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Head and neck cancer subtypes with biological and clinical relevance: Meta-analysis of gene-expression data.

De Cecco L, Nicolau M, Giannoccaro M, Daidone MG, Bossi P, Locati L, Licitra L, Canevari S - Oncotarget (2015)

Bottom Line: Based on their main biological characteristics and de-regulated signaling pathways, the subtypes were designed as immunoreactive, inflammatory, human papilloma virus (HPV)-like, classical, hypoxia associated, and mesenchymal.Our findings highlighted a more aggressive behavior for mesenchymal and hypoxia-associated subtypes.The Genomics Drug Sensitivity Project was used to identify potential associations with drug sensitivity and significant differences were observed among the six subtypes.

View Article: PubMed Central - PubMed

Affiliation: Functional Genomics and Bioinformatics, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

ABSTRACT
Head and neck squamous cell carcinoma (HNSCC) is a disease with heterogeneous clinical behavior and response to therapies. Despite the introduction of multimodality treatment, 40-50% of patients with advanced disease recur. Therefore, there is an urgent need to improve the classification beyond the current parameters in clinical use to better stratify patients and the therapeutic approaches. Following a meta-analysis approach we built a large training set to whom we applied a Disease-Specific Genomic Analysis (DSGA) to identify the disease component embedded into the tumor data. Eleven independent microarray datasets were used as validation sets. Six different HNSCC subtypes that summarize the aberrant alterations occurring during tumor progression were identified. Based on their main biological characteristics and de-regulated signaling pathways, the subtypes were designed as immunoreactive, inflammatory, human papilloma virus (HPV)-like, classical, hypoxia associated, and mesenchymal. Our findings highlighted a more aggressive behavior for mesenchymal and hypoxia-associated subtypes. The Genomics Drug Sensitivity Project was used to identify potential associations with drug sensitivity and significant differences were observed among the six subtypes. To conclude, we report a robust molecularly defined subtype classification in HNSCC that can improve patient selection and pave the way to the development of appropriate therapeutic strategies.

No MeSH data available.


Related in: MedlinePlus

Distribution of the PAM classifier genes in the HNSCC subtypes identified in the training datasetHeatmap of the expression values of the 2843 classifier genes.
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Figure 6: Distribution of the PAM classifier genes in the HNSCC subtypes identified in the training datasetHeatmap of the expression values of the 2843 classifier genes.

Mentions: Eleven independent datasets were retrieved from public domains (GEO and TCGA). Two datasets, GSE39368 and TCGA, were profiled on Agilent and Illumina RNAseq platforms, respectively. The remaining nine datasets comprising a total of 358 samples were profiled on different types of chips belonging to the Affymetrix platform and were computationally integrated through a meta-analysis approach to build a unique independent validation set, hereafter named MetaHNC-B (Figure 1). The subtype membership on these datasets was predicted using PAM. First, we developed a prediction algorithm based on PAM using 40 ‘core samples’ for each subtype as established by Silhouette analysis. A total of 2843 genes entered into the classifier, yielding a cross-validation mis-classification rate of 5%. Figure 6 shows the heatmap of the classifier genes on MetaHNC-A, providing evidence that each subtype has its own distinct expression pattern. The list of genes, shrunken centroid values for each subtype and the algorithm to classify a new sample are reported in Table S3.


Head and neck cancer subtypes with biological and clinical relevance: Meta-analysis of gene-expression data.

De Cecco L, Nicolau M, Giannoccaro M, Daidone MG, Bossi P, Locati L, Licitra L, Canevari S - Oncotarget (2015)

Distribution of the PAM classifier genes in the HNSCC subtypes identified in the training datasetHeatmap of the expression values of the 2843 classifier genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496244&req=5

Figure 6: Distribution of the PAM classifier genes in the HNSCC subtypes identified in the training datasetHeatmap of the expression values of the 2843 classifier genes.
Mentions: Eleven independent datasets were retrieved from public domains (GEO and TCGA). Two datasets, GSE39368 and TCGA, were profiled on Agilent and Illumina RNAseq platforms, respectively. The remaining nine datasets comprising a total of 358 samples were profiled on different types of chips belonging to the Affymetrix platform and were computationally integrated through a meta-analysis approach to build a unique independent validation set, hereafter named MetaHNC-B (Figure 1). The subtype membership on these datasets was predicted using PAM. First, we developed a prediction algorithm based on PAM using 40 ‘core samples’ for each subtype as established by Silhouette analysis. A total of 2843 genes entered into the classifier, yielding a cross-validation mis-classification rate of 5%. Figure 6 shows the heatmap of the classifier genes on MetaHNC-A, providing evidence that each subtype has its own distinct expression pattern. The list of genes, shrunken centroid values for each subtype and the algorithm to classify a new sample are reported in Table S3.

Bottom Line: Based on their main biological characteristics and de-regulated signaling pathways, the subtypes were designed as immunoreactive, inflammatory, human papilloma virus (HPV)-like, classical, hypoxia associated, and mesenchymal.Our findings highlighted a more aggressive behavior for mesenchymal and hypoxia-associated subtypes.The Genomics Drug Sensitivity Project was used to identify potential associations with drug sensitivity and significant differences were observed among the six subtypes.

View Article: PubMed Central - PubMed

Affiliation: Functional Genomics and Bioinformatics, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

ABSTRACT
Head and neck squamous cell carcinoma (HNSCC) is a disease with heterogeneous clinical behavior and response to therapies. Despite the introduction of multimodality treatment, 40-50% of patients with advanced disease recur. Therefore, there is an urgent need to improve the classification beyond the current parameters in clinical use to better stratify patients and the therapeutic approaches. Following a meta-analysis approach we built a large training set to whom we applied a Disease-Specific Genomic Analysis (DSGA) to identify the disease component embedded into the tumor data. Eleven independent microarray datasets were used as validation sets. Six different HNSCC subtypes that summarize the aberrant alterations occurring during tumor progression were identified. Based on their main biological characteristics and de-regulated signaling pathways, the subtypes were designed as immunoreactive, inflammatory, human papilloma virus (HPV)-like, classical, hypoxia associated, and mesenchymal. Our findings highlighted a more aggressive behavior for mesenchymal and hypoxia-associated subtypes. The Genomics Drug Sensitivity Project was used to identify potential associations with drug sensitivity and significant differences were observed among the six subtypes. To conclude, we report a robust molecularly defined subtype classification in HNSCC that can improve patient selection and pave the way to the development of appropriate therapeutic strategies.

No MeSH data available.


Related in: MedlinePlus