Functional Drug Testing & AI for Personalized Cancer Treatment
Featuring: Noah Berlow, PhD, Diana Azzam, PhD
In short
Noah Berlow and Diana Azzam walk through a pipeline that takes a fresh tumor biopsy, runs functional drug sensitivity testing alongside DNA and RNA sequencing, and uses an AI matching engine to recommend personalized drug combinations — all within two weeks. The approach is aimed at advanced cancer patients who have run out of standard treatment options, and real patient cases show it can extend progression-free survival with drugs that doctors might have written off or wouldn't have considered.
- •If you've exhausted standard treatments, ask your oncologist whether functional drug sensitivity testing on a fresh tumor biopsy is available or being studied in a clinical trial near you.
- •Drug combinations tested all at once — rather than one drug at a time — may reduce the chance that cancer cells adapt and become resistant; this is worth discussing with your care team.
- •Frozen tissue from a biopsy can sometimes be thawed and retested for new drug combinations, meaning a single biopsy may give doctors more than one round of answers without requiring another procedure.
- •Drugs approved for other conditions (in these cases, an asthma medication and an allergy medication) showed up as effective in tumor testing — functional testing can surface options that genomics alone might miss.
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Brad Power July 20, 2022 “We get the fresh sample, we process it, we use what we need for drug testing, and we freeze them down. If we need to go back and test combinations, we're throwing those out fresh and testing within a few days.” - Diana Azzam
Meeting Summary
Presentation
Highlights
Noah Berlow, PhD, CTO, First Ascent Biomedical, and Diana Azzam, PhD, Assistant Professor at Florida International University , led a discussion on their approach to functional drug testing and using AI/ML to guide complex treatment decisions for advanced cancer patients. Diana’s expertise is in functional precision medicine, and Noah’s is in AI/ML and bioinformatics.
Together they have put together a pipeline that takes in fresh patient tissue and turns out treatment recommendations in two weeks. They use Diana’s functional drug testing protocols and send out some of the tumor tissue for DNA and RNA sequencing, then put the test results into Noah’s matching engine to report treatment options to patients.
Diana shared several examples of advanced cancer patients who had failed standard treatments and were desperate for treatment options, which were discovered by the drug testing she ran. Many of the treatment options were unexpected. Some were chemotherapy drugs that the patient had already seen and were assumed would be ineffective. One drug was an approved drug – for asthma. The drugs were delivered in combinations.
All extended “progression free survival”, and one patient experienced a particularly durable response. These patients had urgent needs since they had failed their previous lines of treatment, and the analysis was completed within two weeks to give the patients timely treatment recommendations.
Noah described his work in integrating and interpreting the inputs of functional drug testing and sequencing data for individual patients with information about the drugs and their real world mechanisms to derive a personalized “tumor circuit” – a holistic view of tumor drivers and weaknesses to find the best combination of drugs for a patient.
He shared his research in applying this analysis in mice, where he was able to find personalized drug combinations that performed better than a control or the individual drugs. He also showed how the same analysis that they built to find individualized combinations for patients can be applied to discover better biomarkers.
Highlights
We support administering multiple drugs together, instead of single drugs one-by-one, because you give the cancer cells the chance to adjust to every chemical you throw at them. Noah Berlow: In many ways I'm in agreement.
Some of the other work that I've done has been on showing the difference between sequential combinations or simultaneous combinations using the AI to find a combination and then testing that ex vivo on a couple of cell models derived from the same type of cancer, showing the combination can essentially stop tumor regrowth.
But at the same time, we're showing in a practical setting, when you give the drugs at the same time, the effect is that the cancer cells go away and don't come back. Which of course is the key end goal. We have heard concerns about toxicity from treating physicians regarding the kinds of drug combinations you are recommending. How did you overcome those concerns?
Diane Azzam: I have not really seen toxicity concerns because our patients haven’t had other options. We look at the drugs’ concentrations in the blood and use Cmax. ) In the case of the osteosarcoma patient, they administered the drugs in a rapid sequence - a few weeks.
In the case of the rhabdomyosarcoma patient, we had seen in functional testing that one of the drugs (vincristine) was stronger as a single agent, so they administered that first, then the other two in a rapid sequence. You're working off fresh tissue. Can you run one functional test, then come up with a new hypothesis, and run functional testing again without getting a new biopsy? Is the tissue still viable for testing after 48 hours?
Diana Azzam: We get a second shot. That's what happened in all the cases, because we get the fresh sample, we process it, we use what we need for drug testing, and we freeze them down. ”, and that's what we've done. It's been very helpful for the doctor.
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You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. Meeting Notes Diana Azzam: I'm an assistant professor at Florida International University. My PhD focused on ovarian cancer stem cells and then my postdoc focused on high throughput drug discovery.
My research interests at FIU include implementation of functional precision medicine in clinical trials to guide individualized treatments in advanced cancer patients, and a major focus of my lab is to understand the resistance of cancer stem cells and how they play a role in metastasis. Noah Berlow: I have a couple roles. Right now I'm most accurately serving as founder and CTO of First Ascent Biomedical.
My background is Al engineering, computer science, and math. I did my PhD in electrical engineering. I got involved in pediatric cancer research using artificial intelligence machine learning to start solving some real big challenges in that space. I did my postdoc in bioinformatics and molecular biology. I eventually started getting more involved in the sort of day-to-day laboratory side of things.
All of that was happening at the Children's Cancer Therapy and Development Institute, where I am an assistant member. My main focus is on First Ascent, which is bringing functional precision medicine and AI analysis for individualized treatment options into the clinic for patients in need. Diana and I are collaborating to put together this entire pipeline to use functional precision medicine to guide individualized treatments.
Together we are taking a biopsy sample from a patient's cancer, doing a rapid culture and drug sensitivity testing protocol, using Diana's technology that she's developed over the past decade. Functional drug testing is side one. Side two is taking the same tissue and sending it for DNA and RNA sequencing analysis.
Where I come in is taking both of those data sets and ingesting them into the AI/ML engine that I've also been researching and building over the past decade. From the drug testing data and the sequencing data, we can better understand the weaknesses underlying a patient's tumor. Another way of saying that is we take all the drugs that are working, and all the drugs that aren't, to understand the mechanisms that are really driving drug sensitivity.
When we try to build combinations around the patient's key mechanistic targets, it becomes a lot easier since we need to target this gene, and we can find the right drug already available in the clinic that best fits that need. Our goal is to deliver all of this data within two weeks, making this as clinically usable as possible because that turnaround is absolutely critical to meeting the needs of patients.
We've already done a lot of this work in the clinic already. Diana Azzam: The goal is to be able to implement this in the clinic, and we have two feasibility studies, both in pediatric and adult cancer patients, in collaboration with Nicklaus Children's Hospital and Cleveland Clinic Florida , to test whether this is feasible. Can we recommend treatments in a clinically actionable timeframe?
And if these treatments are recommended, how do the patients respond? This is a seven-year-old girl with metastatic rhabdomyosarcoma, a particularly difficult-to-treat cancer. She has been through multiple treatments. None of them were effective. We received a piece of her tumor. We confirmed we had the right cells by looking at the different markers of rhabdomyosarcoma. We tested our panel of drugs and multiple drug combinations for the patient.
We were able to deliver this data to the doctor and the molecular tumor board in about one week. Brad Power: I helped Kasey Altman, a young woman with alveolar rhabdomyosarcoma, with her hackathon. I learned that rhabdomyosarcoma responds to chemo, but just about nothing else. So if you find more chemo drugs, you're probably going to delay progression, but you're not really going to come up with a durable response. Is that correct?
Diana Azzam: We found chemotherapy drugs that were effective, and we also found targeted drugs.
•dasatinib , (Dasatinib is in a class of medications called kinase inhibitors. It works by blocking the action of an abnormal protein that signals cancer cells to multiply.),
•HDAC inhibitors (Histone DeACetylase inhibitors are in a class of anti-cancer agents that play important roles in epigenetic or non-epigenetic regulation, inducing death, apoptosis, and cell cycle arrest in cancer cells.),
•lenalidomide (Lenalidomide is in a class of medications called immunomodulatory agents. It works by helping the bone marrow to produce normal blood cells and by killing abnormal cells in the bone marrow.), and
•an mTOR inhibitor. (mTOR inhibitors are a class of drugs that inhibit the mechanistic Target of Rapamycin.) One of the challenges about recommending treatments is whether doctors have access to these drugs. With children we customize the library of drugs based on what's available in the pharmacy at Nicklaus Children's Hospital. In this case we wanted to treat the patient quickly. So they picked chemotherapy drugs (vincristine, irinotecan, and temozolomide), which they thought wouldn't be effective anymore, but we found were still sensitive on the tumor cells (from our functional drug testing). Before enrolling in our trial, she only had two weeks of progression-free survival, which wasn't really effective. Then she was enrolled in our clinical trial and the molecular tumor board recommended treatment based on the drug sensitivity testing. Her tumor decreased, and we observed that her progression-free survival was six months. She responded to the guided drug regimen. Unfortunately, the patient passed away because of metastasis in the lungs. This patient was a nine-year-old girl with metastatic osteosarcoma, a rare bone cancer which is incredibly difficult to treat, especially when it's metastatic. We received her tumor sample, and we did drug testing and sequencing. The results from the sequencing, molecular tumor analysis, and drug sensitivity testing were used to identify a combination that would be effective for her cancer. As you can see, for the NTRK fusion you have larotrectinib, which is a targeted drug (an inhibitor of tropomyosin kinase receptors), which showed up in the molecular tumor analysis. When we looked at the drug sensitivity testing, the most effective drug here was Idarubicin (a chemotherapy), which was recommended, and also Montelukast, which is an allergy medication. This is a beautiful example of how results from both genomics and drug sensitivity testing were used in her treatment recommendation. When we looked at her clinical course before enrolling in our trial, her cancer kept growing despite multiple treatments. When she received a three- drug combination based on functional precision medicine (idarubicin, montelukast, and larotrectinib), she had a complete response, and her progression-free survival is one and a half years. If you compare the progression-free survival of the patient based on what was recommended by functional precision medicine versus her previous regimen, you can see that there's an improved survival benefit. Brad Power: Was that a three-drug combination? Diana Azzam: Yes: this was a combination of three drugs: Larotrectinib, idarubicin, and Montelukast. Brad Power: Put a pin in that. Drug combinations have been one of our favorite topics. Diana Azzam: That's exactly where machine learning is going to be very important. We've been able to optimize the drug sensitivity testing on at least 13 different tumor types. We can do this in liquid leukemias and in different types of solid tumors, as I've shown in a few case studies. We are analyzing the data from a group of patients for their best overall responses. If you compare patients that were guided by our approach versus their previous regimen or standard of care, you can see that there's a huge improved overall response. We can recommend treatments within clinically actionable timeframes, and patients that were guided by our approach have improved overall responses compared to their previous regimen and compared to those that went through standard of care. Noah Berlow: I have been focused on the AI and machine learning components – an AI engine that is able to pull in functional drug testing data and sequencing data integrated for an individual patient, and pulling in information about the drugs and their real world mechanisms, and putting all of this together to build out an understanding of the patient's tumor weaknesses and creating a tumor circuit. The idea with the tumor weaknesses is to find the multivariate genetic targets that are driving drug sensitivity, the main things we'd really want to hit with the drugs we would give to the patient. The tumor circuit is the holistic view of what this patient really looks like. When we put all of these individual weaknesses together, what can we learn about the patient, and what can we do to really drive forward individualized combinations? The goal of this has always been to find the right combination of drugs for the right patient. This work started at my beginning of grad school, since about 2012, so about a decade. We have validated this approach in multiple cancer types. This is data from a publication from a couple years ago. This is in a genetically engineered mouse model of alveolar rhabdomyosarcoma. We followed the exact same pipeline that we've been showing. We took tumor tissue from a mouse, did drug testing using 60 agents, some of which were FDA approved, some of which were in phase two or phase three studies, and used all of that data, plus the genomics data, to build a circuit of the patient, finding that a combination of OSI-906 (linsitinib, a potent and selective oral inhibitor of dual IGF-1R/IR kinase) and pictilisib (a potent and selective oral inhibitor of PI3K - phosphatidylinositol 3 kinase)
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