Molecular Classification of Lymph Node Metastases Subtypes Predict for Survival in Head and Neck Cancer
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PROVIDER: EGAS00001003233 | EGA |
REPOSITORIES: EGA
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Project description:PURPOSE:In advanced stage head and neck squamous cell cancers (HNSCC), approximately half of the patients with lymph node metastases (LNM) are not cured. Given the heterogeneous outcomes in these patients, we profiled the expression patterns of LNMs to identify the biological factors associated with patient outcomes.Experimental Design: We performed mRNAseq and miRNAseq on 72 LNMs and 29 matched primary tumors from 34 patients with HNSCC. Clustering identified molecular subtypes in LNMs and in primary tumors. Prediction Analysis of Microarrays algorithm identified a 73-gene classifier that distinguished LNM subtypes. Gene-set enrichment analysis identified pathways upregulated in LNM subtypes. RESULTS:Integrative clustering identified three distinct LNM subtypes: (i) an immune subtype (Group 1), (ii) an invasive subtype (Group 2), and (iii) a metabolic/proliferative subtype (Group 3). Group 2 subtype was associated with significantly worse locoregional control and survival. LNM-specific subtypes were not observed in matched primary tumor specimens. In HNSCCs, breast cancers, and melanomas, a 73-gene classifier identified similar Group 2 LNM subtypes that were associated with worse disease control and survival only when applied to lymph node sites, but not when applied to other primary tumors or metastatic sites. Similarly, previously proposed prognostic classifiers better distinguished patients with worse outcomes when applied to the transcriptional profiles of LNMs, but not the profiles of primary tumors. CONCLUSIONS:The transcriptional profiles of LNMs better predict outcomes than transcriptional profiles of primary tumors. The LNMs display site-specific subtypes associated with worse disease control and survival across multiple cancer types.
| S-EPMC6420850 | biostudies-literature
Project description:In head and neck squamous cell carcinoma (HNSCC) pathologic cervical lymph nodes (LN) remain important negative predictors. Current criteria for LN-classification in contrast-enhanced computed-tomography scans (contrast-CT) are shape-based; contrast-CT imagery allows extraction of additional quantitative data ("features"). The data-driven technique to extract, process, and analyze features from contrast-CTs is termed "radiomics". Extracted features from contrast-CTs at various levels are typically redundant and correlated. Current sets of features for LN-classification are too complex for clinical application. Effective eliminative feature selection (EFS) is a crucial preprocessing step to reduce the complexity of sets identified. We aimed at exploring EFS-algorithms for their potential to identify sets of features, which were as small as feasible and yet retained as much accuracy as possible for LN-classification. In this retrospective cohort-study, which adhered to the STROBE guidelines, in total 252 LNs were classified as "non-pathologic" (n = 70), "pathologic" (n = 182) or "pathologic with extracapsular spread" (n = 52) by two experienced head-and-neck radiologists based on established criteria which served as a reference. The combination of sparse discriminant analysis and genetic optimization retained up to 90% of the classification accuracy with only 10% of the original numbers of features. From a clinical perspective, the selected features appeared plausible and potentially capable of correctly classifying LNs. Both the identified EFS-algorithm and the identified features need further exploration to assess their potential to prospectively classify LNs in HNSCC.
| S-EPMC8833684 | biostudies-literature
Project description:ObjectivesTo evaluate the relationship between deep inguinal lymph node metastasis (ILNM) and pelvic lymph node metastasis (PLNM) and explore the prognostic value of deep ILNM in penile squamous cell carcinoma (PSCC).Materials and methodsThe records of 189 patients with ILNM treated for PSCC were analysed retrospectively. Logistic regression models were used to test for predictors of PLNM. Cox regression was performed in univariable and multivariable analyses of cancer-specific survival (CSS). CSS was compared using Kaplan-Meier analyses and log rank tests.ResultsPLNM were observed in 53 cases (28.0%). According to logistic regression models, only deep ILNM (OR 9.72, p<0.001) and number (≥3) of metastatic inguinal lymph nodes (ILNs) (OR 2.36, p=0.03) were independent predictors of PLNM. The incidences of PLNM were 18% and 19% with negative deep ILNM and extranodal extension (ENE); and 76% and 42% with positive deep ILNM and ENE, respectively. The accuracy of deep ILNM, ENE, bilateral involvement and number (≥3) of ILNMs for predicting PLNM were 81.0%, 65.6%, 63.5% and 67.2%, respectively. The CSS was significantly different in patients with positive and negative deep ILNM (median 1.7 years vs not reached, p<0.01). Patients who presented with deep ILNM had worse CSS (median 3.8 years vs not reached, p<0.01) in those with negative PLNs.ConclusionsDeep ILNM is the most accurate factor for predicting PLNM in PSCC according to our data. We recommend that patients with deep ILNM should be referred for pelvic lymph node dissection. Involvement of deep ILNs indicates poor prognosis. We propose that patients with metastases of deep ILNs may be staged as pN3.
| S-EPMC8479104 | biostudies-literature
Project description:NBTXR3 nanoparticle injection is a relatively novel radioenhancer for treatment of various cancers. CT scans following NBTXR3 injection of metastatic lymph nodes from head and neck squamous cell carcinoma were reviewed in a small series of patients. The radioenhancing appears as hyperattenuating, with a mean attenuation of the injected material of 1516 HU. The material was found to leak beyond the margins of the tumor in some cases.
| S-EPMC9498208 | biostudies-literature
Project description:BackgroundWe aimed to evaluate multiparametric ultrasound, to achieve a better understanding of the baseline characteristics of suspected cervical lymph node metastases in head and neck cancer before induction chemotherapy or chemoradiation.MethodsFrom February 2020 to April 2021, our complete ultrasound examination protocol was carried out on clinically evident malignant lymph nodes of histologically proven HNSCC in the pre-therapeutic setting.ResultsA total of 13 patients were eligible for analysis. Using elastography, irregular clear hardening in areas in the center of the lymph node could be detected in all cases. Elastographic Q-analysis showed a significantly softer cortex compared to the center and surrounding tissue. The time-intensity curve analysis showed high values for the area under the curve and a short time-to-peak (fast wash-in) in all cases compared to the surrounding tissue. A parametric evaluation of contrast enhanced the ultrasound in the early arterial phase and showed an irregular enhancement from the margin in almost all investigated lymph nodes. These results show that the implementation of comprehensive, multiparametric ultrasound is suitable for classifying suspected lymph node metastasis more precisely than conventional ultrasound alone in the pre-therapeutic setting of HNSCC. Thus, these parameters may be used for improvements in the re-staging after chemoradiation or neoadjuvant therapy monitoring, respectively.
| S-EPMC9406677 | biostudies-literature
Project description:ImportanceSentinel lymph node biopsy (SLNB) provides prognostic information for melanoma; however, a survival benefit has not been demonstrated.ObjectiveTo assess the association of SLNB with survival for melanoma arising in head and neck subsites (HNM).Design, setting, and participantsPropensity score-matched retrospective cohort study using the Surveillance Epidemiology and End Results (SEER) database to compare US patients with HNM meeting current recommendations for SLNB, treated from 2004 to 2011 with either (1) SLNB with or without neck dissection, or (2) no SLNB or neck dissection.InterventionsSLNB with or without neck dissection.Main outcomes and measuresDisease-specific survival (DSS) estimates based on the Kaplan-Meier method, and Cox proportional hazards modeling to compare survival outcomes between matched pair cohorts.ResultsA total of 7266 patients with HNM meeting study criteria were identified from the SEER database. Matching of treatment cohorts was performed using propensity scores modeled on 10 covariates known to be associated with SLNB treatment or melanoma survival. Cohorts were stratified by tumor thickness (thin, >0.75-1.00 mm Breslow thickness; intermediate, >1.00-4.00 mm; and thick, >4.00 mm) and exactly matched within 5 age categories. In the intermediate-thickness cohort, 2808 patients with HNM were matched and balanced by propensity score for SLNB treatment; the 5-year DSS estimate for those treated by SLNB was 89% vs 88% for nodal observation (log-rank P = .30). The hazard ratio for melanoma-specific death was 0.87 for those undergoing SLNB (95% CI, 0.66-1.14; P = .31). In each of the other cohorts analyzed, including those with thin and thick melanomas, and cohorts with melanoma overall, no significant difference in DSS was demonstrated.Conclusions and relevanceThis SEER cohort analysis demonstrates no significant association between SLNB and improved disease-specific survival for patients with HNM.
| S-EPMC4294459 | biostudies-literature
Project description:Melanoma associated antigens (MAGE) are potential targets for immunotherapy and have been associated with poor overall survival (OS) in head and neck squamous cell carcinoma (HNSCC). However, little is known about MAGE in lymph node metastases (LNM) and recurrent disease (RD) of HNSCC.To assess whether MAGE expression increases with metastasis or recurrence, a tissue microarray (TMA) of 552 primary tumors (PT), 219 LNM and 75 RD was evaluated by immunohistochemistry for MAGE antigens using three monoclonal antibodies to multiple MAGE family members. Mean expression intensity (MEI) was obtained from triplicates of each tumor specimen.The median MEI compared between PT, LNM and RD was significantly higher in LNM and RD. In paired samples, MEI was comparable in PT to respective LNM, but significantly different from RD. Up to 25% of patients were negative for pan-MAGE or MAGE-A3/A4 in PT, but positive in RD. The prognostic impact of MAGE expression was validated in the TMA cohort and also in TCGA data (mRNA). OS was significantly lower for patients expressing pan-MAGE or MAGE-A3/A4 in both independent cohorts.MAGE expression was confirmed as a prognostic marker in HNSCC and may be important for immunotherapeutic strategies as a shared antigen.
| S-EPMC5362438 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-03-01 | E-GEOD-39368 | biostudies-arrayexpress
Project description:CONTEXT:Most papillary microcarcinomas (PMCs) are indolent and subclinical. However, as many as 10% can present with clinically significant nodal metastases. OBJECTIVE AND DESIGN:Characterization of the genomic and transcriptomic landscape of PMCs presenting with or without clinically important lymph node metastases. SUBJECTS AND SAMPLES:Formalin-fixed paraffin-embedded PMC samples from 40 patients with lateral neck nodal metastases (pN1b) and 71 patients with PMC with documented absence of nodal disease (pN0). OUTCOME MEASURES:To interrogate DNA alterations in 410 genes commonly mutated in cancer and test for differential gene expression using a custom NanoString panel of 248 genes selected primarily based on their association with tumor size and nodal disease in the papillary thyroid cancer TCGA project. RESULTS:The genomic landscapes of PMC with or without pN1b were similar. Mutations in TERT promoter (3%) and TP53 (1%) were exclusive to N1b cases. Transcriptomic analysis revealed differential expression of 43 genes in PMCs with pN1b compared with pN0. A random forest machine learning-based molecular classifier developed to predict regional lymph node metastasis demonstrated a negative predictive value of 0.98 and a positive predictive value of 0.72 at a prevalence of 10% pN1b disease. CONCLUSIONS:The genomic landscape of tumors with pN1b and pN0 disease was similar, whereas 43 genes selected primarily by mining the TCGA RNAseq data were differentially expressed. This bioinformatics-driven approach to the development of a custom transcriptomic assay provides a basis for a molecular classifier for pN1b risk stratification in PMC.
| S-EPMC6733494 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2013-03-01 | GSE39368 | GEO