Advancements in profiling technologies have deepened our understanding of cancer biology, particularly in non-small cell lung cancer (NSCLC). Genomic data have demonstrated profound clinical impact, enabling the rational design of therapeutics and achieving excellent clinical outcomes for patients with oncogene-addicted disease. While proteomics, transcriptomics, epigenetics, metabolomics, and microbiomics have also generated a wealth of data in NSCLC, their clinical impact is comparatively limited. The increasing use of multiomic profiling has the capacity to change this paradigm, offering new opportunities for improving patient care, particularly for those with non-oncogene-addicted (NOA) NSCLC. This review will summarize the current landscape of multiomic research in NSCLC, emphasizing the potential role in precision oncology. Each omic field is discussed in turn, describing potential clinical applications and challenges of each. In addition, the developing fields of spatial-omic and integrated multiomics, which are becoming increasingly important in understanding cancer biology, are discussed. NSCLC samples have been extensively profiled across different omic technologies, revealing a range of biomarkers associated with prognosis or response to therapy and potential drug targets, many of which are being investigated. While the patient groups analyzed differ between studies, most are performed on early-stage resection samples, and many studies do not stratify results by genomic status, limiting our understanding of NOA-NSCLC. In addition, while many studies independently analyze several omics, fewer use multiomic integration algorithms. Despite significant research into spatial-omic and multiomics in NSCLC, only genomics has significantly affected NSCLC clinical care, leaving an unmet need in NOA-NSCLC. However, nongenomic technologies have significant potential for precision oncology, particularly when used alongside multiomic integration to discover biomarkers or to identify future precision medicine targets.

This study aimed to characterize the genomic landscape of Korean gastric cancer and evaluate associations among oncogenic alterations, established biomarkers, demographics, and treatment outcomes.

A total of 1,283 patients with gastric cancer who underwent tumor-only targeted sequencing as part of practice and received palliative treatment between January 2017 and August 2025 at the Samsung Medical Center were included.

Among 1,283 patients (median [IQR] age, 61 [52-68] years; 827 males [64.46%]), TP53 (51.91%), ARID1A (19.02%), ERBB2 (12%), KRAS (10.29%), and PIK3CA (9.12%) were the most frequently altered genes. Epstein-Barr virus-positive tumors exhibited enrichment of BCOR, PIK3CA, and ARID1A alterations and reduced TP53 mutations (false discovery rate [FDR] adjusted P < .01). Human epidermal growth factor receptor 2-positive tumors were characterized by coamplification of ERBB2, CCNE1, and MYC (FDR adjusted P < .001), whereas PD-L1 positivity was associated with KRAS and CDKN2A alterations (FDR-adjusted P < .05). Among patients treated with first-line nivolumab plus chemotherapy (n = 269), those with high tumor mutational burden (TMB; ≥10 mutations per megabase) had improved overall survival (v the low TMB subgroup; hazard ratio [HR], 0.48 [95% CI, 0.25 to 0.93]; P = .03), particularly when combined with PD-L1 positivity (v all other biomarker-defined subgroups; HR, 0.33 [95% CI, 0.14 to 0.76]; P = .006). Moreover, as TMB levels increased, patients derived greater survival benefit from nivolumab plus chemotherapy versus chemotherapy alone, even among those with microsatellite-stable tumors. Across treatment regimens, FGFR2 and MET alterations were linked to poorer outcomes, whereas PIK3CA mutations were observed in patients with longer overall survival after first-line chemotherapy.

Our findings provide a comprehensive genomic landscape of Korean gastric cancer and underscore the clinical relevance of integrating genomic and established biomarkers to advance precision oncology.

Although KRAS mutations represent the primary oncogenic driver in pancreatic ductal adenocarcinoma (PDAC), the association between codon-specific alterations and patient outcomes remains poorly elucidated, largely because of a lack of data sets coupling genomic profiling with rich clinical annotations across disease stages.

We used American Association for Cancer Research's GENIE Biopharma Consortium Pancreas v1.2 data set to test the association of codon-specific KRAS mutations with clinicogenomic features and patient outcomes in patients with PDAC diagnosed with localized (stages I to III) and advanced disease (stage IV). Overall survival (OS) was compared using Kaplan-Meier and multivariable Cox proportional hazards methods.

Among 1,032 eligible patients, 949 (92%) exhibited mutant KRAS. These mutations were predominantly observed at G12D (n = 390, 41%), G12V (n = 305, 32%), and G12R (n = 149, 16%). In the group of patients who presented with localized disease, those with G12V mutation had notably longer survival compared with G12D mutation (P = .03). By contrast, patients with G12V mutation who presented with metastatic disease experienced shorter OS compared with those with G12R (P = .04) and G12D mutations (P = .04). Furthermore, no significant differences were observed in the frequencies of coaltered driver genes, including TP53, CDKN2A, and SMAD4, across the different KRAS mutations.

These findings demonstrated that codon-specific KRAS mutations affect PDAC outcomes differently based on disease stage at diagnosis. As studies testing KRAS inhibitors continue to emerge and mature, the prognostic variability of individual KRAS mutations must be carefully considered to avoid confounding and ensure accurate evaluation of therapeutic efficacy in early-phase studies.

Posterior Reversible Encephalopathy Syndrome (PRES) is one of the most common neurological complications in pediatric onco-hematology. Hematologic malignancies and cytotoxic chemotherapy are involved in its pathogenesis. It's a clinical and radiological entity: the diagnosis of PRES is based on both clinical symptoms and neuroimaging data.

Here we reported a series of four cases of children/ adolescents treated by cytotoxic chemotherapy for hematologic malignancies who developed neurologic disorders and their magnetic resonance imaging findings were in favor of PRES.

In onco-hematology, children/ adolescents who present with new seizures, visual deficits, or other neurologic signs, PRES should be considered as a part of the differential diagnosis as a good outcome relies on rapid management of this complication.

Osteoid osteoma and osteoblastoma are benign bone tumors with similar histologic features, often distinguished by size and clinical behavior. Their relationship remains a topic of debate.

An 8-year-old boy presented with a femoral diaphyseal lesion initially diagnosed as osteoid osteoma based on resection biopsy. However, within six months, the boy experienced increased pain and rapid growth, with subsequent biopsy revealing aggressive osteoblastoma. This suggests the initial lesion may have been an early-stage osteoblastoma.

This case challenges the concept of osteoid osteoma transforming into osteoblastoma. While histologically similar, these tumors should be considered distinct entities, and size alone may not be a reliable differentiating factor. Careful clinical and pathological correlation, with attention to growth rate, is crucial for accurate diagnosis and management.

Cancer is a common cause of morbidity and mortality in domestic cats. Because the mutational landscape of domestic cat tumors remains uncharacterized, we performed targeted sequencing of 493 feline tumor-normal tissue pairs from 13 tumor types, focusing on the feline orthologs of ~1000 human cancer genes. TP53 was the most frequently mutated gene, and the most recurrent copy number alterations were loss of PTEN or FAS or gain of MYC. By identifying 31 driver genes, mutational signatures, viral sequences, and tumor-predisposing germline variants, our study provides insight into the domestic cat oncogenome. We demonstrate key similarities with the human oncogenome, confirming the cat as a valuable model for comparative studies, and identify potentially actionable mutations, aligning with a "One Medicine" approach.

In early breast cancer surgery (EBC), free margins significantly decrease local recurrence in breast-conserving surgery (BCS), while metastatic status of sentinel lymph node (SLN) impacts axillary management. However, intraoperative visualization of margins and metastatic SLNs (mSLNs) remains challenging. Here, we report a second-window near-infrared (NIR-II) fluorescence probe by conjugating ICG to TROP2-targeting cyclic peptide (TTP-ICG). Our results showed that TTP-ICG specifically binds to TROP2-expressing cells in vitro and identifies TROP2-positive tumors in vivo. In addition, TTP-ICG enabled efficient intraoperative evaluation of surgical margins and visualization of mSLNs at a submillimeter level in preclinical models. Additionally, we optimized the rapid incubation imaging method (RIIM) by shortening the procedure to less than 8 min, illustrating TTP-ICG's high performance in distinguishing malignant from normal tissues/fibroadenoma lesions as well as detecting metastatic lymph nodes in a cohort of 59 patients. Thus, this TROP2-targeting probe demonstrates its significance for fluorescence imaging-guided surgery with dual applicability in EBC, providing translational potential for further clinical decision-making.

Adoptive cell therapies that genetically engineer immune cells with chimeric antigen receptors (CARs) have shown limited success against solid tumors due to the immunosuppressive tumor microenvironment (TME) and logistical challenges of ex vivo cell manipulation. Here, we introduce an immune cell-tropic lipid nanoparticle (LNP) platform that enables systemic delivery of CAR-encoding mRNA for the in vivo generation of panCAR immune cells. A single intravenous injection of this LNP system efficiently and transiently engineers T cells, macrophages, dendritic cells, and NK cells across the spleen, bone marrow, and peripheral blood, yielding a synergistic, multilineage antitumor response. Using human epidermal growth factor receptor 2 (HER2) as a CAR target, we demonstrate that repeated administration of LNP formulated with HER2-CAR mRNA (LNP-panCAR^HER2) effectively inhibits tumor growth and prolongs overall survival in three murine syngeneic xenograft tumor models, without causing obvious side effects. Immune profiling of treated tumors reveals a remodeled TME with a shift toward an immunostimulatory phenotype, characterized by reduced M2-like macrophages and an increased presence of effector T cell subsets. Our findings establish LNP-panCAR as a broadly applicable, off-the-shelf in vivo CAR cell therapy platform for solid tumor immunotherapy and beyond.

Early and accurate classification of breast tumors from MRI scans is essential for improving patient outcomes. However, a key limitation of conventional deep learning models, such as Convolutional Neural Networks (CNNs), is their difficulty in capturing the subtle, spatially variant features that are crucial for precise medical image interpretation.

To address this limitation, we propose a novel deep learning framework called AdaptiveInvolutionNet (AIN). This hybrid architecture is specifically designed to improve discriminative feature learning for breast tumor classification by integrating two key mechanisms: spatially-adaptive involution layers and channel-wise attention.

Our AIN model employs a unique strategy for feature extraction. In its early layers, it utilizes spatially-adaptive involution kernels, which are highly effective at capturing fine-grained, localized features. As the network deepens, it transitions to conventional convolutions to maintain computational efficiency. To further enhance its diagnostic capabilities, we have embedded channel-wise attention mechanisms (specifically, squeeze-and-excitation modules) within the residual connections of the network. This allows the model to dynamically and selectively amplify diagnostically relevant features while suppressing less important ones. The model was rigorously trained and evaluated on a large, balanced dataset of 6,000 breast MRI images (3,000 benign, 3,000 malignant) using a robust five-fold cross-validation protocol.

AIN demonstrated superior performance, achieving a high test accuracy of 97%. This performance was consistent and reliable across all folds, with an average accuracy of 96% (± 1%). The model also showed strong agreement with true labels, indicated by a high Cohen's Kappa score of 0.93 (± 0.01), and produced well-calibrated, trustworthy predictions with a low Brier score of just 0.0241.

By successfully uniting an adaptive spatial feature extraction method with powerful attention mechanisms, AIN represents a significant advancement in medical image analysis. Its high accuracy, robust generalization, and consistent reliability demonstrate a strong potential for it to serve as a valuable and dependable computer-aided diagnostic tool for breast cancer detection in clinical settings.