Biliary tract cancers (BTCs) are aggressive malignancies with limited treatment options. Despite advances with durvalumab plus chemotherapy, prognosis remains poor. The tumor immune microenvironment (TIME) is increasingly recognized as a determinant of immunotherapy response, yet its spatial organization in BTC is poorly understood. We applied multiplex immunofluorescence to pre-treatment tumor samples from 11 patients with advanced BTC, staining for CD4, CD8, CD20, and CD163 to identify T cells, B cells, and macrophages. Across 198 regions of interest sampled at the invasive tumor margin, approximately 400,000 individual cells were segmented and phenotyped. Spatial organization was analyzed using hierarchical clustering, distance-based approaches, and cellular neighborhood classification. Unsupervised analysis identified two distinct immune phenotypes, one enriched in CD4+ T cells and CD163+ macrophages and the other in CD8+ T cells and CD20+ B cells. These phenotypes displayed distinct spatial architectures, with CD4+ T cells and CD20+ B cells acting as organizing hubs, respectively. Targeted sequencing, available for a subset of cases, showed a higher frequency of co-occurring TP53 and ARID1A alterations in the B-cell-enriched phenotype. No statistically significant differences in overall or progression-free survival were observed between immune phenotypes. However, among patients treated with adjuvant cisplatin/gemcitabine alone, lower CD20/CD8 fractions and higher CD163 abundance at baseline were associated with improved survival. Spatial immune profiling revealed distinct BTC immune architectures at the invasive margin, with different possible impact on survival, thus warranting further characterization and validation in larger cohorts.
This brief research report summarizes our experience with the molecular differential diagnosis of clinically or pathologically suspected partial hydatidiform moles and discusses practical diagnostic options when economic resources or laboratory infrastructure are limited. We analyzed the genome composition of 68 suspected cases using short tandem repeat genotyping based on quantitative fluorescent PCR and fragmentation analysis. Genetic testing excluded a partial mole in 35 cases (51%) by demonstrating a diploid or monogynic monoandric diploid genome, while in 33 cases (49%) a partial mole was confirmed through identification of diandric triploidy (31 cases) or triandric tetraploidy (2 cases). No instances of digynic triploidy were detected in this cohort. Our findings indicate that, in the absence of access to a DNA laboratory, most suspected partial moles can still be accurately evaluated using ploidy assessment by more widely available methods, such as fluorescent in situ hybridization, DNA flow cytometry, or conventional karyotyping, given the apparent rarity of digynic triploidy among these cases.
Colorectal cancer (CRC) is a significant health issue worldwide, but represents biologically heterogeneous disease, necessitating improved characterization of molecular and immunophenotypic subgroups. In this retrospective study, we evaluated the prognostic significance and clinicopathological associations of p53 and HER2 expression in 290 surgically resected CRCs. Tissue microarrays from archival formalin-fixed paraffin-embedded samples were analyzed by immunohistochemistry. p53 expression was categorized into wild-type, overexpression, and null patterns, while HER2 was scored according to College of American Pathologists criteria used for gastric carcinoma. Survival analyses were performed using Kaplan-Meier estimates, univariable and multivariable Cox regression, and restricted mean survival time, and immunohistochemical results were correlated with established clinicopathological and molecular features. Neither aberrant p53 expression nor HER2 positivity had a significant impact on 10-year overall survival. The p53 null phenotype showed a significantly increased relative risk of death within 5 years and a significant adverse effect in multivariable Cox regression of 5-year survival adjusted for age. Aberrant p53 expression was significantly associated with left-sided tumor location, mismatch repair proficiency, CK20 positivity, and MUC6 negativity, consistent with the chromosomal instability pathway of CRC. HER2 expression was infrequent and showed no prognostic relevance; however, HER2-positive tumors were significantly associated with CK7 expression, supporting an "extraintestinal" immunophenotypic profile reminiscent of right-sided or non-intestinal differentiation. In conclusion, while p53 and HER2 expression lacked independent prognostic value, their distinct associations with tumor phenotype underscore biologically relevant CRC subgroups.
Background/ObjectivesThe COVID-19 pandemic influenced cancer care worldwide, delaying diagnosis and treatment. We compared waiting times — time to initial diagnostic imaging, time to treatment, time to histopathological diagnosis, and length of hospital stay — of kidney cancer patients between pre-COVID-19 and COVID-19 periods at a large regional Hungarian clinical center. We also aimed to identify factors predicting prolonged waiting times.MethodsData from 400 adult kidney cancer patients (all histologically renal cell carcinoma) at the University of Pécs Urology Clinic were analyzed retrospectively, for two periods (1 January 2019–15 March 2020, pre-pandemic; 16 March 2020–13 May 2021, pandemic). Demographic and clinical characteristics were collected, and time intervals calculated from electronic health records, followed by statistical analyses.ResultsMedian time from symptom onset to initial diagnostic imaging increased significantly from 7.5 to 34 days during the pandemic (p = 0.026), while time to treatment (p = 0.492), time to histopathological diagnosis (p = 0.575), and length of hospital stay (p = 0.319) remained stable. Median healthcare-related waiting times (time to treatment and time to histopathological diagnosis) were comparatively long (range: 95.5–111 days). Advanced-stage disease (III–IV) was protective for prolonged time to initial diagnostic imaging (OR 0.205, 95% CI 0.074–0.568) pre-pandemic and for prolonged time to histopathological diagnosis (OR 0.496, 95% CI 0.254–0.971) during the pandemic. No other demographic or clinical factors influenced waiting times significantly.ConclusionThe pandemic prolonged the interval from symptom onset to initial diagnostic testing, likely due to fear-driven healthcare avoidance by patients, but did not affect healthcare-related waiting times. Advanced-stage disease predicted shorter waiting times, with variable influence across periods. Our findings highlight the need for patient education and careful prioritization of care, with waiting times exceeding those reported in other international settings.
BackgroundPrediction of pathologic features and outcome in patients with rectal cancer is challenging as a result of lack of a significant biomarker and heterogeneity between and within tumors. This study aims to evaluate the potential of Magnetic Resonance Imaging (MRI)-based radiomics in predicting key pathological features and long-term survival outcomes in patients.MethodsA retrospective study was conducted on 510 rectal cancer patients treated between 2015 and 2019. The inclusion criteria required pre-therapeutic MRI performed on a Discovery MR750W 3.0T machine and known KRAS mutation status. Forty-seven patients met the criteria. MRI sequences included T1-weighted, T2-weighted fat-saturated (T2FS), high-resolution T2-weighted (T2HR), and diffusion-weighted imaging (DWI). Radiomic features were extracted using PyRadiomics, and machine learning models were developed using XGBoost and LightGBM classifiers. Feature selection was performed using Sequential Feature Selector (SFS) and Minimum Redundancy Feature Selection (mRMR).ResultsThe model for KRAS mutation status achieved an Area Under the ROC curve (AUC) of 0.7475 (training) and 0.75 (testing). Lymph node invasion prediction had an AUC of 0.7892 (training) and 0.7984 (testing). Vascular invasion prediction yielded an AUC of 0.6989 (training) and 0.7143 (testing). The 5-year survival prediction model showed an AUC of 0.7848 (training) and 0.7750 (testing). Metastasis prediction achieved an AUC of 0.6627 (training) and 0.6857 (testing).ConclusionMRI-based radiomics demonstrates significant potential in predicting key pathological features and long-term survival outcomes in rectal cancer patients. Integrating multimodal imaging data and clinical information, along with automated segmentation techniques, could further enhance model accuracy and clinical utility.
