Accurately targeted immunotherapies through reliable neoantigen recognition enable personalized medicine development.
Wide-scale resources have increasingly been deployed to find, characterize, validate, bank, and file patents for cancer neoantigens. It has been a biological landgrab echoing the single nucleotide polymorphism (SNP) fervor at the turn of the century. In a conversation late in 2023 with Maravai’s chief innovation officer Kate Broderick and Accuitas’ CEO Tom Madden on future applications for messenger RNA (mRNA)-based therapeutics, the notion came up as a potential lynch pin. Broderick commented that, “there was a beautiful study that came out of a large group in the UK [United Kingdom] called the TRACERx Study where they actually monitored people's cancers over time and fairly frequently, and within a matter of a month, some of these tumors had completely changed their neoantigen profile. So, to [Madden]'s point, the need to have really unprecedented speed to make this, the promise of personalized vaccines prominent … is absolutely key now” (1). However, lacking in mature and effective tools and equipment, activities have been labor intensive and, for the most part, produced only halting successes.
Enter Miller et al. from the La Jolla Institute for Immunology with a new platform approach described in a February American Association for the Advancement of Sciences (AAAS) paper (2). This new approach appears to work well on cancers pictured as evading patients’ immune systems, in part because these cancers do not carry many mutations. However, while these cancers are not infiltrated by cancer-fighting immune cells, the tumors do appear to trigger some cancer-fighting T cells and can perhaps be entrapped, thus “heating up” cold cancers.
The first principles that follow might seem complex, but some amount of future therapeutic target tractability may well require basic understanding of those principles. From the paper’s introduction “the genetic instability underlying neoplastic transformation can also provide tumor-specific targets for host T cells. These include function-altering mutations in so-called ‘driver’ genes, which endow the tumor with growth and survival advantages, as well as tumor-specific variants of ‘passenger’ genes that lack an obvious protumorigenic function” (2).So far so good; cancers behave and interact differently from regular tissue in ways that leave characteristic biological trails that can be tracked. Along this trail researchers recognize mutated peptides bound to the surface of human leukocyte antigen (HLA) molecules for recognition as neoantigens by patients’ CD8 and CD4+ T lymphocytes. Researchers have been aware of neoantigens for some time and have wanted to target these tumor specific targets; however, it has been a substantial challenge to do so.
A typical traditional neoantigen discovery setup relies on next-generation sequencing (NGS), complex algorithms/databases, and a certain distrust regarding the utility of the eventually upturned somatic nonsynonymous mutations (NSMs). The process is expensive and slow. A second method assesses T cells in peripheral blood or tumor-infiltrating lymphocytes (TILs). This can be “technically challenging or require specialized cellular reagents, such as autologous dendritic cells, thus limiting their utility in routine neoantigen screening using nominal clinical samples” (1). The La Jolla team set out instead to forge an “unbiased and HLA-independent strategy for the functional validation of spontaneous neoantigen-specific CD4+ and CD8+ T cell responses that uses NGS-guided selection to nominate NSMs for functional recognition testing in a short-term culture of autologous peripheral blood mononuclear cells (PBMCs), which reports on both T helper cell 1 (TH1) and TH2 cytokines. Using this approach (herein referred to as ‘IPV’ for identify-prioritize-
validate), we now show that preexisting CD4+ and CD8+ T cell responses can be identified and the T cell receptors (TCRs) that mediate them can be isolated at rates that are about 10-fold higher than class I HLA binding–based predictive approaches across a spectrum of tumor types and degrees of tumor mutational burden using routinely available clinical samples” (2). It will likely have been understood that by integrating elements of both traditional approaches, this new platform performs in a more HLA-agnostic and clinic-capable manner and is, henceforth, a healthy step ahead in terms of reliably churning out tumor target information—especially regarding low mutation tumor profiles. It is also a step toward a better understanding of tumor microenvironment interactions with an individual’s immune system, which alone makes it an effort worth celebrating.
There are some drawbacks and limitations, however, and these can be found in the discussion section of the AAAS paper. But the expectation to “routinely and reliably identify natural tumor-specific neoantigens and the TCRs that recognize them” will indeed “be crucial to the development of precisely and accurately targeted immunotherapies that can effectively offer therapeutic benefit while avoiding off-tumor toxicities” (2), which is a long-sought goal in oncology research now visibly present as a functional platform technology.
1. Spviey, C. Exploring mRNA’s Potential. PharmTech.com, Oct. 17, 2023.
2. Miller, A.; Schoenberger, S.; Peters, B.; Cohen, E. A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer. Sci. Transl. Med. 2024, 16 (736), eabj9905. DOI:10.1126/scitranslmed.abj9905
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