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Vascular anomalies are a heterogeneous group of rare diseases caused by defects in the early stages of vascular development that result in abnormal growth and development of vessels. The treatment typically consists of surgical approaches; unfortunately, many anomalies cannot be cured. Therefore, the goal of the treatment is to control the lesion, and progression or recurrence after an intervention is no exception. In the last 15 years, tremendous progress in understanding the molecular basis of the disease has been made, with alterations in more than 40 genes proven to be causal in the disease pathogenesis. Interestingly, such alterations are also found in many human cancers, which recently raised the question of whether repurposing anti-cancer drugs could be utilized in this setting. Tissue biopsies from 38 patients with vascular anomalies have been analyzed using different approaches ranging from Sanger sequencing to NGS-based detection. In 31 patients, known causal alterations of either germline or somatic origin were found. For the remaining seven patients, sampling issues were considered a cause of the negative result. Given that most patients suffered from a venous type of malformation, most alterations were found in TEK and PIK3CA genes, which are commonly altered in this subgroup. Other findings included alterations of KRAS, GLMN, PTEN, or IDH2 genes. TEK gene mutations were predominantly located in exon 17, which encodes for a part of the tyrosine kinase domain of the protein, with frequent L914F substitution being found in 12 cases. Identified PIK3CA gene mutations were all well-characterized activating alterations described in cancer-related context. Based on an identification of either TEK or PIK3CA mutation, 13 patients were administered targeted treatment using selective PI3K alpha subunit inhibitor alpelisib. For all patients, improvement in quality of life, lesion reduction and normalization of coagulation parameters was achieved. A deeper understanding of the pathophysiology of vascular anomalies can significantly contribute to patient stratification, identification of potential therapeutic targets, and overall better clinical management. At the same time, identifying potential therapeutic targets could lead to the administration of novel anti-cancer drugs within the concept of drug repurposing and better overall disease control compared to routinely used approaches. Supported by the Ministry of Health of the Czech Republic, grant nr. NU20-03-00240 and by the project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102) - Funded by the European Union - Next Generation EU. Citation Format: Petra Pokorna, Renata Faberova, Olga Koskova, Martin Sterba, Hana Palova, Katerina Kozelkova, Robin Jugas, Dagmar Al Tukmachi, Tana Machackova, Jiri Sana, Peter Mudry, Jaroslav Sterba, Ondrej Slaby. Precision medicine and cancer drug repurposing in the management of vascular anomalies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1063.

Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0–3 and 0–20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes., Graphical Abstract Unlabelled Image, Highlights • Optimization of 1D numerical representation for DNA to protein translation. • Reducing genetic code degeneracy in numerical representation of DNA sequences. • More robust numerical conversion used for genomic-proteomic analysis.

Despite the great achievements in treating pediatric cancer patients in the last several decades, approximately one fifth of patients remains uncurable using standard therapeutic modalities and require search for innovative therapeutic approaches. Advances in sequencing techniques and bioinformatic data processing enabled identification of wide spectrum of molecular alterations including single nucleotide variants, copy number aberrations, fusion genes or changes in expression and methylation patterns, which could serve as therapeutic targets. Translation of comprehensive molecular profiling into clinical practice is still limited, however, multiple precision oncology initiatives have already explored feasibility of this approach. From September 2016 to December 2020, a total of 160 patients with high-risk solid tumors that were treated or consulted at Department of Pediatric Oncology of University Hospital Brno were subjected to molecular analysis of tumor tissue using whole-exome sequencing, targeted RNA sequencing, whole-transcriptome profiling and array-CGH. In 18 patients, 2 or more biopsies were analyzed due to relapse or progression of the disease. In the cohort, CNS tumors were the most prevalent (41%), followed by sarcomas (33%) and neuroblastoma (9%). All patients were presented at multidisciplinary molecular tumor board, where treatment recommendations were discussed. In 37% of patients (n = 59), therapeutic targets were identified. Most commonly identified targets included BRAF (n = 9), FGFR1 (n = 7), NF1 (n = 6), NRAS (n = 5) and PIK3CA (n = 4), making RAS/MAPK signaling most frequently altered pathway in the subgroup. Single nucleotide variants or small indels accounted for 65% of actionable findings, followed by fusion genes (12%), copy number aberrations (9%), CD274 expression (7%, confirmed by IHC staining for PD-L1 protein), and high tumor mutational burden (7%). Clinically relevant fusions were found in 25% of patients and 20% of identified fusions were targetable. 8 patients were eligible for immunotherapy based on either PD-L1 expression, or high tumor mutational burden (>10 mut/Mb). Using molecular-based approach in treating high-risk patients represents a promising strategy and helps to understand the complexity of pediatric malignancies though examining tumor biology at multiple levels. Implementing the concept of precision oncology into clinical practice could not only be beneficial in the context of chances for improved survival of high-risk patients, but might also be convenient for other patients, whose successful treatment comes at cost of various secondary complications due to intensive chemotherapy/radiotherapy approaches. Supported by Ministry of Health of the Czech Republic, grant nr. NV19-03-00562, NV19-03-00501, NV19-03-00559 and NU20-03-00240. All rights reserved. Citation Format: Petra Pokorna, Hana Palova, Tina Catela Ivkovic, Sona Adamcova, Michal Kyr, Vojtech Bystry, Robin Jugas, Karolina Trachtova, Dagmar Al Tukmachi, Tomas Merta, Jaroslav Juracek, Jiri Sana, Sona Mejstrikova, Marta Jezova, Peter Mudry, Zdenek Pavelka, Jaroslav Sterba, Ondrej Slaby. Comprehensive genomic profiling as an approach to guide therapeutic planning in pediatric patients with high-risk solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 76.

Discovering copy number variation (CNV) in bacteria is not in the spotlight compared to the attention focused on CNV detection in eukaryotes. However, challenges arising from bacterial drug resistance bring further interest to the topic of CNV and its role in drug resistance. General CNV detection methods do not consider bacteria's features and there is space to improve detection accuracy. Here, we present a CNV detection method called CNproScan focused on bacterial genomes. CNproScan implements a hybrid approach and other bacteria-focused features and depends only on NGS data. We benchmarked our method and compared it to the previously published methods and we can resolve to achieve a higher detection rate together with providing other beneficial features, such as CNV classification. Compared with other methods, CNproScan can detect much shorter CNV events.