Machine learning prediction of malaria vaccine efficacy based on antibody profiles

Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (PfSPZ) under chloroquine chemoprophylaxis, using the PfSPZ Chemoprophylaxis Vaccine (PfSPZ-CVac), induces high-level protection against controlled human malaria infection (CHMI). Humoral and cellular immunity contribute to vaccine efficacy but only limited information about the implicated Pf-specific antigens is available. Here, we examined Pf-specific antibody profiles, measured by protein arrays representing the full Pf proteome, of 40 placebo- and PfSPZ-immunized malaria-naïve volunteers from an earlier published PfSPZ-CVac dose-escalation trial. For this purpose, we both utilized and adapted supervised machine learning methods to identify predictive antibody profiles at two different time points: after immunization and before CHMI. We developed an adapted multitask support vector machine (SVM) approach and compared it to standard methods, i.e. single-task SVM, regularized logistic regression and random forests. Our results show, that the multitask SVM approach improved the classification performance to discriminate the protection status based on the underlying antibody-profiles while combining time- and dose-dependent data in the prediction model. Additionally, we developed the new fEature diStance exPlainabilitY (ESPY) method to quantify the impact of single antigens on the non-linear multitask SVM model and make it more interpretable. In conclusion, our multitask SVM model outperforms the studied standard approaches in regard of classification performance. Moreover, with our new explanation method ESPY, we were able to interpret the impact of Pf-specific antigen antibody responses that predict sterile protective immunity against CHMI after immunization. The identified Pf-specific antigens may contribute to a better understanding of immunity against human malaria and may foster vaccine development.Author summary: Developing an effective malaria vaccine is challenging. Malaria is a life-threatening disease caused by the plasmodium parasite, which has a complex multi-stage life-cycle and expresses several thousand proteins in a highly coordinated manner. To date, our understanding of the immune mechanisms mediating protection against Plasmodium falciparum (Pf) is incomplete. Proteome microarrays have been used earlier by our clinical collaboration partners to identify Pf-specific antibody profiles of malaria-naïve volunteers during immunization with attenuated Pf sporozoites (PfSPZ). We reused this data to compare the ability of three supervised machine learning methods to identify predictive antibody profiles after immunization and before controlled human malaria infection (CHMI). We adapted a multitask support vector machine (SVM) approach to analyze time-dependent Pf-induced antibody profiles from several time points in a single prediction model. Our multitask SVM approach outperforms the studied standard approaches in classification performance. Additionally, we developed a new explanation method, named fEature diStance exPlainabilitY (ESPY), to interpret the impact of Pf-specific antigens. We applied ESPY on the multitask SVM model and identified diverse Pf-specific antigen sets after immunization and before CHMI. Furthermore, we showed that the identified Pf-induced antibody profiles vary among protected and non-protected individuals who had been exposed to different doses of PfSPZ.