Bio

Bio


Alokkumar Jha, Ph.D., is a translational researcher working into healthcare data science and disease modeling using multi-dimensional omics and diagnostic imaging data.
He holds background with tumorigenesis, metastasis, tumor evolution and cell-cell communication. His earlier research yielded clinically actionable biomarkers for gynecologic cancers, breast cancer, pancreatic cancer, multiple myeloma, and prostate cancer.
He primarily focused on building the prediction model using artificial intelligence and deep learning for clinical patient stratification and drug dosage balancing. He also developed several novel methods for biomarker discovery such as graph motif mining, Kirchoff's law traversal, deep convolution neural network, and the semantic web.
His recent research is focused on explaining mosaicism genetics for cardiac amyloidosis and multiple myeloma.

Academic Appointments


Honors & Awards


  • (CLARIFY) Cancer Long Survivors Artificial Intelligence Follow Up Co-PI, European Commission Horizon2020 (H2020-SC1-DTH-2019) https://cordis.europa.eu/project/id/875160 (1 January 2020-31 December 2022)

Professional Education


  • PhD, Data Science Institute, National University of Ireland, Galway & Beth Israel Deaconess Medical Center (Exchange Student), Harvard University, Data Science, Cancer Genomics, Machine Learning, Biomarker Discovery (2019)
  • MS, Manipal University, Udupi, India, Medical Data Science, Genomics (2014)
  • BS, Memchandracharya North Gujarat University, Gujarat, India, Electronics and Communication (2010)

Publications

All Publications


  • A community effort to create standards for evaluating tumor subclonal reconstruction. Nature biotechnology Salcedo, A., Tarabichi, M., Espiritu, S. M., Deshwar, A. G., David, M., Wilson, N. M., Dentro, S., Wintersinger, J. A., Liu, L. Y., Ko, M., Sivanandan, S., Zhang, H., Zhu, K., Ou Yang, T. H., Chilton, J. M., Buchanan, A., Lalansingh, C. M., P'ng, C., Anghel, C. V., Umar, I., Lo, B., Zou, W., Simpson, J. T., Stuart, J. M., Anastassiou, D., Guan, Y., Ewing, A. D., Ellrott, K., Wedge, D. C., Morris, Q., Van Loo, P., Boutros, P. C. 2020; 38 (1): 97–107

    Abstract

    Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

    View details for DOI 10.1038/s41587-019-0364-z

    View details for PubMedID 31919445

  • GenomicsKG: A Knowledge Graph to Visualize Poly-Omics Data Journal of Advances in Health Jha, A., Khan, Y., Verma, G., Zehra, D., Mehmood, Q., Sahay, R., Rebholz-Schuhmann, D., Dangwal, S., d'Aquin, M. 2019; 2 (2): 70-84
  • Abstract 750: Gene Signatures to Distinguish Amyloid Cardiomyopathy Risk in Multiple Myeloma Patients Circulation Research Jha, A., Morgado, I., Lee, D. ., Alexander, K., Tsai, C., Schimmel, K., Ward, J., Witteles, R., Liedtke, M., Liao, R., Dangwal, S. 2019
  • Alteration in ventricular pressure stimulates cardiac repair and remodeling. Journal of molecular and cellular cardiology Unno, K., Oikonomopoulos, A., Fujikawa, Y., Okuno, Y., Narita, S., Kato, T., Hayashida, R., Kondo, K., Shibata, R., Murohara, T., Yang, Y., Dangwal, S., Sereti, K. I., Yiling, Q., Johnson, K., Jha, A., Sosnovik, D. E., Fann, Y., Liao, R. 2019

    Abstract

    The mammalian heart undergoes complex structural and functional remodeling to compensate for stresses such as pressure overload. While studies suggest that, at best, the adult mammalian heart is capable of very limited regeneration arising from the proliferation of existing cardiomyocytes, how myocardial stress affects endogenous cardiac regeneration or repair is unknown. To define the relationship between left ventricular afterload and cardiac repair, we induced left ventricle pressure overload in adult mice by constriction of the ascending aorta (AAC). One week following AAC, we normalized ventricular afterload in a subset of animals through removal of the aortic constriction (de-AAC). Subsequent monitoring of cardiomyocyte cell cycle activity via thymidine analog labeling revealed that an acute increase in ventricular afterload induced cardiomyocyte proliferation. Intriguingly, a release in ventricular overload (de-AAC) further increases cardiomyocyte proliferation. Following both AAC and de-AAC, thymidine analog-positive cardiomyocytes exhibited characteristics of newly generated cardiomyocytes, including single diploid nuclei and reduced cell size as compared to age-matched, sham-operated adult mouse myocytes. Notably, those smaller cardiomyocytes frequently resided alongside one another, consistent with local stimulation of cellular proliferation. Collectively, our data demonstrate that adult cardiomyocyte proliferation can be locally stimulated by an acute increase or decrease of ventricular pressure, and this mode of stimulation can be harnessed to promote cardiac repair.

    View details for DOI 10.1016/j.yjmcc.2019.06.006

    View details for PubMedID 31220468

  • One Size Does Not Fit All: Querying Web Polystores IEEE ACCESS Khan, Y., Zimmermann, A., Jha, A., Gadepally, V., D'Aquin, M., Sahay, R. 2019; 7: 9598–9617
  • Ranked MSD: A New Feature Ranking and Feature Selection Approach for Biomarker Identification Cross Domain Conference for Machine Learning and Knowledge Extraction co-located with ARES 2019 Verma, G., Jha, A., Rebholz-Schuhmann, D., Madden, M. G. 2019
  • Determination of system level alterations in host transcriptome due to Zika virus (ZIKV) Infection in retinal pigment epithelium SCIENTIFIC REPORTS Singh, P., Khatri, I., Jha, A., Pretto, C. D., Spindler, K. R., Arumugaswami, V., Giri, S., Kumar, A., Bhasin, M. K. 2018; 8: 11209

    Abstract

    Previously, we reported that Zika virus (ZIKV) causes ocular complications such as chorioretinal atrophy, by infecting cells lining the blood-retinal barrier, including the retinal pigment epithelium (RPE). To understand the molecular basis of ZIKV-induced retinal pathology, we performed a meta-analysis of transcriptome profiles of ZIKV-infected human primary RPE and other cell types infected with either ZIKV or other related flaviviruses (Japanese encephalitis, West Nile, and Dengue). This led to identification of a unique ZIKV infection signature comprising 43 genes (35 upregulated and 8 downregulated). The major biological processes perturbed include SH3/SH2 adaptor activity, lipid and ceramide metabolism, and embryonic organ development. Further, a comparative analysis of some differentially regulated genes (ABCG1, SH2B3, SIX4, and TNFSF13B) revealed that ZIKV induced their expression relatively more than dengue virus did in RPE. Importantly, the pharmacological inhibition of ABCG1, a membrane transporter of cholesterol, resulted in reduced ZIKV infectivity. Interestingly, the ZIKV infection signature revealed the downregulation of ALDH5A1 and CHML, genes implicated in neurological (cognitive impairment, expressive language deficit, and mild ataxia) and ophthalmic (choroideremia) disorders, respectively. Collectively, our study revealed that ZIKV induces differential gene expression in RPE cells, and the identified genes/pathways (e.g., ABCG1) could potentially contribute to ZIKV-associated ocular pathologies.

    View details for DOI 10.1038/s41598-018-29329-2

    View details for Web of Science ID 000439686700032

    View details for PubMedID 30046058

    View details for PubMedCentralID PMC6060127

  • Using Machine Learning to Distinguish Infected from Non-infected Subjects at an Early Stage Based on Viral Inoculation International Conference on Data Integration in the Life Sciences Verma, G., Jha, A., Rebholz-Schuhmann, D., Madden, M. G. 2018

    View details for DOI 10.1007/978-3-030-06016

  • Features’ compendium for machine learning in NGS data Analysis Journal of Advanced Research in Biology Jha, A., Khare, A., Randeep Singh, et al 2018; 1 (2)
  • Deep Convolution Neural Network Model to Predict Relapse in Breast Cancer Jha, A., Verma, G., Khan, Y., Mehmood, Q., Rebholz-Schuhmann, D., Sahay, R., Wani, M. A., Kantardzic, M., Sayedmouchaweh, M., Gama, J., Lughofer, E. IEEE. 2018: 351–58
  • Linked Data Based Multi-omics Integration and Visualization for Cancer Decision Networks International Conference on Data Integration in the Life Sciences Jha, A., Khan, Y., Mehmood, Q., Rebholz-Schuhmann, D., Sahay, R. 2018
  • FedS: Towards Traversing Federated RDF Graphs Mehmood, Q., Jha, A., Rebholz-Schuhmann, D., Sahay, R., Ordonez, C., Bellatreche, L. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 34–45
  • E-selectin ligands recognised by HECA452 induce drug resistance in myeloma, which is overcome by the E-selectin antagonist, GMI-1271 LEUKEMIA Natoni, A., Smith, T. G., Keane, N., McEllistrim, C., Connolly, C., Jha, A., Andrulis, M., Ellert, E., Raab, M. S., Glavey, S. V., Kirkham-McCarthy, L., Kumar, S. K., Locatelli-Hoops, S. C., Oliva, I., Fogler, W. E., Magnani, J. L., O'Dwyer, M. E. 2017; 31 (12): 2642–51

    Abstract

    Multiple myeloma (MM) is characterized by the clonal expansion and metastatic spread of malignant plasma cells to multiple sites in the bone marrow (BM). Recently, we implicated the sialyltransferase ST3Gal-6, an enzyme critical to the generation of E-selectin ligands, in MM BM homing and resistance to therapy. Since E-selectin is constitutively expressed in the BM microvasculature, we wished to establish the contribution of E-selectin ligands to MM biology. We report that functional E-selectin ligands are restricted to a minor subpopulation of MM cell lines which, upon expansion, demonstrate specific and robust interaction with recombinant E-selectin in vitro. Moreover, an increase in the mRNA levels of genes involved in the generation of E-selectin ligands was associated with inferior progression-free survival in the CoMMpass study. In vivo, E-selectin ligand-enriched cells induced a more aggressive disease and were completely insensitive to Bortezomib. Importantly, this resistance could be reverted by co-administration of GMI-1271, a specific glycomimetic antagonist of E-selectin. Finally, we report that E-selectin ligand-bearing cells are present in primary MM samples from BM and peripheral blood with a higher proportion seen in relapsed patients. This study provides a rationale for targeting E-selectin receptor/ligand interactions to overcome MM metastasis and chemoresistance.

    View details for DOI 10.1038/leu.2017.123

    View details for Web of Science ID 000417177100013

    View details for PubMedID 28439107

    View details for PubMedCentralID PMC5729350

  • Towards precision medicine: discovering novel gynecological cancer biomarkers and pathways using linked data JOURNAL OF BIOMEDICAL SEMANTICS Jha, A., Khan, Y., Mehdi, M., Karim, M., Mehmood, Q., Zappa, A., Rebholz-Schuhmann, D., Sahay, R. 2017; 8: 40

    Abstract

    Next Generation Sequencing (NGS) is playing a key role in therapeutic decision making for the cancer prognosis and treatment. The NGS technologies are producing a massive amount of sequencing datasets. Often, these datasets are published from the isolated and different sequencing facilities. Consequently, the process of sharing and aggregating multisite sequencing datasets are thwarted by issues such as the need to discover relevant data from different sources, built scalable repositories, the automation of data linkage, the volume of the data, efficient querying mechanism, and information rich intuitive visualisation.We present an approach to link and query different sequencing datasets (TCGA, COSMIC, REACTOME, KEGG and GO) to indicate risks for four cancer types - Ovarian Serous Cystadenocarcinoma (OV), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) - covering the 16 healthy tissue-specific genes from Illumina Human Body Map 2.0. The differentially expressed genes from Illumina Human Body Map 2.0 are analysed together with the gene expressions reported in COSMIC and TCGA repositories leading to the discover of potential biomarkers for a tissue-specific cancer.We analyse the tissue expression of genes, copy number variation (CNV), somatic mutation, and promoter methylation to identify associated pathways and find novel biomarkers. We discovered twenty (20) mutated genes and three (3) potential pathways causing promoter changes in different gynaecological cancer types. We propose a data-interlinked platform called BIOOPENER that glues together heterogeneous cancer and biomedical repositories. The key approach is to find correspondences (or data links) among genetic, cellular and molecular features across isolated cancer datasets giving insight into cancer progression from normal to diseased tissues. The proposed BIOOPENER platform enriches mutations by filling in missing links from TCGA, COSMIC, REACTOME, KEGG and GO datasets and provides an interlinking mechanism to understand cancer progression from normal to diseased tissues with pathway components, which in turn helped to map mutations, associated phenotypes, pathways, and mechanism.

    View details for DOI 10.1186/s13326-017-0146-9

    View details for Web of Science ID 000411379200001

    View details for PubMedID 28927463

    View details for PubMedCentralID PMC5606033

  • A linked data approach to discover HPV oncoprotiens and RB1 induced mutation associations for the retinoblastoma research Jha, A., Khan, Y., Rebholz-Schumann, D., Sahay, R. AMER ASSOC CANCER RESEARCH. 2017
  • Drug Dosage Balancing Using Large Scale Multi-omics Datasets Jha, A., Mehdi, M., Khan, Y., Mehmood, Q., Rebholz-Schuhmann, D., Sahay, R., Wang, F., Yao, L., Luo, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 81–100
  • Querying Web Polystores Khan, Y., Zimmermann, A., Jha, A., Rebholz-Schuhmann, D., Sahay, R., Nie, J. Y., Obradovic, Z., Suzumura, T., Ghosh, R., Nambiar, R., Wang, C., Zang, H., BaezaYates, R., Hu, Kepner, J., Cuzzocrea, A., Tang, J., Toyoda, M. IEEE. 2017: 3190–95
  • A 13-Glycosylation Gene Signature in Multiple Myeloma Can Predicts Survival and Identifies Candidates for Targeted Therapy (GiMM13) Connolly, C., Jha, A., Natoni, A., O'Dwyer, M. E. AMER SOC HEMATOLOGY. 2016
  • A Linked Data Visualiser for Finite Element Biosimulations INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING Mehdi, M., Khan, Y., Jares, J., Freitas, A., Jha, A., Sakellarios, A., Sahay, R. 2016; 10 (2): 219–45