Computational Analysis of Prostate Cancer Networks Points Way to Patient-Specific Therapies

Computational Analysis of Prostate Cancer Networks Points Way to Patient-Specific Therapies

Researchers have created a detailed map showing of how genes and proteins interact and cause prostate cancer cells to multiply and escape treatment. They then developed a computational tool to analyze patient-specific information, helping clinicians to choose the most effective treatments for individual prostate cancer patients.

The study, by cancer researchers at the University of California Santa Cruz (UCSC) and colleagues at the University of California, Los Angeles (UCLA), is titled “Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer” and published in the journal Cell.

Working with autopsy tissue samples from metastatic prostate cancer patients, the researchers used a range of specialized techniques to characterize, in detail, individual patient cells. Then, using the computational approach developed, they generated personalized diagrams of signaling pathways corresponding to the cells of each patient. These diagrams may help to identify therapeutic targets for individual patients.

Dr. Josh Stuart, a professor of biomolecular engineering at Santa Cruz, director of cancer and stem cell genomics at the UCSC Genomics Institute, and a senior study author, said in a press release: “It’s like having a blueprint for each tumor. This is our dream for personalized cancer therapy, so we’re not just guessing any more about which drugs will work but can choose drug targets based on what’s driving that patient’s cancer.”

Prostate cancer, like many other types of cancer, is caused by mutations in genes involved in cell growth and proliferation. In each individual case, however, the causative mutation may be different.

By mapping the key pathways of prostate cancer cells from different individuals, the researchers were able to identify the “master switches” in those pathways that cause tumor growth. This information could be used by doctors to look for drugs that target these switches and halt the progression of the cancer or, possibly, reverse it.

“Therapies for metastatic prostate cancer are urgently needed. This type of interdisciplinary research is critical as we seek to pinpoint the cellular changes occurring in aggressive prostate cancer and cross new boundaries in understanding the disease,” said  Dr. Owen Witte, founding director of the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA, s professor of microbiology, immunology and molecular genetics at the UCLA David Geffen School of Medicine, and a senior study author.

The study concentrated on the phosphorylation state of key proteins involved in prostate cancer, whose changes are of great importance for cancer development. Using this information, researchers produced a database of protein phosphorylation in prostate cancer cells and tissues. Then, using computational analyses, they integrated the so-called phosphoproteomic data with genomic and gene expression data to provide a unified view of the activated signaling.

“Having the phosphoproteomics data in addition to the traditional genomics and transcriptomics enabled us to get a more comprehensive view of aberrant signaling in this disease,” said Evan Paull, the co-first author of the study. “We developed a method to integrate these multiple large datasets to understand what’s driving the disease in individual patients.”

A main approach in treating advanced prostate cancer is androgen deprivation therapy. But most metastatic prostate cancer patients develop resistance to these therapies. According to the researchers, mutations in androgen receptors may mean that androgen deprivation therapies will not be effective. In some other cases, blocking the androgen receptors may not be sufficient to stop cancer cells to keep growing.

Such personalized information can help identify drugs that are most likely to be effective in each individual prostate cancer patient.