UB CCR provides high performance computing and storage resources to support researchers at Roswell Park Comprehensive Cancer Center. Many of these researchers hold dual appointments at UB or are actively collaborating with UB faculty members. Collaborations and support for RPCI research dates back more than 15 years. More recently, CCR system administrators began providing maintenance for RPCI compute nodes in the faculty cluster as they begin to migrate their HPC workloads from their on-site cluster to CCR. Below is a subset of highlights of some of the exciting and ground breaking work done by RPCI researchers at CCR.
Principal Investigator: Dr. Song Yao
Funded by the National Cancer Institute, Dr. Yao’s research seeks to: (1) explain why aggressive forms of breast cancer are more common in African-American women, (2) learn how cancer metastasizes and develops resistance to treatment, (3) explore opportunities to enhance patient recovery following surgery, and (4) accelerate the development of new immunotherapies.
Principal Investigator: Dr. Alan Hutson
Dr. Hutson’s research group is leveraging high performance computing to accelerate the development of immune-based cancer treatment approaches and prevention strategies. The research supports the Immuno-Oncology Translational Network (IOTN) – a consortium of research centers that are participating in the Cancer Moonshot program sponsored by the National Cancer Institute
Principal Investigator: Dr. Daryl Nazareth
Dr. Nazareth’s group has been using the UB CCR supercomputer to develop optimized radiation treatment plans for cancer patients. Using a technique known as simulation-based optimization, the supercomputer identifies treatment plans that maximize the radiation dose delivered to the tumor region while simultaneously minimizing the dose absorbed by healthy tissue.
Principal Investigator: Dr. Simon Fung-Kee-Fung
Dr. Fung-Kee-Fung and his team are developing a machine learning model for classifying the severity of radiation burns that are a common byproduct of radiation therapy. Initial datasets have focused on breast cancer patients and have achieved 85% classification accuracy.
Principal Investigator: Flow Cytometry
The Flow Cytometry Shared Resource is accelerating its workflow by using “t-SNE” (t-distributed stochastic neighbor embedding) - an unsupervised machine learning algorithm for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions.
Principal Investigator: Dr. Anh Le / John Asbach
This research is using an adaptation of the U-Net neural network model architecture for automatic OAR (organ-at-risk) segmentation of medical images. The current focus is on the automatic segmentation of head and neck cancers.