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
SPEAKER:

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

Abstract:

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​