Proteomic Cancer Testing: Protein Analysis for Treatment Targets

•Prostate cancer: A prostate cancer diagnostics project found biomarkers that can be used from the first prostate biopsy to accurately predict whether patients will need a prostatectomy without needing a second biopsy sample.

•Ovarian cancer: A study of proteins in ovarian cancer patients found that proteins could provide better treatment information than DNA or RNA. The proteomic analysis focused on “phosphorylation”, identifying a distinctive signature from a map of the handoffs of phosphorus in communication pathways from the membrane of the cancer cell to the nucleus, which correlated with patients’ long- or short-term survival. Karin said that Brian could develop a similar map, including his BRAF overexpression, if he sent his fresh frozen tumor tissue to a lab like Olink, and then worked with experts with a systems biology background.

•Acute myeloid leukemia : Using protein and phospho protein analysis, they found they could predict drug response better than genomics or mRNA, particularly in understanding resistance in late phase AML, and they developed a drug combination to apply in the early phase to reduce late phase resistance. Discussion of Testing and Treatment Improvements

•Drug Combinations : Drugs are given in sequence. You're given a drug, you respond, you develop resistance, the disease comes back, then they put you on the next drug. Every time you do that, you are selecting new clones with new survival mechanisms. If we knew what combinations to use early on, could we kill every tumor cell, and there would be nothing left to recur? The FDA slows adoption of new approaches because they are very conservative. They do not want to approve something that kills 10,000 people, even if it saves 10 million, because the 10,000 that died will be blasted all over the media.

•Combination Testing : Payment is the big issue. As long as payers will only authorize payment for the second test after the first one has failed, you're not going to have physicians ordering multiple tests at the same time. We have a federal agency that makes recommendations against tests, the U.S. Preventive Services Task Force.

•Proteomics Adoption : These proteomics tests are available now, or soon, if you find the right academic medical center which is doing the right clinical trial. It's a very hot area of research. To make it standard of care, the most optimistic estimate is three years. You should work with your friendly doctor who is willing to go outside the standard of care to write up a case report on your success. Requests

•Do you or anyone you know have the skills to take the results of a proteomic analysis to build a pathway map?

•Given the potential of protein analysis to guide clinical decisions for advanced cancer, what can be done to accelerate its use? The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab/Prostate Cancer Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. — Meeting Notes Brad Power: We're happy to have with us Karin Rodland, a friend who is based in the Portland, Oregon, area, and southern Washington state, and is an expert in proteomics. I saw Karin speak at an online conference on the potential of proteomics, and since we're very interested in novel testing and how it might inform patients' decisions, Karin graciously agreed to give us a tutorial and introduce us to the potential of proteomics to inform clinical decision-making. Karin Rodland: A lot of you are in the biotech industry and are familiar with the technology, and all of you are self-educated and have learned a lot about this. Cancer is personal for almost everyone. I used to give talks to encourage community support for Oregon Health Sciences University, and I would ask the audience to raise their hand if anyone in their family had ever had cancer. And every hand in the audience went up routinely. That's my motivation here. Hacking the Proteome for Cancer Treatment is something that I'm very passionate about. The object of the game – and I know that this is why you have the Prostate Cancer Lab and hackathon – is improving cancer survival, and the way we have tackled the problem at PNNL (the Pacific Northwest National Laboratory) is early detection and targeted therapeutics. I recognize that this group is past the stage of early detection. Your motivation is that you have been diagnosed with prostate cancer, and it has metastasized or recurred, and you're wondering what your treatment options are, and that's where the targeted therapy comes in. But one of the great strengths of proteomics is in the quest for biomarkers, for early detection and for accurate prognosis and guidance of initial therapy, as well as guidance of later therapy. I probably don't have to tell you folks that if you find almost any cancer in stage one, while it is localized and can be removed, surgery is pretty darn curative for almost all cancers. But the later the stage of the cancer, the worse the prognosis. How can we use proteomics to improve cancer survival? I have a protein bias. I know that a lot of the biotech people from San Diego have either a genome or transcriptome bias. There's a plethora of RNA signatures for prognosis of cancer. The way we tend to do precision medicine is on actionable mutations, but it's really the proteins that execute the signals that are in the genome and messaged in the RNA. You have to study the proteins to really understand the state of the biological system and to understand what is going on in multiple dimensions within the cell at the same time, because it's usually never a single mutation. We don't know a lot about how multiple mutations in the same tumor interact with each other and change the response to a drug, for example. When it comes to the field of biomarkers, proteins are kind of the “go to molecule'' because they're stable, and they're easy to measure. RNA is more labile (likely to change, easily broken down or displaced). We are now tending towards cell-free DNA or circulating tumor DNA. That's relatively new technology, but over time, protein-based biomarkers have really established themselves. For example, there are the early detection biomarkers of PSA for prostate cancer and CA125 for ovarian cancer. The big problem with these biomarkers is that they're normal function proteins that are specific to the tissue of the prostate or the ovary. So there are a lot of false negatives. They're statistically correlated because you have a bigger prostate if you have prostate cancer, but they're not mechanistically driving the cancer. So we are still looking for better biomarkers that say something about the state of cancer and particularly about the immune response of the body to the cancer. There's a lot of potential that I want to focus on in therapeutic biomarkers that actually change the treatment decision. The two that have been around the longest are the HER2 gene in breast cancer and the BCR-ABL chimeric gene in leukemia. Because BCR-ABL activates the ABL kinase, you can use ABL kinase inhibitors to treat Philadelphia chromosome positive leukemias . Because HER2 drives proliferation in breast cancer, you can use anti-HER2 strategies to treat breast cancer. I just saw a new trial that's using HER2-targeted antibodies to deliver doxorubicin specifically to breast cancer cells and allow a higher local dose, another example of a protein- driven therapeutic option that is emerging. You guys know this story. You have been through this. The way we treat cancer patients these days is population science. The way it's always been done. The gold standard for physicians is what treatment works the best in the greatest number of patients. And even five years ago, in Rodland) You guys know this story. You have been through this. The way we treat cancer patients these days is population science. The way it's always been done. The gold standard for physicians is what treatment works the best in the greatest number of patients. And even five years ago, there was a feeling that if you did anything that was outside of the gold standard of treatment, you were letting your patients down. The interview would go something like this: “Your PSA is really high. What should we do now? (I know you've been through this.) Well, we'll do a biopsy of your prostate. You're Gleason 7. That's the hard one to call, so we can do a radical prostatectomy, or we can do another seven needle biopsy next year.” I'm a girl, so I don't know exactly how painful that is, but I can imagine it is not a pleasant experience. It's a pretty coarse decision point. One of the projects that at PNNL has been trying to develop a biomarker that would allow you at the initial biopsy to predict the risk of progressing to aggressive prostate cancer that metastasizes, and therefore have a very objective quantitative, mechanistically-relevant decision tree for determining whether you can just go on active surveillance with repeat biopsies or whether you should have a radical prostatectomy right now. Because I'm a researcher, I have these slides on how he went about doing that. This is an example of how the technology of proteomics can be applied to problems in cancer biology. This type of work is very well funded. I'm sure that you guys in biotech with the startup companies know how this works. We started with a list of prognosis-associated genes in prostate cancer, and other genes that were based on RNA seq experiments and tissue, and other genes from the curated literature that were either prostate-cancer-specific or just plain cancer-related. We ended up with about 62 targets. And we had 340 FFPE samples that were provided by the Center for Prostate Disease Research at the Uniformed Services University . The tissue came from a radical prostatectomy that had been performed within six months of diagnosis with no neoadjuvant therapy. Every individual had at least 10 years of follow up. For the “no events”, we had 10 years of follow up with no increase in PSA and no metastasis or recurrence. We had 171 of those patients. We had 114 with rising PSA, and we had 57 with metastatic disease. We used a technology that we've developed at PNNL which is highly sensitive. It uses fractionation to increase the sensitivity of the mass spectrometry. We call it “ PRISM-SRM”. SRM is “selected reaction monitoring”. You have a synthetic peptide for the protein of interest. You spike that into the lysate (disruption of the plasma membrane of a population of cells), and you can get very good quantification and increase the sensitivity because you know exactly what you're looking for. We were able to see 21 proteins with a standard technology for targeted proteomics. We were able to see an additional 42 using our PRISM-SRM technology. There were 10 that we could not see at all. As always happens using this panel, we were able to not only detect statistically significantly different proteins between metastatic disease and indent disease, but we were able to use machine learning to develop a signature to predict distant metastasis within the next 10 years. We compared it against the biopsy based model, which is the criteria that are used to determine whether you get a biopsy or not. The standard predictive risk stratification is from the NCCN (National Comprehensive Cancer Network standard of care guidelines), which includes your age at diagnosis and your race. We were able to increase the area under the curve from 0.7 to 0.88 almost. (Estimates of the “area under the curve” - AUC - is a means of comparing predictive models. “Receiver Operating Characteristic” - ROC - curves plot the true positive rate against the false positive rate.) Here's the increase using the protein biomarkers. The other comparison was with the pathology standard, which is the Gleas on grade called by the pathologist on that biopsy and the surgical margins. The increase that we saw was not as great there because the pathology-based model all by itself was 0.80. We got about 0.86 with our model. Here's the point: The pathology model requires that you have a biopsy. The biopsy model is the criteria that are used to determine whether or not you will have a biopsy. And if we can match the performance of the biopsy with just the initial diagnostic biopsy – without doing the additional seven-needle or whatever biopsies – we are hoping for significant impacts on the quality of life. That's our prostate cancer project. We're trying to convert this into a less invasive protocol rather than using biopsy samples. We're also working on the sensitivity issues with a single diagnostic biopsy. That's coming along nicely. We have not been able to use urine to replicate our protein signature. We're hoping to be able to use blood, and we're working on that now. Precision medicine is tailoring treatment to the individual with targeted therapies. Here's basically how precision medicine is being done right now. You get the patient's tumor