A gene expression profile test to resolve head & neck squamous versus lung squamous cancers

Wild-Type Anaplastic Lymphoma Kinase Expression in Solitary Pulmonary Nodules: A Potential Marker for Primary Lung Squamous Cell Carcinoma in Patients With Previous Neck Squamous Cell Carcinoma

In patients with a history of head and neck squamous cell carcinoma (HNSCC), distinguishing between primary lung squamous cell carcinoma (LSCC) and pulmonary metastasis of HNSCC is critical when a solitary pulmonary nodule is observed. However, differentiation in clinical practice remains challenging because no golden-standard immunohistochemical (IHC) marker has been established to identify the primary organ of squamous cell carcinoma (SCC). The anaplastic lymphoma kinase (ALK) gene harbors rearrangements in approximately 4-6% of non-small cell lung cancer (NSCLC) cases. The detection of ALK rearrangements is well-established through anti-ALK IHC. While anti-ALK IHC is primarily positive in adenocarcinoma within NSCLC, wild-type ALK without rearrangements is occasionally detected in other histological types, such as SCC. We report two surgical cases with a history of laryngeal cancer that exhibited solitary pulmonary SCC, in which only the lung lesions demonstrated positivity for wild-type ALK through IHC and fluorescence in-situ hybridization method, allowing for the diagnosis of primary LSCC and following postoperative strategy.

Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin

Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient’s tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.

Gene expression profiling for the diagnosis of multiple primary malignant tumors

BACKGROUND

The incidence of multiple primary malignant tumors (MPMTs) is rising due to the development of screening technologies, significant treatment advances and increased aging of the population. For patients with a prior cancer history, identifying the tumor origin of the second malignant lesion has important prognostic and therapeutic implications and still represents a difficult problem in clinical practice.

METHODS

In this study, we evaluated the performance of a 90-gene expression assay and explored its potential diagnostic utility for MPMTs across a broad spectrum of tumor types. Thirty-five MPMT patients from Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University and Fudan University Shanghai Cancer Center were enrolled; 73 MPMT specimens met all quality control criteria and were analyzed by the 90-gene expression assay.

RESULTS

For each clinical specimen, the tumor type predicted by the 90-gene expression assay was compared with its pathological diagnosis, with an overall accuracy of 93.2% (68 of 73, 95% confidence interval 0.84-0.97). For histopathological subgroup analysis, the 90-gene expression assay achieved an overall accuracy of 95.0% (38 of 40; 95% CI 0.82-0.99) for well-moderately differentiated tumors and 92.0% (23 of 25; 95% CI 0.82-0.99) for poorly or undifferentiated tumors, with no statistically significant difference (p-value > 0.5). For squamous cell carcinoma specimens, the overall accuracy of gene expression assay also reached 87.5% (7 of 8; 95% CI 0.47-0.99) for identifying the tumor origins.

CONCLUSIONS

The 90-gene expression assay provides flexibility and accuracy in identifying the tumor origin of MPMTs. Future incorporation of the 90-gene expression assay in pathological diagnosis will assist oncologists in applying precise treatments, leading to improved care and outcomes for MPMT patients.

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases

Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.

Rapid Nanophotonics Assay for Head and Neck Cancer Diagnosis

Efficient and timely diagnosis of head and neck squamous cell carcinoma (HNSCC) is a critical challenge, particularly in low and middle income countries. These regions, which are expected to witness a drastic increase in HNSCC rates, are ill-prepared to handle the diagnostic burden due to limited resources, especially the low ratio of pathologists per population, resulting in delayed diagnosis and treatment. Here, we demonstrate the potential of an alternative diagnostic method as a low-cost, resource-efficient alternative to histopathological analysis. Our novel technology employs unique surface-enhanced Raman scattering (SERS) "nanorattles" targeting cytokeratin nucleic acid biomarkers specific for HNSCC. In this first study using SERS diagnostics for head and neck cancers, we tested the diagnostic accuracy of our assay using patient tissue samples. In a blinded trial, our technique demonstrated a sensitivity of 100% and specificity of 89%, supporting its use as a useful alternative to histopathological diagnosis. The implications of our method are vast and significant in the setting of global health. Our method can provide a rapid diagnosis, allowing for earlier treatment before the onset of distant metastases. In comparison to histopathology, which can take several months in remote limited-resources regions, our method provides a diagnosis within a few hours.

Immunohistochemical pattern analysis of squamous cell carcinoma: Lung primary and metastatic tumors of head and neck

OBJECTIVES

This study aimed to develop an immunohistochemical (IHC) diagnostic algorithm for primary lung squamous cell carcinoma (LSCC) and pulmonary metastasis of head and neck SCC (HNSCC).

MATERIALS AND METHODS

We selected three antibodies (CK19, MMP3, and PI3) from a web-based gene expression database and an IHC analysis available online. We developed an IHC diagnostic algorithm using tissue microarrays from 39 LSCCs and 48 HNSCCs as the training set. It was validated using whole tumor sections of 32 LSCCs and 23 HNSCCs. The algorithm was applied to 28 cases with a history of HNSCC and who underwent resection of pulmonary squamous cell tumors.

RESULTS

The sensitivity, specificity, and accuracy of the algorithm were 90%, 62%, and 77%, respectively, in the training set and 96%, 44%, and 65%, respectively, in the validation set. Twenty-three of 28 SCCs were diagnosed as metastasis of HNSCC; the remaining five tumors were diagnosed as LSCC. Among the patients in the HNSCC group, 18 developed postoperative recurrence and 11 died of the disease, whereas only one patient in the LSCC group had recurrence. Compared with the LSCC group, the HNSCC group had poorer prognosis (P=0.07). IHC diagnosis coincided with the retrospective diagnosis in 22 (79%) of the 28 patients (sensitivity, 95%; specificity, 44%).

CONCLUSION

The IHC diagnostic algorithm may be clinically useful for distinguishing between LSCC and pulmonary metastasis of HNSCC.

Differential diagnosis between primary lung squamous cell carcinoma and pulmonary metastasis of head and neck squamous cell carcinoma

Differentiation between lung squamous cell carcinoma and pulmonary metastasis of head and neck squamous cell carcinoma is clinically important because the prognoses and therapeutic options are considerably different. However, the clinical, pathological, and immunohistochemical diagnostic methods have not yet been fully established. Although various molecular methods have been developed, they have not yet been practically applied. A combined approach involving molecular and immunohistochemical analysis, such as one that uses antibodies selected on the basis of comprehensive genetic analysis results, may be effective. We suggest a new diagnostic criteria using the clinical characteristics and the result of immunohistochemical analysis. However, there are two underlying problems in the development of new diagnostic methods: tumor heterogeneity and determination of the diagnostic accuracy.

Clinical parameters predicting development of pulmonary malignancies in patients treated for head and neck squamous cell carcinoma

BACKGROUND

As the locoregional control rates in head and neck squamous cell carcinoma (HNSCC) have increased, these patients may suffer distant metastasis in a higher proportion of cases. Clinicopathological characteristics allowing prediction of high-risk profile would allow adapting posttreatment surveillance to individual risk.

METHODS

A retrospective review of all patients with HNSCC treated at the Jewish General Hospital, McGill University, Montreal, Quebec, Canada, between 1999 and 2008 was conducted for this study.

RESULTS

The study included 428 patients with a mean follow-up of 65 months (±SEM 1.7). Eighty patients (18.6%) developed pulmonary malignancy during follow-up. In multivariate Cox-regression analysis, locoregional failure and current smoking were associated with higher risk of pulmonary malignancy (p < .001 and p = .008, respectively).

CONCLUSION

Locoregional failure and smoking persistence are predictors of pulmonary malignancy in patients with HNSCC. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1277-E1280, 2016.

High precision prediction of functional sites in protein structures

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta.

Combined effects of 17-DMAG and TNF on cells through a mechanism related to the NF-kappaB pathway

Objective

The tumor necrosis factor (TNF) and the cellular NF-κB pathway protein IKKβ play important roles in various cellular processes such as cell proliferation, survival, differentiation, and apoptosis. A heat shock protein 90 inhibitor, 17-DMAG, can induce apoptosis of some tumor cells. This study is to determine the combined effects of 17-DMAG and TNF on malignant cells and the related mechanisms.

Methods

We have determined effects of 17-DMAG, an Hsp90 inhibitor, and TNF treatments on the small cell lung cancer cell line (MS-1), the adenocarcinoma cell line (A549), the squamous-cell carcinoma cell line (LK-2), and the normal human bronchial epithelium cell line (NuLi-1) by using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrozolium bromide assay. To determine if 17-DMAG inhibit the expression of IKKβ in the normal human NuLi-1 cells, and the malignant MS-1, A549, and LK-2 cells, immunoblotting assays and luciferase assays were performed.

Results

It was found that the combined treatments resulted in synergistic killing of malignant cells, which was confirmed by the apoptosis determination using a fluorescence microscopic assay following staining of the drug-treated cells with Hoescht 33258. The immunoblotting results indicated that the synergistic killing due to 17-DMAG and TNF treatments may be related to the decreases in IKKβ levels in the presence of 17-DMAG.

Conclusions

The results suggest that combination of 17-DMAG and TNF treatments might be useful for treating malignancies upon further study in the further.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2041198513886824