Dr. Ivet Bahar comments on the MMBioS collaboration in a Genetic Engineering & Biotechnology News article:

The National Center for Multiscale Modeling of Biological Systems (MMBioS) is a joint effort between the University of Pittsburgh, Carnegie Mellon University, the Pittsburgh Supercomputing Center, and the Salk Institute for Biological Studies.

MMBioS makes use of so called Big Data to develop multiscale simulations to bridge molecular events and disease and organ functions, as well as sustaining technology development projects in molecular modeling, cell modeling, and image processing.

Dr. Bahar states:

“This phenomenal type of joint effort is very useful. The problems we are dealing with are much more complicated that an individual laboratory can handle. Our role is to build the technology, which we devise in response to existing research needs and challenges.

The computations we develop are very fast, efficient, and inexpensive so in silico experiments can minimize the wet lab benchtop effort. Computations serve two important roles: they help interpret experimental data in the framework of well-defined quantitative models and methods, and they help build new hypotheses, which are then tested experimentally.”

Labant, MaryAnn (2015) “Big Sequencing Beclouds Big Data” Genetic Engineering & Biotechnology News Vol. 35, No. 11.
http://www.genengnews.com/gen-articles/big-sequencing-beclouds-big-data/5513/ 

Prof. Benos, Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, in collaboration with Prof. Glymour, Department of Philosophy, Carnegie Mellon University, has been awarded $1.3M from the National Institutes of Health to develop efficient graphical algorithms to model causal relationships in large, multi-modal datasets. Together with UPCI researchers, Prof. Benos’ group will use these algorithms to address important problems in metastatic melanoma.

“Computational Histopatholomics for Deep Interrogation of Hyperplexed Heterogenous Tumor Data”

Noon – 1 p.m.
Lecture Room 6
Scaife Hall

Topic Overview:

Comprehensive genetic profiling of tumors has revealed intrinsic molecular variability, or intratumor heterogeneity (ITH), in multiple cancers, including breast, prostate, glioblastoma, colorectal, and ovarian. Heterogeneity is rooted in both genetic and nongenetic factors and evolves through a supportive tumor microenvironment (TME). Not surprisingly, genetic, phenotypic, and TME heterogeneity present major obstacles to cancer treatment.

While genetic testing flags the presence and type of ITH, the spatial distribution of the heterogeneity can be appreciated only with in situ imaging of tissue sections or tumor microarrays using methods such as fluorescence-based immunohistochemistry. In situtechniques probe the tumor and surrounding tissue for the expression of proteins, DNA, and RNA in the context of individual cells and tissue slices. Although such imaging has typically been restricted to no more than four to seven proteins labeled per slide, new technological advances now allow up to 60 proteins and a few RNA or DNA probes—with a theoretically unlimited number possible—to be labeled on the same multicellular tissue slice of up to 10 mm, thus yielding large-scale, whole-slide hyperplexed fluorescence images. However, imaging at this scale raises several new big-data challenges and opportunities for automated image analysis, including how to quantitate and characterize spatial ITH, how to harmonize computational methodologies across diagnostic labs and clinical trials, and how to translate ITH analysis into research insights and clinical use.

Chennubhotla and colleagues’ computational histopatholomic approach tackles these challenges with algorithms designed for deep investigation of hyperplexed ITH data. The tools they are developing allow end users to characterize disease subtypes, to study phenotypic heterogeneity effects of genomic alterations, and identify potential associations between heterogeneity and clinical features of interest (e.g., surveillance of progression, risk of invasion, risk of metastasis, drug response, etc.). Their image-based prognostic algorithms help reduce intra- and interobserver variability in quantifying heterogeneity at diagnostic laboratories and support the development of an ITH index that, combined with genetic rating scales (e.g., Oncotype Dx), will better predict tumor progression and patient outcome.