Burning Rock Biotech (NASDAQ:BNR) Q1 2021 Earnings Call

0:00 Good day and thank you for standing by. Welcome to Burning Rocks 2021 First Quarter Endings Conference Call. At this time, all participants are in a listen-only mode. After speaker's presentation, there will be a question and answer session. To ask a question during the session, you will need to press star 1 on your telephone. Please be advised that today's conference is being recorded. Before we begin, I'd like to remind you that this conference call contains forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934 as amended and as defined in the U.S. Private Securities Litigation Reform Act of 1995.

0:46 These forward-looking statements can be identified by terminology such as will, expects, anticipates, future, intents, Thank you for your attention. all of which are difficult to predict and many of which are beyond Burning Rock's control. Fault-looking statements involve risks, uncertainties, and other factors that could cause actual results to differ materially from those contained in any such statements.

1:39 Burning Rock does not undertake any obligation to update any fault-looking statement as a result of new information, future events, or otherwise, except as required under applicable law. And now I'd like to hand the conference over to the management team of Burning Rock. Thank you. Please go ahead. Thank you. Welcome to Burning Rock's earning call. I'm Yusheng Han, the CEO and founder of Burning Rock. And today we also have our CEO, Shannon Chai, our CTO, Zhou Zhang, and our CFO, Leo Li, in this call. The burning rock is a Chinese molecular diagnostic leader for precision oncology. There are two parts of our business. The first one is early detection using liquid biopsy for pan-cancer. And the second is for therapy selection and MRG.

2:37 We will turn to page four. So today we're going to recap the recent programs for dose early detection and therapy selection. So for early detection, We are very excited to launch the multi-omic 22 cancer test, which is called Present. And the second thing is that the PREDICT trial for nine cancer tests is going well, smoothly. And our COO, Shannon Tsai, will talk about these two trials in detail. And in the meantime, The preparation of commercialization of the safe cancer test is ongoing, and we are continuously building our commercial and operating team and optimizing the SOPs.

3:34 Nothing significantly important to report for the safe cancer so far, but if you have any questions, welcome to us at the Q&A session. And for the therapy selection, we have great news that finally the result of a liquid biopsy card of SEQC2 is published in Nature Biotech Knowledge, which proves that burning wax quality is the top tier level in the world. And our CTO, Joe, will talk about that in detail. And after that, our CFO, Leo, will talk about the financial numbers. So let's turn to Shannan first about the early detection part. Shannan?

4:22 All right. Thanks, Yusheng. So if we go to page six, this is to recap the product development roadmap for our early detection programs. So we started with the proof of concept on lung cancer, and the study and the methodology has been actually most recently accepted for publication. and a manuscript is pending publication right now. And then we moved on to three cancer tests which we presented the data last year in January in the AACR special conference on liquid biopsy We were able to achieve 95% specificity and 81% sensitivity. And then most recently, I think a lot of you are familiar with our six cancer test results that we released last year in November on the East Asia. The six cancer tests include or covers lung cancer, colorectal cancer, liver, ovarian, pancreatic, and esophageal.

5:28 and the data showed that we were able to maintain our 81% sensitivity while improving the specificity to about 98%. And we also were able to achieve a reasonably good accuracy for TOO analysis, tissue of origination analysis from this as high as about 81% accuracy from the six cancer tests. in the results we released last year. And then for the six cancer tests, after the Thunder study, which was the case control study to validate the specificity and sensitivity of the product, we also were planning to move on to a prospective interventional study for asymptomatic populations.

6:22 So that study is currently under planning and I'm going to show you an overview in later pages. And today, we really wanted to focus on the most recent, very exciting progresses that we are making on the NYE cancer test and looking forward to the 22 cancer test. As Yusheng has mentioned, for the NYE cancer test, our current status is that the PREDICT study that we started earlier is going very smoothly. The progress is as expected. And also for the 22 cancer tests, first of all, the 22 cancer will eventually cover about 80% of China's cancer incidence altogether.

7:11 And then for this 22 cancer test, we have just kicked off a large clinical development study a few weeks ago in May 2021, and it's called Prescient. So in the later pages, I'm going to tell you a little bit about the design and timeline for both the PREDICT study and the Prescient study. So if we go to page seven, this is sort of an overview of the clinical programs that we are looking at for our early detection programs. So for each product or each version or each generation of our products, there are roughly four phases for the product development or clinical development. The first is the assay development in which we do the panel design, the marker selection, and also the assay chemistry finalization.

8:11 And then we do the analytical validation to validate the performance analytically including using reference materials or clinical samples to validate the performance specifications. And then we move on to the case control study. And for this one, you can sort of think of the CCGA studies from Grail. It's similar to what we're laying out here as the case control study. In these studies, they are real clinical studies. cases and controls or healthy controls. And in these studies, we will be able to validate the sensitivity, specificity, and TO accuracy, the key statistical performance features for the OA detection products. And then after that, eventually, we would want to move on to the asymptomatic population in which we will be able to to validate again the sensitivity specificity to accuracy in these eventually intended to use population.

9:18 And for this one, you can sort of compare that to the Pathfinder trial from GRIL as well, in which it's testing gallery on the asymptomatic population in a prospectively recruited cohort. So for our three cancer tests, after we finished the essay development and analytical validation, we didn't move on to the clinical development, so we moved forward to the six cancer tests. And for the six cancer tests, we have so far completed the essay development and analytical validation, as well as the case control study, which was what we mentioned in the last page as the thunder study.

10:04 were able to show in a training and a pre-specified validation cohort the 81% of sensitivity and 98% of specificity. And then, again, a prospective interventional study on the asymptomatic population for the sixth cancer test is currently under planning. We hope that we will be able to disclose more details about that study once it's finalized and released or kicked off in the near future. And then, in the meanwhile, in a parallel, we are also moving on to cover more cancer types, of course. So for that night cancer test, we have already finished the assay development, which means that we have finalized the chemistry and also the marker selection, the panel design, et cetera.

11:02 And the analytical validation for that product is currently ongoing. And in the meanwhile, we were able to start the enrollment on the case control, the predict study in parallel to sort of shorten the development time overall. and the PREDICT study, if you might recall, actually contains two phases. And in phase two, it does contain a factor of testing or validating among a small asymptomatic cohort or healthy controls. So that's why over here, we are sort of standing that a little bit beyond the case control study, even though it's not fully powered. to test on the healthy or asymptomatic population.

11:56 So in the later pages, I will be able to give you more details of how the two phases of this study will work out. And then for the 22 cancer test, we are actually working on developing this next generation of the product in the meanwhile, and it's currently under the assay development stage. However, because we collected a lot of information or preliminary results from the previous versions of the products, we were able to finalize the design of that depression study as the case control study that we are planning for the 22 cancer tests.

12:41 So that's why we have kicked off the enrollment of this study so we will be able to recruit samples, clinical samples for future testing or validation for these 22 cancer tests in parallel with the product development efforts. So if we move on to page eight, this outlines the study design of the PREDICT study. Again, it covers nine cancer types, which are listed on the upper right here. Overall, this study contains more than 14,000 participants, about 55% of them coming from cancer, about 10%, less than 10% coming from benign diseases, and the rest from healthy controls. One thing we wanted to point out is that more than 75%, at least 75% of the cancer participants will be from stage 1 to 3. So most of the cases we will focus primarily on the early stage patients we wanted to know or be able to assess our sensitivity among the early stage patients because that's what really matters.

14:03 And then in terms of the study design, as I mentioned, there will be two phases. The phase one will be an open label design which means that for Phase 1, we will divide the Phase 1 samples into the pre-specified training and testing steps. And within the training, we will be able to customize or tune or model and cut off and then to be able to report the results or performances on the validation set within the Phase 1 cohorts. And then after that, the model, both the assay and the model will be locked and then we move on to phase two sample processing. So in phase two, we will have a totally independent set of data to test or to validate the performance of the locked model or method from phase one and to be able to have a rather for the accurate assessment of the model performance for the night cancer test. And then one thing we also wanted to point out is that for predict study participants, we are planning for a 12-month follow-up, especially on the healthy controls,

15:33 which will have a positive testing result so that we will be able to have a positive predictive value assessment among these healthy control cohorts over the follow-up. So on the next page, we listed out the objectives and timeline for PREDICT. So of course, the primary objective will be to test the to train and validate the sensitivity specificity NTO analysis of our CFDNA methylation-based model for these nine types of cancers. And then for the secondary objectives, we, of course, wanted to learn about the performance among different types of cancers and also among different stages of cancers.

16:26 And also we wanted to know whether and other biomarkers, including protein biomarkers that we are processing in parallel in these PREDICT samples, whether they will help in any way, which we do expect they might help in at least some of the cancer types, and how do they help and how do we combine them with the methylation markers. That's something we will explore from the PREDICT data. And then last but not least, we will have a chance to evaluate the PPV in this cohort as well after the 12-month follow-up period. And then in terms of the timeline, we expect the Phase I enrollment to finish, to complete by 2022. And then we will be able to have a readout of the Phase I data by the end of 2022.

17:21 And then by the end of 2023, we will have the readout for Phase II data. and by 2024, we will be able to finish the follow-up and have the complete data set for the PREDICT study. On page 10, we wanted to briefly mention the kind of attention that the PREDICT study has attracted among the Chinese oncology community. And this is the picture of the PREDICT Dr. Jia Fan, when he presented as a keynote speech on the National Oncology Conference on Standardized Diagnosis and Treatment Conference that was held a couple of weeks ago in Beijing.

18:06 And the predict study design and also the news was announced during that conference by Dr. Jia Fan, and it has attracted a lot of interest. and attention from the community so far. And then moving on to the next page, page 11 lays out the study design for depression study. So compared to the PREDICT study, depression study actually has two dimensions of expansion. The first is obviously extending from the nine cancer types to the 22 cancer types which are listed on the upper right again. and also it has similar design but not divided into the two phases. However, it has another dimension of extension is that beyond methylation and protein markers, we will profile other omics of biomarkers in the prescient samples as well. We won't be able to disclose too much detail on what exactly are we testing there, but the expansion will be, the exploration from the prescient study will be focused not just on the cancer type expansion but also on the omics combination.

19:23 And then on page 12, again, the objective for depression study as to we will be able to validate the methylation plus protein markers among the 22 cancer types. And then we will be able to assess different types The performance among different stages and different types of the cancer. And then very importantly, we will be able to evaluate the potential combination, including methylation protein and other genetic epigenetic biomarkers from the prescient study. In terms of the timeline, we expect to complete enrollment for prescient by 2023.

20:12 and then for depression study, we will divide it again into specified training and validation sets. So by the end of 2023, we expect to be able to lock the model for the 22 cancer types from the training set. And then in another year, we will have the study read out for the validation set. So on page 13, this is again to introduce to you the principal investigators for PREDICT and Crescent. We are very proud that we have successfully attracted the top tier oncologists in China to lead for the PREDICT and PRESSION trials.

21:07 So on top, Dr. Jia Fan is the leading PI for the PREDICT trial. He is a fellow of the Chinese Academy of Sciences and also the president of the Shanghai Zhongshan Hospital. So for those of you who are not very familiar with the Chinese hospitals, Shanghai Zhongshan Hospital is one of China's are the world's largest comprehensive academic hospital. And it performs more than 100,000 operations each year and serves about 169,000 inpatients per year. And in 2019, it was ranked top five among China's general hospitals.

21:55 So on the bottom line, Dr. Jie He, he is the leading PI of our prescient study and he is also a fellow of the Chinese Academy of Sciences and also president of the hospital called Cancer Hospital of the Chinese Academy of Medical Sciences. This hospital is arguably the top cancer specialist hospital in China. So we are very proud that these top oncologists are leading our predict and present studies. And it actually reflects the sharply growing interest and acknowledgement from the oncologist community in China for cancer early detection, especially in the past year or so. It did attract a lot of attention in this field.

22:55 and also we think high quality of cohorts and data will ensure timely recruitment and of course successful operation of the study which will serve as a key in establishing a leadership position and maintaining our leadership position in the development of our cancer early detection products. So we are excited to share with you these PIs and their results. their level among the oncologist community in China. So with that, I think I'll pass to our CTO, Dr. Zhou Jian, to tell you more about our recent results released from the SQC2 study.

23:41 Zhou. Thanks, Shannon. So I'm going to cover a little bit about the therapy selection part. So for the slide 15, which is a highlight, what's the strengths of Bernie Rock, in terms of a therapy selection business. So with regards to superior products and the NPR approval process for the different IBD kit in the pipeline, but also the commercial penetration. But today I'm going to focus on the first bullet point, which is the superior products, which one of the evidence showing is a paper published last month in Nature Biotechnology. This is in the slide 16. This is basically a leading effort led by consortium, a community effort led by a consortium led by FDA, which they call MAQC Consortium, and focusing on the quality control of the sequencing business. So what you can see here, so the paper has been published.

24:46 We participated both the liquid biopsy part as well as the pan cancer, which is tissue-based So the liquid biopsy study has been published last month. So page 17 basically highlights what's the participating assay as well as the study design. So there are five different companies participating. They are all kit vendors, which means all being capable to produce the liquid biopsy panel as well as sell as a kit format and let the customer to use them. So Burning Rock is the only Chinese vendor participating in this study. and each vendor will distribute their kit to different labs. Also, the lab will receive the FDA distributed reference material and perform the assay based on the vendor's kit guidance and trying to generate the library and the sequence and also using the kit vendor's bioinformatics pipeline to perform analysis.

25:45 Then all the result will be submitted to FDA and for the principal investigator of this study, look at the data and based on the peer project also led by SEQC2 effort and trying to know what's the true ground truth for this data and trying to evaluate sensitivity and specificity we call it for positive rate as well as evaluate the reproducibility within lab also across labs. So, burning rock using the long plasma V4 panel, which we currently call ENCODE Compass Target Panel for the liquid biopsy study. And it covers 168 genes being listed in the top table here.

26:35 So, for the next slide, slide 18 basically highlights several key performance comparison across different kind of panel and product. So, burning rock has been boxed in the green color. And as you can see at the top part, we see the fragment depth, which means based on the sequencing without how many unique fragment that we can collect it or recover it from 25 nanogram reference material. As you can see here, the data is showing the higher the better, which means with limited amount of DNA input, how many real fragment you can collect from them out. And the bottom panel compared covered uniformly, which means across this panel, what's the average coverage and how uniform this panel will be. And as you can see here, the burners are also showing good performance compared to other vendors.

27:35 All the coverage is similar to each other. So for the next slide, slide 19, which compares four different hybridization capture panels, and Xiaozhi Hu. whether this panel can be able to call it, and each column represents one sample, one replicates in one site, in one lab.

28:20 So, burning rocks on the right, most one basically, if it's been colored, which means this variant has been called, if it's blank, which means this variant's been missed, and we call false negative here. As you can see here, basically, almost all the colors are being filled for burning rock, even if it's like 0.1% to 0.2% variable frequency and the calling percentage is higher than some other panel. So this is just to give us a lot of confidence showing like our panel as well as our bioinformatics pipeline showing pretty top performance compared to the other vendors, especially this kind of classic molecular biology vendors. So for the reproducibility slide and the next slide, slide 20, they just compare across different kind of panel, look at how reproducibility it is across panel, across lab or within lab. So each lab process same sample four times. Also, there's a multiple labs performance.

29:23 As you can see here, the reproducibility also, Bernie Rock's data showing pretty good performance compared to other vendors' kit. For the slide 21 is just briefly compare different kind of input of DNA amount and compare the sensitivity as we know like the more DNA put in there and within the then the higher sensitivity there is each panel showing this kind of trend for the for the green line basically represented burning rocks assay and for both sensitivity as well as reproducibility showing the burning rocks panel as a pretty good performance and very stable on that and also even at the variable frequency 0.1, 0.5% showing high performance.

30:10 For the slide 22, very briefly, they just compare analytical accuracy based on the sensitivity and precision curve. And this is based on 25 nanogram of reference material input compared to four different panels. and the precision representing the positive predict value, PPV. And the sensitivity here means the recall, which means how many the true positive rate it is. So the closer to the top right corner of this graph means the better performance. As you can see here, the overall analytical accuracy and the specificity of Bernie Rock showing the best compared to other panels.

30:56 So all this information just give us from the paper published and this is basically a relative like say fair comparison across different panel and using same reference material and gave us a lot of confidence showing that our product in the surface selection zone showing the top performance not only in China but also compared to the world you know a lot of famous kit vendor and we've been doing pretty good on that. So here basically I just conclude the presentation for the therapy selection part highlight. I will hand over to Leo talking about financials. Thanks. Thank you, Joe. Our financials are shown on page 25 of our presentation.

31:42 And for this call, we'll focus mostly on our top line numbers. And first, we recap that all our revenues are generated from our therapy selection business, so there is no contribution from early detection yet, which is still under In the first quarter, we are happy with the year-over-year growth that we've been able to achieve. We grew our revenues by 58% on a year-over-year basis. We grew our gross profits by 72% on a year-over-year basis. By candle, our central lab revenue grew 62%. Our in-hospital revenues grew 70%. In our observation of some anecdotal industry data points, this is above industry growth rate, indicating that we've been able to gain some share in this period. Within the first quarter, talking about the monthly and the sequential trends, January was impacted by COVID resurgence in Beijing, Shanghai, and a few other key cities in China.

32:49 So that did have a negative impact on Thank you very much. Thank you. for the 2021 full year, which is unchanged from our previous earnings release. We have not hit the monthly round rate yet to achieve that full year target, so there is certainly more work that we need to do.

33:36 And in terms of what we're doing, in terms of driving additional NGS penetration, we are doing, number one, executing our in-hospital strategy, including our tests available at more hospitals, which we think is important for building MGS penetration because this is the most typical format of testing in China. And second will be the continued execution of our multi-year NMP registration pipeline process, which will be key in terms of competitive differentiation. So we remain focused on these initiatives for driving the long-term success of our therapy selection business. And with that, we conclude our prepared remarks, and we open up for questions, please.

34:22 Thank you. Ladies and gentlemen, if you wish to ask a question now, please press star 1 on your telephone and wait for your name to be announced. If you wish to cancel your request, please press the pound or hash key. Your first question comes from the line of Doug Shankle from Cohen. Please go ahead. Hi, good day, and thank you for taking my questions. Starting on the topic of asymptomatic screening, I appreciate all the detail you provided today. Regarding the six cancer, nine cancer, and 22 cancer asymptomatic screening programs, three things that are pretty important remain unclear to me.

35:10 One, do you believe studies like Thunder, Predict, and Prescient will be sufficient to allow for product launch from a regulatory standpoint and reimbursement? Second, if not, how big a study will be required to allow for regulatory approval and reimbursement? And third, what is the acceptable target from this perspective when it comes to sensitivity and specificity? I want to go back to these questions because you referenced CCGA and Pathfinder in your prepared remarks as good precedents or at least comparable studies. Neither of these studies are sufficient in the United States to support FDA approval or CMS reimbursement.

36:02 Most companies, in fact, that are based in the West have indicated FDA approval and reimbursement would require large randomized prospective studies. And by large, I mean over 100,000 patients. So I understand your programs are not targeted at the U.S. or Western markets. They're targeted at China. So it just would be helpful to understand, again, what the answers are to my three questions as it relates to asymptomatic screening, given the market is different. Okay, hi, Dot. Thanks for your question. I'll take your question. I'll try. So first of all, a very straightforward answer for your first question. No, we don't think the predict or prescient will be enough for testing the asymptomatic population because they are apparently not powered enough to have a precise enough assessment on the sensitivity, especially the sensitivity for the asymptomatic population. And also the recruitment strategy actually naturally conflicts with prospective asymptomatic validation study because in this study the participants, the control arm actually

37:34 We define them as the healthy quote-unquote because they need to go through a health checkup or physical examination at the recruiter point. But actually for a purely asymptomatic study, they don't necessarily have to go through that. It's just symptom-free and relying on whatever health checkup habits they're going through in their real life. So predict and question are not designed to give us answers for the asymptomatic population performances. They are, on the other hand, powered or designed to give us answers for the case control cohorts, which will help us to design the future asymptomatic, post-active, or even interventional and Xiaozhi Hu.

38:33 However, for the six cancer tests, the study that I just mentioned that's under planning, that one will be designed and powered to give us a definite answer for the asymptomatic population for the six cancer tests. So that one, we do Thank you very much. The question about an individual level, whether the benefits would be enough to pass the product through the registration, at least as a pay-out-of-pocket product. However, for reimbursement, I agree with you that it's a completely different story because for reimbursement, you not only have to established an individual level benefit. You have to establish a population level benefit.

40:02 So in order to do that, there are a lot more you need to evaluate beyond just sensitivity, specificity on that individual level for different cancer types, et cetera. You also have to establish benefits in terms of health economics, et cetera. So in China, I think we also talked about this A few times before. In China, the market is an out-of-pocket market. So we do believe that in China it's possible to have the registration and reimbursement. There are sort of two things, and we will be able to have the registration by just showing the individual level of benefit.

40:54 But of course, Again, this is preliminary thoughts and things might change and we might have new information as time goes on. Thanks so much for that, Shannon. That was really helpful. I think my other topics are probably more for Leo. So, Leo, just in terms of the quarter and specific to the central lab, Volume dropped relative to Q4. Maybe that wouldn't have been shocking regardless of how January and March went given Lunar New Year was in the quarter and wasn't in Q4. That being said, volume was also lower than Q3. Additionally, revenue dropped back to levels not seen since the second quarter of last year in this channel. and that was largely a function of both the volume dynamics and maybe just as if not more importantly ASPs dropping a bit.

41:54 So on the topic of ASPs, because it sounds like you don't think there's any competitive pressure on volume, it sounds like you think that's just the market. So when it comes to ASPs, were there market pricing pressures in the quarter or was that a function of product mix? And then, you know, kind of building off of that, how are you thinking about volume and pricing in the central lab channel over the balance of the year? You know, essentially, what's built into guidance? Yeah, so for the central lab channel, we did see ASP fluctuations quarter over quarter, and that was more to do with product mix.

42:39 So we have not made any pricing changes for that channel during the first quarter. So pretty much driven by product mix shifts and some seasonalities. So that was for the first quarter. For the remainder of the year, for the central lab, we think there are structural challenges that we do need to work through. And if we look at, as we mentioned earlier, if we look at building NGS penetration, We are putting efforts into the in-hospital channel. We think this will be very important for the future growth of NGS penetration as the most typical formats of testing.

43:24 The central lab channel is a more fragmented channel with lower entry barriers, whereas the in-hospital channel is a more institutionalized channel where our product strength will be able to compete better, we believe, versus other non-products and some aggressive commercial factors in the central lab channel. So we think, looking for the rest of the year, in-hospital channel will be important in terms of driving growth. For the in-hospital, for the central lab channel, we have been building our sales team and Headcounts. So we have seen sales and marketing expenses increasing over time, and that's mostly due to Headcount increases.

44:15 So we are putting more manpower on the ground, speaking to more physicians to build up this channel, but we think that this will take time. So Leo, also keeping in mind that in-hospital revenue dropped below levels generated in both the third and fourth quarter of last year, would you attribute the performance in Q1 largely to normal seasonality and thus you feel pretty confident about a more pronounced ramp in the in-hospital channel versus the central lab over the coming quarters? The first Q drop of the in-hospital channel was expected, as we were expecting Chinese New Year, and typically there was not a lot of ordering during that month.

45:05 Then the January COVID resurgence was unexpected, so that did hit us. Without that, we would have been better. Looking at volume trends, we were happy about the in-hospital volumes for the month of March, which grew double digits. and we are keeping our watch on the second quarter as we have not closed the second quarter yet. Okay, and that's a perfect segue to my last question, which again is on guidance. You know, obviously you knew Lunar New Year was in Q1 as it always is. It sounds like what surprised you was the COVID impact on January and probably more the central lab performance in March versus the in-hospital performance in March. What is it that you saw coming out of the quarter and over the early part of Q2 which made you confident in reaffirming guidance in spite of the fact that it does seem like there were more headwinds in the first quarter than you might have anticipated?

46:14 Yeah, as we were building the guidance, we were expecting a second half heavy versus first half light of the year. And it played out that way. And we did leave some buffer for COVID fluctuations, and we did get hit by that in January. So not a lot of surprises in terms of looking at our guidance. Looking forward for the rest of the year, we do need to ramp up our monthly revenue round rates, which we haven't hit the round rate yet to be able to achieve that four-year guidance. So we need to go back and work hard, and we look forward to update you guys in the next earnings call.

47:00 Okay. Thanks to all of you. I appreciate all the details. Thanks, Doc. Thank you. Our next question comes from Ethan Cho from Bank of America. Please ask your question. Thank you for taking my question. I'm Yisheng from Bank of America, and I'll ask two questions on behalf of our analyst, David Li. The first is that can we have some update information about our six cancer tests? Is there any changes for the timetable guidance? For early detection commercialization? Yes, and the approval and like our discussion with NMPA. NMPA, is there any updates?

47:53 You know, last time we talked about that we're going to do the EAP and also the prospective clinical trial. And in terms of the commercialization timeline, Now, we are building our teams for commercialization and operation. So, we think that the key point for the early detection is for the consumer side. So, we recently are recruiting a team from consumer industry and also the internet industry. We believe that we have already found the right way to commercialize that.

48:40 But at the same time, that's a totally new thing in the market. So how to build up an SOP needs time, and we need time to optimize the whole process. So far, we're seeing that everything is on the right track. as we said that the commercialization will start early next year. And in terms of the clinical trial, will Shannon talk about that? Right. I don't think we ever gave any guidance on registration because we honestly are having an ongoing conversation with NMPA. So as I said, there's nothing I'm sure at this point. And also we are, of course, for the whole field, the early detection products, the registration pathway for that is not clear yet. So I think it's a dynamic process or discussion within MPA. So we don't have a specific timetable that we could give out yet.

49:53 But as Yusheng said, for all the progresses or efforts, Thank you, very clear. And the second question is about the participants in our PREDICT and PRECENT study. We think that for the PREDICT study, there are around 14,000 participants, while for the PRECENT, there are around 12,000.

50:39 And can you help us to illustrate more about how this number has been confirmed and why there's a difference and why the PRECENT has fewer number of participants? Thank you. Oh, thanks. It's a very good question. Thank you for noticing that. Actually, for PREDICT, because we have quite rich preliminary results for those night cancers, at least six out of the nine cancers, and there's a little bit of data on the other three cancer types. So we were able to design the study and planning for the sample size on a stage-specific for the sensitivity and TO accuracy. So that's why for predict, even with fewer cancer types, we are planning for a larger sample size because the sample size was calculated so that on each stage, each cancer type, each stage, we will have a precise enough estimate for the sensitivity. However, when we are planning for depression study because it's longer down the road and also because Thank you very much.

52:22 Even among predict and question, each cancer type actually has different sample size plants in terms of or depending on our estimated sensitivity that we will be able to reach or achieve. Especially for depression study, we actually have fewer samples planned for the nine cancer types that were already covered in the predict. And we allocated more samples for the other 13 cancer types. But all in all, the design, the sample size calculation was based on different objectives. That's why you see different sample sizes for each cancer type.

53:12 Okay, thank you. Very clear. I saw my questions. Thank you. Our next question comes from Sean Ru from Morgan Stanley. Please ask your question. Okay, thank you for taking my question. And I actually am also quite curious about the school numbers used for the school studies. You have exactly 14,026 for predict and 11,879. How did you come up with those kind of numbers? That's just from my curiosity. For one study, you are doing nine archetypes. The other, 22. In some sense, why don't you just combine them together? Those two studies, supposedly, clearly are designed for different purposes, I suppose.

54:00 For 9-1-2, should we expect that you will get a more conclusive result from the 9-1 compared to the 22 archetypes? And also, your competitors, some of them have come out with a perspective of design that, you know, for one type of cancer detection, like liver cancer or prostate cancer. What's the difference, the advantage of multi versus single one? For liver cancer, clearly, if people drink a lot and have hepatitis, this one type of medication is also very important for them. And finally, I think you have signed up some very good oncologists at the top hospitals I'm going to ask you to do your finished files.

54:46 How are you so successful with getting so much, so many key APIs to buy in and be a part of your program? Thank you very much. Okay. Well, first of all, For the study sample size, as I just previously explained, Predict and Prescient are designed based on different objectives. For Predict, we are aiming to estimate stage and cancer type-specific sensitivity. So for each cancer type, we allocated more sample size. But for Prescient, because we didn't have as much previous knowledge to support Predict, and Xiaozhi Hu.

55:50 are all sample size plans for depression study. And also for your question, why don't we just combine the two? Because they are for two different products. Because for the PREDICT study, we're using our non-cancer test product. And then for depression study, we will use our next generation of 22 cancer test product. They're not just, it's not an add-on relationship. between the two products. Actually, the chemistry and also the marker selection and the model will all change and hopefully will all improve between the two generations. That's why for the new generation, we will have to retest its performance to see whether it holds or even improve on the existing nine cancers that were already tested in PREDECT.

56:47 And for your last question about the principal investigators, thank you for your comments. We are also very proud, and as I said, it reflects actually the strong interest and attention that early detection has drawn among the oncologist community. I would say about three years ago, none of them really believed in The new technology is getting close to real clinical application or to make real contribution to cancer early detection. But nowadays, a lot of them believe in that and they think the new technology, especially the epigenetics-based biomarkers plus machine learning and next generation sequencing is Finally, it's bringing into reality that early detection can be realized in a large scale, especially on a multi-cancer application.

57:45 That's for one. And for two, there are actually very few hospitals in China that have the capability and capacity to be able to host studies like PREDICT or PRESSION or lead studies like these. It actually requires a lot of organization skills Power and also the impact from the principal investigators. So that's why actually only the top clinicians or oncologists in China have the capability and impact to be able to operate these really large cohort studies. I think that's also why they have the passion and the ambition as well to fulfill these very innovative studies.

58:37 All right, thank you. So ladies and gentlemen, we have reached the end of the question and on to the session. So with that, we conclude our conference for today. Thank you for participating. May all disconnect.