To develop an AI model for predicting future lung cancer in low-dose screening CT

Screening with low-dose CT has been shown to significantly reduce lung cancer related mortality in high-risk ever-smokers. Interval cancer (IC) is a rising challenge in lung cancer screening because it usually presents in an advanced stage (stage III/IV non-small cell cancer) or is more biologically aggressive (i.e., small cell histology) and have a poorer prognosis than prevalent cancers. The dilemma is how to catch IC early because the regularly scheduled follow-up CT is often too late.

Advanced Precision Medicine in Radiation Oncology: Identifying Predictive Markers of Treatment Response

Half of all cancer patients receive radiotherapy, and of this group 15% will develop a tissue injury, or side effect, from the treatment. At present, however, there are no reliable methods for predicting a patient’s response to radiation therapy. We have studied mice with different genetic make ups and determined that there are certain genes which affect the mouse response to radiation. We propose to test whether the human versions of these same genes influence a person’s response to radiation by reviewing the radiation reactions of BC Cancer patients who previously consented to participate.

Quantitative Digital Pathology Algorithm for an Automated Cancer Slide Imaging System

Digital Pathology provides a great reliable source for cancer diagnosis; however, this relatively new technique lacks an appropriate method of regulation and standardization. As a result the information obtained from cancer imaging system might suffer from inconsistency of the results (repeatability and reproducibility issues). Working with the BC Cancer Research Center, Logipath Medical Inc. has developed a number of quantitative pathology systems some of which have been spun off to commercial entities.

A Study of the Molecular Mechanisms Underlying Pediatric Medulloblastoma Mediated By YB-1

Medulloblastoma is the most common form of pediatric brain cancer with a five-year survival rate of approximately 70%, yet for some children’s survival is as low as 40%. Many of the treatment options for these patients may be effective in extending the five-year survival rate, however, quality of life issues still persist for these young patients including learning and developmental deficits. These side effects arise from damage to normal tissue in the developing brain by surgery and/or drug and radiation therapy.

Investigation into the role of YB-1 in childhood sarcomas

Metastatic tumors are a major concern in childhood cancer and the single most prominent cause of patient mortality. Metastasis is a complex process involving several cellular processes, each of which involves numerous extra and intracellular events. Therapeutic targeting strategies are hampered by a large degree of redundancy in the systems controlling metastatic behaviour and by the lack of specific markers associated with tumor dissemination.

Role of ROS regulation by Hace1 in modulating “stemness” versus differentiation of stem cells

The Sorensen laboratory-based Childhood Cancer Research Program is specifically focused on elucidating the genetic and biological determinants of the metastatic process in childhood cancer. Metastatic disease remains the single most dominant driver of adverse outcome in most childhood cancers, particularly in childhood sarcomas. Cancer stem cells, malignant cells that share many characteristics with normal stem cells, have been implicated to have a central role in the metastatic process.

Low-cost complex genome assembly and annotation

There is a pressing need to apply genomics-based tools for rapidly evaluating the impact of industrial and municipal waste management practices and climate change. In order to do this, information about the genome from relevant North American species is needed, yet acquiring these complex genome sequences had been cost and resource prohibitive until recently.

GPU-oriented Structural Variation Detection in Human Genomes

Fast structural variation (deletion, insertion and inversion) detection between genome of different individuals is the main goal of this project. The internship team is planning in extending new algorithms to reduce the number of false positive calls (especially for deletions) and to parallelize it using Graphics Processing Unit (GPU). The standard approach implementation of the algorithms, as a result of high computational needs, is not fast enough for every day use by health science centers (such as hospitals).

GPU based High Throughput Sequence Mapping for Re-Sequencing Short Reads

The throughput of next-gen sequencers is about 20 to 90 million base pairs per hour and increasing. To map this huge volume of data to reference genome and reduce the computation time, current mapping tools are installed on the clusters. Although using a cluster reduces the computation time but the cost of having such a cluster is considerable. So, there is a trade-off between computation time and computation cost. This project’s goal is to reduce the computation time as well as to reduce the computation cost.

Approximate Algorithms for Resequencing of Human Genome using High Throughput Short

Human genetic variation has been traditionally studied at the single nucleotide polymorphism (SNP) level. It is clear that, human genetic variation extends beyond single nucleotide polymorphism. New projects aimed at identifying structural variation have been initiated through the use of high throughput sequencing technologies. Although the new sequencing technologies produce short reads with respect to Sanger sequencing experiments, the focus of search for differences between a sample and its reference is moving from single based differences to structural variations.