Patients data: We had 39 patients of 2 categories: with an objective response on dendritic cell vaccine therapy (20) and with disease progression without a response (19). All of them had 21 biomarkers (antigen concentration) as features. The positive effect means that the patient responded to therapy. The features data has quantitative (continuous) values, but we made it categorical by determining the 6 intervals, so each of the biomarkers was replaced with 6 encoding (‘dummy’) variables with possible values 1 or 0, depending on if the patient’s biomarker value belongs to this interval.
Methods: The machine learning algorithm for response prediction is called JSM method for automatic support of scientific research (JSM method ASSR). It allows conducting a plausible reasoning that is realized in hypotheses generating and keeping only those that remain after each database enlargement. The reasoning is based on the similarity of the objects, that can be obtained with patients’ (objects’) features intersection using the statements from the set theory. According to it, the object is representing by a set of features, and hypotheses about its belongings to a class are also sets of features, that are specific for the current class. So, for each class there is a separate amount of hypotheses is generating. On the prediction stage each object given for the prediction is being checked for how many hypotheses are entering into it, or, in other words – is a subset of this object. Based on this information prediction is making: it depends on which hypotheses (of which class) are prevailing in entering in the object. This kind of machine learning approach also allows us to get the reasons why the particular object is classified into his class. So it can be used not only for the classification problem but also for the knowledge discovery about effects’ reasons. We divided the database into 2 batches: source base (18 objects) and first enlargement (17 objects) for the learning, and the rest 4 objects were left for testing. The source base and its enlargement are being permutated during the learning process for more reliability and robustness. We applied a cross-validation, according to which each object was at least 1 time in the test group. So it was 10 learning launches with predictions: 9 with 4 test examples and the rest 1 - with 3 test examples.
Results: On all 10 cross-validation launches, there were 26 correct predictions. Also were 5 cases with a failure, 5 false-positive predictions, and 3 false-negative ones. Recall of the model was 85%, and precision is 77%, F1 score = 0.81. We also obtained reasons, which were common for all the database permutation. It meant that patients who will not respond to therapy should have CD8 value at interval 39.9-54.1 and IRI at interval 0.29-0.7.
Discussion: Actually 39 samples are a small amount of data even for the JSM method ASSR, but we showed the suitability of described approach for the quantity data predicting and the reasons extracting. With the enlargement of the source database, it will be possible to get higher results.
Citation Format: Dmitrii K. Chebanov, Irina N. Mikhaylova, Nadezhda S. Tatevosova. Method for predicting the effectiveness of the developed immune dendritic cell vaccine in melanoma patients based on cell surface antigens and machine learning with non-classical logic [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PO086.