Background: GATA2-related myelodysplastic syndrome (GATA2-MDS) is a unique predisposition syndrome with a high risk of leukemic transformation. This systematic review synthesizes current literature and presents two illustrative pediatric GATA2-MDS cases. Methods: Data retrieval from eight cohort and case–control studies provides comprehensive analysis on disease features, diagnostic complexities, management, and outcomes related to hematopoietic stem cell transplantation (HSCT) in GATA2-related myeloid malignancies. Additionally, two pediatric cases are included to exemplify clinical and therapeutic challenges in real-world setting. Results: The literature data demonstrates high incidence of monosomy 7, and recurrent infections as the most common clinical feature, followed by immunodeficiency and lymphedema. Prognosis clearly worsens with age and HSCT remains the only curative treatment. GATA2 patients undergoing HSCT experience high rates of graft versus host disease (GvHD) as well as unique neurological, thrombotic, and infectious complications. Transplant-related mortality (TRM) is linked to GvHD and infections. Post-transplant cyclophosphamide (PT/Cy) strategies seem to improve survival by reducing GvHD incidence. Overall survival (OS) remains variable across groups. The first case presents rapid disease progression to pulmonary alveolar proteinosis (PAP) and leukemic transformation, further developing severe HSCT complications. The second case addresses novel GATA2 mutation and raises concerns regarding alternative prophylactic and therapeutic strategies in transplant setting. Conclusions: Collaborative efforts aim to enhance understandings of GATA2-related myeloid malignancies and guide towards more effective management approaches.

The rapid expansion of next-generation sequencing technologies has generated unprecedented volumes of genomic data; however, translating these data into reliable and clinically actionable insights remains a major challenge in precision medicine. Artificial intelligence (AI) has emerged as a key enabling technology across the genomic medicine pipeline, supporting variant detection, variant interpretation, polygenic risk prediction, disease subtyping, biomarker discovery and treatment–response modelling. This review provides a clinically oriented, pipeline-based synthesis of contemporary AI applications in genomic medicine. Major computational paradigms, including machine learning, deep learning, ensemble methods, multimodal AI, explainable AI frameworks and emerging foundation models, are discussed in the context of their contribution to genomic analysis and clinical decision support. Particular emphasis is placed on the factors that determine model robustness and clinical utility, including dataset composition, class imbalance, label noise, calibration, ancestry representation, distributional shift and external validation. Evidence from rare genetic disorders, cardiovascular genetics and precision oncology is examined to illustrate both successful translational applications and persistent barriers to implementation. The review further analyses common sources of failure in real-world genomic AI systems, including overfitting, limited transportability across populations and sequencing environments, inadequate interpretability, and insufficient prospective validation. Ethical and regulatory challenges are discussed in relation to clinical accountability, genomic privacy, algorithmic bias and equitable implementation. Ultimately, the successful clinical translation of genomic AI will depend not only on methodological innovation, but also on rigorous validation, transparent reporting, continuous calibration, robust governance and sustained expert oversight.

(1) Background: This study investigates the tumor immune microenvironment, focusing on immune cell distribution in lung adenocarcinoma. (2) Methods: We evaluated fifty cases of lung adenocarcinoma, and suitable areas for further studies were annotated on the histological slides. Two tumor cores per case were obtained, one from the tumor’s center and another from its periphery, and introduced into three paraffin receptor blocks for optimized processing efficiency. The 4-micrometer-thick tissue microarray sections were stained for H&E and for CD68, CD163, CD8, CD4, and PD-L1; (3) Results: Our investigation revealed significant correlations between PD-L1 expression in tumor cells and the presence of CD163+ macrophages, between CD4+ cells and CD8+, CD68+, and CD163+ cells, and also between CD8+ T cells and CD163+ cells. Additionally, while we observed some differences in cellular components and densities between the tumor center and periphery, these differences were not statistically significant. However, distinct correlations between PD-L1 and immune cells in these regions were identified, suggesting spatial heterogeneity in the immune landscape. (4) Conclusions: These results emphasize the intricate interactions between immune cells and tumor cells in lung adenocarcinoma. Understanding patient spatial immune profile could improve patient selection for immunotherapy, ensuring that those most likely to benefit are identified.

Background: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis, even in resectable disease. Molecular alterations in the four major driver genes (KRAS, SMAD4, CDKN2A, and TP53) have been associated with disease progression and patient outcomes. However, reliable prognostic biomarkers remain limited, particularly in resected PDAC, where recurrence rates remain high. Case presentation: We report the case of a 71-year-old woman incidentally diagnosed with PDAC carrying KRAS G12D/SMAD4 co-mutations with low baseline neutrophil-to-lymphocyte ratio (NLR) and circulating cell-free DNA (ccfDNA) levels. The patient underwent distal pancreatectomy with splenectomy and portal vein resection with multiple postoperative complications requiring re-intervention. She experienced a disease stability under adjuvant chemotherapy, and subsequent second lines of treatment, achieving an overall survival (OS) of 53-months. Conclusions: This case highlights the potential value of integrating molecular alterations with inflammatory and liquid biopsy biomarkers, including NLR and ccfDNA levels for improving risk stratification in PDAC. A focused review of the literature further supports the need for multimodal prognostic assessment in resected disease.

Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy and genomic profiling has become increasingly vital for risk stratification. We conducted a retrospective single-center study including 96 newly diagnosed pediatric patients with ALL and mixed phenotype acute leukemia to characterize the genomic landscape using conventional and molecular cytogenetics, multiplex ligation-dependent probe amplification and targeted next-generation sequencing and to evaluate associations with treatment response and survival. Mutation co-occurrence analysis revealed a NRAS/KRAS/JAK cluster and an ETV6::RUNX1 cluster characterized by relative mutational exclusivity, with additional associations between NOTCH1–CDK4 and CDK4–CCND3 alterations. DelCDKN2A/CDKN2B alterations showed a trend toward higher day-15 minimal residual disease (MRD) levels and inferior 2-year event free survival rate (EFS). DelIKZF1-positive cases showed lower 2-year EFS. In contrast, ETV6::RUNX1 and high hyperdiploidy were associated with favorable early response and EFS. DelPAX5 did not independently influence outcome. ZNF384 positive cases showed higher early MRD levels despite excellent survival outcomes. NRAS/KRAS mutations were significantly associated with higher positive day-15 MRD (Wilcoxon, p = 0.0067) and remained independently associated after adjustment for white blood cell count and cytogenetic subgroup. Intermediate risk (IR) and high-risk groups showed comparable 2-year EFS, indicating limited discrimination by conventional risk stratification. The IR group displayed a heterogeneous genomic profile, with NRAS/KRAS, Ph-like mutations and delCDKN2A/CDKN2B among the most frequent alterations. These observations highlight the potential of integrated genomic profiling to refine risk stratification, particularly by identifying clinically relevant subgroups within the IR category.

Background and Clinical Significance: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis, even in resectable disease. Molecular alterations in the four major driver genes (KRAS, SMAD4, CDKN2A, and TP53) have been associated with disease progression and patient outcomes. However, reliable prognostic biomarkers remain limited, particularly in resected PDAC, where recurrence rates remain high; Case presentation: We report the case of a 71-year-old woman incidentally diagnosed with PDAC carrying KRAS G12R with an ultra-low-variant allele frequency (VAF) SMAD4 mutation, with low baseline neutrophil-to-lymphocyte ratio (NLR) and circulating cell-free DNA (ccfDNA) levels. The patient underwent distal pancreatectomy with splenectomy and portal vein resection with multiple postoperative complications requiring re-intervention. The patient demonstrated excellent tolerance to the therapy, experiencing no significant adverse events and reaching an overall survival of 53 months. Conclusions: This case highlights the potential value of integrating molecular alterations with inflammatory and liquid biopsy biomarkers, including NLR and ccfDNA levels for improving risk stratification in PDAC. A focused review of the literature further supports the need for multimodal prognostic assessment in resected disease.