Ekta Khurana, Ph.D.

Assistant Professor of Physiology and Biophysics

  • Assistant Professor of Computational Genomics in Computational Biomedicine in the Institute for Computational Biomedicine


1305 York Avenue, Room Y-13.06
New York, NY 10021


Research Areas

Research Summary:

The research interests of the lab fall under the broad categories of genomics, computational biology and systems biology. We participate in multiple international genomics consortia and collaborate with scientists at Weill Cornell to develop novel approaches to understand the role of sequence variants in human disease. The decreasing costs of genome sequencing are leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. This is also the case for somatic variants in cancer. An average cancer genome contains thousands of somatic variants – but the functional implications of these variants on cancer progression and growth are not clear. We develop integrative computational models to understand the relationship between genomic sequence variation and disease. The impact of sequence variants in non-protein-coding regions of the genome is especially less-well-understood. We have developed muliple computational approaches (for example, FunSeq and RegNetDriver) that integrate large-scale data from multiple resources to identify the DNA point mutations and rearrangements in protein-coding genes and non-coding regulatory regions leading to human disease, in particular cancer.

Recent Publications:

  1. Han, T, Goswami, S, Hu, Y, Tang, F, Zafra, MP, Murphy, C et al.. Lineage reversion drives WNT independence in intestinal cancer. Cancer Discov. 2020; :. doi: 10.1158/2159-8290.CD-19-1536. PubMed PMID:32546576 .
  2. Xu, D, Gokcumen, O, Khurana, E. Loss-of-function tolerance of enhancers in the human genome. PLoS Genet. 2020;16 (4):e1008663. doi: 10.1371/journal.pgen.1008663. PubMed PMID:32243438 PubMed Central PMC7159235.
  3. Trieu, T, Martinez-Fundichely, A, Khurana, E. DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure. Genome Biol. 2020;21 (1):79. doi: 10.1186/s13059-020-01987-4. PubMed PMID:32216817 PubMed Central PMC7098089.
  4. Kumar, S, Warrell, J, Li, S, McGillivray, PD, Meyerson, W, Salichos, L et al.. Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences. Cell. 2020;180 (5):915-927.e16. doi: 10.1016/j.cell.2020.01.032. PubMed PMID:32084333 PubMed Central PMC7210002.
  5. PCAWG Transcriptome Core Group, Calabrese, C, Davidson, NR, Demircioğlu, D, Fonseca, NA, He, Y et al.. Genomic basis for RNA alterations in cancer. Nature. 2020;578 (7793):129-136. doi: 10.1038/s41586-020-1970-0. PubMed PMID:32025019 PubMed Central PMC7054216.
  6. Rheinbay, E, Nielsen, MM, Abascal, F, Wala, JA, Shapira, O, Tiao, G et al.. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature. 2020;578 (7793):102-111. doi: 10.1038/s41586-020-1965-x. PubMed PMID:32025015 PubMed Central PMC7054214.
  7. Li, Y, Roberts, ND, Wala, JA, Shapira, O, Schumacher, SE, Kumar, K et al.. Patterns of somatic structural variation in human cancer genomes. Nature. 2020;578 (7793):112-121. doi: 10.1038/s41586-019-1913-9. PubMed PMID:32025012 PubMed Central PMC7025897.
  8. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature. 2020;578 (7793):82-93. doi: 10.1038/s41586-020-1969-6. PubMed PMID:32025007 PubMed Central PMC7025898.
  9. Carlevaro-Fita, J, Lanzós, A, Feuerbach, L, Hong, C, Mas-Ponte, D, Pedersen, JS et al.. Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis. Commun Biol. 2020;3 (1):56. doi: 10.1038/s42003-019-0741-7. PubMed PMID:32024996 PubMed Central PMC7002399.
  10. Reyna, MA, Haan, D, Paczkowska, M, Verbeke, LPC, Vazquez, M, Kahraman, A et al.. Pathway and network analysis of more than 2500 whole cancer genomes. Nat Commun. 2020;11 (1):729. doi: 10.1038/s41467-020-14367-0. PubMed PMID:32024854 PubMed Central PMC7002574.
  11. Paczkowska, M, Barenboim, J, Sintupisut, N, Fox, NS, Zhu, H, Abd-Rabbo, D et al.. Integrative pathway enrichment analysis of multivariate omics data. Nat Commun. 2020;11 (1):735. doi: 10.1038/s41467-019-13983-9. PubMed PMID:32024846 PubMed Central PMC7002665.
  12. Zhang, Y, Chen, F, Fonseca, NA, He, Y, Fujita, M, Nakagawa, H et al.. High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations. Nat Commun. 2020;11 (1):736. doi: 10.1038/s41467-019-13885-w. PubMed PMID:32024823 PubMed Central PMC7002524.
  13. Shuai, S, PCAWG Drivers and Functional Interpretation Working Group, Gallinger, S, Stein, L, PCAWG Consortium. Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nat Commun. 2020;11 (1):734. doi: 10.1038/s41467-019-13929-1. PubMed PMID:32024818 PubMed Central PMC7002750.
  14. Liu, EM, Martinez-Fundichely, A, Diaz, BJ, Aronson, B, Cuykendall, T, MacKay, M et al.. Identification of Cancer Drivers at CTCF Insulators in 1,962 Whole Genomes. Cell Syst. 2019;8 (5):446-455.e8. doi: 10.1016/j.cels.2019.04.001. PubMed PMID:31078526 PubMed Central PMC6917527.
  15. Bailey, MH, Tokheim, C, Porta-Pardo, E, Sengupta, S, Bertrand, D, Weerasinghe, A et al.. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018;174 (4):1034-1035. doi: 10.1016/j.cell.2018.07.034. PubMed PMID:30096302 .
  16. Backenroth, D, He, Z, Kiryluk, K, Boeva, V, Pethukova, L, Khurana, E et al.. FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications. Am. J. Hum. Genet. 2018;102 (5):920-942. doi: 10.1016/j.ajhg.2018.03.026. PubMed PMID:29727691 PubMed Central PMC5986983.
  17. Bailey, MH, Tokheim, C, Porta-Pardo, E, Sengupta, S, Bertrand, D, Weerasinghe, A et al.. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018;173 (2):371-385.e18. doi: 10.1016/j.cell.2018.02.060. PubMed PMID:29625053 PubMed Central PMC6029450.
  18. Kim, J, Geyer, FC, Martelotto, LG, Ng, CK, Lim, RS, Selenica, P et al.. MYBL1 rearrangements and MYB amplification in breast adenoid cystic carcinomas lacking the MYB-NFIB fusion gene. J. Pathol. 2018;244 (2):143-150. doi: 10.1002/path.5006. PubMed PMID:29149504 PubMed Central PMC5839480.
  19. Dhingra, P, Martinez-Fundichely, A, Berger, A, Huang, FW, Forbes, AN, Liu, EM et al.. Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network. Genome Biol. 2017;18 (1):141. doi: 10.1186/s13059-017-1266-3. PubMed PMID:28750683 PubMed Central PMC5530464.
  20. Romanel, A, Garritano, S, Stringa, B, Blattner, M, Dalfovo, D, Chakravarty, D et al.. Inherited determinants of early recurrent somatic mutations in prostate cancer. Nat Commun. 2017;8 (1):48. doi: 10.1038/s41467-017-00046-0. PubMed PMID:28663546 PubMed Central PMC5491529.
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