Workshop in Biostatistics

DATE: March 31, 2016
TIME: 1:30 - 3:00 pm
LOCATION: Medical School Office Building, Rm x303
TITLE: Computational pathology for data-driven cancer diagnostics

Andrew Beck
Assistant Professor of Pathology, Harvard Medical School and 
Director of Bioinformatics, Cancer Research Institute at Beth Israel Deaconess Medical Center


Pathology is the medical specialty tasked with providing an accurate mapping from tissue samples to diagnoses and treatment strategies.   I will present work focused on the analysis of two primary data types used in cancer pathology (transcriptomic data and morphologic data).  I will discuss our work to use these data to better understand carcinogenesis and to build improved cancer diagnostics.

Suggested readings:

Computational approaches for identification of biomarkers and signatures from clinically annotated transcriptome data:

  1. ​​Nabavi S, Schmolze D, Maitituoheti M, Malladi S, Beck AH.​ EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes. Bioinformatics. 2016 Feb 15;32(4):533-41.  PMID: 26515818 
  2. Beck AH, Knoblauch NW, Hefti MM, Kaplan J, Schnitt SJ, Culhane AC, Schroeder MS, Risch T, Quackenbush J, Haibe-Kains B.​ Significance analysis of prognostic signatures. PLoS Comput Biol. 2013;9(1):e1002875. PMID: 23365551

Computational approaches for the analysis of morphologic data in cancer:

  1. Dong F, Irshad H, Oh EY, Lerwill MF, Brachtel EF, Jones NC, Knoblauch NW, Montaser-Kouhsari L, Johnson NB, Rao LK, Faulkner-Jones B, Wilbur DC, Schnitt SJ, Beck AH.​ Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PLoS One. 2014 Dec 9;9(12):e114885. doi: 10.1371/journal.pone.0114885. eCollection 2014. PMID: 25490766
  2. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, West RB, van de Rijn M, Koller D.​ Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011 Nov 9;3(108):108ra113. PMID: 22072638 

Computational approaches for the integration of morphologic and transcriptomic information to build epithelial-stromal co-expression networks and to identify signatures driving cancer morphologic phenotypes:

  1. ​​Oh EY, Christensen SM, Ghanta S, Jeong JC, Bucur O, Glass B, Montaser-Kouhsari L, Knoblauch NW, Bertos N, Saleh SM, Haibe-Kains B, Park M, Beck AH.​  Extensive rewiring of epithelial-stromal co-expression networks in breast cancer.  Genome Biol. 2015 PMID: 26087699
  2. Comprehensive molecular portraits of human breast tumours. Cancer Genome Atlas Network. Nature. 2012 Oct 4;490(7418):61-70. PMID: 23000897​