Author Interviews, Cancer Research, Prostate Cancer / 03.06.2019
SPARTAN Study: Older Castration-Resistant Prostate Cancer Patients Can Tolerate and Benefit from Apalutamide (ERLEADA®)
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Dr. Julie Graff[/caption]
MedicalResearch.com Interview with:
Julie N. Graff, MD
Associate Professor of Medicine
Knight Cancer Institute
Chief of Hematology/Oncology
VA Portland Health Care System
MedicalResearch.com: What is the background for this study?
Response: Androgen deprivation therapy is often deployed in patients with a rising PSA after local therapy (such as radical prostatectomy or radiation therapy). With time, the prostate cancer can develop resistance to ADT, at which point it is called castration resistant prostate cancer (CRPC). There were 6 treatments for metastatic CRPC that have shown improved survival. However, in non-metastatic disease, there was nothing that showed improved survival.
The SPARTAN study was designed to determine if a next generation androgen receptor antagonist could delay the time to metastatic disease. Overall survival was a secondary endpoint.
Dr. Julie Graff[/caption]
MedicalResearch.com Interview with:
Julie N. Graff, MD
Associate Professor of Medicine
Knight Cancer Institute
Chief of Hematology/Oncology
VA Portland Health Care System
MedicalResearch.com: What is the background for this study?
Response: Androgen deprivation therapy is often deployed in patients with a rising PSA after local therapy (such as radical prostatectomy or radiation therapy). With time, the prostate cancer can develop resistance to ADT, at which point it is called castration resistant prostate cancer (CRPC). There were 6 treatments for metastatic CRPC that have shown improved survival. However, in non-metastatic disease, there was nothing that showed improved survival.
The SPARTAN study was designed to determine if a next generation androgen receptor antagonist could delay the time to metastatic disease. Overall survival was a secondary endpoint.

Jasleen Grewal, BSc.
Genome Sciences Centre
British Columbia Cancer Research Centre
Vancouver, British Columbia, Canada
MedicalResearch.com: What is the background for this study?
Response: Cancer diagnosis requires manual analysis of tissue appearance, histology, and protein expression. However, there are certain types of cancers, known as cancers of unknown primary, that are difficult to diagnose based purely on their appearance and a small set of proteins. In our precision medicine oncogenomics program, we needed an accurate approach to confirm diagnosis of biopsied samples and determine candidate tumour types for where the primary site of the cancer was uncertain. We developed a machine learning approach, trained on the gene expression data of over 10,688 individual tumours and healthy tissues, that has been able to achieve this task with high accuracy.
Genome sequencing offers a high-resolution view of the biological landscape of cancers. RNA-Seq in particular quantifies how much each gene is expressed in a given sample. In this study, we used the entire transcriptome, spanning 17,688 genes in the human genome, to train a machine learning method for cancer diagnosis. The resultant method, SCOPE, takes in the entire transcriptome and outputs an interpretable confidence score from across a set of 40 different cancer types and 26 healthy tissues.