Razelle Kurzrock: Thank you for having me here. Can you hear me okay? Yes. So I'm going to talk about impacting complex genomic and immune landscape, and doing this by looking at precision medicine in the real world cancer clinic. And these are my disclosures. And this is what I do. I'm very passionate about the Center for Personalized Cancer Therapy at UCSD. And this center has been devoted to developing clinical trials and precision medicine.
Before I was at UCSD, I was at MD Anderson Cancer Center. And I founded and built the department with early phase clinical trials. And then these patients that were really end stage metastatic patients, we began to have the first insights into what precision medicine could do for our patients.
Next slide. So these are first generation studies and first generation technology. We started at MD Anderson Cancer Center about 2007. And we started collecting really real-world data from patients that were navigated to clinical trials. And this started with a master protocol, which we call PREDICT, Profile Related Evidence Determining Individualized Cancer Therapy. And this included any type of patient, was an umbrella trial, and that included individual histologies, regardless of their genomics, and it was a basket trial, and that it included cross tumor type genomic subsets.
And this is what we began to see. Next slide, please. What we began to see with our patients, these are waterfall plots. And any patient below the horizontal line is a responder, and any patient above the horizontal line is a progression. So the response rate in these patients with end stage disease, median of about four or five prior therapies when given phase one clinical trials was about 5%.
But what we saw was, when we began to match patients, and this is first generation matching, starting in 2007. So we did not have next generation sequencing yet, but when we began to match patients based on their alterations, the response rates really increased very dramatically.
Next slide. But this was just the beginning. And we realized that things have moved really quickly in the last, approximately 13 years. And we now have a new era with tissue agnostic approvals that are driven by genomics. The first one, and this was really key. So I put up the date, was when the FDA approved pembrolizumab for solid tumors. And there were three key features of those approval that made it really transformative.
The first was that it was tissue agnostic, it was the first time, but certainly not the last time that we had a tissue agnostic approval. The second key feature was that it was approval based on the genomic marker, mismatch repair gene defect. And then the third part of this approval, which I think was even more important, was that the approval was based in part on retrospective, real-world data. And I think this is the first time that the FDA, but not the last time, began to think about looking at approvals based on real-world data.
Next slide. But as we began to do next generation sequencing, we were really excited to have this powerful, new tool. And then, very soon afterwards, we realized that there was a challenge that came with this new tool. And that challenge was what if every patient with metastatic disease is different? I live in San Diego, but I was born in Canada, and know a little bit about snowflakes. And we began to think about tumors as metastatic, the equivalent of metastatic snowflakes, every snowflake has a unique crystal structure, and every snowflake is very complicated.
Next slide. And this can be shown in this slide. We've actually done thousands of patients, but this is what fits on the slide. There is several patients with breast cancer. And you can see that patient 42, and patient 25 both have ERBB2 amplification. And so what we've normally done is given both of these patients ERBB2 targeted agents, and of course, that's the right thing to do.
But when you look at the rest of their genomic aberration portfolio, it's different. And so now the question arises, do these people, both of these patients really belong on the same regimen? Or do we need to individualize the therapy to each of these patients? And I think the answer is the latter. But this is very different than anything we've done before.
Next slide, please. So, genomics has unveiled disruptive complexity. If there's 300 drugs for oncology, there's almost 4.5 million three drug combinations. And how are we going to go do all these clinical trials, individualizing all this therapy? Next slide. And then we need to layer on this all the single nucleotide polymorphisms, and all the snips. And then of course, we've talked about genomics, but there's transcriptomics, proteomics, epigenomics, and so forth.
Next slide. So we need real-world data and machine learning to sort all of this out. Next slide, please. So at UCSD, we created a real-world data master protocol, which is really an umbrella protocol over the entire institution. And, next slide, what helps us with this is our molecular tumor board. And I want to talk a little bit about what we concentrated on. We concentrated on the pillars of precision medicine, which includes genomics and immunotherapy. And people think of them separately, but they're really a couple, and they're married to each other.
And this marriage, next slide, is exemplified by the fact that the higher the tumor mutational burden, the better the response to immune checkpoint blockade. So overall, if you have a low mutational burden, your response rate is 4%. If you have a very high mutational burden, based on our real-world data, the response rate can go up to 67%.
Next slide. So I want to give you some examples of what we're seeing and what we're doing in the clinic. And so this is one patient, this patient came to see me in January 2016, she had a hybrid neuroendocrine cervical cancer, this is a rare tumor, I run a rare tumor clinic, she came from the Middle East, she'd had prior surgery radiation therapy, and chemotherapy. Next slide. And she was very end stage with bowel obstruction and kidney obstruction.
Next slide. And this is, we didn't really have a lot of time. So what we did is we did profiling on her blood. And what we saw, the usual patient has three or four abnormalities. She had about 20 abnormalities. And we assumed that this was hyper-mutated DNA. Next slide, please. And this meant, we assumed that she might have a high tumor mutational burden, remember, this is January 2016. And we began to treat her with immunotherapy, with checkpoint blockade. And you can see her CAT scan before and after therapy. And essentially, she is now, I guess, about four and a half years now with an ongoing response.
Next slide, please. I also wanted to say that real-world data can tell us not only who we should be treating, but who we shouldn't be treating. And one of the things that we've seen is this phenomenon of hyper progressors. These are patients who have accelerated progression after immune checkpoint blockade, and this is a patient. You can see in the left that this patient's tumors were stable from March till June. And then she started on immune checkpoint blockade, and six weeks later, she just exploded.
And what we saw in this patient is that she has MDM2 amplification. When we did an analysis of our data, next slide, we found that this was very common in our patients with hyper progression. They had MDM2 amplification, and the other alteration that they had was EGFR alterations. Now, that's not to say that every patient with MDM2 or EGFR will hyper progress, or that every hyper progressor will have MDM2 or EGFR.
But my point here is that some of these patients are at risk of hyper progression, and that genomics can not only tell us who may respond to therapy, but it may tell us who may be resistant or do poorly on therapy, and who in whom we shouldn't choose these therapies. Next slide, please. So I'll just talk a little bit about our study, the I-PREDICT Study, next slide, that we published in Nature Medicine a few months ago, where we gave customized combinations to newly diagnosed patients with lethal malignancies
And this is really an n-of-1 study. And, next slide, this is the outcomes on this study. So what you can see here is with these n-of-1 combinations, we found that patients that were highly matched to therapy had a response rate of 45%. Patients with low matches had a response rate of about 16%. And progression free survival and overall survival was also increased, with overall survival being about 60% at two years in patients with high match, and about 20% in patients with low match. And this tells us that is not just about, yes, patients are matched or no, they're not matched to therapy, but it's the degree of matching that is important.
Next slide, please. So this is an example of a patient. This patient had a carcinoma of unknown primary, had a KRAS mutation and then ARID1A mutation, KRAS, we targeted with the MEK inhibitor trametinib, and ARID1A, there's literature suggesting that it can be targeted by the PARP inhibitor, this patient has had a beautiful response as you can see in this slide. And therapy is ongoing with very little in the way of side effects that 17 plus months.
Next slide, please. I also wanted to mention some new trial types such as the master observational trial that we've been involved with. And this, we recently published in Cell. This is structuring real-world data within the concepts of a clinical trial. Next slide. So thank you for listening, I want to give special thanks to our precision medicine team, and especially to our patients because they do all the work. And thank you again for inviting me.