Journal of Hematology & Oncology最新文献

The paramount challenge in precision oncology lies in further improving quality of life and response rates for individual patients. Efforts toward these goals are steadily expanding the scope of clinical implementation, despite ongoing challenges such as standardization, cost-effectiveness, and data harmonization. Building upon this maturing foundation, generative AI-which has evolved dramatically in recent years-is particularly valuable at this stage of advancing efficiency and adoption as an auxiliary technology linking literature, guidelines, trial protocols, and patient data. Specifically, through mutation interpretation, trial eligibility matching, and tumor board support, it is expected to contribute to advancing standardization, improving cost-effectiveness, accelerating data harmonization, and further accelerating human-centered decision-making. Accordingly, this review surveys the development history of generative AI and its current healthcare applications, organizing its implementation potential for precision oncology along three axes: (1) generative AI-based interpretation of genetic mutations and estimation of their pathological significance; (2) generative AI-driven verification of clinical trial eligibility; and (3) multimodal foundation models for imaging and pathology that compute "tumor phenotypes" using real-world data, contributing to report drafting and molecular surrogate estimation. In response, we propose a strategy centered on retrieval-augmented generation (RAG) and human-in-the-loop (HITL) workflows, encompassing data preparation based on OMOP, mCODE, and FHIR; multicenter prospective evaluation; auditable logs and governance aligned with Good Manufacturing Practice (GMP) and the EU AI Act; and a synthetic data strategy including differential privacy. Ultimately, this approach validates value through real-world outcomes and charts a path toward "learning oncology," accelerating patient-centered decision-making and clinical trial development.

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of lung cancer over the past decade, markedly improving antitumor responses, overall survival, and quality of life. However, durable clinical benefit is achieved in only a subset of patients, and resistance to ICIs remains a major clinical challenge. Mechanistically, resistance arises from multiple, often overlapping processes, including inadequate tumor antigen presentation, dysfunctional T-cell priming and expansion, and the presence of physical and immunosuppressive barriers within the tumor microenvironment that limit immune cell infiltration and effector function. Cancer vaccines have re-emerged as a rational immunotherapeutic strategy to overcome these obstacles by inducing de novo or amplifying pre-existing tumor-specific immune responses, thereby enhancing long-term immunological memory while maintaining a favorable safety profile. Advances in antigen discovery, neoantigen prediction, and vaccine platforms have accelerated the development of both personalized and off-the-shelf neoantigen vaccines. Although personalized neoantigen vaccines have gained considerable attention following the success of mRNA-based COVID-19 vaccines, off-the-shelf approaches offer advantages in scalability, cost, and manufacturing timelines, facilitating broader clinical implementation. Accumulating preclinical and clinical evidence suggests that cancer vaccines are more effective in the adjuvant setting than in the metastatic setting, where high tumor burden and an immunosuppressive tumor microenvironment constrain vaccine-induced immune responses. Consistent with their limited efficacy as monotherapy, contemporary clinical trials increasingly evaluate cancer vaccines in combination with ICIs or other immunotherapeutic agents to enhance T-cell activation, reverse immune suppression, and restore antitumor immunity. This review synthesizes current mechanistic insights, highlights ongoing clinical efforts, and discusses future directions for rational cancer vaccine development in lung cancer, with an emphasis on overcoming resistance to ICI.

Background: Upper tract urothelial carcinoma (UTUC) poses diagnostic challenges due to its anatomical complexity and limited accessibility.

Methods: This prospective multicenter study (NCT05043662) evaluated the diagnostic performance of UroCAD, a novel non-invasive assay based on chromosomal copy number variation (CNV) analysis, in 244 patients with suspected UTUC or benign upper urinary tract conditions.

Results: Urine samples were analyzed for arm-level chromosomal aberrations (|Z-score|≥ 3.21), with histopathology as the reference standard. UroCAD demonstrated excellent diagnostic accuracy: sensitivity 91.0%, specificity 97.0%, and overall accuracy 93.4%. Performance was consistent across tumor locations (renal pelvis: 96.8%, ureter: 86.4%) and grades (high-grade: 93.5%, low-grade: 76.2%). Compared to urine cytology, UroCAD showed significantly higher sensitivity (91.2% vs. 52.9%, P < 0.001). CNV profiling revealed grade-associated patterns, with high-grade tumors frequently exhibiting amplifications on 1q and 8q. The extent (MaxZ) and number (CountZ) of chromosomal alterations correlated positively with tumor grade, supporting UroCAD's potential utility in molecular grading.

Conclusions and clinical implications: The UroCAD test demonstrates robust diagnostic performance for UTUC detection with high specificity and high sensitivity. The consistent performance across various clinical scenarios and the identification of specific chromosomal alteration patterns support its potential as a valuable clinical tool for both initial diagnosis and treatment monitoring. These findings warrant larger-scale validation studies to confirm its utility in routine clinical practice.

Distal cholangiocarcinoma (dCCA) arises from the distal bile duct and is anatomically embedded within the pancreatic head, adjacent to abundant autonomic nerve plexuses. This unique location renders dCCA particularly prone to perineural invasion (PNI), a pathological hallmark that contributes to its dismal prognosis. However, the spatial architecture and molecular drivers that orchestrate PNI remain poorly defined. Here, we applied Xenium subcellular resolution spatial transcriptomics platform to profile resected tumor tissues from dCCA patients stratified by PNI status pathologically. A spatially resolved atlas comprising a total of 20 cell types was generated, uncovering enrichment of Schwann cells, type 2 conventional dendritic cells (cDC2), M2-like macrophages, cancer associated fibroblasts (CAFs) and B/plasma cells in PNI-high tumors, along with depletion of exhausted CD8⁺ T cells. Heterogeneous malignant cells in PNI-high tumors demonstrated activation of extracellular matrix remodeling and axonogenesis pathways, in line with the initial pathological classification. Spatial mapping further revealed distinct PNI-associated niches, notably matrix-producing CAFs (mCAFs)-macrophage clusters exhibiting coordinated enrichment of inflammatory and fibrotic programs. We further identified the LAMB3-DAG1 axis as a potential mediator of dCCA cells-Schwann cell interaction, while the preferential proximity of arteries to Schwann cells suggested additional microenvironmental support for nerve invasion. Collectively, our study provides a comprehensive subcellular atlas of PNI in dCCA, uncovering coordinated epithelial, stromal, and immune remodeling that drives perineural invasion. The identified biomarkers not only hold promise for patient stratification but may also guide intraoperative navigation and surgical margin determination, offering new avenues for precision therapy.

Most patients with large B-cell lymphoma (LBCL) progressing after CAR-T therapy experience poor survival and lack standardized treatment strategies. Prognostic tools are needed to guide decision-making at relapse. The Post-CAR Prognostic Index (PC-PI), recently proposed by Iacoboni et al., combines five routine clinical variables to stratify outcomes after CAR-T failure: ECOG (> 0), hemoglobin (< 10 g/dL), LDH (≥ 2xULN), number of extranodal sites (> 1) and time from CAR-T to progression (< 4 months). We evaluated the PC-PI in a retrospective multicenter study including 125 LBCL patients relapsing or refractory after axicabtagene-ciloleucel or tisagenlecleucel, treated between 2019 and 2023 across 16 Italian centers belonging to the Fondazione Italiana Linfomi network. Median overall survival (OS) was 4.9 months, with 6- and 12-month OS rates of 44.9% and 28.5%, respectively. The PC-PI discriminated prognosis effectively: high-risk patients had a median OS of 1.8 months, intermediate-high 2.2, intermediate-low 8.7, while in the low-risk group median OS was not reached (p<0.0001). Results remained consistent after excluding patients receiving only palliative care. Post-progression therapy markedly influenced survival: patients receiving active treatment achieved a median OS of 7.3 months versus 0.7 without further therapy (p<0.0001). Bispecific antibodies conferred the best outcomes (HR 0.44, p=0.02), with 6- and 12-month OS rates of 90% and 55%. Our findings confirm the prognostic value of the PC-PI and support its use in clinical practice as a tool for risk-adapted management of LBCL after CAR-T failure.