A: I love this question. The return on investment is a direct function of the probability of clinical success. Throughout my professional career that probability has been zero and nobody has ever gotten a positive ROI on a cancer vaccine. My first project as a post doc with Steve Rosenberg 30 years ago was to develop phase one cancer vaccine program, and a couple of years later we even published on a nascent mRNA cancer vaccine that worked in a mouse model. While some of these mice models predicted the ability to generate reactive T cells in patients, none translated into clinical efficacy.

So why is this time different and I'm referring to the randomized phase 2 an adjuvant melanoma of Moderna's personalized cancer vaccine. I think for three reasons. First, is that we've learned that when the immune system does see an antigen in the context of cancer, it is far far more likely to be a neo epitope than it is to be encoded by a non-mutated protein. And we now have both the sequencing tools and bond for attic tools to be able to quickly analyze a patient's tumor for those new epitopes that are most likely to bind to that patient's HLA.

The second is that mRNA vaccine provide a uniquely potent T cell vaccine. It is worth noting that this is not by our clever design but rather by evolution. When you concatenate 30 or more short peptides and one long mRNA chain, what comes off the ribosome is a misfolded protein that you would expect get preferentially shuttle to the protozoal machinery and displayed for recognition because this is how the immune system has evolved T cells to recognize RNA virally infected cell cells.

Recognizing these two points and the complete lack of predictability of urine models when it comes to cancer vaccines is why we advanced Moderna's cancer vaccine to the clinic without attempting to cure mice first finally, the third factor is the clinical context. We very carefully chose the first study to be an adjuvant melanoma in combination with Keytruda. The heavier, mutation burden in melanoma suggests that it is the place where we are most likely to pick up relevant new epitopes; the proven activity of Keytruda in adjuvant melanoma combined with solid pre-clinical data suggesting that PD one blockade can enhance vaccination made this the obvious combination.

Finally, treatment in the edge of event, setting provides us with the smallest tumor burden, and the longest time window during which we can prime, boost and further stimulate an immune response. If you look at the history of developing cancer medicines, every medicine that ever worked in late metastatic disease works better the earlier you go. So coming back to your question, I think the clinical trial design is a big factor in terms of having a higher probability of success, and therefore providing a positive return on investment. By extension, I am much more hesitant to predict the utility of a personalized cancer vaccine, even if combined with a checkpoint inhibitor, in later stage metastatic, epithelial tumors. I think this has been born out, even in melanoma by some of the competitors trials.

If one desires, a positive return on investment, one should conduct a definitive experiment in the context where it is most likely to give a positive answer. That means a randomized phase 2, rather than a phase 1, when combining with another medication and studying at the adjuvant rather than the metastatic setting.

I think both the manufacturing and logistics are no longer an issue. These were significant challenges when we started back in 2015 but based on the success of mRNA vaccines for infectious disease as well as the investment that have put into improving the manufacturing footprint. At this point, this should not be an impediment for a personalized cancer vaccine to provide a solid business case for Moderna. That said this, of course, is true for Moderna and maybe Biontech, but would be a challenge for any newcomer to the field, who would need to consider the costs of CMC in their overall ROI.

Logistics are even simpler. I don't want to trivialize this as it requires very careful and close coordination between the treating centers, the sequencing and bond for Matic analysis, and the manufacturer. At a high level, in an era where we can provide personalized cell therapy, a personalized vaccine is much easier by comparison.

A: I think people need to understand why we were able to move fast in Covid so that they better understand what is or is not relevant. A speed of development of the COVID-19 vaccines was enabled by three factors.

1. The first is that we had the technology from a CMC and manufacturing standpoint to go very quickly from the initial designed phase to the start of a phase 1 trial. This was a function of previous investments in the mRNA platform and can't really be replicated from a standing start. People forget that it took us eight years to get to January 2020. During that time we had tried to immunize humans in clinical trials against a different viruses. Our success rate was eight out of eight. So COVID-19 was our ninth virus. And the investment in CMC and manufacturing accelerated in the early part of 2022, match the clinical development timelines and ultimately global deployment

2. The second was close regulatory support, and guidance to move from phase one to phase 3. And disregard, I think we're seeing US biotechs learning to leverage places in the world where the regulatory framework is much more agile and quick to start, such as Australia and even China.

3. The final determination of timeline as it is for any phase 3 trial, is how quickly do you see endpoints. If you look at the original COVID-19 protocol, we thought it would take us 12 months to reach the number of events required to demonstrate efficacy. As it happened, the infectious rates across the US during the summer of 2020 were horrendous, but in a way that enabled us to reach our endpoints, i.e. cases on controls versus placebo, much quicker. 11 is winning studies and cancer gear towards overall survival, thankfully from a patient's perspective the endpoints take a long time. In fact with more and more available lines of therapy, these trials, especially in the earlier lines of treatment are taking longer than ever.

A: I think there are many missing components. Even in cancers that are expected to be checkpoint responsive, there are many patients were not helping. In those that lack the high frequency of mutations that we see in melanomas and maybe even a lung cancer, are there other sources of new antigens that can be leveraged?

Since the amazing success of the first checkpoint inhibitors, we as a field have spent billions trying to find the next ones, with little to show for these efforts. Except maybe the realization that there is only so much we can learn from mouse biology.

I very much hope that this first personalized cancer vaccine will in fact be positive in the phase 3 that has now completed enrollment. I am also hopeful that there will be ways to improve on its efficacy, even by such immediate parameters as increasing the number of new epitopes and improving our ability to predict which ones are relevant within a given patients immunology and biology, without having to repeat the entire clinical development paradigm.

Finally, we need to continue to improve our methods of understanding, human immunology, and human cancer immunology from human disease. The abject failure of my models to predict the efficacy of both cancer, vaccines, and novel I owe combinations hoses a unique funding challenge that is going to be difficult to overcome because it means it is challenging to de-risk and Investmont based on pre-clinical studies.

This problem is further compounded for cancer vaccines because we continue to lack for bio markers that predict efficacy. Demonstrating one can illicit tumor active teasels in the periphery is likely important, but clearly insufficient to predict for efficacy, which has been a painful lesson over the last 30 years.

A: One centralized hub supports multiple research locations across the U.S., so we can open a trial in multiple states simultaneously and manage operations from a single team.

A: Traditional models are site by site and slow. Ours is a distributed, digitally connected network with centralized activation, oversight, and support, built to scale rapidly without overhauling core systems.

A: It expands access to emerging, life saving treatments closer to home, especially for communities that typically don’t have trial options.

A: “Offer all our patients, regardless of where they live, the opportunity to participate in clinical trials”, bringing promising options earlier than standard care.

A: The operational efficiency of the centralized model reduces time to first patient, increases enrollment, and keeps trials moving across a wide geographic footprint.

A: NIH sponsored, industry sponsored, and investigator initiated trials, including City of Hope developed investigational medicines.

A: The hub provides centralized processes and clinical decision making expertise to all sites, so protocol execution and patient management are standardized.

A: We’re the first academic center with a national clinical trial network paired to a large, diverse patient population, a combination that enables unprecedented access and scalable operations.

A: Continued network expansion and portfolio growth, so investigators can bring groundbreaking science to every City of Hope location without sacrificing speed or quality.

A: If you need multi state activation, faster enrollment, and consistent execution, a centralized hub national network is the shortest path.

A: The future of this emerging technology is extremely promising for changing the way we not only diagnose new cases, but how we manage active treatment cases. The fact that we may now be able to determine disease response and/or recurrence earlier than currently possible with anatomic imaging technologies will significantly enhance the way we manage our patients. It will allow for earlier intervention with potentially more effective alternative therapies when we know a patient is not responding to what is being used.

A: We have utilized next generation genomic and proteomic sequencing technologies to identify those molecular alterations and/or pathways associated with resistance mechanisms to targeted therapies. As stated above, these type of data could lead to even earlier alternative interventions when our initial therapies have fail to achieve the desired clinical outcomes.

A: The validation that these newer technologies can give accurate information much earlier than the older anatomic-based methods of evaluating disease status could bring significant improvements in how we manage cancer. It would be very useful, indeed critical, to know when cessation of an approved but possibly ineffective therapeutic approach for an individual patient is failing to achieve the desired clinical effect. The sooner we have these type of data, the better for our patients and our ability to optimally manage their disease. That alone could result in significant savings to a health system where cancer drug costs are so high. In addition, the cost of deploying ct-effective and accurate genomic tests for screening, diagnostic and response assessment activity should make such an approach even more cost-effective than our many of our current anatomic-based methods for these decisions.