Periodic Reporting for period 4 - SUMMIT (Stepping Up mRNA Mutanome Immunotherapy)
Reporting period: 2023-02-01 to 2023-07-31
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 demonstrated in a first-in-human trial potent tumor-directed immune responses in every single vaccinated patient. Moreover, clinical activity of a novel mRNA-based mutanome vaccine was observed. Given that mutations are a hallmark of cancer, mRNA mutanome vaccines are universal drugs and their efficacy is unaffected by the cancer type.
The aim of this research project was 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 the 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. Mutation prediction algorithms for targetable variants deciphering clonal heterogeneity to inform rational vaccine design and countermeasures against selection of target escape variants were developed.
(iii) Tumor cell resistance to vaccine-induced immune cells due to antigen presentation defects were addressed by developing strategies for mobilizing the full repertoire of immune effector mechanisms, such as antibodies and NK cells. Immune cell exhaustion was 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 was undertaken in the final stage of the project.
The aim of this research project was 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 the 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. Mutation prediction algorithms for targetable variants deciphering clonal heterogeneity to inform rational vaccine design and countermeasures against selection of target escape variants were developed.
(iii) Tumor cell resistance to vaccine-induced immune cells due to antigen presentation defects were addressed by developing strategies for mobilizing the full repertoire of immune effector mechanisms, such as antibodies and NK cells. Immune cell exhaustion was 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 was undertaken in the final stage of the project.
For the entire duration of the project August 2018 – July 2023, the following activities were undertaken (with indicated results and their exploitation and dissemination):
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, and tools for the discovery of somatic point mutations and short insertions and deletions from whole exome sequencing data are nearing completion. EasyFuse, a pipeline for accurate fusion gene detection from RNA-seq data has been developed. splice2neo, an automated, stable and efficient pipeline for detection of neoantigens candidates from mutation-induced alternative splicing events has been implemented.
ii. Neoepitope predictors were evaluated based on their performance in two independent immunogenicity studies and six independent checkpoint blockade trials. Moreover, the NeoFox tool to annotate neoantigen candidates with these published neoantigen features has been developed.
iii. The detection of residual tumor DNA in liquid biopsies was tested and applied to clinical samples. We developed an advanced error correction method based on AI technologies which significantly lowers the false discovery rate. 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. We have generated a dataset of mutations annotated with information such as variant allele frequency, depth, cancer cell fraction, and labelled as truncal or non-truncal.
This dataset of mutations will be used to develop a machine learning tool to classify mutations as truncal or non-truncal.
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.
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, and tools for the discovery of somatic point mutations and short insertions and deletions from whole exome sequencing data are nearing completion. EasyFuse, a pipeline for accurate fusion gene detection from RNA-seq data has been developed. splice2neo, an automated, stable and efficient pipeline for detection of neoantigens candidates from mutation-induced alternative splicing events has been implemented.
ii. Neoepitope predictors were evaluated based on their performance in two independent immunogenicity studies and six independent checkpoint blockade trials. Moreover, the NeoFox tool to annotate neoantigen candidates with these published neoantigen features has been developed.
iii. The detection of residual tumor DNA in liquid biopsies was tested and applied to clinical samples. We developed an advanced error correction method based on AI technologies which significantly lowers the false discovery rate. 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. We have generated a dataset of mutations annotated with information such as variant allele frequency, depth, cancer cell fraction, and labelled as truncal or non-truncal.
This dataset of mutations will be used to develop a machine learning tool to classify mutations as truncal or non-truncal.
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, fusion genes and alternative splicing variants in individual patient samples. Furthermore, we have shown that these newly discovered epitopes can elicit an effective T cell response, thus enhancing the options for vaccine design. Also, we have investigated a second crucial step in any discovery pipeline for anti-tumor vaccines, namely the prediction or selection of relevant targets out of the individual mutanome. Both procedures will enable improved vaccine desings for a greater number of patiens.
Secondly, by characterizing tumor heterogeneity and tumor evolution, we have further improved our neoepitope selection algorithms, which will ultimately ensure a more effective vaccine design by adapting to the changed individual mutanome which changes over time through tumor evolution.
Thirdly, we have explored 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.
Secondly, by characterizing tumor heterogeneity and tumor evolution, we have further improved our neoepitope selection algorithms, which will ultimately ensure a more effective vaccine design by adapting to the changed individual mutanome which changes over time through tumor evolution.
Thirdly, we have explored 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.