Enhanced clinical outcomes with radiotherapy in diagnostically challenging intracranial plasmacytomas: Analysis of 190 cases.

BACKGROUND: Intracranial plasmacytomas are rare tumors arising from plasma cells with approximately half of the cases progressing to multiple myeloma (MM). However, there is a lack of comprehensive clinical cohort analysis on the clinical and pathological features, progression, and outcomes of intracranial plasmacytomas. METHODS: A retrospective analysis of 190 cases was conducted, combining data from 38 cases in a single institution and 152 cases from the literature. Patient demographics, clinical presentations, tumor locations, imaging features, surgical treatments, and follow-up outcomes were collected and analyzed. Survival analysis and Cox regression analysis were performed to identify prognostic factors. RESULTS: A total of 190 intracranial plasmacytoma patients with an average age of 55.4 years were included in the study. The preoperative misdiagnosis ratio was high at 55.3%, and 59.7% of the tumors affected the calvaria convexity, compared to 40.3% located at the skull base. Resection and biopsy were achieved in 72.4% and 27.6% patients, respectively. Among them, 34.2% (65/190) of patients were initially diagnosed with MM with intracranial plasmacytoma as their first presentation (MM-IPFP), while 63.2% (120/190) of patients were diagnosed with solitary intracranial plasmacytoma (SIP), including 61 extramedullary plasmacytomas and 59 solitary bone plasmacytomas. In the SIP group, 22.4% (24/107) of patients experienced disease progression leading to the development of MM during a median follow-up time of 42.6 months (range 1-230 months). Multivariate analysis unveiled that radiotherapy (HR, 0.05; 95% CI, 0.00-0.87; p = 0.04), not surgery, was a protective prognostic factor for overall survival in MM-IPFP patients. Comparison between the SIP progression group and non-progression group revealed a significant difference of Ki-67 index (non-progression vs. SIP progression, 8.82% ± 7.03 vs. 16.5% ± 10.5, p < 0.05). AUC analysis determined that a cutoff value of 9.0% was the best predictor of SIP progression, with an area under the curve of 0.712. CONCLUSIONS: This retrospective clinical analysis highlights the potential role of radiotherapy, rather than surgical resection, in improving the outcomes of intracranial plasmacytoma. Additionally, the Ki-67 index is identified as a valuable marker for predicting disease progression. This would provide some evidence for the paradigm of diagnosis and treatment modalities for intracranial plasmacytomas from the large cohort.

Multiscale Transcriptomic Integration Reveals B-Cell Depletion and T-Cell Mistrafficking in Nasopharyngeal Carcinoma Progression.

Nasopharyngeal carcinoma (NPC), featured by Epstein-Barr virus (EBV) infection and regional epidemiology, is curable when detected early, but highly lethal at an advanced stage. The molecular mechanism of NPC progression toward a clinically uncontrollable stage remains elusive. In this study, we developed a novel computational framework to conduct multiscale transcriptomic analysis during NPC progression. The framework consists of four modules enabling transcriptomic analyses spanning from single-cell, bulk, microenvironment, to cohort scales. The bulk-transcriptomic analysis of 133 NPC or normal samples unraveled leading functional enrichments of cell-cycle acceleration, epithelial-mesenchymal transition, and chemokine-modulated inflammatory response during NPC progression. The chemokine CXCL10 in the NPC microenvironment, discovered by single-cell RNA sequencing data analysis, recruits cytotoxic T cells through interacting with its receptor CXCR3 at early but late stages. This T-cell mistrafficking was featured by the decline of cytotoxic T cells and the increase of regulatory T cells, accompanied with B-cell depletion confirmed by immunohistochemistry staining. The featured immunomodulatory chemokines were commonly upregulated in the majority of cancers associated with viral or bacterial infections.

Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors.

Metastatic cancer is associated with poor patient prognosis but its spatiotemporal behavior remains unpredictable at early stage. Here we develop MetaNet, a computational framework that integrates clinical and sequencing data from 32,176 primary and metastatic cancer cases, to assess metastatic risks of primary tumors. MetaNet achieves high accuracy in distinguishing the metastasis from the primary in breast and prostate cancers. From the prediction, we identify Metastasis-Featuring Primary (MFP) tumors, a subset of primary tumors with genomic features enriched in metastasis and demonstrate their higher metastatic risk and shorter disease-free survival. In addition, we identify genomic alterations associated with organ-specific metastases and employ them to stratify patients into various risk groups with propensities toward different metastatic organs. This organotropic stratification method achieves better prognostic value than the standard histological grading system in prostate cancer, especially in the identification of Bone-MFP and Liver-MFP subtypes, with potential in informing organ-specific examinations in follow-ups.