Explore this Laboratory


The Sarcoma Computational Biology Laboratory is under the direction of Dr. Dimitrios Spentzos, MD, M.MSc. The laboratory collaborates with a number of oncology, radiation oncology, and pathology investigators at the Center for Sarcoma and Connective Tissue Oncology and the Mass General Cancer Center. It also works closely with other Harvard Medical School research affiliates such as the Department of Biostatistics at the Harvard School of Public Health, and the Harvard Initiative for RNA Medicine (HIRM) and the Pediatric Oncology Program at Boston Children’s Hospital.

The mission of the laboratory is to uncover the mechanisms of tumor behavior and understand how they show different responses to conventional and novel treatments, but studying genome-wide DNA, RNA, microRNA, and methylation profiles and determining their relationship with biologic and clinical outcomes. We utilize microarray and deep sequencing platforms and advanced biostatistical and computational analyses methods to detect biologic and clinical signal in highly dimensional and often noisy genomic data. Our studies aim to develop molecular patterns that can function as biomarkers to guide treatment strategies and can also be used as functional “read outs” of response to therapy.

In addition, we are exploring novel molecular classification approaches to sarcomas based on genomic profiling, that can complement traditional histology-based classification, as it is recognized that if often does not capture the entire biologic diversity of sarcomas or their differential clinical response patterns. For example, novel insights into osteosarcoma subtypes may be gained by recent work on the role of a non-coding 14q32 chromosome based cluster in osteosarcoma. Finally, in close collaboration with our computational biology and bioinformatics affiliate centers we are interested in assisting in the development and implementation of novel bioinformatics approaches to analyze highly dimensional genomic data in a clinically and biologically relevant manner.

Our laboratory is funded by the NIH and is also part of Cancer Center efforts to utilize generous philanthropic contributions to cancer research at Mass General.

Team Members

Principal Investigator:


Research Technician

  • Christopher Lietz

Research Projects

  • MicroRNA patterns and their role in osteosarcoma
  • Methylation biomarkers of osteosarcoma outcome
  • Genomic biomarkers of response to novel cancer treatments
  • Novel bioinformatics methodologies to assess clinically and biologically relevant patterns in highly dimensional genomic data (collaborating with the Center for Cancer Computational Biology at DFCI)

Research Positions

The laboratory generally employs research assistants with a clear academic orientation, who typically move on to graduate (PhD, MD, or MD/PhD) studies, and we are in the process of expanding with new and higher-level post graduate positions. In addition, we provide mentorship to undergraduate and graduate students who are interested to participate in our research.

Selected Publications

  • Lietz CE, Garbutt C, Barry WT, Deshpande V, Chen YL, Lozano-Calderon SA, Wang Y, Lawney B, Ebb D, Cote GM, Duan Z, Hornicek FJ, Choy E, Petur Nielsen G, Haibe-Kains B, Quackenbush J, Spentzos D. MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma. Sci Rep. 2020 03 10; 10(1):4409. PMID: 32157112.
  • Guarnerio J, Zhang Y, Cheloni G, Panella R, Katon JM, Simpson M, Matsumoto A, Papa A, Loretelli C, Petri A, Kauppinen S, Garbutt C, Nielsen GP, Deshpande V, Castillo-Martin M, Cordon-Cardo C, Spentzos D, Clohessy JG, Batish M, Pandolfi PP. Author Correction: Intragenic antagonistic roles of protein and circRNA in tumorigenesis. Cell Res. 2020 Feb; 30(2):188. PMID: 31911670.
  • Lozano Calderón SA, Garbutt C, Kim J, Lietz CE, Chen YL, Bernstein K, Chebib I, Nielsen GP, Deshpande V, Rubio R, Wang YE, Quackenbush J, Delaney T, Raskin K, Schwab J, Cote G, Spentzos D. Clinical and Molecular Analysis of Pathologic Fracture-associated Osteosarcoma: MicroRNA profile Is Different and Correlates with Prognosis. Clin Orthop Relat Res. 2019 Sep; 477(9):2114-2126. PMID: 31389890.
  • Hill KE, Kelly AD, Kuijjer ML, Barry W, Rattani A, Garbutt CC, Kissick H, Janeway K, Perez-Atayde A, Goldsmith J, Gebhardt MC, Arredouani MS, Cote G, Hornicek F, Choy E, Duan Z, Quackenbush J, Haibe-Kains B, Spentzos D. An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets. J Hematol Oncol. 2017 05 15; 10(1):107. PMID: 28506242.
  • Osaka E, Kelly AD, Spentzos D, Choy E, Yang X, Shen JK, Yang P, Mankin HJ, Hornicek FJ, Duan Z. MicroRNA-155 expression is independently predictive of outcome in chordoma. Oncotarget. 2015 Apr 20; 6(11):9125-39. PMID: 25823817.
  • Glass K, Quackenbush J, Spentzos D, Haibe-Kains B, Yuan GC. A network model for angiogenesis in ovarian cancer. BMC Bioinformatics. 2015 Apr 11; 16:115. PMID: 25888305.
  • Kelly AD, Hill KE, Correll M, Hu L, Wang YE, Rubio R, Duan S, Quackenbush J, Spentzos D. Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: preliminary assessment of cross-platform concordance. Genomics. 2013 Jul; 102(1):8-14. PMID: 23562991.