Atlas of the surgical wound/cancer immune microenvironment in time
"Atlas of the surgical wound/cancer immune microenvironment in time" This project started Sept 2021 Although surgery is the mainstay treatment in nearly all cancers, it is currently not well understood how the wound healing response impacts cancer growth and the local immune microenvironment. This project will be the first of its kind to investigate how surgery impacts cancer cells and how the local immune response interacts with cancer cells. This project uses a combination of technologies (bulk RNAseq, single-cell RNAseq, and spatial transcriptomics) to look at what genes are being switched on and off, over time and where they are located in relation to the cancer after surgical intervention.
Area of science
Medical And Health Sciences
Systems used
Nimbus
Applications used
R, Python, kallistoThe Challenge
For patients with a cancer known as “soft tissue sarcoma”, relapse after surgery is common, resulting in a considerable increase in morbidity and mortality. Soft tissue sarcoma is a group of cancers derived from muscle, fat or connective tissues, characterised by local aggressive growth. Hundreds of patients in WA are diagnosed with sarcoma each year. To try prevent relapse, extensive chemotherapy is given, causing frequent hospitalisation due to side effects. Sadly, despite these treatments, relapses occur in approximately half of the patients with sarcoma, and two in three patients with relapse will die of their disease within 5 years. Other cancers with high relapse rates include pancreatic cancer (recurrence rate ~70%), liver cancer (~70%) and brain cancer (~99%).
The Solution
This project aims to gain an understanding of the tumour-surgery microenvironment and knowledge on how to target the cancer, the immune response, and to improve upon current immune therapies.
Using this information, we aim to develop effective new treatments to prevent relapse of sarcoma after surgery. Treatments which prevent relapse will have the potential to provide multiple benefits to patients:
1) Reduced mortality
2) A reduced need for aggressive chemotherapy and radiotherapy, reducing both short- and long-term side effects (e.g. secondary cancers)
3) Reduced side effects from current therapies, requiring less hospitalisations
4) Alleviate the need for wide resection margins, sparing healthy tissue, which may improve quality of life
The Outcome
This project uses technologies that generate large datasets: millions of genes per sample. The size of the files is not only large but analysing these data uses significant computing power. Using a Nimbus Instance allows me to use Pawsey’s computing power from my own computer.
