Biology subjects

Rajkumar, U.

Publications and source records attributed to Rajkumar, U..

6 recordsLinked to original sources

bioRxiv2025

Accurate Prediction of ecDNA in Interphase Cancer Cells using Deep Neural Networks

Oncogene amplification is a key driver of cancer pathogenesis and is often mediated by extrachromosomal DNA (ecDNA). EcDNA amplifications are associated with increased pathogenicity of cancer and poorer outcomes for patients. EcDNA can be detected accurately using fluorescence in situ hybridization (FISH) when cells are arrested in metaphase. However, the majority of cancer cells are non-mitotic and must be analyzed in interphase, where it is difficult to discern extrachromosomal amplifications from chromosomal amplifications. Thus, there is a need for methods that accurately predict oncogene amplification status from interphase cells. Here, we present interSeg, a deep learning-based tool to cytogenetically determine the amplification status as EC-amp, HSR-amp, or not amplified from interphase FISH images. We trained and validated interSeg on 652 images (40,446 nuclei). Tests on 215 cultured cell and tissue model images (9,733 nuclei) showed 89% and 97% accuracy at the nuclear and sample levels, respectively. The neuroblastoma patient tissue hold-out set (67 samples and 1,937 nuclei) also revealed 97% accuracy at the sample level in detecting the presence of focal amplification. In experimentally and computationally mixed images, interSeg accurately predicted the level of heterogeneity. The results showcase interSeg as an important method for analyzing oncogene amplifications.

cancer biology↗

bioRxiv2023

Breakage fusion bridge cycles drive high oncogene copy number, but not intratumoral genetic heterogeneity or rapid cancer genome change

Oncogene amplification is a major driver of cancer pathogenesis. Breakage fusion bridge (BFB) cycles, like extrachromosomal DNA (ecDNA), can lead to high copy numbers of oncogenes, but their impact on intratumoral heterogeneity, treatment response, and patient survival are not well understood due to difficulty in detecting them by DNA sequencing. We describe a novel algorithm that detects and reconstructs BFB amplifications using optical genome maps (OGMs), called OM2BFB. OM2BFB showed high precision (>93%) and recall (92%) in detecting BFB amplifications in cancer cell lines, PDX models and primary tumors. OM-based comparisons demonstrated that short-read BFB detection using our AmpliconSuite (AS) toolkit also achieved high precision, albeit with reduced sensitivity. We detected 371 BFB events using whole genome sequences from 2,557 primary tumors and cancer lines. BFB amplifications were preferentially found in cervical, head and neck, lung, and esophageal cancers, but rarely in brain cancers. BFB amplified genes show lower variance of gene expression, with fewer options for regulatory rewiring relative to ecDNA amplified genes. BFB positive (BFB (+)) tumors showed reduced heterogeneity of amplicon structures, and delayed onset of resistance, relative to ecDNA(+) tumors. EcDNA and BFB amplifications represent contrasting mechanisms to increase the copy numbers of oncogene with markedly different characteristics that suggest different routes for intervention.

cancer biology↗

bioRxiv2021

Principles of ecDNA random inheritance drive rapid genome change and therapy resistance in human cancers

The foundational principles of Darwinian evolution are variation, selection, and identity by descent. Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumour growth, drug resistance, and shorter survival in patients1-4. Currently, the impact of non-chromosomal oncogene inheritance--random identity by descent--is not well understood. Neither is the impact of ecDNA on variation and selection. Here, integrating mathematical modeling, unbiased image analysis, CRISPR-based ecDNA tagging, and live-cell imaging, we identify a set of basic "rules" for how random ecDNA inheritance drives oncogene copy number and distribution, resulting in extensive intratumoural ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted cancer treatment. Observed ecDNAs obligatorily benefit host cell survival or growth and can change within a single cell cycle. In studies ranging from well-curated, patient-derived cancer cell cultures to clinical tumour samples from patients with glioblastoma and neuroblastoma treated with oncogene-targeted drugs, we show how these ecDNA inheritance "rules" can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are entailed by their ability to rapidly change their genomes in a way that is not possible for cancers driven by chromosomal oncogene amplification. These results shed new light on how the non-chromosomal random inheritance pattern of ecDNA underlies poor outcomes for cancer patients.

cancer biology↗

bioRxiv2020

Indian Red Jungle fowl depicts close genetic relationship with Indian native chicken breeds as evidenced through whole mitochondrial genome intersection

Native chickens are dispersed in a wide range of geometry and they have influenced hereditary assets that are kept by farmers for various purposes. Mitochondrial DNA (mtDNA) is a widely utilized marker in molecular study because of its quick advancement, matrilineal legacy, and simple molecular structure. In a genomics study, it is important for understanding the origins, history, and adjustment of domestication. In this report, for the first time, we utilized Next-generation sequencing (NGS) to investigate the mitochondrial genomes and to evaluate the hereditary connections, diversity, and measure of gene stream estimation in seven Indian native chicken breeds along with twenty-two Asian native breeds. The absolute length of each mtDNA was 16775bp harboring 4 tRNA genes, 2 rRNA genes, 12 protein-coding genes, and 1 D-loop region. The chicken breeds were genotyped by using the D-loop region and 23 haplotypes were identified. In addition, when compared to only Indian native breeds more haplotypes were identified in the NADH dehydrogenase subunit (ND4 and ND5), Cytochrome c oxidase subunit (COXI and COXII), Cytochrome b, mitochondrial encoded ATP synthase membrane subunit 6, and Ribosomal RNA genes. The phylogenetic examination utilizing N-J computational algorithms indicated that the analyzed all native chicken breeds were divided into six significant clades: A, B, C, D, E, and F. All Indian native breeds are coming under the F clade and it says all Indian breeds are domesticated in India. Besides, the sequencing results effectively distinguished SNPs, INDELs, mutations, and variants in seven Indian native breeds. Additionally, our work affirmed that Indian Red Jungle Fowl is the origin of reference Red Jungle Fowl as well as all Indian breeds, which is reflected in the dendrogram as well as network analysis based on whole mtDNA and D-loop region. Albeit, Indian Red Jungle Fowl is distributed as an outgroup, proposing that this ancestry was reciprocally monophyletic. The seven Indian native chickens of entire mtDNA sequencing and disclosure of variations gave novel insights about adaptation mechanisms and the significance of important mtDNA variations in understanding the maternal lineages of native chickens.

evolutionary biology↗

bioRxiv2020

EcDNA hubs drive cooperative intermolecular oncogene expression

Extrachromosomal DNAs (ecDNAs) are prevalent in human cancers and mediate high oncogene expression through elevated copy number and altered gene regulation1. Gene expression typically involves distal enhancer DNA elements that contact and activate genes on the same chromosome2,3. Here we show that ecDNA hubs, comprised of ~10-100 ecDNAs clustered in the nucleus of interphase cells, drive intermolecular enhancer input for amplified oncogene expression. Single-molecule sequencing, single-cell multiome, and 3D enhancer connectome reveal subspecies of MYC-PVT1 ecDNAs lacking enhancers that access intermolecular and ectopic enhancer-promoter interactions in ecDNA hubs. ecDNA hubs persist without transcription and are tethered by BET protein BRD4. BET inhibitor JQ1 disperses ecDNA hubs, preferentially inhibits ecDNA oncogene transcription, and kills ecDNA+ cancer cells. Two amplified oncogenes MYC and FGFR2 intermix in ecDNA hubs, engage in intermolecular enhancer-promoter interactions, and transcription is uniformly sensitive to JQ1. Thus, ecDNA hubs are nuclear bodies of many ecDNAs tethered by proteins and platforms for cooperative transcription, leveraging the power of oncogene diversification and combinatorial DNA interactions. We suggest ecDNA hubs, rather than individual ecDNAs, as units of oncogene function, cooperative evolution, and new targets for cancer therapy.

cancer biology↗

bioRxiv2020

AmpliconReconstructor: Integrated analysis of NGS and optical mapping resolves the complex structures of focal amplifications in cancer

Oncogene amplification, a major driver of cancer pathogenicity, is often mediated through focal amplification of genomic segments. Recent results implicate extrachromosomal DNA (ecDNA) as the primary mechanism driving focal copy number amplification (fCNA) - enabling gene amplification, rapid tumor evolution, and the rewiring of regulatory circuitry. Resolving an fCNAs structure is a first step in deciphering the mechanisms of its genesis and the subsequent biological consequences. Here, we introduce a powerful new computational method, AmpliconReconstructor (AR), for integrating optical mapping (OM) of long DNA fragments (>150kb) with next-generation sequencing (NGS) to resolve fCNAs at single-nucleotide resolution. AR uses an NGS-derived breakpoint graph alongside OM scaffolds to produce high-fidelity reconstructions. After validating performance by extensive simulations, we used AR to reconstruct fCNAs in seven cancer cell lines to reveal the complex architecture of ecDNA, breakage-fusion-bridge cycles, and other complex rearrangements. By distinguishing between chromosomal and extrachromosomal origins, and by reconstructing the rearrangement signatures associated with a given fCNAs generative mechanism, AR enables a more thorough understanding of the origins of fCNAs, and their functional consequences.

bioinformatics↗