Biology subjects

Khurana, E.

Publications and source records attributed to Khurana, E..

At least 19 recordsLinked to original sources

bioRxiv2025

cfOncoXpress: Tumor gene expression prediction from cell-free DNA whole-genome sequences

Cell-free DNA (cfDNA) fragments in the plasma capture cellular nucleosomal profiles since nucleosome-protected regions escape enzymatic degradation while nucleosome-depleted regions can not. We developed cfOncoXpress, a machine learning framework that uses fragmentation patterns to predict oncogene expression from cfDNA WGS. cfOncoXpress incorporates gene copy number aberrations inferred from cfDNA, including those associated with extrachromosomal DNA. Its application in prostate and breast cancers shows it can predict tumor subtype based on expression of signature genes and activated pathways. cfOncoXpress shows superior performance relative to other state-of-the-art methods and can be used to predict tumor gene expression when tissue biopsies are infeasible.

genomics↗

bioRxiv2024

Three-dimensional regulatory hubs support oncogenic programs in glioblastoma

Dysregulation of enhancer-promoter communication in the context of the three-dimensional (3D) nucleus is increasingly recognized as a potential driver of oncogenic programs. Here, we profiled the 3D enhancer-promoter networks of primary patient-derived glioblastoma stem cells (GSCs) in comparison with neuronal stem cells (NSCs) to identify potential central nodes and vulnerabilities in the regulatory logic of this devastating cancer. Specifically, we focused on hyperconnected 3D regulatory hubs and demonstrated that hub-interacting genes exhibit high and coordinated expression at the single-cell level and strong association with oncogenic programs that distinguish IDH-wt glioblastoma patients from low-grade glioma. Epigenetic silencing of a recurrent 3D enhancer hub--with an uncharacterized role in glioblastoma--was sufficient to cause concordant downregulation of multiple hub-connected genes along with significant shifts in transcriptional states and reduced clonogenicity. By integrating published datasets from other cancer types, we also identified both universal and cancer type-specific 3D regulatory hubs which enrich for varying oncogenic programs and nominate specific factors associated with worse outcomes. Genetic alterations, such as focal duplications, could explain only a small fraction of the detected hyperconnected hubs and their increased activity. Overall, our study provides computational and experimental support for the potential central role of 3D regulatory hubs in controlling oncogenic programs and properties. HIGHLIGHTS- 3D regulatory "hubs" in glioblastoma enrich for highly coregulated genes at a single-cell level and expand oncogenic regulatory networks. - Targeted perturbation of a highly recurrent 3D regulatory hub in GSCs results in altered transcriptional states and cellular properties. - 3D regulatory hubs across cancer types associate with tumor-specific and universal oncogenic programs and worse outcomes. - The majority of hyperconnected hubs do not overlap with structural variants, suggesting epigenetic mechanisms. eTOCHere we profile the 3D enhancer connectomes of primary patient-derived human glioblastoma stem cells (GSCs), identify hyperconnected 3D regulatory "hubs", and examine the impact of 3D hub perturbation on the transcriptional program and oncogenic properties.

genomics↗

bioRxiv2024

TAD hierarchy restricts poised LTR activation and loss of TAD hierarchy promotes LTR co-option in cancer

Transposable elements (TEs) are abundant in the human genome, and they provide the sources for genetic and functional diversity. The regulation of TEs expression and their functional consequences in physiological conditions and cancer development remain to be fully elucidated. Previous studies suggested TEs are repressed by DNA methylation and chromatin modifications. The effect of 3D chromatin topology on TE regulation remains elusive. Here, by integrating transcriptome and 3D genome architecture studies, we showed that haploinsufficient loss of NIPBL selectively activates alternative promoters at the long terminal repeats (LTRs) of the TE subclasses. This activation occurs through the reorganization of topologically associating domain (TAD) hierarchical structures and recruitment of proximal enhancers. These observations indicate that TAD hierarchy restricts transcriptional activation of LTRs that already possess open chromatin features. In cancer, perturbation of the hierarchical chromatin topology can lead to co-option of LTRs as functional alternative promoters in a context-dependent manner and drive aberrant transcriptional activation of novel oncogenes and other divergent transcripts. These data uncovered a new layer of regulatory mechanism of TE expression beyond DNA and chromatin modification in human genome. They also posit the TAD hierarchy dysregulation as a novel mechanism for alternative promoter-mediated oncogene activation and transcriptional diversity in cancer, which may be exploited therapeutically.

cancer biology↗

bioRxiv2022

Tumor-intrinsic PRC2 inactivation drives a context-dependent immune-desert tumor microenvironment and confers resistance to immunotherapy

Immune checkpoint blockade (ICB) has demonstrated clinical success in "inflamed" tumors with significant T-cell infiltrates, but tumors with an immune-desert tumor microenvironment (TME) fail to benefit. The tumor cell-intrinsic molecular mechanisms of the immune-desert phenotype remain poorly understood. Here, we demonstrate that inactivation of the Polycomb-repressive complex 2 (PRC2) core components, EED or SUZ12, a prevalent genetic event in malignant peripheral nerve sheath tumor (MPNST) and sporadically in other cancer types, drives a context-dependent immune-desert TME. PRC2 inactivation reprograms the chromatin landscape that leads to a cell-autonomous shift from primed baseline signaling-dependent cellular responses (e.g., interferon {gamma}) to PRC2-regulated development and cellular differentiation transcriptional programs. Further, PRC2 inactivation reprograms the TME, leads to diminished tumor immune infiltrates and immune evasion through reduced chemokine production and impaired antigen presentation and T-cell priming, and confers ICB primary resistance through blunted T-cell recruitment in vivo. We demonstrate that strategies that enhancing innate immunity via intratumoral delivery of inactivated modified vaccinia virus Ankara (MVA) leads to increased tumor immune infiltrates and sensitizes PRC2-loss tumors to ICB. Our results provide novel molecular mechanisms of context-dependent dysfunctional epigenetic reprogramming that underline the immune-desert phenotype in MPNST and other cancers with PRC2 inactivation. Importantly, our findings highlight genetic-inactivation of PRC2 as a novel context-dependent ICB therapeutic resistance biomarker in cancer, and caution that therapeutic strategies that non-selectively target PRC2 in the host may lead to undesirable context-dependent immune evasion and ICB resistance in tumors. Our studies also point to intratumoral delivery of immunogenic therapeutic viruses as an initial strategy to modulate the immune-desert TME and capitalize on the clinical benefit of ICB.

cancer biology↗

bioRxiv2022

Discovery of novel therapeutic targets in cancer using patient-specific gene regulatory networks.

Most cancer types lack effective targeted therapeutic options and in cancers where first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of ATAC-seq and RNA-seq on patient biopsies in a high-throughput manner. Here we present a computational approach that leverages these datasets to identify novel drug targets based on tumor lineage. We constructed patient-specific gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained using three-dimensional genomic data for enhancer to promoter contacts. Next, we identify the key transcription factors (TFs) in these networks, which are used to identify therapeutic vulnerabilities either by direct targeting of TFs or proteins that they co-operate with. We validate four novel candidates identified for neuroendocrine, liver and renal cancers, which have a dismal prognosis with current therapeutic options. We present a novel approach to use the increasing amounts of functional genomics data from patient biospecimens for identification of novel drug targets.

systems biology↗

bioRxiv2021

Recapitulation of patient-specific 3D chromatin conformation using machine learning and validation of identified enhancer-gene targets

Regulatory networks containing enhancer to gene edges define cellular state and their rewiring is a hallmark of cancer. While efforts, such as ENCODE, have revealed these networks for reference tissues and cell-lines by integrating multi-omics data, the same methods cannot be applied for large patient cohorts due to the constraints on generating ChIP-seq and three-dimensional data from limited material in patient biopsies. We trained a supervised machine learning model using genomic 3D signatures of physical enhancer-gene connections that can predict accurate connections using data from ATAC-seq and RNA-seq assays only, which can be easily generated from patient biopsies. Our method overcomes the major limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers in different samples, which is a hallmark of network rewiring in cancer. Our model achieved an AUROC (area under receiver operating characteristic curve) of 0.91 and, importantly, can distinguish between active regulatory elements with connections to target genes and poised elements with no connections to target genes. Our predicted regulatory elements are validated by multi-omics data, including histone modification marks from ENCODE, with an average specificity of 0.92. Application of our model on chromatin accessibility and transcriptomic data from 400 cancer patients across 22 cancer types revealed novel cancer-type and subtype-specific enhancer-gene connections for known cancer genes. In one example, we identified two enhancers that regulate the expression of ESR1 in only ER+ breast cancer (BRCA) samples but not in ER-samples. These enhancers are predicted to contribute to the high expression of ESR1 in 93% of ER+ BRCA samples. Functional validation using CRISPRi confirms that inhibition of these enhancers decreases the expression of ESR1 in ER+ samples.

cancer biology↗

bioRxiv2021

Modeling tissue-specific breakpoint proximity of structural variations from 2,382 whole-genomes to identify cancer drivers

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, little is known about the genomic features related to the breakpoint distribution of SVs in different cancers, a prerequisite to distinguish loci under positive selection from those with neutral evolution. We developed a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in novel cancers that have previous evidence of therapeutic relevance in other cancers.

cancer biology↗

bioRxiv2021

Systems Biology Analysis of Human Genomes Points to Key Pathways Conferring Spina Bifida Risk

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance and environmental influences that hamper GWAS approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases vs. controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation and essential transcriptional regulation, indicating their impact on the pathogenesis of human SB. Additionally, interrogation of conserved non-coding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to interrogation of coding and non-coding sequence variant contributions to rare complex genetic disorders.

genomics↗

bioRxiv2020

Chromatin accessibility profiles of castration-resistant prostate cancers reveal novel subtypes and therapeutic vulnerabilities

In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR)-dependence due to lineage plasticity, which has become more prevalent, leads to clinically highly aggressive tumors with few therapeutic options and is mechanistically poorly defined. To identify the master transcription factors (TFs) of CRPC in a subtype-specific manner, we derived and collected 29 metastatic human prostate cancer organoids and cell lines, and generated ATAC-seq, RNA-seq and DNA sequencing data. We identified four subtypes and their master TFs using novel computational algorithms: AR-dependent; Wnt-dependent, driven by TCF; neuroendocrine, driven by ASCL1 and NEUROD1 and stem cell-like (SCL), driven by the AP-1 family. The transcriptomic signatures of these four subtypes enabled the classification of 370 patients. We find that AP-1 co-operates with the inhibitable YAP/TAZ/TEAD pathway in the SCL subtype, the second most common group of CRPC tumors after AR-dependent. Together, this molecular classification reveals new drug targets and can potentially guide therapeutic decisions.

cancer biology↗

bioRxiv2020

Developmental chromatin programs determine oncogenic competence in melanoma

Oncogenes are only transforming in certain cellular contexts, a phenomenon called oncogenic competence. The mechanisms regulating this competence remain poorly understood. Here, using a combination of a novel human pluripotent stem cell (hPSC)-based cancer model along with zebrafish transgenesis, we demonstrate that the transforming ability of BRAFV600E depends upon the intrinsic transcriptional program present in the cell of origin. Remarkably, in both systems, melanocytes (MC) are largely resistant to BRAF. In contrast, both neural crest (NC) and melanoblast (MB) populations are readily transformed. Molecular profiling reveals that NC/MB cells have markedly higher expression of chromatin modifying enzymes, and we discovered that the chromatin remodeler ATAD2 is required for response to BRAF and tumor initiation. ATAD2 forms a complex with SOX10, allowing for expression of downstream oncogenic programs. These data suggest that oncogenic competence is mediated by developmental regulation of chromatin factors, which then allow for proper response to those oncogenes.

cancer biology↗

bioRxiv2020

CNCDatabase: a database of non-coding cancer drivers

Most mutations in cancer genomes occur in the non-coding regions with unknown impact to tumor development. Although the increase in number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies and thus it is difficult to bridge the understanding of non-coding alterations, the genes they impact and the supporting evidence for their role in tumorigenesis across multiple cancer types. To address this gap, we have developed CNCDatabase, Cornell Non-Coding Cancer driver Database (https://cncdatabase.med.cornell.edu/) that contains detailed information about predicted non-coding drivers at gene promoters, 5 and 3 UTRs (untranslated regions), enhancers, CTCF insulators and non-coding RNAs. CNCDatabase documents 1,111 protein-coding genes and 90 non-coding RNAs with reported drivers in their non-coding regions from 32 cancer types by computational predictions of positive selection in whole-genome sequences; differential gene expression in samples with and without mutations; or another set of experimental validations including luciferase reporter assays and genome editing. The database can be easily modified and scaled as lists of non-coding drivers are revised in the community with larger whole-genome sequencing studies, CRISPR screens and further experimental validations. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations and their associations with gene expression in human cancers.

cancer biology↗

bioRxiv2020

Lineage reversion drives WNT independence in intestinal cancer

The WNT pathway is a fundamental regulator of intestinal homeostasis and hyperactivation of WNT signaling is the major oncogenic driver in colorectal cancer (CRC). To date, there are no described mechanisms that bypass WNT dependence in intestinal tumors. Here, we show that while WNT suppression blocks tumor growth in most organoid and in vivo CRC models, the accumulation of CRC-associated genetic alterations enables drug resistance and WNT-independent growth. In intestinal epithelial cells harboring mutations in KRAS or BRAF, together with disruption of p53 and SMAD4, transient TGF{beta} exposure drives YAP/ TAZ-dependent transcriptional reprogramming and lineage reversion. Acquisition of embryonic intestinal identity is accompanied by a permanent loss of adult intestinal lineages, and long-term WNT-independent growth. This work delineates genetic and microenvironmental factors that drive WNT inhibitor resistance, identifies a new mechanism for WNT-independent CRC growth and reveals how integration of associated genetic alterations and extracellular signals can overcome lineage-dependent oncogenic programs.

cancer biology↗

bioRxiv2019

LESSeq: Local event-based analysis of alternative splicing using RNA-Seq data

Alternative splicing, which can be observed genome-wide by RNA-Seq, is important in cellular development and evolution. Comparative RNA-Seq experiments between different cellular conditions allow alternative splicing signatures to be detected. However, inferring alternative splicing signatures from short-read technology is unreliable and still presents many challenges before biologically significant signatures may be identified. To enable the robust discovery of differential alternative splicing, we developed the Local Event-based analysis of alternative Splicing using RNA-Seq (LESSeq) pipeline. LESSeq utilizes information of local splicing events (i.e., the partial structures in genes where transcript-splicing patterns diverge) to identify unambiguous alternative splicing. In addition, LESSeq quantifies the abundance of these alternative events using Maximum Likelihood Estimation (MLE) and provides their significance between different cellular conditions. The utility of LESSeq is demonstrated through two case studies relevant to human variation and evolution. Using an RNA-Seq data set of lymphoblastoid cell lines in two human populations, we examined within-species variation and discovered population-differential alternative splicing events. With an RNA-Seq data set of several tissues in human and rhesus macaque, we studied cross-species variation and identified lineage-differential alternative splicing events. LESSeq is implemented in C++ and R, and made publicly available on GitHub at: https://github.com/gersteinlab/LESSeq

bioinformatics↗

bioRxiv2019

Loss-of-function tolerance of enhancers in the human genome

Previous studies have surveyed the potential impact of loss-of-function (LoF) variants and identified LoF-tolerant protein-coding genes. However, the tolerance of human genomes to losing enhancers has not yet been evaluated. Here we present the catalog of LoF-tolerant enhancers using structural variants from whole-genome sequences. Using a conservative approach, we estimate that each individual human genome possesses at least 28 LoF-tolerant enhancers on average. We assessed the properties of LoF-tolerant enhancers in a unified regulatory network constructed by integrating tissue-specific enhancers and gene-gene interactions. We find that LoF-tolerant enhancers are more tissue-specific and regulate fewer and more dispensable genes. They are enriched in immune-related cells while LoF-intolerant enhancers are enriched in kidney and brain/neuronal stem cells. We developed a supervised learning approach to predict the LoF-tolerance of enhancers, which achieved an AUROC of 96%. We predict 5,677 more enhancers would be likely tolerant to LoF and 75 enhancers that would be highly LoF-intolerant. Our predictions are supported by known set of disease enhancers and novel deletions from PacBio sequencing. The LoF-tolerance scores provided here will serve as an important reference for disease studies.

genomics↗

bioRxiv2019

A deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure

Three-dimensional structures of the genome play an important role in regulating the expression of genes. Non-coding variants have been shown to alter 3D genome structures to activate oncogenes in cancer. However, there is currently no method to predict the effect of DNA variants on 3D structures. We propose a deep learning method, DeepMILO, to learn DNA sequence features of CTCF/cohesin-mediated loops and to predict the effect of variants on these loops. DeepMILO consists of a convolutional and a recurrent neural network, and it can learn features beyond the presence of CTCF motifs and their orientations. Application of DeepMILO on a cohort of 241 malignant lymphoma patients with whole-genome sequences revealed CTCF/cohesin-mediated loops disrupted in multiple patients. These disrupted loops contain known cancer driver genes and novel genes. Our results show mutations at loop boundaries are associated with upregulation of the cancer driver gene BCL2 and may point to a possible new mechanism for its dysregulation via alteration of 3D loop structures.

bioinformatics↗

bioRxiv2018

Pathway and network analysis of more than 2,500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

cancer biology↗

bioRxiv2018

Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed \"putative passengers\") are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

genomics↗

bioRxiv2017

Germline determinants of the somatic mutation landscape in 2,642 cancer genomes

Cancers develop through somatic mutagenesis, however germline genetic variation can markedly contribute to tumorigenesis via diverse mechanisms. We discovered and phased 88 million germline single nucleotide variants, short insertions/deletions, and large structural variants in whole genomes from 2,642 cancer patients, and employed this genomic resource to study genetic determinants of somatic mutagenesis across 39 cancer types. Our analyses implicate damaging germline variants in a variety of cancer predisposition and DNA damage response genes with specific somatic mutation patterns. Mutations in the MBD4 DNA glycosylase gene showed association with elevated C>T mutagenesis at CpG dinucleotides, a ubiquitous mutational process acting across tissues. Analysis of somatic structural variation exposed complex rearrangement patterns, involving cycles of templated insertions and tandem duplications, in BRCA1-deficient tumours. Genome-wide association analysis implicated common genetic variation at the APOBEC3 gene cluster with reduced basal levels of somatic mutagenesis attributable to APOBEC cytidine deaminases across cancer types. We further inferred over a hundred polymorphic L1/LINE elements with somatic retrotransposition activity in cancer. Our study highlights the major impact of rare and common germline variants on mutational landscapes in cancer.

genomics↗