Progress towards accurate prediction of overall survival in men with metastatic castration-resistant prostate cancer

Prostate cancer (PC) is the most frequently diagnosed male malignancy and the 2^nd or 3^rd leading cause of cancer deaths in men in the developed countries. The disease progresses from locally invasive carcinoma to metastatic prostate cancer (mPC). While PC metastasizes to the liver and lung, bone is the most frequent site of PC metastasis. Distant metastasis likely marks the point of no return progress towards the worst prognosis. Owning to the landmark discovery that metastatic PC requires androgen receptor (AR) signaling by Charles Huggins in 1941, androgen deprivation therapy (ADT) remains the standard of care for mPC patients. Although the treatment provides initial benefits in the majority of patients with mPC, metastatic castration-resistant prostate cancer (mCRPC) inevitably arises. Prior to 2011, docetaxel was the only second line-therapy (1), and prolonged median overall survival (OS) in patients with mCRPC by 3 months. Since then, the second generation anti-androgens (abiraterone and enzalutamide), radium-223, cabazitaxel, and Sipuleucel-T have become available in the clinic. Although these therapies are not curative, they extend OS in patients with mCRPC (2). As these drugs have different mechanisms of action, they could be used in a variety of combinations either sequentially or simultaneously to maximize benefits to patients with mPC or mCRPC. For example, ADT plus docetaxel is superior to either alone (3) and is becoming the new standard of care for patients with mPC with good performance status. Clearly, improving our knowledge on the course of mCRPC will contribute to the development of rational treatment plans with the currently available medicines and thereby improves patient management. In this regard, identifying parameters to accurately predict survival of patients with mCRPC is an area of active research; there are 113 and 40 published studies related to the topic of mCRPC and prognostic biomarkers or prognostic models in PubMed (https://www.ncbi.nlm.nih.gov/pubmed/advanced) up to Dec 3, 2016. In order to yield a robust predictive model, it will be essential for a team with combined expertise in clinic and machine learning to analyze comprehensive sets of clinical data.