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Stepping Up mRNA Mutanome Immunotherapy

Periodic Reporting for period 2 - SUMMIT (Stepping Up mRNA Mutanome Immunotherapy)

Reporting period: 2020-02-01 to 2021-07-31

Immunotherapy is fundamentally changing the treatment of cancer patients. Personalized vaccines eliciting immune responses against individual cancer mutations have moved into the spotlight. We have pioneered the field and moved ´cancer mutanome vaccines´ from a mere vision into a disruptive medical concept compatible with current standards of drug development and health care practice. Solving key scientific and technological challenges and building on extensive preclinical studies, we showed in a first-in-human trial potent tumor-directed immune responses in every single vaccinated patient, and clinical activity of a novel mRNA-based mutanome vaccine. Given that mutations are a hallmark of cancer, mRNA mutanome vaccines are universal drugs the efficacy of which are unaffected by the cancer type.

The aim of this research program is to ignite the next wave of advancement by addressing four key constraints challenging a full clinical realization of such vaccines.
(i) The scarcity of point mutations in many tumors is to be addressed by extending new target discovery to the full spectrum of genetic aberrations.
(ii) Cancers are heterogeneous and outgrowth of clones unaccounted for by the vaccine is an efficient escape mechanism. Targetable mutation prediction algorithms deciphering clonal heterogeneity to inform rational vaccine design and countermeasures against selection of target escape variants will be developed.
(iii) Tumor cell resistance to vaccine-induced immune cells due to antigen presentation defects will be addressed by developing strategies for mobilizing the full repertoire of immune effector mechanisms, such as antibodies and NK cells. Immune cell exhaustion will be tackled by vaccination protocols promoting long-lived memory responses and by combination treatments counteracting tumor-mediated immunosuppression.
(iv) Finally, translation of the scientific findings via collaboration with clinical and industrial partners will be undertaken in the last stage of the program.
For the scientific reporting period August 2018 – January 2021, the following activities were undertaken (with indicated results):

A) Expansion and optimal exploitation of individual neoepitope repertoire
i. Software tools for improved detection of structural variants from whole genome sequencing data have been generated. Software tools for somatic point mutations from whole exome sequencing data, and aberrant splicing are in progress.
ii. Neoepitope predictors were evaluated based on their performance in two independent immunogenicity studies and six independent checkpoint blockade trials. Moreover, a tool to annotate neoantigen candidates with these published neoantigen features has been developed.
iii. The detection of residual tumor cells in liquid biopsies was tested. Automated selection of mutations for sequencing panels for different cancer entities and a general detection pipeline is in progress.
iv. Investigation of tumor evolution (new mutation acquisition over time to generate distinct clones) and intratumoral heterogeneity by identifying (early) truncal mutations was initiated. A machine learning model to “learn” what features and feature combinations distinguish truncal from non-truncal mutations will be developed with the aim to prioritize truncal mutations.

B) Optimizing immunotherapeutic mechanisms
The functionality of a combined approach driving cytokine- and antibody-mediated effector mechanisms to counteract T-cell-resistant tumor clones was investigated in a preclinical mouse model. The mechanisms behind improved tumor killing upon RNA vaccination therapy were evaluated by single-cell RNA-Seq analysis.
First of all, we have broadened the space of neoepitope repertoire beyond point mutations by developing software tools that can detect structural variants and alternative splicing variants. Furthermore, we have shown that these newly discovered epitopes can elicit an effective T cell response, thus enhancing the options for vaccine design.

Secondly, by characterizing tumor heterogeneity and tumor evolution, we have improved our neoepitope selection algorithms, which will ultimately ensure an effective vaccine design, which can be adapted based on tumor evolution.

Thirdly, we are exploring how to counteract tumor resistance to vaccine-induced T-cells. Inhibiting the resistance against T-cell killing will improve vaccine efficacy and lead to a more sustained immune response.