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Molecular signatures of metastasis in head and neck cancer.

ABSTRACT: Metastases are the primary cause of cancer treatment failure and death, yet metastatic mechanisms remain incompletely understood.We studied the molecular basis of head and neck cancer metastasis by transcriptionally profiling 70 samples from 27 patients-matching normal adjacent tissue, primary tumor, and cervical lymph node metastases.We identified tumor-associated expression signatures common to both primary tumors and metastases. Use of matching metastases revealed an additional 46 dysregulated genes associated solely with head and neck cancer metastasis. However, despite being metastasis-specific in our sample set, these 46 genes are concordant with genes previously discovered in primary tumors that metastasized.Although our data and related studies show that most of the metastatic potential appears to be inherent to the primary tumor, they are also consistent with the notion that a limited number of additional clonal changes are necessary to yield the final metastatic cell(s), albeit in a variable temporal order.

SUBMITTER: Colella S

PROVIDER: S-EPMC4136479 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Molecular signatures of metastasis in head and neck cancer.

Colella Stefano S Richards Kristy L KL Bachinski Linda L LL Baggerly Keith A KA Tsavachidis Spiridon S Lang James C JC Schuller David E DE Krahe Ralf R

Head & neck 20081001 10

<h4>Background</h4>Metastases are the primary cause of cancer treatment failure and death, yet metastatic mechanisms remain incompletely understood.<h4>Methods</h4>We studied the molecular basis of head and neck cancer metastasis by transcriptionally profiling 70 samples from 27 patients-matching normal adjacent tissue, primary tumor, and cervical lymph node metastases.<h4>Results</h4>We identified tumor-associated expression signatures common to both primary tumors and metastases. Use of matchi ...[more]

PMID: 18642293

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Project description:For many years, head and neck squamous cell carcinoma (HNSCC) has been considered as a single entity. However, in the last decades HNSCC complexity and heterogeneity have been recognized. In parallel, high-throughput omics techniques had allowed picturing a larger spectrum of the behavior and characteristics of molecules in cancer and a large set of omics web-based tools and informative repository databases have been developed. The objective of the present review is to provide an overview on biological, prognostic and predictive molecular signatures in HNSCC. To contextualize the selected data, our literature survey includes a short summary of the main characteristics of omics data repositories and web-tools for data analyses. The timeframe of our analysis was fixed, encompassing papers published between January 2015 and January 2019. From more than 1000 papers evaluated, 61 omics studies were selected: 33 investigating mRNA signatures, 11 and 13 related to miRNA and other non-coding-RNA signatures and 4 analyzing DNA methylation signatures. More than half of identified signatures (36) had a prognostic value but only in 10 studies selection of a specific anatomical sub-site (8 oral cavity, 1 oropharynx and 1 both oral cavity and oropharynx) was performed. Noteworthy, although the sample size included in many studies was limited, about one-half of the retrieved studies reported an external validation on independent dataset(s), strengthening the relevance of the obtained data. Finally, we highlighted the development and exploitation of three gene-expression signatures, whose clinical impact on prognosis/prediction of treatment response could be high. Based on this overview on omics-related literature in HNSCC, we identified some limits and strengths. The major limits are represented by the low number of signatures associated to DNA methylation and to non-coding RNA (miRNA, lncRNA and piRNAs) and the availability of a single dataset with multiple omics on more than 500 HNSCC (i.e. TCGA). The major strengths rely on the integration of multiple datasets through meta-analysis approaches and on the growing integration among omics data obtained on the same cohort of patients. Moreover, new approaches based on artificial intelligence and informatic analyses are expected to be available in the next future.

Project description:BackgroundFluorescence molecular imaging using ABY-029, an epidermal growth factor receptor (EGFR)-targeted, synthetic Affibody peptide labeled with a near-infrared fluorophore, is under investigation for surgical guidance during head and neck squamous cell carcinoma (HNSCC) resection. However, tumor-to-normal tissue contrast is confounded by intrinsic physiological limitations of heterogeneous EGFR expression and non-specific agent uptake.ObjectiveIn this preliminary study, radiomic analysis was applied to optical ABY-029 fluorescence image data for HNSCC tissue classification through an approach termed "optomics." Optomics was employed to improve tumor identification by leveraging textural pattern differences in EGFR expression conveyed by fluorescence. The study objective was to compare the performance of conventional fluorescence intensity thresholding and optomics for binary classification of malignant vs. non-malignant HNSCC tissues.Materials and methodsFluorescence image data collected through a Phase 0 clinical trial of ABY-029 involved a total of 20,073 sub-image patches (size of 1.8 × 1.8 mm2) extracted from 24 bread-loafed slices of HNSCC surgical resections originating from 12 patients who were stratified into three dose groups (30, 90, and 171 nanomoles). Each dose group was randomly partitioned on the specimen-level 75%/25% into training/testing sets, then all training and testing sets were aggregated. A total of 1,472 standardized radiomic features were extracted from each patch and evaluated by minimum redundancy maximum relevance feature selection, and 25 top-ranked features were used to train a support vector machine (SVM) classifier. Predictive performance of the SVM classifier was compared to fluorescence intensity thresholding for classifying testing set image patches with histologically confirmed malignancy status.ResultsOptomics provided consistent improvement in prediction accuracy and false positive rate (FPR) and similar false negative rate (FNR) on all testing set slices, irrespective of dose, compared to fluorescence intensity thresholding (mean accuracies of 89% vs. 81%, P = 0.0072; mean FPRs of 12% vs. 21%, P = 0.0035; and mean FNRs of 13% vs. 17%, P = 0.35).ConclusionsOptomics outperformed conventional fluorescence intensity thresholding for tumor identification using sub-image patches as the unit of analysis. Optomics mitigate diagnostic uncertainties introduced through physiological variability, imaging agent dose, and inter-specimen biases of fluorescence molecular imaging by probing textural image information. This preliminary study provides a proof-of-concept that applying radiomics to fluorescence molecular imaging data offers a promising image analysis technique for cancer detection in fluorescence-guided surgery.

Dataset Information

Molecular signatures of metastasis in head and neck cancer.

Publications

Molecular signatures of metastasis in head and neck cancer.

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