Mapping the interactions of metastatic prostate tumor gene expression and protein phosphorylation can yield detailed, patient-specific signalling pathway diagrams, and help to identify “master-switch” tumor progression-driving targets for personalized treatment.
Mapping the interactions of metastatic prostate tumor gene expression and protein phosphorylation can yield detailed, patient-specific signalling pathway diagrams, and help to identify “master-switch” tumor progression-driving targets for personalized treatment, according to a study published Aug. 11 in the journal Cell.
“It’s like having a blueprint for each tumor,” said Joshua M. Stuart, PhD, Professor at UC Santa Cruz, a senior coauthor of the paper, in a news release. “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.”
“For now it’s a research tool, but the hope is to have a strategy like this to use in the clinic,” Dr. Stuart said.
The study identified gene mutations that alter androgen receptor proteins and tumors’ use of alternative kinase signaling pathways to circumvent androgen-receptor blockade-mechanisms that drive tumor resistance to antiandrogen therapy.
The authors used a “multi-omic” approach, integrating data about tumors’ protein phosphorylation (the “phosphoproteomes”), genomes, and gene expression for a clearer picture of activated signaling pathways in each of six patients’ metastatic prostate tumor samples, which were obtained at autopsy. Kinase enzymes’ phosphorylation of proteins functions like a molecular switch, activating or deactivating proteins, and so phosphoproteomic data offers more detail than genome sequencing or gene expression transcriptome data alone.
The integrated datasets should prove helpful in identifying the “minimum combination of targets” needed to best disrupt each patient’s tumor signaling pathways, the coauthors reasoned. Their analysis implicated several aberrant signaling proteins as “possible new therapeutic targets and/or biomarkers in prostate cancer,” including PRKDC, PRKAA2, PTK2, RPS6KA4, and members of the CDK family of proteins.
Despite very similar RNA-transcriptome profiles, several patients’ tumors had exhibited “marked differences” in responses to antiandrogen therapy or chemotherapy, the authors noted. Phosphorylation of signaling proteins might help explain those differences in treatment responses and “offer new treatment options to abrogate the signaling upstream of these TF [transcription factor]-driven circuits.”
But the authors found that genomic or phosphoroproteomic data alone might be able to predict tumors’ drug sensitivities. The authors used the integrated –omics data set to build a “generic model” of metastatic prostate cancer signaling networks, which can be refined with patient-specific data like tumor-sample gene mutations, using the pCHIPs computations tool.
“These mutations in the genome create a lot of havoc in the cell, and trying to interpret the genomic information can be overwhelming,” said Dr. Stuart. “You need the computer to help you make sense of it and find the Achilles heel in the network, that you can hit with a drug.”