Machine learning for personalized drug response prediction for bladder and prostate cancer patients

Lead Investigator: Marianna Kruithof-de Julio, University of Bern
Title of Proposal Research: Machine learning for personalized drug response prediction for bladder and prostate cancer patients
Vivli Data Request: 7312
Funding Source: Swiss National Foundation and contract at University of Bern
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer-associated death in men. The survival rate of PCa patients is mostly determined by the extent of the tumor. If the cancer is confined to the prostatic gland, the median survival can be anticipated in excess of 5 years. If PCa has spread to distant organs, current therapies are not curative, and the median survival drops to 1 to 3 years.

Currently, PCa can be successfully treated surgically when still in its first phase of androgen dependency. Follow up with androgen deprivation therapy will contain the cancer and reduce the possibility of metastasis. However, once the cancer becomes androgen independent or “castration resistant,” therapy is no longer useful or successful.

Urothelial carcinoma of the bladder (BlCa) is the fifth most common cancer in the Western world. BlCa can be classified in non-muscle invasive (NMIBC) and muscle invasive (MIBC). Patients with low grade NMIBC have a good prognosis but show frequent recurrence. Those with high grade NMIBC show an even higher rate of recurrence and progression. Patients with NMIBC are followed up by cystoscopy and cytology every 3-6 months for at least 5 years.

Unfortunately, cystoscopy is invasive, and cytology has a low sensitivity, ranging from 20% to 53%. As a result of the need for this procedure-based, long-term follow-up, bladder cancer management costs more per patient lifetime than any other cancer. MIBC is a highly aggressive disease with a 60% 5-year overall survival, presumably due to early metastatic dissemination.

As of today, there are no preemptive diagnostic tests to identify increased risk to develop PCa or BlCa and patients are still treated with standard of care drugs. Standard of care therapy does not work for every patient. The development of machine learning algorithms for automatic prediction of drug response for new patients and new drugs will allow us to select the most promising drugs for each patient for further experimental evaluation. We will be able to determine therapy response based on multiple parameters defined by the study of the individual tumor including genomic and transcriptomic sequencing. This will have a huge impact on the patient population by reducing the number of patients that will undergo surgery.

Requested Studies:

Genomic, Transcriptomic, and Drug Screening Data from a Pan-cancer Organoid Cohort with Source Tumor Samples
Data Contributor: Tempus Labs, Inc.
Study ID: T21.01
Sponsor ID: T21.01