Biosimulation for Cancer: Personalized Treatment & Predicting Response
Featuring: Michael Castro, MD
In short
Oncologist Michael Castro, MD, explains how biosimulation — using detailed genomic testing and computer modeling — can map a person's unique cancer network and identify combination therapies tailored to it, rather than relying on a one-size-fits-all standard of care. The approach focuses on 'synthetic lethal' vulnerabilities, points of weakness specific to cancer cells that, when targeted together, may collapse the cancer without harming healthy tissue. This is relevant to patients with complex or advanced cancers who have not responded well to standard treatments and want to understand emerging personalized options.
- •Ask your oncologist about whole exome and RNA sequencing (transcriptome testing) — standard single-gene tests may miss dozens of genomic changes that could be targetable.
- •The oncogene-addiction approach (blocking one pathway with one drug) is relevant for only about 9–10% of cancers; if your cancer doesn't fit that profile, a network-targeting combination strategy may be worth exploring.
- •Synthetic lethal targeting means attacking vulnerabilities that exist in your cancer cells but not in normal cells — this is why some multi-drug combinations in this approach have shown little to no toxicity in cases described.
- •The Cellworks biosimulation tool used in this approach is currently investigational and not commercially sold, so ask your care team how to access this type of analysis and what observational trial options, such as I-PREDICT, might apply to your situation.
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Brad Power March 4, 2024 “A network view of cancer provides a revolutionary, modern view of what cancer is. Beyond identifying oncogenic drivers, if also find synthetic lethal vulnerabilities and the master regulator constellation that underlies the malignant phenotype. The challenge is to develop a functional understanding of the impact of all the genomic aberrations in a cancer - and there may be dozens or hundreds of abnormalities.
To achieve ‘network consciousness’ in molecular diagnosis provides an understanding of the functional organization of malignant behavior in mechanistic terms - that forms the requisite foundation for curing advanced complex malignancies. Without that, treatment will always just be a shot in the dark.
For most of the history of cancer treatment, there was no hope of understanding the disease at this level, but the ready availability of whole exome NGS and the tools to make sense of it using biosimulation has brought us to an inflection point in the history of medicine where we can finally see what we should be targeting. Somehow, we need to evolve patient-centric combination therapies that target the cancer network where it matters most.
” – Michael Castro, MD “Synthetic lethality can kill cancer cells without causing toxicity to normal tissues, thus bypassing the traditional oncologic requirement to give maximum tolerated dose and the necessity for toxicity. ” – Michael Castro, MD “The forefront of cancer research is decades ahead of the clinic.
Unfortunately, most of what is being learned by the great public investment in cancer will never be translated into therapy, because that translation is driven by one thing, and that's return on investment… Without profit, the translation of science into practice is usually deemed “uninvestable.. But the issue is that it’s not a matter of whether we can treat the disease better, but a matter of whether anyone can get rich in the process.
Meeting Summary
The “one-size fits all” approach to treating cancer (the “standard of care”) derived through evidence from randomized clinical trials has drawbacks. In some situations, approved therapies may have no disease impact for patients. We can do a lot better by personalizing treatment. For example, in prostate cancer a study showed that the chemotherapy docetaxel was superior to another chemotherapy mitoxantrone plus the steroid prednisone.
We suppose that all patients would be better off with docetaxel. However, for some patients the combination of mitoxantrone plus prednisone is four-fold more effective, and docetaxel has no impact, the very opposite of what the randomized trial concluded. Ideally, one would get a comprehensive molecular diagnosis of cancer (Whole exome NGS and transcriptome).
This is seldom done and most oncologists shun the approach because they don’t know what to do with the results. The oncogene-addiction paradigm is relevant fo0r 9-10% of patients only. The single drug approach to block a single pathway in cancer, such as targeting EGFR in lung cancer or the androgen receptor (AR) in prostate cancer, doesn't get very far.
In the other 90% of cancers, there are numerous genomic aberrations causing disruption of dozens of signaling pathways. The idea is to measure all of this so that the master regulator network and synthetic lethal vulnerabilities in cancer can be determined. Once achieved, it is possible to understand the disease as a functional network that seeks robust perfect adaptation under stress, that is, therapeutic resistance.
” Using computational biosimulation at Cellworks, it is possible to simulate how drugs and drug combinations interact with the network. If you attack as many of the disease-specific nodes in the network as possible with combination therapy, that is “Network-targeting Combination Therapy (NTCT)”, the cancer will collapse.
Because the Cellworks model is based on mathematics (differential equations), you get a quantitative measure of how one treatment compares to other treatments and the signaling pathways that show you why. Michael Castro, MD, and Chief Medical Officer of Cellworks, is uniquely qualified as a clinician who is also an expert in molecular pathology, to look at molecular pathways and personalize treatments for cancer patients.
Cellworks provides a report on the signaling pathways and the convergence of particular biochemical enzymes which he uses to understand master regulators, network nodes, DNA repair, and transcriptional (RNA) drivers. Identifying the dysregulated proteins that contribute to your cancer is crucial for developing personalized treatment plans. The Cellworks model is an investigational tool and not yet sold.
How does this biosimulation approach to personalized treatment work? Conduct detailed and extensive genomic testing (whole exome, RNA sequencing, proteomics, liquid biopsy) to uncover your unique molecular profile. Identify as many susceptible nodes (synthetic lethal nodes, oncogenic nodes, or master regulator nodes) that can be treated as possible in your cancer network.
One approach, VIPER (Virtual Inference of Protein-activity by Enriched Regulon) analyzes protein activity from gene expression data (RNA sequencing) to depict your unique cancer network. Synthetic lethal nodes in a cancer network are extremely important because normal tissue doesn't have them.
Treat with a combination of as many therapies as possible that target your personalized susceptible nodes, with an emphasis on synthetic lethal targeting, where you can stack multiple therapies simultaneously without impacting normal tissues much.
For example, using this approach, a patient with an aggressive brain cancer (glioblastoma) got a comprehensive molecular diagnosis that led to the identification of a disease network with about a dozen synthetic lethal nodes.
She was treated with a five-drug combination (lomustine, olaparib, digoxin, metformin, and high dose intravenous ascorbate) targeting the synthetic lethal nodes, with intra-patient dose escalation to safely deliver the treatment. There was no toxicity. 5 years from relapse, without a trace of disease. What are the challenges to building a personalized network model of your disease?
•Complexity: There are hundreds of pathways >1500 transcription factors, 2500 microRNAs that modulate translation of mRNA into protein. Each of these pathways is complex. DNA repair has about 497 genes in it. Transcription factors are at the end of signaling pathways that are also complexly dysregulated in the tumor. They compete against each other to upregulate and downregulate gene expression.
•Interpretation: There's no one alive who could sit down and tell you all the protein interactions in the body. That is why a model of signaling pathway behavior and the consequences of genomic, epigenetic, transcriptional, posttranslational impacts is desperately needed.
•Statistical validation : Statistical validation requires data sets of patients, preferably with controls. Such data sets are nearly impossible to come by. However, Cewllworks has such a data set coming for NSCLC addressing the issue of which patients benefit from the addition of chemotherapy to immunotherapy.
•Data: You need the right data for the question you’re trying to answer. For a model that tries to predict clinical response to combination therapies using genomic signatures, the good news is that we have (arguably) lots of cancer genomics data, but the problem is that a limited amount of that data has comprehensive clinical outcome data, and that data is presumably biased to represent the standard of care. As such, we’re probably able to build a decent model to predict whether a patient will respond better to medicine A vs. medicine B.
•Model validation: The current Cellworks model is informed by scientific work. Though it is unvalidated, the decision for patients is whether to prefer a blind evidence based approach where everyone is considered the same for the sake of treatment, or whether to allow scientific discoveries to inform clinical decision making. I prefe th4e lights on approach,
•Synthetic lethal nodes : Synthetic lethal nodes are a “holy grail” for cancer because if you can find a drug that is very effective only when a certain mutation is present, the selectivity impact against cancer compared to normal could be enormous. We have started to0 understand that when chemotherapy really works well, it is often because there is a synthetic lethal vulnerability that confers responsiveness. But without that synthetic lethal susceptibility, many types of chemotherapy may indeed do little. What are the concerns and countermeasures to drug combinations and other non- standard (“off label”) drug recommendations?
•Toxicity: Conventional wisdom in cancer treatment strategy, largely derived from chemotherapy, argues that the similarities between normal human cells and cancer cells means there is no way to avoid toxicity for normal tissue when you treat cancer cells with drug combinations. With combinations of five or more toxic treatments, eventually the human body can only take so much. However, if you kill cancer by attacking synthetic lethal nodes, you won’t harm normal tissue. (Synthetic lethality is where mutations in two genes together result in cell death, but a mutation in either gene alone does not.) This could be the key for how we're going to go forward in combination therapy for cancer. You can stack therapies, one on top of the other, without really ever getting anywhere near the maximum tolerated dose.
•Lack of evidence from clinical trials : No one has done clinical trials on many of the recommendations that derive from molecular biology, so they are viewed as speculative by treating physicians. However, molecular biology researchers have taken these pathway models and drug relationships as facts. The breakdown is in translational medicine, taking molecular biology insights from laboratories into clinical practice. Jason Sicklick and Razelle Kurzrock have done observational clinical trials (I-PREDICT) which show drug combinations are effective and safe. Statistician Drew Watson is taking a given parameter, weighting that with exponents according to its relationship with survival, and then in the end, and creating an equation which will tell you whether or not immunotherapy is going to work, and what the patient's survival will be. How viable is the biosimulation approach for targeting disease?
•Likely Pharma interest : Biosimulation tools are of very high interest to a variety of pharma teams that plan/design trials, especially for identifying hypotheses for medications that work synergistically in combination.
•Computationally possible : Modeling genome-wide networks has been a tractable computational problem for a number of years now. There were a number of problems that were challenging to tackle about a decade ago, but they’ve been largely solved through the combination of cloud computing and more efficient numerical algorithms. You can always think up a more elaborate model, but as long as you’ve got the money, then you can find a computer to build/run it. Should you pursue this method of personalized network modeling and drug combinations? It comes down to your philosophy about how you want to be treated, and where your comfort level is. You can either go with the “standard of care" (the NCCN guidelines, consensus medicine), or you can ask, “Is there anything else that's been learned about cancer that could help me?” “Is there anything that anyone could learn about my tumor that I can leverage as a therapy?” You should pursue this if you believe in following cutting edge scientific research and are willing to go outside the box and be in an uncomfortable relationship with your doctors who may say, “There's no evidence for this. Therefore, I wouldn't do it.” There's an old saying that, “All models are wrong, but some of them are useful.” While the models are incomplete, they are often further ahead than any doctors can do on their own. What can we do to accelerate the exploration and adoption of this new approach?
•Stop doing things that don't work . If one adopts this approach in the clinic, it leads to radical re-thinking because the standard of care is predicated on giving drugs that don’t work for most patients based on the randomized trial method.
•Gather evidence: We're going to need to do a prospective randomized trial with these biosimulation tools to change medicine. This will need to be funded.
•Educate: Increase understanding of molecular biology among clinicians and patients.
•Expand the data inputs : We can get the entire genome and transcriptome. We need to increase experience with the transcriptome and look at the proteome as an input.
•Certify molecular oncologists : There is a chasm between molecular biology and clinical medicine. We need a hybrid person who has both skills and has AI to unravel this complex molecular biologic system. There are a few molecular pathologists, and there is no certification for molecular oncologists. The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. 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 KEYWORDS patients, work, cancer, pathways, question, docetaxel, drug, proteomics, mutation, targeting, oncogenes, cell, disease, gene, network, therapy, nodes, synthetic, people, treatment SPEAKERS Michael Castro (78%), Allen Morris (8%), Frank Nothaft (4%), Brian McCloskey (3%), Brad Power (3%), Ian Lewington (2%), Robert Gurmankin (1%), Vanessa Hugo (1%) OUTLINE 1.Cancer treatment strategies using computational biology. (0:00) 2.Using AI to analyze cancer genomics and identify potential treatments. (6:17) 3.Personalized cancer treatment based on audio transcript. (12:39) 4.Personalized cancer treatment using liquid biopsies and bio simulations. (19:22) 5.Targeting cancer cells with synthetic lethality. (26:40) 6.Personalized cancer treatment using liquid biopsy. (35:35) 7.Personalized cancer treatment using AI and synthetic lethal targeting. (43:23) 8.Personalized cancer treatment using multi-omics data. (52:51) 9.Using proteomics and transcriptomics to understand cancer. (58:48) 10.Personalized cancer treatment and the limitations of current methods. (1:05:13) 11.Molecular biology and immunology in cancer treatment. (1:12:04) 12.Cancer biology and treatment strategies. (1:18:57) SUMMARY
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