Prognostic significance of tertiary lymphoid structures in gastric neuroendocrine carcinoma with association to delta-like ligand 3 and neuroendocrine expressions.

BACKGROUND: Gastric neuroendocrine carcinomas (NECs) are rare cancers with highly aggressive behavior. Although tertiary lymphoid structures (TLSs) are well-known prognostic factors in various cancers, their role in gastric NECs remain unexplored. Unique immunohistochemical subtypes of pulmonary NECs have been discovered, however, their feasibility in gastric NECs is unknown. METHODS: The presence and maturation of TLSs (lymphoid aggregates, primary and secondary follicles) were assessed in 48 surgically resected gastric NECs and were compared with immunohistochemical subtypes, using a panel of ASCL1, NeuroD1, POU2F3, YAP1, and DLL3 with three neuroendocrine (NE) markers. RESULTS: Patients with secondary follicles had significantly better overall survival (OS) and recurrence-free survival (RFS; both, p = 0.004) than those without them. Based on the hierarchical clustering, gastric NECs were classified into all low/negative (31%), high-YAP1 (19%), high-DLL3/low-NE (29%), and high-NE (21%) expression groups. The high-DLL3/low-NE group was associated with absent TLSs (p = 0.026) and showed the worst OS (p = 0.026). Distant metastasis and a lack of secondary follicles were poor independent prognostic factors of OS and RFS. CONCLUSION: The assessment of TLSs is a feasible and potent biomarker for gastric NECs, thus enabling better prognosis and more effective immunotherapy. Furthermore, gastric NECs can be categorized as four immunohistochemically distinct groups, of which the high-DLL3/low-NE group has the worst OS with lack of TLSs.

From molecular subgroups to molecular targeted therapy in rheumatoid arthritis: A bioinformatics approach.

1Background: Rheumatoid Arthritis (RA) is a heterogeneous autoimmune disease with multiple unidentified pathogenic factors. The inconsistency between molecular subgroups poses challenges for early diagnosis and personalized treatment strategies. In this study, we aimed to accurately distinguish RA patients at the transcriptome level using bioinformatics methods. 2Methods: We collected a total of 362 transcriptome datasets from RA patients in three independent samples from the GEO database. Consensus clustering was performed to identify molecular subgroups, and clinical features were assessed. Differential analysis was employed to annotate the biological functions of specifically upregulated genes between subgroups. 3Results: Based on consensus clustering of RA samples, we identified three robust molecular subgroups, with Subgroup III representing the high-risk subgroup and Subgroup II exhibiting a milder phenotype, possibly associated with relatively higher levels of autophagic ability. Subgroup I showed biological functions mainly related to viral infections, cellular metabolism, protein synthesis, and inflammatory responses. Subgroup II involved autophagy of mitochondria and organelles, protein localization, and organelle disassembly pathways, suggesting heterogeneity in the autophagy process of mitochondria that may play a protective role in inflammatory diseases. Subgroup III represented a high-risk subgroup with pathological processes including abnormal amyloid precursor protein activation, promotion of inflammatory response, and cell proliferation. 4Conclusion: The classification of the RA dataset revealed pathological heterogeneity among different subgroups, providing new insights and a basis for understanding the molecular mechanisms of RA, identifying potential therapeutic targets, and developing personalized treatment approaches.

Pediatric WNT medulloblastoma predisposition in intraoperative blood loss: a retrospective observational cohort study.

Introduction: Molecular subgroups influence the vascular architecture within medulloblastomas, particularly the wingless (WNT) subgroup, which contributes to its propensity for primary tumor hemorrhage. Whether this mechanism affects intraoperative blood loss remains unknown. This study aimed to assess the association between WNT medulloblastoma and the predisposition for blood loss. Methods: This was a retrospective observational study using data from a neuro-oncology center comprising molecular data on patients treated between December 31, 2014, and April 30, 2023. Differences between WNT and other subgroups in the risk of primary outcome-intraoperative blood loss were assessed using multivariable-adjusted linear regression. Results: Of the 148 patients included in the analysis, 18 patients (12.2%) had WNT, 42 (28.4%) had sonic hedgehog (SHH) TP53-wildtype, 7 (4.7%) had SHH TP53-mutant, and 81 (54.7%) were non-WNT/ non-SHH. The WNT subgroup more frequently underwent primary intratumoral hemorrhage (22% vs. 3.8%; p = 0.011). The median intraoperative blood loss was 400.00 (interquartile range [IQR] 250, 500) mL for WNT and 300.00 [200, 400] mL for the other subgroups (p = 0.136), with an adjusted ß of 135.264 (95% confidence intervals [CI], 11.701-258.827; p = 0.032). Similar results were observed in both midline and noninfiltrative margin medulloblastoma. Discussion: WNT medulloblastoma is typically associated with primary intratumoral hemorrhage and intraoperative blood loss. The validity of determining the surgical approach based on predicted molecular subtypes from imaging data is questionable. However, attempting to engage in risk communication with patients in a molecular-specific way is worthwhile to validate.

Neurosurgical short-term outcomes for pediatric medulloblastoma patients and molecular correlations: a 10-year single-center observation cohort study.

This study examined the risk factors for short-term outcomes, focusing particularly on the associations among molecular subgroups. The analysis focused on the data of pediatric patients with medulloblastoma between 2013 and 2023, as well as operative complications, length of stay from surgery to adjuvant treatment, 30-day unplanned reoperation, unplanned readmission, and mortality. 148 patients were included. Patients with the SHH TP53-wildtype exhibited a lower incidence of complications (45.2% vs. 66.0%, odds ratio [OR] 0.358, 95% confidence interval [CI] 0.160 - 0.802). Female sex (0.437, 0.207 - 0.919) was identified as an independent protective factor for complications, and brainstem involvement (1.900, 1.297 - 2.784) was identified as a risk factor. Surgical time was associated with an increased risk of complications (1.004, 1.001 - 1.008), duration of hospitalization (1.006, 1.003 - 1.010), and reoperation (1.003, 1.001 - 1.006). Age was found to be a predictor of improved outcomes, as each additional year was associated with a 14.1% decrease in the likelihood of experiencing a prolonged length of stay (0.859, 0.772 - 0.956). Patients without metastasis exhibited a reduced risk of reoperation (0.322, 0.133 - 0.784) and readmission (0.208, 0.074 - 0.581). There is a significant degree of variability in the occurrence of operative complications in pediatric patients with medulloblastoma. SHH TP53-wildtype medulloblastoma is commonly correlated with a decreased incidence of complications. The short-term outcomes of patients are influenced by various unmodifiable endogenous factors. These findings could enhance the knowledge of onconeurosurgeons and alleviate the challenges associated with patient/parent education through personalized risk communication. However, the importance of a dedicated center with expertise surgical team and experienced neurosurgeon in improving neurosurgical outcomes appears self-evident.

Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle / Clasificación basada en aprendizaje automático de pacientes con linfoma difuso de células B grandes mediante una firma de 7 ARNm enriquecida con infiltración inmune y ciclo celular

Background: Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment. Methods: Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312). Results: Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05). Conclusion: This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.(AU)

Development and validation of a 23-gene expression signature for molecular subtyping of medulloblastoma in a long-term Chinese cohort.

PURPOSE: Medulloblastoma is the most common childhood malignant brain tumor and is a leading cause of cancer-related death in children. Recent transcriptional studies have shown that medulloblastomas comprise at least four molecular subgroups, each with distinct demographics, genetics, and clinical outcomes. Medulloblastoma subtyping has become critical for subgroup-specific therapies. The use of gene expression assays to determine the molecular subgroup of clinical specimens is a long-awaited application of molecular biology for this pediatric cancer. METHODS: In the current study, we established a medulloblastoma transcriptome database of 460 samples retrieved from three published datasets (GSE21140, GSE37382, and GSE37418). With this database, we identified a 23-gene signature that is significantly associated with the medulloblastoma subgroups and achieved a classification accuracy of 95.2%. RESULTS: The 23-gene signature was further validated in a long-term cohort of 142 Chinese medulloblastoma patients. The 23-gene signature classified 21 patients as WNT (15%), 41 as SHH (29%), 16 as Group 3 (11%), and 64 as Group 4 (45%). For patients of WNT, SHH, Group 3, and Group 4, 5-year overall-survival rate reached 80%, 62%, 27%, and 47%, respectively (p < 0.0001), meanwhile 5-year progression-free survival reached 80%, 52%, 27%, and 45%, respectively (p < 0.0001). Besides, SHH/TP53-mutant tumors were associated with worse prognosis compared with SHH/TP53 wild-type tumors and other subgroups. We demonstrated that subgroup assignments by the 23-gene signature and Northcott's NanoString assay were highly comparable with a concordance rate of 96.4%. CONCLUSIONS: In conclusion, we present a novel gene signature that is capable of accurately and reliably assigning FFPE medulloblastoma samples to their molecular subgroup, which may serve as an auxiliary tool for medulloblastoma subtyping in the clinic. Future incorporation of this gene signature into prospective clinical trials is warranted to further evaluate its clinical.

Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment. METHODS: Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312). RESULTS: Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05). CONCLUSION: This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.

C11orf95-RELA, YAP1-MAMLD1, and YAP1-FAM118B Fusion Negative Anaplastic Ependymoma with Lipogenic Differentiation.

Lipogenic differentiation in ependymoma is an infrequent occurrence with very few reported cases. The grading was done solely based on the histomorphology and molecular subtyping was not described in such ependymomas. New molecular classification divided ependymomas in nine different subgroups, of which supratentorial location tumor usually exhibits C11orf95-RELA, YAP1-MAMLD1, and YAP1-FAM118B fusion proteins. A 46-year-old female presented with headache and right-sided parapresis. Radilogy revealed a large intraxial left parietooccipital mass lesion, which histologically and immuohistochemically confirmed as anaplastic ependymoma with extensive lipogenic changes. The ependymal origin of the tumor was corroborated by the immunohistochemistry and ultrastructural studies. Molecular studies for C11orf95-RELA, YAP1-MAMLD1, and YAP1-FAM118B fusion proteins were negative. This is the first documentation of fusion negative supratentorial anaplastic ependymoma with lipogenic differentiation. This novel finding needs further reinforcement by similar studies to identify its impact on the disease outcome.

Incidence and Risk Factors for Necessitating Cerebrospinal Fluid Diversion Following Medulloblastoma Surgery in Children.

BACKGROUND: There are insufficient data on pediatric patients with medulloblastoma who require cerebrospinal fluid (CSF) diversion following resection. Therefore, this study aimed to determine the incidence and the characteristics associated with it in this subset of patients. METHODS: We conducted a single-center, retrospective, observational cohort study of patients aged 18 years or less who underwent medulloblastoma resection at our department between 2010 and 2021. The primary outcome was the incidence of CSF diversion surgery required after resection. Participant demographics, tumor biology, and interventions were analyzed using univariate- and multivariate-adjusted models. RESULTS: Of the 183 patients admitted to our department, 131 (71.6%) participated in this study. The incidence of permanent CSF diversion was 26.0% (95% confidence interval [CI]: 18.7 to 34.3). Factors independently associated with requirement of permanent CSF diversion were medulloblastoma volume >46.4 cm3 (odds ratio [OR]: 2.919, 95% CI: 1.191 to 7.156) and CSF channel invasion (OR: 2.849, 95% CI: 1.142 to 7.102). The duration of manifestation may be a covariate of tumor volume with increased risk of requirement for permanent CSF diversion (OR: 1.006, 95% CI: 1.000 to 1.013), and tumor volume may be a predictor in patients who underwent subtotal resection (OR: 4.900, 95% CI: 0.992 to 24.208, P = 0.05). Finally, patients who required permanent CSF diversion were divided according to medulloblastoma molecular subgroups, and no significant differences were found. CONCLUSION: We report major predictive factors for permanent CSF diversion surgery in patients with medulloblastoma. Our study suggests that the presence of postresection hydrocephalus is not high enough to warrant permanent, prophylactic CSF diversion in all patients.

Clinical, Histological, and Molecular Prognostic Factors in Childhood Medulloblastoma: Where Do We Stand?

Medulloblastomas, highly aggressive neoplasms of the central nervous system (CNS) that present significant heterogeneity in clinical presentation, disease course, and treatment outcomes, are common in childhood. Moreover, patients who survive may be diagnosed with subsequent malignancies during their life or could develop treatment-related medical conditions. Genetic and transcriptomic studies have classified MBs into four subgroups: wingless type (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4, with distinct histological and molecular profiles. However, recent molecular findings resulted in the WHO updating their guidelines and stratifying medulloblastomas into further molecular subgroups, changing the clinical stratification and treatment management. In this review, we discuss most of the histological, clinical, and molecular prognostic factors, as well the feasibility of their application, for better characterization, prognostication, and treatment of medulloblastomas.

Medulloblastoma: Current Perspectives and Recent Advances.

Medulloblastoma is the most common embryonal tumor of the central nervous system in childhood. Combined multimodality approaches, including surgery, radiation, and chemotherapy, have improved the outcome of medulloblastoma. Advances in genomic research have shown that medulloblastoma is not a biologically or clinically discrete entity. Previously, the risk was divided according to histology, presence of metastasis, degree of resection, and age at diagnosis. Through the development of integrated genomics, new biology-based risk stratification methods have recently been proposed. It is also important to understand the genetic predisposition of patients with medulloblastoma. Therefore, treatment goal aimed to improve the survival rate with minimal additional adverse effects and reduced long-term sequelae. It is necessary to incorporate genetic findings into the standard of care, and clinical trials that reflect this need to be conducted.

Radiomics signature for the prediction of progression-free survival and radiotherapeutic benefits in pediatric medulloblastoma.

PURPOSE: To develop and validate a radiomics signature for progression-free survival (PFS) and radiotherapeutic benefits in pediatric medulloblastoma. MATERIALS AND METHODS: We retrospectively enrolled 253 consecutive children with medulloblastoma from two hospitals. A total of 1294 radiomic features were extracted from the region of tumor on the T1-weighted and contrast-enhanced T1-weighted (CE-T1w) MRI. Radiomic feature selection and machine learning modelling were performed to build radiomics signature for the prediction of PFS on the training set. Moreover, the prognostic performance of the clinical parameters was investigated for PFS. The Concordance index (a value of 0.5 indicates no predictive discrimination, and a value of 1 indicates perfect predictive discrimination) was used to measure and compare the prognostic performance of these models. RESULTS: The radiomics signature for the prediction of the PFS yielded Concordance indices of 0.711, 0.707, and 0.717 on the training and held-out test sets 1 and 2, respectively. The radiomics nomogram integrating the radiomics signature, age, and metastasis performed better than the nomogram incorporating only clinicopathological factors (C-index, 0.723 vs. 0.665 and 0.722 vs. 0.677 on the held-out test sets 1 and 2, respectively), which was also validated by the good calibration and decision curve analysis. Further analysis demonstrated that patients with lower value of radiomics signature were associated with better clinical outcomes after postoperative radiotherapy (p < 0.001). CONCLUSION: The radiomics signature and nomogram performed well for the prediction of PFS and could stratify patients underwent postoperative radiotherapy into the high- and low-risk groups with significantly different clinical outcomes.

Treatment and outcome of pediatric intracranial ependymoma after first relapse.

INTRODUCTION: Almost 50% of children with intracranial ependymoma experience disease relapse, and their outcomes are extremely poor. The aim of this study was to investigate optimal salvage treatment for pediatric intracranial ependymoma after the first relapse and to identify prognostic factors affecting survival. METHODS: We conducted a retrospective analysis of 159 children who underwent initial treatment for intracranial ependymoma at Beijing Tiantan Hospital from 2013 to 2017. RESULTS: Relapse was observed in 73 patients (73/159; 45.9%), with a median age of 7.2 ± 3.5 years old. Molecular subgrouping analysis identified H3K27me3-negative PF-EPNs in 74% of patients, ST-RELA EPNs in 21% of patients, and H3K27me3-positive PF-EPNs in 5% of patients. The 5-year event-free survival (EFS) and overall survival (OS) rates after first relapse were 21.1% (95% CI 16.0-26.2) and 30.5% (95% CI 19.8-30.8), respectively. Patients with GTR at first relapse had higher 5-year EFS and 5-year OS than those with STR (P = 0.031 and P = 0.003) or no surgery (P = 0.007 and P = 0.001). Radiotherapy or re-radiotherapy at first relapse significantly prolonged 5-year EFS and OS (both P < 0.001). Patients with H3K27me3-negative PF-EPN had worse 5-year EFS and OS than those with ST-RELA EPN (P = 0.001 and P = 0.002). Multivariate analysis showed that both tumor resection and radiotherapy at first relapse had independent prognostic significance for survival (all P < 0.05). CONCLUSION: Children with recurrent intracranial EPN have poor outcomes, and surgery and radiotherapy at first relapse should be encouraged to improve their prognosis.

Risk Stratification of Childhood Medulloblastoma Using Integrated Diagnosis: Discrepancies with Clinical Risk Stratification.

BACKGROUND: Recent genomic studies identified four discrete molecular subgroups of medulloblastoma (MB), and the risk stratification of childhood MB in the context of subgroups was refined in 2015. In this study, we investigated the effect of molecular subgroups on the risk stratification of childhood MB. METHODS: The nCounter® system and a customized cancer panel were used for molecular subgrouping and risk stratification in archived tissues. RESULTS: A total of 44 patients were included in this study. In clinical risk stratification, based on the presence of residual tumor/metastasis and histological findings, 24 and 20 patients were classified into the average-risk and high-risk groups, respectively. Molecular subgroups were successfully defined in 37 patients using limited gene expression analysis, and DNA panel sequencing additionally classified the molecular subgroups in three patients. Collectively, 40 patients were classified into molecular subgroups as follows: WNT (n = 7), SHH (n = 4), Group 3 (n = 8), and Group 4 (n = 21). Excluding the four patients whose molecular subgroups could not be determined, among the 17 average-risk group patients in clinical risk stratification, one patient in the SHH group with the TP53 variant was reclassified as very-high-risk using the new risk classification system. In addition, 5 out of 23 patients who were initially classified as high-risk group in clinical risk stratification were reclassified into the low- or standard-risk groups in the new risk classification system. CONCLUSION: The new risk stratification incorporating integrated diagnosis showed some discrepancies with clinical risk stratification. Risk stratification based on precise molecular subgrouping is needed for the tailored treatment of MB patients.

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles.

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (P < .0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (P = .002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.

The tumor microenvironment and immune responses in prostate cancer patients.

The landscape of cancer treatment has been transformed over the past decade by the success of immune-targeting therapies. However, despite sipuleucel-T being the first-ever approved vaccine for cancer and the first immunotherapy licensed for prostate cancer in 2010, immunotherapy has since seen limited success in the treatment of prostate cancer. The tumour microenvironment of prostate cancer presents particular barriers for immunotherapy. Moreover, prostate cancer is distinguished by being one of only two solid tumours where increased T cell-infiltration correlates with a poorer, rather than improved, outlook. Here, we discuss the specific aspects of the prostate cancer microenvironment that converge to create a challenging microenvironment, including myeloid-derived immune cells and cancer-associated fibroblasts. By exploring the immune microenvironment of defined molecular subgroups of prostate cancer, we propose an immunogenomic subtyping approach to single-agent and combination immune-targeting strategies that could lead to improved outcomes in prostate cancer treatment.

Diffusion Tensor Imaging of Brain Metastases in Patients with Breast Cancer According to Molecular Subtypes.

BACKGROUND AND PURPOSE: Recent studies have shown that diffusion tensor imaging (DTI) parameters are used to follow the patients with breast cancer and correlate well as a prognostic parameter of breast cancer. However, as far as we know, there is no data to compare the DTI features of breast cancer brain metastases according to molecular subtypes in the literature. Our aim is to evaluate whether there are any differences in DTI parameters of brain metastases in patients with breast cancer according to molecular subtypes. METHODS: Twenty-seven patients with breast cancer and 82 metastatic brain lesions were included. We classified subjects into three subgroups according to their hormone expression; Group 0, triple- negative (n; 6, 19 lesions), group 1, HER2-positive (n;16, 54 lesions) and group 2, hormone-- positive group (n; 5, 9 lesions). The apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) values in DTI were measured and compared between three groups. RESULTS: ADC, AD and RD values of group 2 were significantly lower compared to group 0. No significant differences were found in FA, ADC, AD and RD values between the group 0 and 1 and the group 1 and 2. CONCLUSION: Metastasis of aggressive triple-negative breast cancer showed higher ADC values compared to the less aggressive hormone-positive group. Higher ADC values in brain metastases of breast cancer may indicate a poor prognosis, so DTI findings could play a role in planning appropriate treatment.

Multi-omics analysis based on integrated genomics, epigenomics and transcriptomics in pancreatic cancer.

Aim: Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. Materials & methods: The DNA copy number variation, the simple nucleotide variation, methylation and mRNA data of pancreatic cancer patients were obtained from The Cancer Genome Atlas. Four molecular subgroups (iC1, iC2, iC3 and iC4) of pancreatic cancer were identified by integrating analysis. Results: The iC1 subgroup harbors better prognosis, higher immune score, lesser DNA copy number variation mutations and better genomic stability compared with iC2, iC3 and iC4 subgroups. Three new genes (GRAP2, ICAM3 and A2ML1) correlated with prognosis were identified. Conclusion: Integrated multi-omics analysis provides fresh insight into molecular classification of pancreatic cancer, which may help discover new prognostic biomarkers and reveal the underlying mechanism of pancreatic cancer.

Characterization of global 5-hydroxymethylcytosine in pediatric posterior fossa ependymoma.

BACKGROUND: 5-Hydroxymethylcytosine (5hmC) is a novel epigenetic mark and may be involved in the mechanisms of tumorigenesis and malignant transformation. However, the role of 5hmC in ependymoma, the third most common brain tumor in children, remains unclear. The aim of this study sought to identify the characterization of 5hmC levels in pediatric posterior fossa ependymoma and to evaluate whether 5hmC levels could be a potential factor to predict clinical outcomes. RESULTS: Our results showed that 5hmC levels were globally decreased in posterior fossa ependymoma compared with normal cerebellum tissues (P < 0.001). Group A posterior fossa ependymomas had higher 5hmC levels than group B tumors (P = 0.007). Moreover, 5hmC levels positively correlated with Ki-67 index in posterior fossa ependymoma (r = 0.428, P = 0.003). Multivariate Cox hazards model revealed that patients with high 5hmC levels (> 0.102%) had worse PFS and OS than patients with lower 5hmC levels (< 0.102%) (PFS: HR = 3.014; 95% CI, 1.040-8.738; P = 0.042; OS: HR = 2.788; 95% CI, 0.974-7.982; P = 0.047). CONCLUSIONS: Our findings suggest that loss of 5hmC is an epigenetic hallmark for pediatric posterior fossa ependymoma. 5hmC levels may represent a potential biomarker to predict prognosis in children with posterior fossa ependymoma.

PRPS-ST: A protocol-agnostic self-training method for gene expression-based classification of blood cancers.

Gene expression classifiers are gaining increasing popularity for stratifying tumors into subgroups with distinct biological features. A fundamental limitation shared by current classifiers is the requirement for comparable training and testing data sets. Here, we describe a self-training implementation of our probability ratio-based classification prediction score method (PRPS-ST), which facilitates the porting of existing classification models to other gene expression data sets. In comparison to gold standards, we demonstrate favorable performance of PRPS-ST in gene expression-based classification of DLBCL and B-ALL using a diverse variety of gene expression data types and pre-processing methods, including in classifications with a high degree of class imbalance. Tumors classified by our method were significantly enriched for prototypical genetic features of their respective subgroups. Interestingly, this included cases that were unclassifiable by established methods, implying the potential enhanced sensitivity of PRPS-ST.