Mustapha Abubakar, M.D., Ph.D.

Stadtman Investigator

Integrative Tumor Epidemiology Branch

NCI/DCEG

9609 Medical Center Drive
Room SG/7E222
Rockville, MD 20850

240-276-5091

mustapha.abubakar2@nih.gov

Research Topics

With the increasing uptake of population-based cancer screening programs around the world, the incidences of benign and precursor lesions on tissue biopsies performed for suspected breast, colorectal, prostate, or lung cancers are likely to continue to rise, underscoring the need for natural history studies into screening-detectable cancers. Accordingly, Dr. Mustapha Abubakar’s integrative research program in computational pathoepidemiology is focused on advancing scientific understanding into the role of tissue ecosystem disruption in the etiology, natural history, tumor heterogeneity, and clinical outcomes of screening-detectable cancers. Underpinning his line of research is the notion that cellular (e.g., epithelial cells, myoepithelial cells, pericytes, fibroblasts, endothelial cells, immune cells, etc.) and non-cellular (e.g., collagens, proteoglycans, glycoproteins, etc.) tissue components exist in a dynamic equilibrium during normal homeostasis, and that progressive disruption of this equilibrium precedes tumor development, impacting risk, progression, and tumor behavior.

Dynamic Tissue Ecosystems in Cancer Etiology and Natural History

Histologic tissue disorganization represents a highly promising but poorly developed area of biomarker research, presumably due to the limited capacity for tissue characterization on microscopy. Recent advances in digital pathology and artificial intelligence (AI) i.e., computational pathology, have ushered unparalleled opportunities for high-throughput, standardized, and reproducible profiling of histological images to extract complex, multidimensional tissue organizational features on a scale that has never before been possible by using conventional microscopy. Dr. Abubakar’s research aims to: (i) leverage state-of-the-art computational pathology methods to characterize tissues as dynamic ecosystems and to uncover novel tissue disruption phenotypes in normal, precursor, and tumor tissues; (ii) elucidate the genetic, lifestyle, and environmental causes of tissue ecosystem disruption; and (iii) determine how disruptions in organ-specific tissue ecosystems lead to cancer, including tumor etiologic heterogeneity. To advance this line of enquiry, Dr. Abubakar is conducting tissue-based studies within several studies in DCEG with epidemiologically-annotated tissue collections, including the Ghana Breast Health Study (GBHS); Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO); Polish Breast Cancer Study (PBCS); Komen Tissue Bank (KTB); and the CONNECT for cancer prevention cohort. He is also participating in international consortia, such as the Breast Cancer Association Consortium, where he is leading computational pathoepidemiology studies that, together with his other ongoing studies, are aimed at advancing scientific understanding into the role of “tissue-of-origin” in breast cancer etiologic heterogeneity.

Understanding Tumor-associated Tissue Ecosystems

In addition to the neoplastic parenchymal cells, tumors are comprised of a complex mixture of cellular and non-cellular elements that collectively comprise the tumor microenvironment (TME). Emerging evidence indicates that the TME impacts tumor biology, including disease progression, metastasis risk, and treatment response. Nevertheless, conventional approaches for studying tumor biology have focused mostly on the neoplastic parenchyma or, more recently, the TME as somewhat disparate entities. Dr. Abubakar’s research seeks to improve understanding of tumor biology and its impact on treatment response and clinical outcomes by adopting an ecosystem approach that studies both the tumor and TME as dynamically interconnected components of the tumor-associated ecosystem. By leveraging AI, in conjunction with several laboratory staining approaches, he is performing deep phenotypic profiling of tumor tissues to uncover novel features that can be linked with epidemiological and clinical data to inform prognosis and/or treatment decision-making for screening-detectable cancers.

Biography

Dr. Abubakar joined NCI as a postdoctoral fellow in the Integrative Tumor Epidemiology Branch (ITEB) in 2017, was promoted to a research fellow in 2020, and was appointed as an Earl Stadtman tenure-track investigator and selected for the NIH Distinguished Scholars Program in 2022. He earned his medical degree from Bayero University, Kano, Nigeria and trained as a pathologist in the Department of Pathology, Aminu Kano Teaching Hospital, Nigeria. He obtained his M.Sc. in epidemiology from the Imperial College London and his Ph.D. in molecular epidemiology, with concentration in computational pathology and epidemiology, from the University of London’s Institute of Cancer Research (ICR): Royal Cancer Hospital, London, United Kingdom. Dr. Abubakar has received numerous awards for his work, including the DCEG Fellows Award for Research Excellence (D-FARE), Intramural Research Award (IRA), Fellowship Achievement Award, and the AACR NextGen Star Award.

Selected Publications

  1. Abubakar M, Fan S, Bowles EA, Widemann L, Duggan MA, Pfeiffer RM, Falk RT, Lawrence S, Richert-Boe K, Glass AG, Kimes TM, Figueroa JD, Rohan TE, Gierach GL. Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics With Invasive Breast Cancer Risk. JNCI Cancer Spectr. 2021;5(3).
  2. Abubakar M, Zhang J, Ahearn TU, Koka H, Guo C, Lawrence SM, Mutreja K, Figueroa JD, Ying J, Lissowska J, Lyu N, Garcia-Closas M, Yang XR. Tumor-Associated Stromal Cellular Density as a Predictor of Recurrence and Mortality in Breast Cancer: Results from Ethnically Diverse Study Populations. Cancer Epidemiol Biomarkers Prev. 2021;30(7):1397-1407.
  3. Abubakar M, Chang-Claude J, Ali HR, Chatterjee N, Coulson P, Daley F, Blows F, Benitez J, Milne RL, Brenner H, Stegmaier C, Mannermaa A, Rudolph A, Sinn P, Couch FJ, Devilee P, Tollenaar RAEM, Seynaeve C, Figueroa J, Lissowska J, Hewitt S, Hooning MJ, Hollestelle A, Foekens R, Koppert LB, kConFab Investigators, Bolla MK, Wang Q, Jones ME, Schoemaker MJ, Keeman R, Easton DF, Swerdlow AJ, Sherman ME, Schmidt MK, Pharoah PD, Garcia-Closas M. Etiology of hormone receptor positive breast cancer differs by levels of histologic grade and proliferation. Int J Cancer. 2018;143(4):746-757.
  4. Abubakar M, Figueroa J, Ali HR, Blows F, Lissowska J, Caldas C, Easton DF, Sherman ME, Garcia-Closas M, Dowsett M, Pharoah PD. Combined quantitative measures of ER, PR, HER2, and KI67 provide more prognostic information than categorical combinations in luminal breast cancer. Mod Pathol. 2019;32(9):1244-1256.
  5. Abubakar M, Orr N, Daley F, Coulson P, Ali HR, Blows F, Benitez J, Milne R, Brenner H, Stegmaier C, Mannermaa A, Chang-Claude J, Rudolph A, Sinn P, Couch FJ, Devilee P, Tollenaar RA, Seynaeve C, Figueroa J, Sherman ME, Lissowska J, Hewitt S, Eccles D, Hooning MJ, Hollestelle A, Martens JW, van Deurzen CH, kConFab Investigators, Bolla MK, Wang Q, Jones M, Schoemaker M, Wesseling J, van Leeuwen FE, Van 't Veer L, Easton D, Swerdlow AJ, Dowsett M, Pharoah PD, Schmidt MK, Garcia-Closas M. Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups. Breast Cancer Res. 2016;18(1):104.

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This page was last updated on Monday, December 19, 2022