- February 19, 2016
- URMC 2-6408 (K-207 Auditorium)
- Event Sponsor:
Tumor Heterogeneity Studies – Tricks From the Burack Lab
Janice Spence, Ph.D.
Department of Pathology and Laboratory Medicine
While most think of cancer as a clonal process, new approaches to genetic studies of tumors have demonstrated substantial heterogeneity from the founding clone. This genetic instability is emerging as the common theme across all cancers, and is believed to be responsible for treatment failure and subsequent relapse. The Burack Lab has focused on the study of tumor heterogeneity in B-cell lymphomas as both potential diagnostic characteristics and prognostic factors. We are using targeted next generation sequencing approach to identify and quantify heterogeneity within tumors, evaluating the evolutionary-neutral ‘passenger’ mutations as well a unique tumor biomarker in B-cells, the IGH molecule. We focus our analysis on mutational hot spots as prime locations for determining the number and proportions of low variant allele frequency (VAF) tumor subclones. However, low VAF detection is complicated by poor mapping efficiency in regions with high mutation density. Our Deep-Drilling with iterative Mapping (DDiMAP – available on BlueHive) retains variant allele patterns to aid in SNV detection and generation of additional reference alleles, with remapping increasing coverage of highly mutated regions to capture data critical to heterogeneity analysis and enhancing sensitivity. DDiMAP outputs variant patterns with frequencies, enabling rapid phylogenetic analysis of ongoing mutation. We have expanded this dictionary-based approach to IGH sequence analyses, using the longer, single amplicon based reads currently available from MiSeq.
On-going Research Talk: Mapping the Optimal Route Between Two Quantum States
Department of Physics and Astronomy
A central feature of quantum mechanics is that a measurement result is intrinsically probabilistic. Consequently, continuously monitoring a quantum system will randomly perturb its natural unitary evolution. A detailed understanding of this stochastic evolution is essential for the development of optimized control methods. Here we reconstruct the individual quantum trajectories of a superconducting circuit that evolves under the competing influences of continuous weak measurement and Rabi drive. By tracking individual trajectories that evolve between any chosen initial and final states, we can deduce the most probable path through quantum state space.