Hypophosphatasia (HPP) is an inherited disorder that affects bone and tooth mineralization, among other body systems. HPP is caused by pathogenic variants in the alkaline phosphatase-liver (ALPL) gene, which encodes tissue nonspecific alkaline phosphatase. One major challenge in diagnosing HPP is interpreting variant of uncertain significance (VUS), which can create uncertainty for patients and healthcare professionals, leading to delays in diagnosis and treatment or incorrect diagnoses. Since February 2021, the ALPLgene variant consortium has reclassified 100 VUS using adjusted American College of Medical Genetics/Association for Molecular Pathology criteria. Of these, 11 were reclassified as pathogenic, 62 as likely pathogenic, three as likely benign, one as benign, and 23 remained as VUS; out of 100 variants, there were 81 missense, eight frameshifts, four in-frame deletions, two intronic, two synonymous, two nonsense, and one splice site variant. In vitrofunctional testing of the variant’s residual enzymatic activity and its dominant negative effect (DNE) was required for 40 variants.Out of 40 variants tested, 72% showed <30% residual activity and were reclassified as pathogenic or likely pathogenic. Among these, 48% exhibited a DNE. All variants with ˂15% residual activity showed a DNE, while those with over 20% did not. In terms of domains, all tested variants in the active site domain and 75% of those in the homodimeric interface showed <30% residual activity. The consortium’s reclassification of 100 ALPLvariants has resulted in a 21% increase in the number of variants in the Johannes Kepler University ALPLgene variant database. Furthermore, 136 new genotypes with 118 associated phenotypes, including asymptomatic states, have been added. These expanded resources will help improve genetic counseling for patients and families affected by ALPLvariants and enable faster diagnosis and medical decision-making.

Translocations of the IGH locus on 14q32.3 are present in about 8% of patients with chronic lymphocytic leukemia (CLL) and contribute to leukemogenesis by deregulating the expression of the IGH-partner genes. Identification of these genes and investigation of the downstream effects of their deregulation can reveal disease-causing mechanisms. We report on the molecular characterization of a novel t(12;14)(q23.2;q32.3) in CLL. As a consequence of the rearrangement ASCL1 was brought into proximity of the IGHJ-Cμ enhancer and was highly overexpressed in the aberrant B-cells of the patient, as shown by qPCR and immunohistochemistry. ASCL1 encodes for a transcription factor acting as a master regulator of neurogenesis, is overexpressed in neuroendocrine tumors and a promising therapeutic target in small cell lung cancer (SCLC). Its overexpression has also been recently reported in acute adult T-cell leukemia/lymphoma. To examine possible downstream effects of the ASCL1 upregulation in CLL, we compared the gene expression of sorted CD5+ cells of the translocation patient to that of CD19+ B-cells from seven healthy donors and detected 176 significantly deregulated genes (Fold Change ≥2, FDR p ≤ 0.01). Deregulation of 55 genes in our gene set was concordant with at least two studies comparing gene expression of normal and CLL B-lymphocytes. INSM1, a well-established ASCL1 target in the nervous system and SCLC, was the gene with the strongest upregulation (Fold Change = 209.4, FDR p = 1.37E-4). INSM1 encodes for a transcriptional repressor with extranuclear functions, implicated in neuroendocrine differentiation and overexpressed in the majority of neuroendocrine tumors. It was previously shown to be induced in CLL cells but not in normal B-cells upon treatment with IL-4 and to be overexpressed in CLL cells with unmutated versus mutated IGHV genes. Its role in CLL is still unexplored. We identified ASCL1 as a novel IGH-partner gene in CLL. The neural transcription factor was strongly overexpressed in the patient’s CLL cells. Microarray gene expression analysis revealed the strong upregulation of INSM1, a prominent ASCL1 target, which was previously shown to be induced in CLL cells upon IL-4 treatment. We propose further investigation of the expression and potential role of INSM1 in CLL.

Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs.

Background/Objectives: First-line treatment selection for advanced (locally advanced or metastatic) pancreatic ductal adenocarcinoma (PDAC) is currently based on clinical characteristics rather than predictive biomarkers. While homologous recombination deficiency (HRD) is associated with increased sensitivity to platinum-based chemotherapy, established predictive biomarkers, such as pathogenic alterations in BRCA1, BRCA2, and PALB2, identify only a small subset of patients who are likely to benefit from platinum-based first-line treatment. The Genomic Instability Score (GIS) may capture broader HRD-associated genomic scarring beyond single-gene testing and support biomarker-guided platinum selection. However, the clinical utility of the GIS in PDAC remains uncertain, and no PDAC-specific GIS cutoff has yet been established. Methods: In this retrospective multicenter cohort, 127 patients with advanced PDAC underwent TruSight Oncology 500 HRD testing between 2022 and 2025. GIS was evaluable in 99 patients; 80 received first-line therapy. The primary endpoint was time to failure of the first-line strategy (TFS). An exploratory candidate GIS cutoff for platinum sensitivity was identified using maximally selected log-rank statistics, and survival was analyzed using Kaplan–Meier estimates and Cox regression. Results: In the first-line platinum cohort, an exploratory GIS threshold of 39 showed the greatest discrimination for treatment failure-free survival (TFS) within this cohort. Patients with GIS ≥ 39 had significantly longer TFS than those with GIS < 39 (9.9 vs. 5.9 months; HR 0.33, 95% CI 0.13–0.80; p = 0.015), and this association remained significant after multivariable adjustment. Among individual genes, BRCA2-mutated tumors exhibited significantly higher GIS than BRCA2-wild-type tumors (median 54 vs. 19; FDR-adjusted p = 0.002), although 30% of GIS-high tumors lacked BRCA1/2 mutations. Combined stratification by GIS and first-line treatment demonstrated a stepwise improvement in overall survival, with median OS of 8.2 months in non-platinum/GIS < 39 patients, 15.1 months in platinum/GIS < 39 patients, and 30.7 months in platinum/GIS ≥ 39 patients (p = 0.002). Conclusions: GIS may identify patients with advanced PDAC who experience favorable outcomes during first-line platinum-based chemotherapy. The observed association between higher GIS and improved treatment outcomes supports further investigation of GIS as a biomarker for treatment stratification in advanced PDAC and warrants prospective validation.