Cry Me a Biomarker: Using Tears to Screen for Cancer
- Namida Lab is a diagnostic company focused on developing cancer screening tests using biomarkers found in tears.
- Tears have been studied as a diagnostic fluid for decades, and tears and blood are considered two sides of the same picture in terms of diagnostic potential.
- Tears provide a clean and easy-to-collect fluid for testing, containing low molecular weight biomarkers related to cancer.
- Tear collection involves using a Schirmer strip placed inside the lower eyelid, which absorbs proteins of interest from the inner surface of the eye.
- Namida’s Aria test is a companion test that assesses breast cancer risk by examining inflammatory markers found in tears.
- The test can detect elevated markers due to benign and malignant conditions, so it does not differentiate between them.
- The test uses an algorithm that combines protein concentrations with clinical information to calculate a risk score, determining whether an individual falls into the low, medium, or high-risk category.
- The test aims to encourage women to undergo regular screening mammograms by providing information about their breast health and offering a breast health consultation.
- By reaching out to women who may be screening averse or unable to access screening facilities easily, the test aims to bridge gaps in breast cancer screening and improve early detection rates.
Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.
Is it possible that our tears are a signal of more than just our emotions?
The answer is yes. It turns out that the liquid in tears comes from blood plasma, and contains a lot of the same proteins and other biomolecules that circulate in the bloodstream.
But they don’t have a lot of the extra components like antibodies that would get in the way if you were looking for specific biomarkers, such as the low-molecular-weight proteins released as a byproduct of the inflammation around tumors.
My guests today, Anna Daily and Omid Moghadam, are from a startup called Namida Lab that’s the first company to market a lab test using tears to predict cancer risk.
Specifically, Namida’s test assesses the short-term risk that a patient might have breast cancer, as a way of helping them decide how soon to go in for a mammogram.
Namida is actually the Japanese word for tears. And beyond breast cancer, the company aims to build a whole business around risk assessment and diagnostics, using just the biomarkers in tears.
Eventually it could be possible to collect a sample of your tears on a small strip of absorbent paper, send it in to Namida Lab, and find out whether you have colon cancer, pancreatic cancer, prostate cancer, or ovarian cancer.
The breast cancer test and the pan-cancer test that Namida is developing might sound familiar if you remember our January 2023 episode about Grail and their blood-based test, which can screen for cancer in 50 ways in a single blood draw.
One big difference is that tears are way easier to obtain than blood. Another is that Namida’s current test is much cheaper than Grail’s.
Namida’s big vision is to use tear testing to make precision medicine and diagnostics more accessible and affordable, including to patients who might live far away from tertiary care centers.
Omid and Anna and I talked through that vision back in February. Here’s our full conversation.
Harry Glorikian: Welcome to the show. Great to have you guys here.
Omid Moghadam: Thank you, thank you, Harry. Appreciate the invite. And let me say that once you’re being interviewed by the great Harry Glorikian on Moneyball Medicine, you’ve really made it. It’s like playing the main room in Caesar’s Palace.
Harry Glorikian: Uh, [laughter] I’ve got to figure out how to edit that out, but okay.
Omid Moghadam: I’m bringing my A material to your podcast, Harry, what are you talking about?
Harry Glorikian: [laughter] Uh, so. Your company. I want to dig into, you know, what you guys are doing and you know what the company is working on. And, you know, I tried to do all my homework just, but let’s just start from the basic message. You know, the company Namida Lab is working on, you know, gauging breast cancer risk. To decide how soon to have a mammogram simply sending tears to your lab. Right. And to a lot of people, I think the idea that tears might contain cancer biomarkers is probably new and surprising. So I want to step back a little bit and talk about. Maybe you can start telling us a little bit about tears or how they’re produced. What’s the connection between tears and breast cancer or maybe tears and and useful biomarkers and we can start there.
Omid Moghadam: Sure, sure. So Namida Lab, even though our first product is in breast cancer, we are we’re a diagnostic company developing cancer, early cancer screening tests for various cancers using biomarkers found in tears. And I’ll let our chief scientific officer, Anna Daily, tell you more about what’s in tears, what is the relationship between tears and blood, and how these markers end up in tears and what we can see in there?
Anna Daily: Great. Well, so tears have actually been studied as a diagnostic fluid for decades. We are just the first group to actually make a lab developed test using tear fluid. And the interesting thing about tears is we call tears and blood two sides of the same picture. So we know with blood it’s a very well characterized fluid. It’s been studied for a long time, but it does have a lot of extra components in it. So it has lots of you know, you’ve got red blood cells, white blood cells, antibodies, large proteins. There’s a big, the term that we use is dynamic range. So we have a lot of really big proteins down to really small proteins when you’re studying cancer biomarkers. So oncology markers are often in the low end of the dynamic range. They’re often very small, low molecular weight markers and it can take quite a bit of processing in a lab using blood to get down to those molecular levels, low molecular weight. You have to do a lot of cleaning of the fluid. With tears, we can get to those low molecular weight biomarkers in one step. It’s just a really easy, clean fluid because we don’t have a lot of the large molecular weight proteins and cells and things in the way.
Anna Daily: And the way that we collect tear fluid is we use a piece of filter paper. It’s called a Schirmer strip. It’s what they used to use to detect dry eye. And so we place it inside the lower eyelid. We have the patient or customer close their eye. And the action of having something in your eye will cause your eye to water. But really what we’re looking at is the proteins that we’re interested in back and forth across those capillaries on the inner surface of the eye. And because of the characteristics of the protein, they will stick to that filter paper. So we take that filter paper out, we place it in a buffer that is shipped back to our lab and in the process of shipment, the proteins will release off the paper and go back into the buffer. And the buffer itself is what we test in the lab. So it’s just really quick and lends itself very nicely to at home collection. It’s easy to do and easier than even putting in a contact lens and it’s a clean fluid that we can get to those low molecular weight proteins very easily. And the reason that we would see proteins in tears as related to breast cancer is, is simply because we’re looking at, so we’re looking at inflammatory markers that are present in the breast tissue and they’re circulating throughout the body. And so we’re just that is just the place that we are picking them up is inside the lid of our eye.
Harry Glorikian: Because, it was funny because I was thinking like, how are you going to collect the tears? I was thinking like, do I have to watch a movie that like, gets me all welled up and like, they start streaming is the first thing I thought of, because my wife will be like, whenever there’s a sappy movie, you’re always like welling up in some way. And I’m like, I can’t help it. So but you guys, so the company describes the Aria test, which is the test we’re talking about, right, as a companion test that assesses breast cancer risk rather than actually diagnosing cancer. Let’s just ask if that’s correct.
Anna Daily: That is correct. So the markers that we look at, because they’re part of the inflammatory process, can also be elevated due to a benign mass. So at this point, we don’t differentiate between a benign inflammation due to a benign mass, or a malignant tumor.
Harry Glorikian: So we’re going to get into some of those details later. But you don’t see Aria as a substitute for other types of testing like mammograms.
Anna Daily: It’s not designed at this time to be a substitute for mammography.
What are the main ways to screen for cancer?
There are several ways to screen for cancer, including:
- Mammography: This is a screening test for breast cancer that involves taking X-rays of the breasts to look for any abnormalities.
- Pap smear: This test is used to screen for cervical cancer by examining cells from the cervix under a microscope.
- Colonoscopy: This is a test that examines the colon and rectum for polyps or other signs of cancer using a long, flexible tube with a camera.
- PSA test: This is a blood test used to screen for prostate cancer by measuring the level of prostate-specific antigen (PSA) in the blood.
- Skin exam: This is a visual inspection of the skin by a healthcare provider to check for any suspicious moles or other growths that may indicate skin cancer.
- CT scan or MRI: These imaging tests can be used to screen for various types of cancer, such as lung cancer or ovarian cancer.
It’s important to note that not all types of cancer have a reliable screening test, and the effectiveness of screening tests can vary depending on a person’s age, risk factors, and other factors. It’s important to talk to your healthcare provider about which screening tests are appropriate for you.
Harry Glorikian: Okay. So at this point so I mean, I want to step back in time, right? So Anna, you developed the test itself. How did you get interested in tears containing biomarkers and, you know, how did the idea evolve into a company. And then, Omid, how did how did you wake up one morning and go, yep, CEO, I’m going to do that job, sort of thing. So can you guys start, you know, and you start and then you take over?
Anna Daily: Sure. So the early work around tears and breast cancer in Arkansas was done in academics, as it typically is with companies. And so there were some very preliminary research done at UAMS, which is our local medical school here in Arkansas, by a well renowned breast surgical oncologist, Suzanne Klimberg. And she, so if we think of when we’re looking at proteins, proteins, so what she did in her work was she basically found the map and not the name of the roads, kind of how I talk about it. So she figured out sort of the map and the direction. And my job was to come in and figure out what the what the exact roads were and really identify the route. And so the original idea and early work was hers. And then there was an incubator company that sort of came up with the sort of built the company around that idea. And that’s that’s when Omid and I met.
Omid Moghadam: That’s where we met. There was an academic incubator company here that was, that was looking to start some new companies in the life sciences space and I was leading that effort for them. And this was one of the companies, one of the technologies in the portfolio that we were looking at. And Anna was one of our scientists in the incubator. And the way it worked was that we would bring technologies in from universities and then do a proof of concept to make sure that we can validate what they had done in academic research, which most times you can not. So we learned, we learned that fairly quickly, that a lot of the IP coming out of universities was not replicable in real life. But this one, we could. And so we worked on it part time for a few years, recruited a cohort of patients to do studies. And once we figured out what these markers actually were, then we launched a company to commercialize it. And that that company was founded in 2013. And it went on for six years until it was acquired to create Namida. And at the time, we were working on an FDA-cleared device, a cartridge, a paper based cartridge for the diagnostic. But we ran into the Theranos debacle, and finding funding for anything in diagnostic at that time was almost impossible. And so that’s why the work took longer because, you know, we just had to do it with limited resources, but we managed to do it. Went to an acquisition. Namida was founded and the strategy of the company changed, changed to not just the cartridge and FDA cleared, but go for anything. So if it makes sense to be an LDT [laboratory developed test], let’s go do an LDT. If we want to go for a breakthrough device, say if it’s an ovarian cancer, we’ll go for breakthrough device designation. So we just expanded our like regulatory pathways and the first one happened to be in breast cancer. And post-COVID the whole idea of direct to consumer testing or consumer directed testing became more prevalent and well funded. And that’s why we released Aria as a direct to consumer test into that market. You know, using our own developed platform, digital platform.
Harry Glorikian: So let’s talk a little bit more about the science of using tears to screen for cancer and some of the other questions that motivated you guys to start down this path. Right. So there’s a growing recognition that, you know, we can screen for cancer with fluids like blood to look for cancer biomarkers. In fact, I recently had the president of Grail on the show talking about their work, right, to develop a test that can screen for cancer with a blood sample for, say, 50 different cancers by looking at free floating DNA shed by tumors. But I think your argument is that blood may not actually be the best, as you mentioned earlier, to screen for cancer. And tears might actually be better in some cases. So I’d like to unpack that a little more. Right. So are there components in blood that are not found in tears that can get in the way of finding these cancer biomarkers? Are there practical and economical advantages to testing tears rather than blood, right? Because I can imagine that someone arguing that it’s easier to get a tear sample, like you said, than, you know, trying to find a phlebotomist to stick a, you know, needle in my arm. You mentioned that the tears are easier and faster to process than the blood. So walk me through some of that for the listeners.
Anna Daily: Sure. So, you know, there’s a lot to unpack there. But when we think about ease of getting to a protein and here we’re looking at protein. So Grail looks at DNA and they’re looking at DNA shed from tumor cells that are circulating throughout the body. So there’s a lot of different things we can think about when we think of cost of testing. Grail’s test is quite a bit more expensive. Of course, they’re looking at a lot of different cancers, but their test is quite a bit more expensive. So we wanted something that could be easily attainable, could be done in any clinic or any home by our users, and keeping the costs low. And that’s something that tear fluid really lends itself nicely to. And then we’re looking at protein instead of DNA. So looking at protein, we’re doing a sandwich based ELISA in the lab. That’s a fairly cheap, well-established, easy process to do. And we can get, as mentioned, we can get to those low molecular weight biomarkers easily in tears without having a lot of cleanup to be done. Um, and so, you know, and our interest in, in doing a, and when we talk about proteins, we’re talking about a short term real time risk. So if you’re looking at genetic testing to determine your, that looks at your, you’re not looking at tumor shedding, you’re looking at, you know, like the BRCA testing and those kind of things. So that’s looking at the propensity, the possibility that you may at some point in your life develop cancer.
Anna Daily: So that’s a long term risk. Our test is a short term real time risk. We’re looking at what’s going on in your tissue right now that could help you make a decision for your next step of care. And so, you know, there are there are uses for both testing platforms. And, you know, we’re not saying that tears are a better source for, you know, diagnostics and screening, but it is an equally accessible and very nice source for the type of testing that we’re doing. And I think when we look into kind of where diagnostic testing is going and getting that into our, you know, getting that out to people and easy to use, tears are just a beautiful way to do that. And so, there are some components that can get in the way with blood. I think, you know, they, they complement each other very nicely. We’ve done some studies to look at, just to see if we can see things in tears that you can’t see in blood. It’s just easier to get to there. It’s just easier to get to in tears when we’re looking at proteome. So we have done we’ve done some mRNA studies as well with tears. So you can see genetic components in tears as well. But for what we’re doing and how we’re targeting our testing to be, you know, screening and population based testing, then tears are just a really nice fluid and much lower cost than than doing, say, a Grail type test.
Harry Glorikian: Right, Right.
Omid Moghadam: The tears are highly concentrated. So there’s low, abundant proteins are easier to find. So it’s the economics of it works really well. You can still make a profitable test at at a low cost.
Harry Glorikian: So. Now that we’ve jumped into the protein markers you’re talking about, Right? So you guys published, or Anna, you published a paper in PLOS One in 2022. And in the paper you actually named, I guess, the three main biomarkers you’re looking at, S100a8, S100a9 and then, which are generally, if I read it correctly, elevated in breast cancer patients, and Galectin 3 binding protein, which is found at lower levels in breast cancer patients. What’s the relationship between these three proteins to breast cancer. Are they markers of inflammation or some other abnormality in breast tissue? Are these the same proteins you’re looking for in the test or are there others?
Anna Daily: Yeah. So the two, A8 and A9, those are the proteins that we use in our calculation to calculate your score. They are part of the inflammatory process. They’re part of the early process that sort of gets that tissue ready for tumor formation. So recruiting a lot of the immune system to that spot. A9 is part of the cascade that is activated by BRAC 2. So it does have and they have both of those have been extensively studied in tissue in breast cancer as well. So there’s a long sort of academic history of A8 and A9 being studied in breast cancer specifically, and we were just fortunate that we can see them in tears as well quite easily.
Harry Glorikian: So. Let’s talk about the readout from the test. Right. So I understand that women get back what, you know, what is basically a very simple chart that says low, medium, high level risk for breast cancer. What goes into the assessment? I mean, how do you weigh the combined assay results and sort women into one of those three buckets. What is, what does medium mean? What is high mean? What do you recommend that women do with this information? Right. I was sort of looking at the FAQ on the site that the test is intended for women with average to low risk of breast cancer. You know, can you explain that? So I’m just, you know, if you can walk me through what goes into the assessment, what the different buckets might mean to someone who’s looking at them.
Anna Daily: Sure. Yeah. So when you activate your kit, you will fill out a short health survey as well as a breast health survey. And then when we determine your protein concentrations in the lab, we have an algorithm that we use that takes some those two protein concentrations together with some clinical information to calculate their score. And then that score is what we compare to the scale to put them into the low, medium, or high category. So those categories were built using the over 2000 samples that we had studied over ten years of time, and then going back and looking to see how soon—so, if we set someone, we if our test said someone was medium or high, but mammography said they may be normal, how soon did they come back with a finding, based on our our test? So looking out, we went up to five years, I think, to see how soon someone had come back in. And so that’s how we developed the low, medium and high. Those low, medium and high are recommended time points on how soon you might want to consider going in for your mammography or your follow up imaging, depending on your age, your breast tissue and your risk level. So when we first started, we really, because we were utilizing this test to be a patient activation tool to get women in for screening mammography, we said low to average risk because normally if you’re high risk for breast cancer, you’re already on a pretty stringent regime of imaging. Normally that’s a screening mammogram and six months later an MRI and you alternate those every six months. And so we didn’t want to get in the way of what women were already doing. And we’ve kind of lessened that that sort of jargon, because what we’re really wanting to do is not only provide women with information about what’s going on in their body, but part of the test is a breast health consultation.
Anna Daily: So we have RNs and LPNs who’ve spent their career navigating women from everything from a screening mammogram to all the way through breast cancer care. And so, you know, when we look at the statistics, what we see is across the board, about 50% of women will go in to screen for cancer with a mammogram. And when you get down to the lower age range, around 45-50, you’re looking at about 37% of women go in. And that doesn’t matter if you’re low risk, medium or high risk. That doesn’t you know, or if you’ve been told you’re high risk, a lot of women will still not do the recommended imaging because it’s time intensive. And so what we’re wanting to do is provide a conversation that often breast centers and OB-GYNs just don’t have time to have in an extended setting. So you get, say, 15 minutes. Our breast health specialists will spend as long a time with you as you want and discuss your history, your family history, your concerns, maybe a past imaging issue. And we found that to be very successful, to get women who were screening averse, who had no intention of going in for a mammogram, but came back medium or high on our test and then had the conversation with the breast health specialist and then went ahead and went in. And we have had women have findings based on that pathway. And so really what we’re wanting to do is sort of assist that screening conversation education process with this at home test and our breast health consultation process.
Harry Glorikian: Yeah, I was, you know, that was going to be one of my next questions is, like, what is the gap you guys are filling and what behaviors are you trying to change? And so I’ve always believed, like when you have data and you show patients, right, that they will probably, not all the time, but probably change their behavior or, you know, take that next step because there’s a piece of data that says something might be going in the wrong direction. But, you know, from what I’m understanding is you’re trying to fill that gap of, “Hey, once a year might not be enough. You can do this test that might indicate that you need to have that mammogram, you know, within a shorter period of time than you might normally think.”
Omid Moghadam: Right, or do one at all. A lot of women do not do that. We recently had a small scale pilot with a with an employer, with a self-insured employer, with the women in their headquarters. And it was about 100 women. And we found three breast anomalies. And these were women who were not planning on going for mammogram, had no intention of going, or if they had gone, they were already cleared. So it was it was a success. And as a result, the company now wants to do a larger one of a thousand women.
Harry Glorikian: Yeah, that was going to be one of my next questions, about the clinical evidence to drive somebody to have it in a shorter period of time. But but, you know, also, maybe wait longer than 12 months. Right. So, you know, how do you guys think about this the public health aspect, the economic aspect or, you know, I almost think like what you guys are saying is we need to come up with a better job of stratifying access to cancer screening by risk levels.
Anna Daily: Yes. And risk based tests to screen for cancer are definitely interesting. In Arkansas, we’re a pretty rural state, so we have about 23 counties where women have to drive at least an hour or more to get to a screening facility. So these are a lot of places where, you know, they’re just not going to be able to take the time to do that. And so if we can get something to them in their home, then that gives them a means of doing something to monitor their health. And, you know, we know there’s a lot of insurance companies and different payers that are starting to spread out how often you have a screening mammogram. So maybe they’ll only cover every other year up to a certain age. We’re starting to see that more and more. And so, you know, the other thing is, you know, as a country, we do way more screenings than most other countries do. And our quality of life and our longevity is lower than a lot of other places. So we’re obviously not hitting the need that needs to be hit. So, you know, here we’re just trying to, yeah, can we really get the people in who need to be in? And if you can take a little bit more time, let’s see if we can help make that possible.
Harry Glorikian: So obviously my background being diagnostics, right, I’d love to talk a little bit about sensitivity and specificity, right. You know, same sort of thing I did with Grail. But on the website it says that the test will detect 92% of people with a breast abnormality, which translates into a 8% false positive. But the site also says that it will identify 54% of people who don’t have an abnormality, which translates to, I think it’s 46% false negative. I don’t know. That seems a little high, but I’m maybe not. I mean, how do you how do you like to talk about specificity and sensitivity, especially when you compare Aria to tests like mammography?
Anna Daily: Right. So a mammogram given, it depends on what type of breast tissue you have and what type of screening modality you’re using can be anywhere from 60 to 75% sensitive. Specificity can be anywhere from 75 to 90, just depending on, again, breast tissue. It’s lower if you have very high tissue density. We have set the cutoffs. So our cutoffs on our scale have been set to have a higher sensitivity. So we have a lower false negative rate. And if you’re familiar with sensitivity and specificity, you know, it’s a balancing game. So right now our baseline is 92% sensitive, which kind of drops the specificity down around 54%, which means we have a higher false positive rate when we’re talking about a cancer screening diagnostic. You want to have as high a sensitivity as you can get. Now, we have a lot of work and some papers that are coming out that will feel like we’re going to be able to raise that specificity up, which is going to help. But that’s because of the high false positive rates. That’s why we’ve positioned this test to be prior to screening mammography, as a pre screen for cancer. We’re not saying don’t do a mammogram. We’re saying do our test, get some information about yourself, be informed and educated when you go in for your screening and get some extra questions that you can ask. So so that it’s really, you know, we are continuing to to improve that. But, you know, we’re happy with this as a starting point. And we’ve seen this test to do exactly what we have positioned it to do.
Harry Glorikian: Yeah, you always have to worry about like what do people do with the information and then how do you make sure that they’re educated to like, even if they showed up low, not skip the mammogram that they need to take when it when it’s appropriate.
Omid Moghadam: That consultation that’s that’s part of our our product offering, is exactly for tha, so our customers are educated about the results of the tests, they understand their family history and and what they what they need to do for their breast health.
Harry Glorikian: So now let’s jump to the economics, right. Because, you know, how much does a test cost for the average person? You know, do any insurers cover it? I you know, I assume you’re working on getting it covered. And is the test available by prescription only? So I’m trying to just understand where the you know, and all of that complexity things are with the test.
Omid Moghadam: It’s a lab developed test. So, yes, it requires a prescription, which is which is done through our 50-state coverage. We have physicians with 50-state coverage. And so that is, the prescription is generated when you buy the test. The test is $159. It’s at aria.care. And that includes everything, the prescription, the test, back and forth shipping, and results and the consultation at the end.
Harry Glorikian: So are you guys working on an over-the-counter version? I’m just wondering.
Omid Moghadam: Not at the moment. What we’re working on is increasing the specificity and then a diagnostic version of the test.
Harry Glorikian: So. If and if I’m also not mistaken, you guys have another test that’s called Melody. Um, what’s a difference between Melody and Aria?
Anna Daily: Yeah. So Melody is a sort of a the clinical application of the algorithm. So instead of having the kit and the scale and the consultation, Melody would be if we have a physician that’s interested in offering this to their patients, then they can make the interpretation themselves and decide how they want to move forward. So really we see this more in like an ACO setting. Um, and that sort of thing is really where Melody is at.
Omid Moghadam: So Aria puts the customer in one of the three buckets. High, medium and low. Melody has a cut off point, so it’s like Cologuard, clinically relevant, clinically not… I’m sorry, Clinically significant versus clinically not significant. So Melody has that cut off point, which as Anna said, it’s more for a clinical setting. And we’re going after the ACO market for that.
Harry Glorikian: Okay, so. I know we’re talking about, you know, we talked about blood and tears. I’m makes me beg ask the question about I don’t know what about sweat or urine? You know. I’m trying to figure out why tears, I guess, based on all the work that you guys have done.
Anna Daily: Sure. Where there’s you know, there are there is work in other fluids. Urine is a concentration issue. And what you get obviously has a higher volume, so it’s a lot more work in the lab process to get down to the molecular weight proteins. Saliva is a cleanliness issue, again. It can take quite a bit to collect those samples and it’s just the processing work. And you know, tears are just, it’s just an easy way for patients to collect their sample and we can get to those proteins quickly. Um, sweat, I’m not, I’m not sure about that one. Um, I know that there are some, some work in exocrine glands and then there is a breath test as well to look at esophageal and stomach cancer.
Omid Moghadam: Yeah, stomach cancers.
Anna Daily: Um, but yeah, so there’s, you know, there’s lots of work being done in other diagnostic fluids. I think we’re starting to see there’s lots of different ways to figure out what’s going on in the body. And tears are just another really nice way to do that.
Harry Glorikian: So I know you guys are working in breast, right? So you’re the product is now sort of lightning focused on women. But I believe the website also says that you’re working on tests for pancreatic, melanoma, ovarian, colon and prostate. Right. So can you talk a little bit about your pan-cancer study? You know, what are the additional challenges to screen for cancers of other types?
Anna Daily: Well, I will say, when you’re working in tears, the biggest challenge, no matter what type of cancer you’re looking at, is getting samples. Because much like, you know, with blood, there’s often biobanks, you can just purchase samples of anything you’re interested in. Any time we come up with a new question we want to study, we have to collect a new batch of samples. And so that’s really the biggest issue. If we can find a clinic that is excited and wants to collect samples and wants to be part of it, often that’s once that hurdle is overcome, we can get the samples. And we’ve been really lucky to find some very nice clinical partners that have been excited about the pan-cancer study. So we have a nice data set, early data set in prostate, ovarian. And so ovarian is the hardest to get. It’s just a challenging diagnosis. And so just finding the right clinical point to get those samples. And then so we have some early work in prostate. We have a sample bank for colon, pancreatic and a few in melanoma. And so really it’s just kind of moving that discovery work forward. But that’s sort of where the trajectory is going. But really the biggest issue is finding clinical partners. Once we get those samples in from there, it’s it’s pretty straightforward on how we do it.
Harry Glorikian: So I’m going to assume like once you get the samples and you’re doing your analysis, this is where I’m making a grand assumption that there’s some artificial intelligence machine learning approach, right, to decipher all these signals, to understand, you know, how to how to bucket these and therefore apply them to a specific cancer that you’re going to call in the end based on the results of the test.
Anna Daily: Yes. So I’ll let you talk about that.
Omid Moghadam: Well, I mean, we start with I mean, I think the early look at the data is typical normal statistical analysis, because what we’re looking for is the difference between cancerous and non-cancerous spectrum of proteins. But once you get all of the data, all of the patient data, which means their history, all the information from the EMR data that we get in and we marry that to maybe if they had any genetic testing, that big data set is where machine learning comes in, because it’s not just the levels of proteins, but it also that proprietary algorithm that has some demographic and some historical data in it, that would give us that final sort of cutoff point in where we make the clinically significant versus non clinically significant, clinically not significant cutoff. And that’s where the machine learning comes in.
Omid Moghadam: And as a matter of fact, I mean, there’s this sort of like, history around how Aria was developed that had had something to do with that. As Anna said, in our early days, in our early studies we had some false positives of our work that were ruled out by mammograms. But then those patients came back with cancer a few months later. And that sort of prompted this that maybe we should take a look at our bigger data sets, and do some machine learning work and use artificial intelligence to see perhaps something that we’re missing, here, because it seems that we have detected things early enough that mammographers have missed it the first time around, but a few months later they have found it. And that’s sort of what eventually led to this idea that maybe we should make a test that gives you a risk. Gives you a sort of like a 12 month risk. You know, if you’re in the high, 3 to 6 months, 6 to 12 months. and 12 months plus categories of when you get your mammogram. And that actually that came out of a collaboration with the machine learning company. And not only that, but then they gave us, we found some more targets where we can actually take our knowledge of tear proteomics with a proprietary algorithm into a diagnostic test. And that’s our next, you know, that that is also in our R&D pipeline to turn our test into a diagnostic test, which can actually say whether you have breast cancer or not with a tear test.
Harry Glorikian: Yeah, it’s not surprising. I mean, you know, if you know the work, like, you know, not far down the road at Regina Barzilay’s lab at MIT, like she can see something five years before the human will actually, like, be able to point at it, right? So the machines can do a much better job of identifying either images. We can detect stuff at such a low level now, I think that we can get ahead of the actual tumor formation, which is, you know, you got to use your eye and be able to see it. So I think we’re, you know, we’re on the cusp of changing medical practice. I’m not sure that the medical schools and everything are going to be able to keep up. But I think we’re at that stage now with all these technologies that we can throw at this. But what’s the long term plan for Namida? I mean, I’m assuming is it to become a general cancer screening company with tears as the special sauce?
Omid Moghadam: Yes. Yeah. We would like to have a pan cancer test using tears as the medium of testing. Highly concentrated, sterile fluid that’s easy to collect by by the consumer and inexpensive. I think those are all the all the points that we would like to hit. And, you know, with we’ve proven that there is a there there, with our proof of concept, this breast cancer test. And we would like to extend that R&D into into diagnostic tests and also other screening tests.
Harry Glorikian: Well, I think I’ve gone through all of my questions on this end. I don’t think I missed anything, did I?
Omid Moghadam: No, no, that’s. It’s all good. Harry.
Harry Glorikian: So it was great to have you guys on the show. I mean, I only wish you like huge success, and I hope this goes forward because, you know, we need more good diagnostic products out on the market that can help people stratify themselves and understand when to get tested. So, you know, it was great to have you guys on the show today.
Omid Moghadam: Appreciate it, Harry. Thank you for the invitation.
FAQs about Cancer screening
At the end of my podcast transcripts, I like to include a small FAQ section to answer some popular questions on the web. So, here are some FAQs about how to screen for cancer and more.
How can you screen for cancer risk?
In order to screen for cancer risk, you’ll typically need to assess an individual’s personal and family medical history, as well as evaluate certain risk factors associated with specific types of cancer. Here are some ways to screen for cancer risk:
- Medical history: Your healthcare provider will ask you about your personal medical history, including any past diagnoses, treatments, and surgeries, as well as any family history of cancer.
- Genetic testing: Genetic testing can help identify genetic mutations that increase the risk of certain types of cancer. Genetic testing is typically recommended for individuals with a strong family history of certain cancers, such as breast, ovarian, or colon cancer.
- Imaging tests: Some imaging tests, such as mammograms or CT scans, can help detect early signs of cancer, especially in individuals with a higher risk of developing certain types of cancer due to their personal or family medical history.
- Blood tests: Certain blood tests, such as the CA-125 test for ovarian cancer or the PSA test for prostate cancer, can help screen for cancer risk in individuals with a higher risk of developing these types of cancer.
- Lifestyle factors: Certain lifestyle factors, such as tobacco use, alcohol consumption, and diet, can increase the risk of developing cancer. Your healthcare provider may ask about these factors and provide guidance on reducing your risk.
It’s important to note that the way people screen for cancer risk can vary depending on the individual and the type of cancer being screened for. It’s important to talk to your healthcare provider about your personal and family medical history, as well as any concerns or questions you may have about a possible screen for cancer.
What types of cancer are commonly screened for?
Depending on lifestyle choices, different individuals can require different ways to screen for cancer. The types of cancer that are commonly screened for include:
- Breast cancer
- Cervical cancer
- Colorectal cancer
- Lung cancer
- Prostate cancer
How often should I get a screen for cancer risk?
The frequency of cancer screening depends on various factors, such as your age, sex, family history of cancer, personal medical history, and lifestyle. Here are some general guidelines on how often you should screen for cancer:
- Breast cancer: Women should get a mammogram every 1-2 years starting at age 50, or earlier if they have a family history of breast cancer or other risk factors.
- Cervical cancer: Women should get a Pap test every 3-5 years starting at age 21, or earlier if they are at higher risk. HPV testing may also be recommended for certain age groups.
- Colorectal cancer: Adults should get a colonoscopy every 10 years starting at age 45, or earlier if they have a family history of colon cancer or other risk factors.
- Lung cancer: People at high risk for lung cancer, such as heavy smokers, should get an annual low-dose CT scan between ages 55-80.
- Prostate cancer: Men should discuss with their doctor whether they should get a PSA blood test and DRE to screen for prostate cancer starting at age 50, or earlier if they have a family history of the disease or other risk factors.
It is important to note that these are general guidelines and may vary depending on individual circumstances. It is best to talk to your healthcare provider about your personal risk factors and when to start and how often to get screened for cancer.