR26, a type-B response regulator in rice. Our results demonstrate that OsRR26 is responsive to cytokinin, ABA, and salinity stress, serving as the ortholog of Arabidopsis ARR11. OsRR26 primarily localizes to the nucleus and plays a crucial role in seedling growth, spikelet fertility, and the suppression of awn development. Exogenous application of cytokinin led to distinct patterns of reactive oxygen species (ROS) accumulation in the roots of both WT and transgenic plants (OsRR26OE and OsRR26KD), indicating the potential involvement of OsRR26 in cytokinin-mediated ROS signaling in roots. The application of exogenous ABA resulted in varied cellular compartmentalization of ROS between the WT and transgenic lines. Stress tolerance assays of these plants revealed that OsRR26 functions as a negative regulator of salinity stress tolerance across different developmental stages in rice. Physiological and biochemical analyses unveiled that the knockdown of OsRR26 enhances salinity tolerance, characterized by improved chlorophyll retention and the accumulation of soluble sugars, K+ content, and amino acids, particularly proline.

Abstract 4631: Evaluation of the impact of homozygous MTAP truncations on the activity and selectivity of MTA-cooperative PRMT5 inhibitors

Abstract Homozygous deletion of the MTAP gene occurs in 10-15% of all human cancers. To benefit this large and diverse patient population, MTA-cooperative PRMT5 inhibitors, including TNG908 and TNG462, have been developed to leverage the synthetic lethal relationship between MTAP deletion and PRMT5 inhibition. MTA-cooperative PRMT5 inhibitors selectively bind the PRMT5-MTA complex driving selective inhibition of PRMT5 in MTAP-deleted cancers while sparing normal, MTAP-proficient cells. Our PRMT5 inhibitors are currently in Phase I/II clinical trials (NCT05275478 and NCT05732831), and eligibility is restricted to patients with tumors with confirmed MTAP loss either detected by next-generation sequencing (NGS) or immunohistochemistry. MTAP gene loss occurs in cancers because of its chromosomal proximity to one of the most common genetically altered tumor suppressor genes, CDKN2A, but the chromosomal 9p breakpoints for the co-deletion are not uniform. Indeed, while clinical NGS testing and preclinical data confirm that homozygous intragenic MTAP breakpoints occur, the functional consequence of any given breakpoint on MTAP enzymatic activity and protein function remains unknown. Given the potential implications for homozygous intragenic MTAP deletions to impact the clinical response to MTA-cooperative PRMT5 inhibitors, we have started to evaluate the loss-of-function phenotype of various MTAP truncations to determine whether they retain MTAP activity. Here, we present our initial functional genomics analysis of this important diagnostic biomarker using in vitro cDNA reconstitution approaches for MTAP activity combined with analysis of PRMT5 inhibitor sensitivity. Ultimately, these data may help refine patient enrollment on clinical trials to drive the maximum benefit for patients with MTAP-deleted cancers. Citation Format: Matthew R. Tonini, Andre A. Mignault, Douglas A. Whittington, Steven A. Lombardo, Binzhang Shen, Hannah Stowe, Satoshi Yoda, Shangtao Liu, Minjie Zhang, Kevin M. Cottrell, Samuel R. Meier, Heidi Rego, Jennifer Morawiak, Ellen Hooper, Yi Yu, Heather DiBenedetto, Adam S. Crystal, Teng, Kimberly Briggs. Evaluation of the impact of homozygous MTAP truncations on the activity and selectivity of MTA-cooperative PRMT5 inhibitors [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 4631.

Abstract 3242: TNG917 is a clinical-grade, potent and selective inhibitor of EHMT1/2 for the treatment of immune cold tumors

Abstract CRISPR-based functional genomics screening can be designed to identify novel cancer cell intrinsic targets that increase tumor immunogenicity. Using a FACS-based CRISPR sorting screen for PD-L1 expression, we identified Euchromatic histone-lysine-N-methyltransferase 1 and 2 (EHMT1/2) as negative modulators of the interferon signaling pathways. EHMT1 and EHMT2 are histone methyltransferases that mono- and di-methylate lysine 9 of histone H3 to repress gene transcription of defined target genes. Gene knockout or pharmacological inhibition of EHMT1/2 in cancer cells resulted in de-repression of gene promoters, upregulation of interferon-stimulated genes (ISGs), and secretion of pro-inflammatory cytokines. Here, we present the preclinical characterization of TNG917 - an oral and highly selective EHMT1/2 inhibitor with low nanomolar cellular potency, and favorable pharmacodynamic and pharmacokinetic properties. In humanized and syngeneic mouse models, treatment with TNG917 in combination with anti-PD1 promoted a T-cell-infiltrated tumor microenvironment, led to significant anti-tumor activity, and resulted in survival benefit. In summary, our in vitro and in vivo studies provide a rationale for the clinical development path of TNG917, in combination with checkpoint inhibition, in patients with immune cold tumors. Citation Format: Alvin Lu, Brian Haines, Ji Lei, Wenhai Zhang, Minjie Zhang, Douglas A. Whittington, Maria Lucia Ferdinez, Yi Yu, Samuel R. Meier, Ashley Choi, Alborz Bejnood, Madhavi Bandi, Katherine Lazarides, Xuewen Pan, Lina Gu, Alice W. Tsai, Sirimas Sudsakorn, Colin Liang, Jon Come, Brett Williams, Scott Throner, Joseph Vacca, John P. Maxwell, Jannik N. Anderson, Alan Huang, Chengyin Min, Yingnan P. Chen. TNG917 is a clinical-grade, potent and selective inhibitor of EHMT1/2 for the treatment of immune cold tumors [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 3242.

Abstract 6043: Discovery and characterization of a p300-selective degrader with potent anti-tumor activity in CBP mutant cancers

Abstract Although precision medicine strategies for the treatment of human cancers are clinically successful, they have also revealed that only a small fraction of cancers carry actionable alterations in oncogenic drivers. To expand the druggable genome while maintaining an emphasis on genetically targeted therapies, recent efforts have focused on exploiting synthetic lethal relationships. Paralogous protein pairs are particularly compelling targets in this context due to the clear mechanistic basis for the synthetic lethality. As such, functional genomics screens as well as work from Ogiwara et al. have shown compelling rationale for targeting the histone acetylase transferase p300 in the context of CBP-deficient cancer types. We discovered potent heterobifunctional degraders with demonstrated selectivity for p300 over CBP. Our compound exhibits degradation of p300 within 2 hours (DC50 < 10nM, Dmax > 90%) with minimal impact on CBP through 48 h (DC50 >1uM, Dmax = 30%). Utilizing CRISPR-generated isogenic cell lines depleted for either CBP or p300, we observed significant growth inhibition in CBP knockout (KO) cells (gIC50 = 17nM) with minimal impact on growth of p300 KO (gIC50 = >10uM) or parental wild-type cells (gIC50 > 10uM). This translated to downstream pharmacology, where we observed a rapid and potent inhibition of global H3K27 acetylation in the CBP deficient context but a significantly attenuated response in p300 deleted or parental cells. We subsequently employed computational methods to identify and characterize CBP alterations, defining a putative loss-of-function (LoF) phenotype and confirming this sensitivity pharmacologically in cancer cell types harboring these endogenous mutations. Results mirrored what we observed with CBP depletion in these endogenous CBP LoF cell lines, with potent growth inhibition seen in multiple cell lines including LK-2 (gIC50 = 8nM), H1703 (gIC50 = 9nM), and TE-8 cells (gIC50 = 23nM). To confirm the in vitro results in an in vivo setting, we evaluated the response in mice harboring H1703 xenografts. Once daily oral administration of our compound led to almost complete degradation of p300 within H1703 xenograft tumors, resulting in a pronounced inhibition of tumor growth. Lastly, we profiled p300 degradation in human bone marrow derived myeloid progenitor colony-forming assays where we observed a significant reduction in toxicity (IC50 = 3.9uM) compared to a dual CBP/p300 inhibitor (IC50 = 121nM) or degrader (IC50 = 16nM), supporting the hypothesis that a p300-specific mechanism will offer an improved therapeutic index while maintaining anti-tumor efficacy in a biomarker population Taken together, our results suggest that a p300-selective degrader has the potential to serve as an effective therapeutic modality in CBP-mutated cancers. Citation Format: Mike R. Russell, Cassandra L. Lowenstein, Xuqing Zhang, Jeremy Roach, Jianing Song, Rakesh Nagilla, Nathan Kendsersky, Shreyas Joshi, Peter Orth, Matt Tudor, Qiaolin Deng, Clemente Aguilar-Bonavides, Elham Behshad, Sudeep Banjade, Zhihua Sui, Corey Strickland, Larry Jolivette, Helai P. Mohammad. Discovery and characterization of a p300-selective degrader with potent anti-tumor activity in CBP mutant cancers [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 6043.

Abstract 2500: Technical feasibility of CRISPR-based functional profiling for solid tumor precision medicine

Abstract Introduction: Next generation sequencing (NGS) has revolutionized precision oncology by enabling identification of cancer patients harboring actionable mutations who may benefit from corresponding targeted therapies. However, sequencing-based biomarkers are not detected for most patients and imperfectly predict response to therapy even when present. To address this unmet need, we have developed a CRISPR-based diagnostics platform for ‘personalized functional genomics’, allowing identification of patient-specific therapeutic vulnerabilities irrespective of mutation status. We have previously applied this approach to primary hematologic malignancies, and here report application to solid tumor-derived samples. Methods: Patient-derived solid tumor samples from several tumor types including breast, ovarian, colon and lung were obtained from 3 sources: fresh tissue (obtained from patients via core biopsy, surgical resection, or malignant fluid sampling), primary Patient-Derived Cell Lines (PDCLs, generated from surgical material and adapted for ex vivo culture), and commercially obtained viably frozen dissociated tumor cells (DTCs). Samples were dissociated to single cell suspensions and transduced with a lentivirus encoding Cas9 and an sgRNA library covering a custom targeted oncology gene panel. The panel consists of genes encoding targets of FDA-approved oncology drugs, as well as reference genes. After a short culture period, transduced cells were harvested and relative sgRNA distribution was measured by amplicon NGS. Changes in sgRNA abundance were collapsed into gene-level log2-fold changes, and significantly depleted genes scored as functional dependencies. For select PDCLs, cells were treated with cognate small molecule inhibitors over the course of 4 days. Cell viability was read out using CTG assay and a calculated IC50 lower than 500nM inferred as inhibitor sensitive. Results: In a double-blind study using primary PDCLs, functional dependencies identified by CRISPR perfectly discriminated alpelisib sensitive (2/2) from resistant samples (6/6). Using fresh primary samples from varying cancer types and specimen sources, we achieve a technical success rate >70%. Among gene dependencies identified, targets of FDA-approved oncology drugs and those under late-stage clinical investigation were both observed including BRAF, CDK4/6, VEGFR1/2 and PARP1/2. Conclusion: We demonstrate technical proof of concept of the FX platform for CRISPR-based personalized functional genomics. This approach is feasible when applied to primary patient samples from a variety of tumor types and tissue sources, and results are validated by comparison with orthogonal methods. CRISPR-based functional profiling represents a powerful adjunct to traditional genomic profiling, for the first time allowing drug target dependency mapping in a patient-specific and mutation-agnostic manner. Citation Format: Karl Hodel, Andreas Varkaris, Nicholas Chevalier, Maci Meyers, Allison Kehlmann, Stephane Corbel, Anastasia Stavridi, Peter Kilfeather, Leonardo Hagmann, Kathleen Tran, Yali Li, Christian Schmedt, Ioannis Sanidas, Srinath Sampath, Srihari Sampath, Dejan Juric. Technical feasibility of CRISPR-based functional profiling for solid tumor precision medicine [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 2500.