Immunotherapy has shown promising results in a variety of cancers, including melanoma. However, the responses to therapy are usually heterogeneous, and understanding the factors affecting clinical outcome is still not achieved. Here, we show that immunological monitoring of the vaccine therapy for melanoma patients may help to predict the clinical course of the disease.We studied cytokine profile of cellular Th1 (IL-2, IL-12, IFN-γ) and humoral Th2 (IL-4, IL-10) immune response, vascular endothelial growth factor (VEGFA), transforming growth factor-β 2 (TGF-β 2), S100 protein (S100A1B and S100BB), adhesion molecule CD44 and serum cytokines β2-microglobulin to analyze different peripheral blood mononuclear cell subpopuations of patients treated with dendritic vaccines and/or cyclophosphamide in melanoma patients in the course of adjuvant treatment.The obtained data indicate predominance of cellular immunity in the first adjuvant group of patients with durable time to progression and shift to humoral with low cellular immunity in patients with short-term period to progression (increased levels of IL-4 and IL- 10). Beta-2 microglobulin was differentially expressed in adjuvant subgroups: its higher levels correlated with shorter progression-free survival and the total follow-up time. Immunoregulatory index was overall higher in patients with disease progression compared to the group of patients with no signs of disease progression.

The aggressiveness of a tumor depends on its genomic profile. Accordingly, it should be expected that the overall survival of cancer patients also depends on this, in particular, on the number and nature of mutations and the degree of gene activity. In this work, we try to predict overall survival by the genomic profile of the tumor, both by primary DNA and by RNA activity. One of the objectives of the study is to compare which of the presented baseline data better predict overall survival. The data were taken from the pan-cancer TCGA database (33 types of cancer) on DNA and gene expression. They were split into 2 datasets: DNA data only and expression only. In the DNA data, we select only pathogenic and likely pathogenic variants. The total number of genes containing these mutations was 1806, they are accepted as features. In the expression data, we selected only those genes that belong to the cancer-related pathways in the KEGG database (1821 genes). As a prediction effect for both datasets, a 3-year OS was chosen. Accordingly, if a patient crossed the three-year line of OS, he was considered a positive example, otherwise - a negative one. The DNA dataset contained 2159 positive examples and 1687 negative examples. The expression dataset contained 3363 positive and 2212 negative ones. Machine learning algorithms have been implemented using python 3. To determine the significance of the features, we used the Lasso linear regression algorithm with 5-fold cross validation. The result was obtained in the form of list of genes ordered by decreasing importance on the effect. In the DNA dataset, the algorithm selected 64 significant genes, including a sign (plus or minus) indicating an influence on a positive or negative effect, and a coefficient indicating the relative strength of an influence. For example, age 81-90 and EGFR mutations were at the negative end of the scale, while stage I and HRAS mutations were at the positive end. In the RNA dataset, the algorithm selected 75 of such important genes. At the negative end of the scale there were age 81-90 and changes in CDK6 expression, at the positive end - stage I and changes in RPS6 expression. Only 11 of significant features were shared across the two datasets. To predict the effect, we used a logistic regression algorithm with 5-fold cross-validation. Receiver characteristic curves (ROC), reflecting the sensitivity and specificity of the classification, were evaluated by the area under the curve (AUC). For the DNA dataset, the mean ROC-AUC for the 5 predictions was 0.72 (0.64-0.77), for the RNA dataset 0.74 (0.69-0.77). Predicting overall survival is essential for planning treatment strategies and selecting patients for clinical trials. Sufficiently high indicators of the classification quality show that this approach makes sense for further development. Further tuning of the algorithms will make it possible to predict the effect more accurately. Combinations of different input data must be tested. The list of important genes can be helpful in detecting molecular targets in drug discovery. Citation Format: Dmitrii K. Chebanov, Nadezhda S. Tatevosova, Irina N. Mikhaylova. Machine learning for predicting overall survival using whole exome DNA and gene expression data and analyzing the significance of features [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-045.

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We have designed an algorithm implemented in a software platform for the development of new anti-tumor drugs in the form of small molecules. Several molecules were generated for the treatment of patients with lung cancer as an example. At the initial stage, we identified the targets for the therapy. Firstly, we evaluated the expression profile of the genes most associated with poor clinical outcome in patients with lung cancer using deep learning. Additional patients data were gained by generative adversarial neural networks (GAN) technology. As a result, a set of genes was successfully selected, which expression was associated with poor prognosis. We identified the genes that could distinguish normal tissue from tumor tissue using another deep learning model that was trained to predict normal and tumor tissue based on gene expression. The other genes were considered as targets for targeted lung cancer therapy. After that, a module was developed that predicts the interactions of inhibitors with proteins. For this purpose, the amino acid sequences of proteins were represented in vector form, as well as formulas of chemical compounds interacting with proteins. In addition, a deep learning-based module was developed that predicts the IC50 in experiments on cell lines. Virtual pre-clinical trials with the selected inhibitors were performed to identify relevant cell lines for laboratory experiments. As a result, the study obtained formulas of several molecules with the predicted binding to certain proteins.