Therapeutic Cancer Vaccines

Although most people have received preventive vaccines for infectious diseases, the use of therapeutic vaccines is still evolving. After many years of research, the preventive cancer vaccine for human papillomavirus (HPV) is now available and routinely administered. Doug Whitman, Senior Director of the Advanced Technology Group at Luminex, sees the HPV vaccine as analogous to the current development of therapeutic cancer vaccines—nearing a “tipping point” toward treatment breakthroughs. “This is a really exciting time for the vaccine approach to cancer treatment and I hope to see it realize its promise,” he says. A therapeutic vaccine aims to target cancer cells and stimulate a patient’s immune system to strengthen its own defenses. Personalized vaccines appear poised to become an integral part of our cancer-fighting arsenal. Here’s a look at recent developments and challenges in this quickly moving field.

Choosing antigens

Developing any vaccine requires an important decision right off the bat: choosing an antigen to target. As selecting antigens for cancer vaccine development is a complex process, researchers are developing prediction algorithms to aid them. Whitman says that antigen selection requires further optimization, which Diasorin’s bead-based Luminex xMAP platform for highly multiplexed biomarker assays is capable of handling. “A deeper understanding of both healthy human biology and cancer biology calls for a comprehensive approach, with the ability to analyze large numbers of antigens in a scalable and cost-effective workflow,” he says. “Our multiplex bead-based approach supports the analysis of hundreds of antigens per sample in one test, providing the necessary scale to get a complete view of epitope binding, including monitoring of postvaccination peptide-specific IgG levels, for example.”

Therapeutic cancer vaccines are usually designed to target neoantigens, which are unknown proteins resulting from DNA mutations. Because neoantigens are completely unfamiliar to the immune system, they generate a strong immune response and so make good targets for cancer vaccines. Further improvements in algorithm abilities to predict effective antigens are on the horizon. “Overall, more patient data are needed to assess whether the antigens included in vaccines are truly immunogenic and protective, to validate and improve the algorithms used,” says Stefaan De Koker, VP of Technology and Innovation at etherna, an RNA technology company. “With more data emerging from larger-scale clinical studies, and with improvements in machine learning and AI, one can expect strong improvements in epitope and immunogenicity predictions over the next years.”

mRNA-based vaccines

The success of mRNA-based Covid vaccines during the pandemic sparked greater interest in applying this technology to cancer therapies. Compared to traditional protein-based vaccines, mRNA-based vaccines can be altered more quickly to mirror changes that may occur in their targets. “RNA technologies now allow for fast and flexible vaccine manufacturing, thereby enabling personalized vaccine design and short timelines between taking a biopsy and treating the patient with the matching precision vaccine,” adds De Koker. This means researchers can “tweak” the targeting information of an mRNA vaccine on a shorter timescale compared to traditional vaccines. “The good news is the quick turnaround of reformulating mRNA vaccines within weeks to continue to chase neoantigens as they arise during treatment,” explains Whitman.

Initial studies combining cancer vaccines with other therapies following tumor resection look promising. For example, a combination of Moderna’s personalized mRNA cancer vaccine and Merck’s Keytruda, an immune checkpoint inhibitor drug, reduced recurrence or death by 49% (at three years) compared to Keytruda alone, in a study with melanoma patients. In addition, in a three-way combination of BioNTech’s personalized mRNA cancer vaccine, chemotherapy, and an immune checkpoint inhibitor drug, half of pancreatic cancer patients remained cancer-free at 18 months. Furthermore, all patients receiving the vaccine showed expanded neoantigen-specific T cells.

Patients most likely to benefit from therapeutic cancer vaccines today are those in earlier disease stages, who have had a tumor removed, yet may harbor residual cancer cells with potential to cause relapse. “Induction of strong antitumor T cells that eliminate remaining or dormant tumor cells can likely prevent or at least extend the time to relapse in this context,” says De Koker. “The recent successes reported by BioNTech and Moderna when applying their personalized cancer vaccines to patients with advanced but still [resectable] tumors seems to confirm this hypothesis.”

Lipid nanoparticles for vaccine delivery

Lipid nanoparticles (LNPs) are an effective vaccine delivery method recently used to formulate the Pfizer-BioNTech and Moderna Covid-19 vaccines, and undergoing much research as a delivery method for mRNA-based cancer vaccines. LNPs work by encapsulating a vaccine “payload,” which protects it from degradation in the body while being targeted to the intended cells. “LNP properties can be altered to target uptake by specific cells and to have more sustained release, which can lead to a robust and prolonged immune response,” says Keara Marshall, Senior Product Manager, Preclinical System at Precision NanoSystems. “Their inherent adjuvant properties can further enhance an immune response, which provides many benefits in cancer vaccines like better tumor recognition and controlled growth.”

Challenges include optimizing LNP formulations for their specific payloads, as each LNP must be uniquely tailored to its task. “LNP design and delivery needs to be optimized for specific profiles—target cells and tissues, routes of administration, immunogenicity, and patient characteristics are among some of the requirements that can impact LNP design for optimal delivery,” she says. “Size, surface charge, stability, and excipients all play a role in achieving the most efficient delivery of a payload.”

This makes the development of new LNP formulations a pricey and time-consuming process. “Some LNP designs aren't scalable, and they may not be robust enough for downstream processing or to produce in large volumes,” says Marshall. With accessible products such as off-the-shelf LNP kits, Precision NanoSystems is working to help researchers tackle the challenges of developing mRNA-LNP-based cancer vaccines. “We aim to provide a simple, optimized LNP formulation for a given profile that seamlessly transitions from discovery to clinic, enabling drug developers and disease researchers to focus on getting solutions to patients faster,” she says.

Assists from machine-learning and AI

Further analyses of early vaccine trials are likely to advance future endeavors significantly. De Koker suggests that AI-assisted analysis of deep sequencing of tumors may assist clinicians and patients in choosing treatments in conjunction with a cancer vaccine, that together are predicted to give the best therapeutic outcome. “Integrating the collection of big datasets from large patient cohorts with the rapid advances in machine learning and AI to analyze and interpret these data, is likely to transform the treatment options for cancer patients in the foreseeable future,” he says. “By analyzing patient response data to cancer vaccines, researchers will be able to improve and finetune the algorithms used to identify immunogenic neo-antigens, potentially leading to better outcomes.”

Whitman believes that a combination of cancer-fighting approaches, including immune-checkpoint inhibitors, bispecific immunomodulators, and vaccines, will be successful. “A reliable process can be established for the optimization of personalized cancer vaccines,” he says. “I have hope that this approach would save many lives, as it would be a big leap in cancer therapy.”