https://repositorio.fleni.org.ar/xmlui/handle/123456789/84 Sun, 05 Apr 2026 20:36:48 GMT 2026-04-05T20:36:48Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1450 Impact of 10q loss, CDKN2A deletions, EGFR amplification, and trisomy of chromosome 7 in the overall survival of IDH-mutant astrocytoma Mezmezian, Mónica Beatriz; Arakaki, Naomi; Diez, Blanca; Martinetto, Horacio; Sevlever, Gustavo Emilio Introduction: Since progression from grade 2 to grade 4 occurs in the evolution of IDH-mutant astrocytoma (A, IDH-mut), it is crucial to identify the key factors that define the different grades. Aims: To evaluate the impact on overall survival (OS) of molecular alterations traditionally associated with high-grade gliomas within the grading scheme. Materials and methods: We retrospectively analyzed the role of 10q loss, CDKN2A deletions, EGFR amplification (Amp), and trisomy of chromosome 7 (trisomy 7) in 189 A, IDH-mut, reclassified according to the WHO 2021 criteria (grade 2, n = 133; grade 3, n = 18; grade 4, n = 38). Results: Among the 189 cases, 29 presented with CDKN2A hemizygous deletion (hemidel), 17 with CDKN2A homozygous deletion, 18 showed trisomy 7, and 2 showed EGFR Amp. A multivariate test revealed that WHO grade 4 and trisomy 7 significantly impacted OS. CDKN2A hemidel and 10q loss did not influence OS in our cohort. Given that 11 out of 18 cases with trisomy 7 were IDH-mutant grade 2 (G2), we compared G2 cases with and without trisomy 7 and found worse OS in cases with trisomy (p = 0.0034), similar to WHO grade 4. Conclusion: Our results suggest that trisomy 7 plays a significant role in the OS of A, IDH-mut. Further research is needed to determine whether trisomy 7 is an independent marker or if it is associated with other molecular alterations that affect OS. Fri, 01 Aug 2025 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1450 2025-08-01T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1386 The Immunogenic Tumor-Specific Neoantigen Database (ITSNdb): A Tool for Comprehensive Performance Evaluation of Neoantigen Immunogenicity Predictors Nibeyro, Guadalupe; Flesia, Rocío; Orschanski, Daniela; Nava, Agustín; Baronetto, Verónica; Fernández, Elmer A. The identification of tumor-specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based antitumoral vaccines and/or adoptive T cell immunotherapies. In silico immunogenicity prediction of candidate peptides is crucial to speed up the prioritization of such peptides for experimental validation. Up to now, several methods were proposed as TSN immunogenicity predictors, but there are still several drawbacks in both performance and comprehensive performance evaluation, mainly due to the absence of well documented and adequate TSN databases.The Immunogenic Tumor-Specific Neoantigen database (ITSNdb) is a tool developed to fairly benchmark immunogenicity predictors intended to be used over tumoral neopeptides. The proposed ITSNdb enables the analysis of immunogenicity without the interference of other variables such as binding affinity or peptide processing, as they were considered into the inclusion criteria for the curation of neoantigens. ITSNdb, together with a dataset emulating a true patient neoantigens scenario, as a validation strategy for prioritization, and a list of neopeptides predicted to bind to major histocompatibility complex I (MHC-I) from immune checkpoint blockade immunotherapy (ICB) cohorts, along with their associated patient outcomes, is available to evaluate tumor neoantigen burden as a biomarker for ICB response (accessible at https://github.com/elmerfer/ITSNdb). Wed, 01 Jan 2025 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1386 2025-01-01T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1203 Identification of a putative molecular subtype of adult-type diffuse astrocytoma with recurrent MAPK pathway alterations Sievers, Philipp; Bielle, Franck; Göbel, Kirsten; Schrimpf, Daniel; Nichelli, Lucia; Mathon, Bertrand; Appay, Romain; Boldt, Henning B.; Dohmen, Hildegard; Selignow, Carmen; Acker, Till; Vicha, Ales; Martinetto, Horacio; Schweizer, Leonille; Schüller, Ulrich; Brandner, Sebastian; Wesseling, Pieter; Schmid, Simone; Capper, David; Abdullaev, Zied; Aldape, Kenneth; Korshunov, Andrey; Krieg, Sandro M.; Wick, Wolfgang; Pfister, Stefan M.; von Deimling, Andreas; Reuss, David E.; Jones, David T.W.; Sahm, Felix Thu, 18 Jul 2024 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1203 2024-07-18T00:00:00Z https://repositorio.fleni.org.ar/xmlui/handle/123456789/1083 Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation Wang, Wesley; Kumm, Zeynep Temerit; Ho, Cindy; Zanesco-Fontes, Ideli; Texiera, Gustavo; Reis, Rui Manuel; Martinetto, Horacio; Khan, Javaria; McCandless, Martin G.; Baker, Katherine E.; Anderson, Mark D.; Chohan, Muhammad Omar; Beyer, Sasha; Elder, J. Brad; Giglio, Pierre; Otero, José Javier Background Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested the use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision-making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma—amounting to 581 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results We discovered that the serum white blood cell (WBC) count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of WBC count. By utilizing an objective PD-L1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PD-L1 expression in glioblastoma patients with high serum WBC counts. Conclusions These findings suggest that in a subset of glioblastoma patients the incorporation of WBC count and PD-L1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, machine learning models allow the distillation of complex clinical data sets to uncover novel and meaningful clinical relationships. Sat, 11 Nov 2023 00:00:00 GMT https://repositorio.fleni.org.ar/xmlui/handle/123456789/1083 2023-11-11T00:00:00Z