Biology subjects

Arun, G.

Publications and source records attributed to Arun, G..

4 recordsLinked to original sources

bioRxiv2026

Patient-derived organoid xenografts reveal the multifaceted role of the lncRNA MALAT1 in breast cancer progression

Long non-coding RNAs (lncRNAs) have emerged as key regulators of tumor biology, however, thus far none have translated to cancer therapies. The lncRNA MALAT1 is overexpressed in more than 20 cancers, including breast cancer and has been shown to function via various mechanisms in a context-dependent manner, in 2D cell lines and mouse models. However, its functional role and therapeutic potential have not been evaluated in clinically relevant patient-derived models. We investigated the therapeutic potential of MALAT1-targeting antisense oligonucleotides (ASOs) for breast cancer, using clinically relevant 3D human patient-derived organoids (PDOs) and PDO-xenograft (PDO-X) models. We systematically evaluated the efficiency of MALAT1-targeting ASOs using a biobank of 28 PDO models. Across three independent PDO-X models of triple negative breast cancer (TNBC), MALAT1 depletion reproducibly drove widespread alternative splicing changes across all event types, with an enrichment of intron retention. Differentially spliced transcripts were enriched for targets of shared cancer-associated transcription factors, and MALAT1 knockdown specifically altered the relative abundance of previously unannotated splicing isoforms. Beyond tumor-intrinsic effects, tumor-specific MALAT1 depletion induced a consistent reduction in macrophage-associated gene signatures and reduced lung metastatic burden. Our data defines MALAT1s multifaceted role in TNBC, coordinating alternative splicing, tumor-stroma crosstalk, and metastatic progression. Our study provides strong preclinical evidence supporting MALAT1-targeted ASO therapy and establishes PDO-X models as a clinically relevant platform for functional interrogation of TNBC therapies. SignificanceUsing clinically relevant human PDO-X models, we show that MALAT1 influences alternative splicing, metastatic potential and the tumor microenvironment. These data support the potential of oncogenic lncRNA MALAT1 as a therapeutic target.

cancer biology↗

bioRxiv2022

Development and validation of an AI/ML platform for the discovery of splice-switching oligonucleotide targets

This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of novel and verifiable splice-switching oligonucleotide (SSO) targets in-silico. SSOs are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). To leverage the potential of AS research for therapeutic development, we created SpliceLearn, an AI/ML algorithm for the identification of modulatory SSO binding sites on pre-mRNA. SpliceLearn also predicts the identity of specific splicing factors whose binding to pre-mRNA is blocked by SSOs, adding considerable transparency to AI/ML-driven drug discovery and informing biological insights useful in further validation steps. Here we predicted NEDD4L exon 13 (NEDD4Le13) as a novel target in triple negative breast cancer (TNBC) and computationally designed an SSO to modulate NEDD4Le13. Targeting NEDD4Le13 with this SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGF{beta} pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data. SpliceLearn is part of the SpliceCore(R) platform, an AI/ML predictive ensemble for AS-based drug target discovery.

bioinformatics↗

bioRxiv2021

Patient-derived triple negative breast cancer organoids provide robust model systems that recapitulate tumor intrinsic characteristics

Triple negative breast cancer (TNBC) is an aggressive form of breast cancer with poor patient outcomes, and an unmet clinical need for targeted therapies and better model systems. Here, we developed and comprehensively characterized a diverse biobank of normal and breast cancer patient-derived organoids (PDOs) with a focus on TNBCs. PDOs recapitulated patient tumor intrinsic properties and a subset of PDOs can be propagated for long-term culture (LT-TNBCs). Single cell profiling of PDOs identified cell types and gene candidates affiliated with different aspects of cancer progression. The LT-TNBC organoids exhibit signatures of aggressive MYC-driven basal-like breast cancers and are largely comprised of luminal progenitor (LP)-like cells. The TNBC LP-like cells are distinct from normal LPs and exhibit hyperactivation of NOTCH and MYC signaling. Overall, our study validates TNBC PDOs as robust models for understanding breast cancer biology and progression, paving the way for personalized medicine and tailored treatment options. Statement of SignificanceA comprehensive analysis of TNBC patient-derived organoids is presented by genomic, transcriptomic, and in-vivo analyses, providing insights into cellular heterogeneity and mechanisms of tumorigenesis at the single cell level.

cancer biology↗

bioRxiv2019

Comprehensive analysis of structural variants in breast cancer genomes using single molecule sequencing

Improved identification of structural variants (SVs) in cancer can lead to more targeted and effective treatment options as well as advance our basic understanding of disease progression. We performed whole genome sequencing of the SKBR3 breast cancer cell-line and patient-derived tumor and normal organoids from two breast cancer patients using 10X/Illumina, PacBio, and Oxford Nanopore sequencing. We then inferred SVs and large-scale allele-specific copy number variants (CNVs) using an ensemble of methods. Our findings demonstrate that long-read sequencing allows for substantially more accurate and sensitive SV detection, with between 90% and 95% of variants supported by each long-read technology also supported by the other. We also report high accuracy for long-reads even at relatively low coverage (25x-30x). Furthermore, we inferred karyotypes from these data using our enhanced RCK algorithm to present a more accurate representation of the mutated cancer genomes, and find hundreds of variants affecting known cancer-related genes detectable only through long-read sequencing. These findings highlight the need for long-read sequencing of cancer genomes for the precise analysis of their genetic instability.

bioinformatics↗