Multicenter prospective clinical trial of molecular genetic markers for non-invasive differential diagnosis of benign and malignant melanocytic skin lesions

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Background: Improving the accuracy of non-invasive diagnosis of skin tumors is becoming an urgent problem, given the clear increase in the incidence of melanoma in many countries. Dermoscopy and examination by an experienced dermatologist can reduce the NNE to 5–10 per melanoma (M) detected, however, achieving even this, clearly suboptimal, rate requires a long specialist training. Methods: We report results of prospective nonrandomized trial evaluating sensitivity and specificity of RNA profiling of cytological samples from adhesive transparent patches (ATP) applied to skin lesions. Routine pathology reports were obtained for all surgery samples. RNA extracted from ATP specimens (N = 126) was tested for mRNA expression of PRAME and LINC00518 genes. GAPDH was used as a reference gene. Sample was considered marker-positive if either PRAME or LINC00518 expression was detected. Results: Between June 2021 and November 2021, 126 pts undegoing excisional biopsy of skin lesions were included in the study at 5 centers. On pathology, invasive M was detected in 49 (38.9%) patients (mean Breslow thickness was 2.41 mm [95%CI 1,44 to 3.37]), M in situ in 6 (4.8%), non-melanoma skin cancer in 4 (3.2%) and dysplastic nevus in 56 (44.4%). In the day of preplanned excision ATP was applied to the target lesion for 5–6 min and sent to the central lab. Amplifiable mRNA was found in all 126 ATP samples. 82 samples of 126 (65,1%) tested marker-positive. Sensitivity of RNA profiling for detection of skin cancers reached 88.14% (95% CI 77.07% – 95.09%), while specificity was only 53.85% (41.03% – 66.30%), with PPV of 63.41% (56.74% – 69.61%), NPV of 83.33% (70.65% – 91.22%) and accuracy of 70.16% (61.29% – 78.04%). Among the seven false negative results there were 3 M in situ and 4 invasive M. Conclusions: RNA profiling of skin ATP specimens is feasible and has high sensitivity but rather low specificity. Randomized studies are needed to evaluate if this technique can add anything to dermoscopy or other noninvasive diagnostic modalities. Clinical trial information: NCT04353050. [Table: see text]

Mambalgin-2 Inhibits Growth, Migration, and Invasion of Metastatic Melanoma Cells by Targeting the Channels Containing an ASIC1a Subunit Whose Up-Regulation Correlates with Poor Survival Prognosis

Melanoma is an aggressive cancer characterized by the acidification of the extracellular environment. Here, we showed for the first time that extracellular media acidification increases proliferation, migration, and invasion of patient-derived metastatic melanoma cells and up-regulates cell-surface expression of acid-sensitive channels containing the ASIC1a, α-ENaC, and γ-ENaC subunits. No influence of media acidification on these processes was found in normal keratinocytes. To control metastatic melanoma progression associated with the ASIC1a up-regulation, we proposed the ASIC1a inhibitor, -mambalgin-2 from Dendpoaspis polylepis venom. Recombinant analog of mambalgin-2 cancelled acidification-induced proliferation, migration, and invasion of metastatic melanoma cells, promoted apoptosis, and down-regulated cell-surface expression of prooncogenic factors CD44 and Frizzled 4 and phosphorylation of transcription factor SNAI. Confocal microscopy and affinity purification revealed that mambalgin-2 interacts with heterotrimeric ASIC1a/α-ENaC/γ-ENaC channels on the surface of metastatic melanoma cells. Using the mutant variant of mambalgin-2 with reduced activity toward ASIC1a, we confirmed that the principal molecular target of mambalgin-2 in melanoma cells is the ASIC1a subunit. Bioinformatic analysis confirmed up-regulation of the ASIC1 expression as a marker of poor survival prognosis for patients with metastatic melanoma. Thus, targeting ASIC1a by drugs such as mambalgin-2 could be a promising strategy for metastatic melanoma treatment.

Abstract PO-045: Machine learning for predicting overall survival using whole exome DNA and gene expression data and analyzing the significance of features

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.

Abstract PO086: 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

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.

Immunological monitoring for prediction of clinical response to antitumor vaccine therapy

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.

[Melanoma and Human Papillomaviruses: Is There an Outlook for Study?]

Melanoma is one of the most aggressive human malignant tumors. Its incidence and mortality are growing steadily. Ultraviolet irradiation is the main risk factor for melanoma involved in melanomagenesis. The probability of viral etiology of melanoma has been discussed. Human papillomaviruses (HPV) have been mentioned among candidates for its etiologic agents because some HPV types are the powerful carcinogens causing cervical cancer and other cancers. The review analyses the literature data on the association of melanoma with HPV Several groupsfound HPVin skin melanomas as well as in mucosa; viruses of high oncogenic risk were detected in some cases. For some organs the etiological role of high-risk HPV as inducers of invasive carcinomas is confirmed. These organs require special mention: cervix uteri, vulva, vagina, penis, anal region, and oral cavity. However in the majority of the studies in which viral DNA-positive melanomas were found, testing for viral genome expression was not done while this is the fact of primary importance. HPVare found in normal skin and mucous membranes thus creating justifiable threat of tumor specimen contamination with viral DNA in vivo. There are limited data on aggravation of the disease prognosis in papillomavirus-positive melanomas. However, any systematic observation of a sizeable patient group distinguished by that tumor type has not been performed yet. Viral E6 and E7 oncogenes of high-risk papillomaviruses were shown to be able to transform normal human melanocytes in vitro experiments. Thus, we can assume the presence of the association of melanoma with oncogenic HPV. The clinical significance of this problem is indisputable under the conditions of the steady increase in melanoma incidence and mortality rates in Russia and abroad. The problem requires further study.

Biochemical and immunologic markers in patients with metastatic melanoma treated with chemotherapy and dendritic cell vaccine

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Background: The purpose of this sub study was to identify peripheral blood biomarkers associated with the therapeutic effect of immunotherapy with dendritic cell vaccine in patients with metastatic melanoma (MM). Methods: Patients (pts) with low disease burden achieved disease control after two cycles of chemo (cisplatine, vinbalstie, DTIC) were randomized dendritic cell vaccine (DC) or three cycles of chemo. Vaccination schedule consisted of 5 subcutaneous injections of dendritic cell (DC) vaccine (2×106 cells pulsed with autologous lysate) with 14 days intervals. S100B level, LDH level, and peripheral blood lymphocytes immune phenotype were assessed before treatment, after two cycles of chemo and after each vaccination cycle. Results: 104 pts were included in the study, 30 pts were randomized to DC arm, 29 pts were randomized to continue chemo. In pts with rapid disease progression baseline serum S100b level was significantly higher compared to patients with objective response or stable disease (0.874±1.15 mcg/L vs. 0.361±0.66 mcg/L, P=0.002). Similar results were found for baseline serum LDH level (559.8±469.7 U/L vs. 412.5±184.4 U/L, P=0.005) and serum S100b level after 2 cycles of chemo (0.688±0.855 mcg/L vs. 0.187±0,29 mcg/L, P<0.001). Contrary, baseline CD3+HLA-DR+ and CD4+CD25+ lymphocytes levels after 2 cycles of chemo were significantly higher in pts with disease control (11.9±8.9% vs. 8.9±5.3%, P=0.047 and 14.4±8.3% vs. 11.3±5.5%, P=0.036 respectively).In pts randomized to DC arm following markers were associated with long lasting objective response or stable disease course (>6 months): lower baseline S100b level (0.133±0.120 mcg/L vs. 0.445±0.406 mcg/L, P=0.014), lower S100b level after 2 cycles of chemo (0.105±0.095 mcg/L vs. 0.255±0.154 mcg/L, P=0.048), higher proportion of active CD8+lymphocytes prior to vaccination (74.7±3.6% vs. 51.7±14.2%, P=0.05); and more prominent increase of NK-cells CD3-CD16+CD56+ from baseline (increase in 76.5%±41.12% vs. 4.5±28.8%, P=0.01). Conclusions: Biochemical and immunological markers may be helpful when selecting patients with metastatic melanoma for immunotherapy with dendritic cell vaccine.

Adjuvant dendritic cell (DC)-based vaccine therapy of melanoma patients

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Background: Earlier DC vaccine therapy demonstrated clinical benefit in some patients (pts) with advanced melanoma although the effect was seen only in pts with minimal tumor burden (ASCO2007 abstract No: 3077). As there is no effective treatment for high risk resected melanoma we conducted an exploratory trial to assess the effectiveness of DC vaccine in stage III and IV melanoma pts after radical surgery in comparison with observation. Methods: 108 stage III and IV melanoma pts were enrolled in two comparative arms. The arms were well balanced in respect with demographic and prognostic factors. The vaccine was based on mature autologous monocyte-derived DC primed with autologous tumor lysate, administered intradermally every 2-6 weeks until the disease progression. Disease free survival (DFS) and overall survival (OS) were evaluated with Kaplan-Meier method and compared between the two arms using the log rank test. Safety of DC vaccine was also monitored. Results: The vaccine arm included 56 pts (stage III – 46, stage IV – 10 pts) who were surgically rendered free of disease and treated with DC vaccine. 52 pts were enrolled in the control arm (stage III – 47, stage IV – 5 pts), they were treated surgically and observed afterwards. At a median follow-up of 22 months, the HR (DC vs observation) for DFS was 0.45 (p < 0.05; 95%CI 0.29-0.69) and for OS was 0.71 (p=0.23; 95%CI 0.40-1.25). 60% of pts in the DC arm remained alive at this time point. Risk reduction significantly correlated with the strong delayed type hypersensitivity reaction induced by vaccine in 31 (55%) out of 56 vaccinated pts. The vaccine was safe and well tolerated although vitiligo was registered in 4 cases which was associated with more durable time to progression and OS. Conclusions: The immunotherapy with DC vaccine is safe and significantly improves DFS compared with observation in the adjuvant treatment of stage III and IV melanoma.

Cancer/testis genes expression in human melanoma cell lines

We analyzed the expression of 15 cancer/testis and four melanoma differentiation antigens in 21 metastatic melanoma cell lines using reverse transcriptase-polymerase chain reaction (RT-PCR) assay. On the basis of morphological characteristics, tumor cell lines were divided into three groups with high, moderate, and low grade of differentiation. Evaluation of gene expression and melanoma cell morphology has revealed a correlation between increased expression of cancer/testis genes and differentiation grade of cancer cells. The gene expression pattern for lymph node metastases and primary tumors exhibits the distribution of expression level and frequency similar to that found for established cell lines. Nevertheless, only 60% lymph node metastases or primary tumor tissue of randomly selected patients show marked expression of the most prominent cancer/testis genes, and almost 90% lesion tissue expresses at least one of 15 cancer/testis genes.