Sarcoma/Oncology Computational Biology Laboratory

Sarcoma/Oncology Computational Biology Laboratory

The Sarcoma/Oncology Computational Biology Laboratory conducts cancer genomic/bioinformatics studies in order to detect molecular patterns relevant to tumor outcome and response to conventional or novel treatments


The Sarcoma/Oncology 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 Center for Cancer Computational Biology (directed by John Quackenbush, PhD) and many faculty collaborators at the Department of Biostatistics, at the Dana Farber Cancer Institute.

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.

Group Members

Principal Investigator

  • Dimitrios Spentzos, MD


Research Technician

  • Cassandra Garbutt


  • Research assistant
  • Research student

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.


  1. A network model for angiogenesis in ovarian cancer Glass K, Quackenbush J, Spentzos D, Haibe-Kains B, Yuan GC. BMC Bioinformatics. 2015 Apr 11;16:115. doi: 10.1186/s12859-015-0551-y.PMID: 25888305
  2. MicroRNA-155 expression is independently predictive of outcome in chordoma. Osaka E, Kelly AD, Spentzos D, Choy E, Yang X, Shen JK, Yang P, Mankin HJ, Hornicek FJ, Duan Z. Oncotarget. 2015 Apr 20;6(11):9125-39.PMID: 25823817
  3. Prognostic significance of miRNA-1 (miR-1) expression in patients with chordoma. Duan Z, Shen J, Yang X, Yang P, Osaka E, Choy E, Cote G, Harmon D, Zhang Y, Nielsen GP, Spentzos D, Mankin H, Hornicek F. J Orthop Res. 2014 May;32(5):695-701. doi: 10.1002/jor.22589. Epub 2014 Feb 5. PMID: 24501096
  4. Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: preliminary assessment of cross-platform concordance. Kelly AD, Hill KE, Correll M, Hu L, Wang YE, Rubio R, Duan S, Quackenbush J, Spentzos D. Genomics. 2013 Jul;102(1):8-14. doi: 10.1016/j.ygeno.2013.03.008. Epub 2013 Apr 3. PMID: 23562991
  5. Stem cell-like gene expression in ovarian cancer predicts type II subtype and prognosis. Schwede M, Spentzos D, Bentink S, Hofmann O, Haibe-Kains B, Harrington D, Quackenbush J, Culhane AC. PLoS One. 2013;8(3):e57799. doi: 10.1371/journal.pone.0057799. Epub 2013 Mar 11. PMID: 23536770
  6. MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32. Kelly AD, Haibe-Kains B, Janeway KA, Hill KE, Howe E, Goldsmith J, Kurek K, Perez-Atayde AR, Francoeur N, Fan JB, April C, Schneider H, Gebhardt MC, Culhane A, Quackenbush J, Spentzos D. Genome Med. 2013 Jan 22;5(1):2. doi: 10.1186/gm406. eCollection 2013. PMID: 23339462
  7. A microRNA activity map of human mesenchymal tumors: connections to oncogenic pathways; an integrative transcriptomic study. Fountzilas E, Kelly AD, Perez-Atayde AR, Goldsmith J, Konstantinopoulos PA, Francoeur N, Correll M, Rubio R, Hu L, Gebhardt MC, Quackenbush J, Spentzos D. BMC Genomics. 2012 Jul 23;13:332. doi: 10.1186/1471-2164-13-332. PMID: 22823907
  8. Metabolomic profiling from formalin-fixed, paraffin-embedded tumor tissue using targeted LC/MS/MS: application in sarcoma. Kelly AD, Breitkopf SB, Yuan M, Goldsmith J, Spentzos D, Asara JM. PLoS One. 2011;6(10):e25357. doi: 10.1371/journal.pone.0025357. Epub 2011 Oct 3. PMID: 21984915
  9. Keap1 mutations and Nrf2 pathway activation in epithelial ovarian cancer. Konstantinopoulos PA, Spentzos D, Fountzilas E, Francoeur N, Sanisetty S, Grammatikos AP, Hecht JL, Cannistra SA.Cancer Res. 2011 Aug 1;71(15):5081-9. doi: 10.1158/0008-5472.CAN-10-4668. Epub 2011 Jun 15. PMID: 21676886
  10. Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer. Konstantinopoulos PA, Cannistra SA, Fountzilas H, Culhane A, Pillay K, Rueda B, Cramer D, Seiden M, Birrer M, Coukos G, Zhang L, Quackenbush J, Spentzos D. PLoS One. 2011 Mar 29;6(3):e18202. doi: 10.1371/journal.pone.0018202. PMID: 21479231
  11. Gene expression profile of BRCAness that correlates with responsiveness to chemotherapy and with outcome in patients with epithelial ovarian cancer. Konstantinopoulos PA, Spentzos D, Karlan BY, Taniguchi T, Fountzilas E, Francoeur N, Levine DA, Cannistra SA. J Clin Oncol. 2010 Aug 1;28(22):3555-61. doi: 10.1200/JCO.2009.27.5719. Epub 2010 Jun 14. Erratum in: J Clin Oncol. 2010 Nov 10;28(32):4868. PMID: 20547991.


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Dimitrios Spentzos, MD

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