Relapse or reinfection: Classification of malaria infection using transition likelihoods.

In patients with Plasmodium vivax malaria treated with effective blood-stage therapy, the recurrent illness may occur due to relapse from latent liver-stage infection or reinfection from a new mosquito bite. Classification of the recurrent infection as either relapse or reinfection is critical when evaluating the efficacy of an anti-relapse treatment. Although one can use whether a shared genetic variant exists between baseline and recurrence genotypes to classify the outcome, little has been suggested to use both sharing and nonsharing variants to improve the classification accuracy. In this paper, we develop a novel classification criterion that utilizes transition likelihoods to distinguish relapse from reinfection. When tested in extensive simulation experiments with known outcomes, our classifier has superior operating characteristics. A real data set from 78 Cambodian P. vivax malaria patients was analyzed to demonstrate the practical use of our proposed method.

Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks.

A standard competing risks set-up requires both time to event and cause of failure to be fully observable for all subjects. However, in application, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. In this paper, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria.

The Plasmodium liver-specific protein 2 (LISP2) is an early marker of liver stage development.

Plasmodium vivax hypnozoites persist in the liver, cause malaria relapse and represent a major challenge to malaria elimination. Our previous transcriptomic study provided a novel molecular framework to enhance our understanding of the hypnozoite biology (Voorberg-van der Wel A, et al., 2017). In this dataset, we identified and characterized the Liver-Specific Protein 2 (LISP2) protein as an early molecular marker of liver stage development. Immunofluorescence analysis of hepatocytes infected with relapsing malaria parasites, in vitro (P. cynomolgi) and in vivo (P. vivax), reveals that LISP2 expression discriminates between dormant hypnozoites and early developing parasites. We further demonstrate that prophylactic drugs selectively kill all LISP2-positive parasites, while LISP2-negative hypnozoites are only sensitive to anti-relapse drug tafenoquine. Our results provide novel biological insights in the initiation of liver stage schizogony and an early marker suitable for the development of drug discovery assays predictive of anti-relapse activity.