Video Transcription
Ofer Sharon 00:00
Good morning, everyone. Ofer Sharon from OncoHost, and I will be presenting on OncoHost today. And I thought I would start there with a celebration. So last week we had the results of the Harmony 2 trial, a clinical trial phase three, for metastatic non-small cell lung cancer. This is the first bispecific antibody. And when I read the results, I was happy, like everyone, of course, and the stock went up. But when I looked at the results, what we added to patients is about six months. And I was asking myself if in 2024, six months is the right way to go when it comes to a new drug, a super expensive drug, with the price of 20% of the patients suffering from severe adverse events, and I think that the answer is no. And if we look at what's going on in the market, it's unfortunately not going to get any better. So what we have today in the US is that about 50% of the patients are eligible for immunotherapy, but only 14% of them will actually benefit from treatment, and it takes about three to six months to realize which patient will benefit or which patient will not. So we are spending a lot of money on those patients, a lot of time, and unfortunately, reaching mediocre outcomes, as you saw earlier, because in those patients that we saw in the previous trials, I can promise you that we have hidden patients that can live much longer with the treatment only. We are not looking for them. And as I said, this is only going to get worse, because currently, as we speak, there are about 6,000 clinical trials ongoing looking at different combinations of immunotherapy with other treatment modalities. Even a fraction of those, and a fraction of those will be successful. Think about the number of options each clinician will have to treat these patients, and there's no way for them to choose which is the right treatment.
So let me introduce this to OncoHost. OncoHost was founded in 2018. We currently have 45 FTE, 46 actually; somebody just joined yesterday. To date, we raised about $45 million. We have a commercial product in the market, and I’ll tell you a little bit about the Profit platform as we move on. The first product is for metastatic non-small cell lung cancer, but we've already presented results in conferences and publications in small cell, head and neck, renal cell, melanoma, and adverse event predictor, which is the first-in-class product in the market. And we are expanding to other indications as well, like colorectal and triple-negative. To date, as I said, we raised about $45 million for the company. We are now raising round C. We are selling our product in over 100 hospitals in the US, with over 150 clinicians, prescribers, and the average selling price today for this test is around $2,800; this is what we get paid. We are not covered by CMS here; this is in process. So the number here represents what we get from the commercial plans in the US. The product itself is a combination of analysis of proteins in the plasma, systems biology, and machine learning. If we think about the business model, it is very similar to what we are familiar with, with products like Foundation One or Guardant 360. We get a tissue sample; in our case, it's blood to the lab. We have a lab in Cary, North Carolina, and we have a license to sell in all of the US states. We run the assay; we are measuring about 7,000 proteins, and we send back a report, the Profit report, which can be either positive or negative, and I'll explain what that means in the next slide. The test itself is non-invasive. This is a blood test, very easily integrated into the clinical workflow. So one blood test can be for treatment only 100 to 500 microliters of plasma. So I also have the option to create a very good biobank, which I do, of course. The product is supported by a high level of clinical, I would say good quality and good quantity of clinical data and decision impact trials to support the launch. It is a machine learning-based product. So one disclaimer I would make now is that everything you're going to see is based on blind validation. We train the algorithm and validate the algorithm on a completely separated, independent cohort of patients. What we do is we analyze proteins, and we identify proteins that are differentially expressed between responders and non-responders. Once we make sure that indeed, what we see is an overexpressed protein, there's a lot of work done here. We do something simple. Well, it's maybe overly simplistic, but for now, bear with me. For now on that, what we do is we count the number of overexpressed reps for each patient. So patients that have a lot of those reps overexpressed are not likely to benefit from treatment. Patients that have few, if any, of those reps are likely to benefit from treatment, and they will get a Profit positive report; the others will get a Profit negative report.
Let me show you how good the prediction is. So we are going to look at three blind validations. The first one, about two years ago, a cohort of 250 patients, all metastatic non-small cell lung cancer patients treated with immunotherapy, all immunotherapy combined with chemo. This is the actual outcome, correlation of 0.98 with a very good P-value, too good to be true, I know. So we ran another cohort of 50 patients, again, very good performance of the algorithm, and a third one. And you can see that we have a very stable, predictable algorithm with good, repeatable results, with very good predictive capability. And when we stratify the patients based on the Profit result, we see that patients with Profit positive have a median overall survival of 26 months. Patients with Profit negative have a median overall survival of about 11 months. Now, if those numbers are familiar, I'll remind you on my first slide, the negative result is the average result of the phase three trial from last week. So we have a very good predictor and a classifier for the patient, but what I was missing at that point is clinical utility. How do we expect clinicians to use the product? So what we did next is we correlated the Profit result with the PD-L1 level of the patient. And this is where it gets really interesting, because what you see here is that for patients with Profit positive and PD-L1 of over 50, we basically see no difference between immunotherapy combined with chemo or immunotherapy alone, with excellent median overall survival. So for these patients, there's no need to give chemotherapy. You can spare the cost, you can spare the adverse events, you can spare the impact on quality of life. However, on the right-hand side, we see the perfect negative patients, and for these patients, you see that if treated with immunotherapy combined with chemo, they live more than three times longer than those treated with immunotherapy alone. Now this is a resolution that today doesn't exist for oncologists; they are blinded to this. Only by using our test can they see this. So what we did is we took a predictive algorithm with cool mathematics and science and turned it into a tool that clinicians can use every day in the clinic. And the beauty is that our recommendations are still within the NCCN guidelines. I remind you this is the first-line setting, so we are not playing games, trying off-label therapies for these patients. So this is what it looks like without the Profit, and you can see the resolution. And this is what it looks like with the Profit, again, within the guidelines, but much better stratification of patients. And of course, we do that for all PD-L1 levels. Next, we picked everything into a very simple, I call it less than 10 seconds report in the clinic, because this is what you get from clinicians in the clinic: 10 seconds or less. So they have a report that tells them whether the Profit test is positive or negative, how positive or negative, given by the Profit score of zero to 10. And then simply, a simple instruction on what to do, and they can have the confidence that the recommendation is within the guidelines. This is a decision impact study that we ran: 93% of the oncologists changed management for the patient based on the Profit test, and overall, we see treatment accommodation change in 40% of the patients. This is better than NGS, which is what we use in the targeted therapies. Today, we are well published and supported by clinicians that use the product and love it in the US. And currently, you can see a sneak peek into our clinical development plan, very extensive, one with a lot of launches ahead of us. As I mentioned, we raised $45 million. We are now raising a C2 of $30 million. The idea is to expand to Europe, GCC, and add more indications and finalize the reinvestment process. With that, I will end and thank you for your listening. Applause.
