Advanced Prostate Cancer: Making Informed Treatment Decisions

•Bring testing forward in the guidelines to help people at each decision point.

•Embed more real world evidence in the decisions.

•Add more structure and explicit guidance for decisions in the late stages.

•Make the guidelines more dynamic and predictive.

•Put a treatment strategy with guiding principles on top of it. To guide his treatment decision, Rick shared his test results (DNA sequencing, immunohistochemistry, and RNA sequencing). The DNA sequencing identified two CDK12 mutations. The RNA sequencing identified three biomarkers which were overexpressed: AR, PSMA, and B7-H3. Based on the test results and his medical history, Rick and his medical team have identified this list of treatment options:

•Pluvicto (radioligand attacking PSMA, currently unavailable due to production issues)

•Olaparib (targeting CDK12) + CTLA4 inhibitor + PDL1 blockade

•PSMA CAR-T from Poseida (targeting PSMA)

•PSMA bispecific from Calibr (targeting PSMA)

•ARV-766 AR degrader (targeting AR)

•Daiichi antibody drug conjugate to B7-H3 (targeting B7-H3)

•Cabazitaxel (chemotherapy) Requests

•Do you have any feedback on Rick’s testing and treatment options? 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. — 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

•There's IHC, which stands for immunohistochemistry. This is one type of testing that is pretty typical. You can look at whether you have T-cell infiltration into your tumor. It would inform a decision on whether you might be a candidate for PDL1. If you have T cells in your tumor, they would be able to help kill the tumor if they were not inhibited by the Programmed Death-Ligand 1 (PDL1).

•DNA sequencing will inform decisions on all therapy classes.

•RNA-sequencing can inform decisions on all therapy classes as well.

•Spatial analysis is more cutting edge. You take a slice of the tumor and analyze it using different techniques, such as immunofluorescence. You tag antibodies to these types of immunomodulators so that you can see the presence of different proteins in your tumor. And based upon that, you would be able to choose which of the immunomodulator clinical trials might be a good fit for you.

•Organoids are cutting edge. You take a little bit of tumor, grow it up, replicate it into, say, 200 little colonies, and query those 200 colonies with different therapeutic candidates to see what kind of response you get. These more advanced assays that people don't typically get might help inform their decision. We're trying to push the cutting edge. I'm going to go into the assays that have been done on me next and what they mean to me and my oncology care team. This is a screenshot of my genetic testing and DNA sequencing. I have two CDK12 mutations, and they are targetable by FDA-approved drugs, such as Olaparib, for prostate cancer. I have a low tumor mutation burden. My MSI microsatellite status is stable, and I have a gene fusion. This is the highlight of my DNA sequencing report. I would never have known that I have an FDA-approved drug that might help me, if it wasn't for this DNA sequencing. This is within the NCCN guidelines because they recommend testing for these DNA repair genes and CDK12. My specific mutation is not the same as Brian's. CDK12 mutations are a poor prognostic. They happen in about 5% of prostate cancer patients. This mutation will create a lot of gene fusions, which can be a target for T-cells. Therefore, I would hopefully be responsive to PDL1. Saed Sayad: Is there any information about using drugs for CDK12? Rick Stanton: There's a fair bit of publications about the outcomes of using this drug, Olaparib. It was more hopeful upon approval, and as more data was released, there has been a weaker linkage to improved outcomes as time went on. This is maybe not the first drug that my care team would consider, but it's something. When I talked to Dr. Tanya Dorff, she said, “I see Olaparib in your future. You know, one way or another. You could take it sooner. You could take it later. This is a drug that could help you. And when you exhaust other options, you're probably going to try it.” And that's what I know about it. Brad Power: Given our recent conversations with Saed Sayad, Bob Gatenby, and John Laird all promoting the notion of drug combinations and lower dosages, are you thinking of this as a monotherapy, or are you thinking this might be in a drug cocktail you could try? Rick Stanton: That's a great question. It's not just me who will decide. Tanya Dorff, who is one of the authors of the NCCN guidelines, at City of Hope, and a well respected leader, told me of a clinical trial at City of Hope that was a cocktail of Olaparib, a PDL1 inhibitor, and a CTLA4 inhibitor. Those are FDA-approved immunotherapies, but the trial is to have the triplet cocktail. She recommended it should be considered. She felt like Olaparib on its own had a weak response, but in a cocktail triplet it would extend my life. Brad Power: Building on the discussion we had about the CureMatch report, it would be interesting to see how that cocktail of three would overlap and give coverage to the biomarkers you've identified that are unique to you Rick Stanton: This is going to get very interesting because I would like to give my biomarker analysis to CureMatch. I gave Ally Perlina at CureMatch my Tempus report, but she told me there were not enough distinctive biomarkers to run their algorithm. She was able to run a report for Brian, but Brian's Tempus report included a section of overexpressed genes which my report didn't include. The bioinformatics team at Tempus said they don't always include it. I'll do my own assessment of overexpression, with help from TGen, and provide that to Ally. And then we can compare CureMatch’s recommendation to what my care team recommends. Brad Power: Another dimension we've been talking about and you included are organoids. I just listened to a Society for Functional Precision Medicine webinar, where they talked about testing drugs on organoids. It'd be awesome to test your cocktail with organoids. It seems like you have some lead time. It can take months to get the organoids cultured and all set up. Brian, you've talked to SEngine, which provides organoids, what kind of tissue do they need to culture and organize? Is it fresh tissue, and do you have fresh tissue available or whatever the feedstock is for organoids? Rick Stanton: I don't know what is required. I have fresh frozen tissue at City of Hope from my primary tumor. I don't know if that's good enough. Brian McCloskey: They need live, fresh frozen tissue. I don't know the difference here. There's fresh frozen and there's live fresh frozen. I think it has to be shipped within 20 hours. Rick Stanton: This first analysis is molecular testing at the DNA level. This was my report from Ashion who subcontracted to TGen. I have a similar report from Tempus, which agreed. Now I'm going to go to the immunohistochemistry level, then I'm going to go to the RNA seq level. These are the three sets of data that I have. The immunohistochemistry analysis was done on my primary tumor. You stain a slice of the tumor tissue and you look for something. In this case the pathologist looked for tumor infiltrating lymphocytes, which are T-cells and B-cells. My result was zero to 5% TILs, and that's not as good as we were hoping because my mutation on average does attract these TILs, and the T- cells are your immune response that kills the cancer. Having no T-cells, or zero to 5%, is not a good predictor of a response to immunotherapy on the PD1/ PDL1 blockade. Here is a piece from Wikipedia describing how TILs are implicated in killing tumor cells. Their presence is often associated with better clinical outcomes. I would have hoped for a higher number. Immunohistochemistry changes between the primary tumor and metastases, especially as metastases are being pushed by therapies and chemotherapies. One would hope that if a similar IHC stain would be done on one of my metastases we'd see some TILs in there. In case you don't have experience with looking at CD3 staining, here's the slice of tissue, and it's a lymph node biopsy. This is from a paper in Cell on how inactivation of CDK12 (my mutation) delineates a distinct immunogenic class of advanced prostate cancer. So why do we care about this? If you look at the response of the person in this paper called MO_1975, they were given an anti- PD-1 checkpoint inhibitor immunotherapy, their PSA was at 5, and when they were given the PD1 inhibitor, it basically went to zero. This is a tremendous response. And the rationale was here's the stain for CD3, a protein that is present on T-cells. You can look at this slide and where you see brown, you'd say there are T-cells. If the T-cells are in the tumor, this person is a really good candidate for PDL1 blockade. This is the first easiest look at, will you respond or not to a PDL1 blockade. The City of Hope pathologists did not share the image with me, which I wish they would've. All I got was zero to 5% tumor infiltration, and no evidence of PDL1. My tumor would look like this without the brown stain from CD3. I'm not going to respond to PDL1. Brad Power: How would we reflect this on the roadmap? What you're saying is, “if I have an IHC stain, it's going to tell me if I'm likely to respond to immunotherapy.” So, we're going to want to go back to the roadmap and build in these “if, then, else,” conditional logic gates. Rick Stanton: In the roadmap there is some incorporation of molecular testing on whether to include pembrolizumab, a PD1/PDL1 blockade. One is MSI, microsatellite instability high, or mismatch repair genes. From DNA testing, you could pick this, but the roadmap doesn't have a decision point. Relative to IHC staining, to my awareness, there's nothing in the NCCN guidelines that currently says, “if you do an IHC stain, and you test positive, you should go on a PDL1.” Now this is a little interesting because for lung cancer for a long time, chemo was the first line or one of the first line treatments. And then when this PDL1 blockade came out, if you have lung cancer, you will be stained most likely for tumor infiltrating lymphocytes. And if a pathologist scores you above a threshold, the immunotherapy replaces chemotherapy as the first line therapy. So it's happening in lung cancer. Probably not so much in prostate cancer because most prostate cancer primary tumors are known as cold, meaning not a lot of immune infiltration. Next I will go into my RNA sequencing results. One of the neat things about RNA seq is that it is pretty cheap. If you send away your tissue to a DNA sequencing lab, they probably run RNA seq. You're getting the relative abundance of gene expression at the RNA level. This was one view of what Ashion produced from my RNA seq data, and Tempus also produced RNA seq data on me. I want to thank Josh Bell and Wei LIn at TGen. Full credit, you know, you provided the code. Wei Lin: If you guys are interested in learning how this works, I can show a few slides, which are very simple, just to explain why the molecular profiling helps to deconvolute the cell composition in the tissue. For RNA seq we can get the gene expression values of the whole transcription because multiple cells contribute to one gene signal. So we need to do deconvolution. There's a tool called the Epic immune deconvolution, which helps us to input the gene expression values and output the fractions of the immune cells and tumor cells. But there's some assumption of what could be in the tumor, but mostly they are these uncharacterized cells. Usually they are tumors because the tumors are not normal cells. They call it uncharacterized, but for other cells, mostly myeloid lineage cells and the lymphoid cells, which including T-cells, B-cells, and something like neutrophil macrophages, that's how we can get the fractions of those cell types. This is just one algorithm because it's based on different assumptions of the algorithms they give for Rick's case. You can see the other patients we pulled from the TCGA (the Cancer Genome Atlas) consortium data repository, and used them as a reference. And we know for the patient we are interested in what’s at the end. Rick Stanton: I'm the lowest row in that bar graph (labeled People). Wei LIn: This is consistent with the imaging data Rick just showed. In his slide he said zero to 5% TILs. It's low end. It's like a cold tumor. This other algorithm also helps to deconvolute the cell composition even lower. This means that there were no specific patients that showed what end their immune cells are in. I also tried to validate the result because I'm not very familiar with the algorithm. So I compared the expression quantile for those marker genes, for example, like CD1, CD3, which are the marker genes for specific immune cell types. These are the killer cell markers. These are the inflammatory signals from the neutral field, those kinds of things. And for People_1 (Rick’s) expression values, compared to their signals, are mostly at the low end (see the yellow), except this IL7R is at the high end, 90 percentile for others, mostly 10 percentile lower. So we just support the conclusion on these fractions at the low end. That's how it works. It's very simple. Brad Power: I have a very basic question. I don't know what “deconvolution” means. Wei Lin: For example, we have a stew. It's made of tomato, beef, and other things. We want to know after we get the stew, which contributes how much. So we try to figure out how much tomato and beef we have put in.