What’s new in non-small cell lung cancer for pathologists the importance of accurate subtyping, EGFR mutations and ALK rearrangements

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.

Novel image features of optical coherence tomography for pathological classification of lung cancer: Results from a prospective clinical trial

Background

This study aimed to explore the characteristics of optical coherence tomography (OCT) imaging for differentiating between benign and malignant lesions and different pathological types of lung cancer in bronchial lesions and to preliminarily evaluate the clinical value of OCT.

Methods

Patients who underwent bronchoscopy biopsy and OCT between February 2019 and December 2019 at the Chinese PLA General Hospital were enrolled in this study. White-light bronchoscopy (WLB), auto-fluorescence bronchoscopy (AFB), and OCT were performed at the lesion location. The main characteristics of OCT imaging for the differentiation between benign and malignant lesions and the prediction of the pathological classification of lung cancer in bronchial lesions were identified, and their clinical value was evaluated.

Results

A total of 135 patients were included in this study. The accuracy of OCT imaging for differentiating between benign and malignant bronchial lesions was 94.1%, which was significantly higher than that of AFB (67.4%). For the OCT imaging of SCC, adenocarcinoma, and small-cell lung cancer, the accuracies were 95.6, 94.3, and 92%, respectively. The accuracy, sensitivity, and specificity of OCT were higher than those of WLB. In addition, these main OCT image characteristics are independent influencing factors for predicting the corresponding diseases through logistic regression analysis between the main OCT image characteristics in the study and the general clinical features of patients (p<0.05).

Conclusion

As a non-biopsy technique, OCT can be used to improve the diagnosis rate of lung cancer and promote the development of non-invasive histological biopsy.

Diagnostic, Predictive, and Prognostic Biomarkers in Non-Small Cell Lung Cancer (NSCLC) Management

Lung cancer is the leading cause of cancer-related deaths worldwide. Despite growing efforts for its early detection by screening populations at risk, the majority of lung cancer patients are still diagnosed in an advanced stage. The management of lung cancer has dramatically improved in the last decade and is no longer based on the "one-fits-all" paradigm or the general histological classification of non-small cell versus small cell lung cancer. Emerging options of targeted therapies and immunotherapies have shifted the management of lung cancer to a more personalized treatment approach, significantly influencing the clinical course and outcome of the disease. Molecular biomarkers have emerged as valuable tools in the prognosis and prediction of therapy response. In this review, we discuss the relevant biomarkers used in the clinical management of lung tumors, from diagnosis to prognosis. We also discuss promising new biomarkers, focusing on non-small cell lung cancer as the most abundant type of lung cancer.

Molecular profiling and characteristics of non-small-cell lung cancer patients in Georgia

Aims: In patients with advanced non-small-cell lung cancer, the correlation between histopathology, smoking status, driver oncogene mutations and PD-L1 overexpression were investigated. Patients and methods: A total of 202 patients were identified. Research was done in Georgia. Results: EGFR mutations were detected in 6% of the tested cases (12/187) and five out of 12 EGFR+ cases had histology consistent with squamous cell carcinoma. No statistically significant correlation was observed between PD-L1 expression, smoking status and clinicopathological characteristics. However, the correlation between smoking status and histology was statistically significant (p = 0.0264), as never-smokers had a higher incidence of adenocarcinoma histology. Conclusion: The study showed a small percentage of EGFR mutations associated with adenocarcinoma histology and revealed a solid existence of this mutation in squamous cell carcinoma histology. A higher incidence of adenocarcinoma histology was observed in never-smokers.

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

PURPOSE

To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC).

METHODS

A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots.

RESULTS

In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900-0.964), 0.901 (0.840-0.957), respectively) (p < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions.

CONCLUSION

This study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity

Objective

To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients.

Materials and Methods

This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables.

Results

Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1).

Conclusion

A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.

Early lung cancer diagnostic biomarker discovery by machine learning methods

Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients' plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making. Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish AC from SCC patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate AC from SCC patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients.

Use of dual-marker staining to differentiate between lung squamous cell carcinoma and adenocarcinoma

OBJECTIVE

To assess the value of dual-marker immunostaining for detecting p40 and napsin A, and cytokeratin 5/6 (CK5/6) and thyroid transcription factor 1 (TTF1) in single sections of lung cancer tissue, for differentiating between lung squamous cell carcinoma and adenocarcinoma.

METHODS

Lung cancer tissue sections from 58 patients were stained by dual-marker immunostaining using a mixtures of anti-p40 and anti-napsin A, and anti-CK5/6 and anti-TTF1 primary antibodies. Sections stained with single markers were used as controls. Nuclear or cytoplasmic staining was considered as indicating positive p40 or napsin A expression, respectively, and cytoplasmic or nuclear staining was considered as indicating positive CK5/6 or TTF1 expression, respectively.

RESULTS

p40/napsin A and CK5/6/TTF1 dual-marker staining showed high sensitivity, specificity, positive predictive value, and negative predictive value for the diagnosis of squamous cell carcinoma and adenocarcinoma respectively. There were no differences in marker expression between dual-marker and single-marker staining.

CONCLUSIONS

Dual-marker immunostaining is a relatively easy, time- and cost-conserving staining method for detecting two markers in a single section using one procedure and one chromogen. p40 and napsin A, and CK5/6 and TTF1 dual-marker staining were suitable for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma.

Complementary value of electron microscopy and immunohistochemistry in the diagnosis of non-small cell lung cancer: A potential role for electron microscopy in the era of targeted therapy

With the identification of therapeutic targets for lung adenocarcinoma, it has become mandatory to distinguish it from other entities. Some cases remain classified as non-small cell lung carcinoma, not otherwise specified (NSCLC-NOS) with immunohistochemistry. Electron microscopy (EM) can be useful, allowing the identification of glandular differentiation. The aim of this study was to determine the complementary value of immunohistochemistry and EM.Forty-eight NSCLC-NOS cases were selected (PSMAR-Biobank, Barcelona, Spain). Immunohistochemistry (TTF-1, p40) was performed. Tissue was retrieved from paraffin blocks. Results were compared to the final diagnosis, derived from combination of light microscopy, immunohistochemistry, EM, molecular studies and resection specimen.Immunohistochemistry concurred with final diagnosis in 36 cases (75%, Kappa = 0.517). EM agreed with final diagnosis in 35 (72.9%, Kappa = 0.471). Immunohistochemistry had a sensitivity = 73%, specificity = 100%, positive predictive value (PPV) = 100% and negative predictive value (NPV) = 52.4% for adenocarcinoma. All adenocarcinoma cases not solved by immunohistochemistry (n = 10) were classified by EM, and vice versa. Data from EM were identical to those of immunohistochemistry: sensitivity = 73%, specificity = 100%, PPV = 100% and NPV = 52.4%. Combining both techniques, 47 cases were coincident with final diagnosis (97.9%, Kappa = 0.943).EM can provide valuable information in subtyping NSCLC-NOS, being particularly useful when immunohistochemistry is inconclusive. EM could be considered as a complementary tool for decision-making in NSCLC-NOS.

Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to "know everything about all exams and regions". In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, "big data", and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms

PURPOSE

As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment.

METHODS

A local cohort of 85 patients were retrospectively (2010-2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data.

RESULTS

Univariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns.

CONCLUSION

Several radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura's texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.

Prognostic value of KRAS/TP53/PIK3CA in non-small cell lung cancer

The present study explored the association between KRAS proto-oncogene GTPase (KRAS), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α (PIK3CA) and tumor protein p53 (TP53) mutations, and the clinical features and survival prognosis in 50 patients with non-small cell lung cancer (NSCLC). The most common concurrent single gene mutation was TP53, followed by KRAS and PIK3CA. Co-existing mutations were found in 17 patients. KRAS, PIK3CA and TP53 mutations were associated with carbohydrate antigen 19-9 expression, invasive growth, vacuolar signs and margin lobulation on chest CT. The incidence of distant metastasis (bone and adrenal) with KRAS and TP53 mutations was greater than that of local metastasis (pleura). Patients with the wild-type genes experienced longer progression-free survival (PFS) times than those with KRAS, TP53, KRAS/TP53 or PIK3CA/TP53 mutations. Patients with KRAS/TP53 or PIK3CA/TP53 mutations experienced shorter PFS times than those with a single KRAS or TP53 mutation. KRAS, PIK3CA and TP53 mutations were associated with distant metastases and a poor prognosis. Patients with NSCLC should receive routine KRAS, PIK3CA and TP53 gene sequencing to determine mutations for the analysis of clinical characteristics and prognosis.

Significance of the epidermal growth factor receptor mutation status and differences among molecular subgroups in surgically resected lung microinvasive adenocarcinoma

Lung microinvasive adenocarcinoma (MIA) is a newly-defined subtype of early stage non-small cell lung cancer (NSCLC). However, its epidermal growth factor receptor (EGFR) mutation status and clinical significance remain unclear. The present study aimed to determine EGFR mutation characteristics and identify their significance in patients with resected lung MIA. The present study also analyzed clinicopathological differences between EGFR molecular subgroups defined as 19Del and L858R. The present study examined EGFR mutations in 79 consecutive lung MIA resection specimens and compared the differences in clinicopathological features between the EGFR wild-type and mutation groups, as well as between the 19Del and L858R subgroups. EGFR mutations were detected in 60 (75.95%) tumors. The most common mutations were 19Del (28 cases; 35.44%) and L858R (30 cases; 37.97%). Two patients harbored rare mutations and one of them had a concomitant double mutation. EGFR mutations were significantly associated with microinvasion component, thyroid transcription factor 1 (TTF-1) expression, intratumoral fibrosis and inflammatory cell infiltration. Subgroup evaluation indicated that there was a significant association between 19Del and tumor size, maximum diameter of microinvasion, presence of intratumoral fibrosis and inflammatory cell infiltration. Similar associations were observed for the L858R subgroup, and L858R was associated with TTF-1 expression. In particular, 19Del occurred more frequently in MIA with a smaller size, with a smaller microinvasive area, without TTF-1 expression, and lacking intratumoral fibrosis and inflammatory cell infiltration. By contrast, L858R was detected more frequently in MIA with entirely different tumor features. In conclusion, the results of the present study indicated that surgically resected MIA cases harboring different EGFR gene statuses exhibit distinct clinicopathological features. Significant differences in pathological features associated with the tumor microenvironment were identified in MIA with 19Del or L858R mutations. Therefore, the present study proposed that MIA should be classified into molecular subgroups based on EGFR mutation subtypes. The molecular sub-classification should be taken into account for prognostic evaluation and clinical management of MIA.

An Update on Predictive Biomarkers for Treatment Selection in Non-Small Cell Lung Cancer

It is now widely established that management of lung cancer is much more complex and cannot be centered on the binary classification of small-cell versus non-small cell lung cancer (NSCLC). Lung cancer is now recognized as a highly heterogeneous disease that develops from genetic mutations and gene expression patterns, which initiate uncontrolled cellular growth, proliferation and progression, as well as immune evasion. Accurate biomarker assessment to determine the mutational status of driver mutations such as EGFR, ALK and ROS1, which can be targeted by specific tyrosine kinase inhibitors, is now essential for treatment decision making in advanced stage NSCLC and has shifted the treatment paradigm of NSCLC to more individualized therapy. Rapid advancements in immunotherapeutic approaches to NSCLC treatment have been paralleled by development of a range of potential predictive biomarkers that can enrich for patient response, including PD-L1 expression and tumor mutational burden. Here, we review the key biomarkers that help predict response to treatment options in NSCLC patients.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases

BACKGROUND AND OBJECTIVES

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models.

METHODS

local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis.

RESULTS

radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively.

CONCLUSIONS

the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning.

Serum Monitoring and Phenotype Identification of Stage I Non-Small Cell Lung Cancer Patients

A stage I non-small cell lung cancer (NSCLC) serum profiling platform is presented which is highly efficient and accurate. Test sensitivity (0.95) for stage I NSCLC is the highest reported so far. Test metrics are reported for discriminating stage I adenocarcinoma vs squamous cell carcinoma subtypes. Blinded analysis identified 23 out of 24 stage I NSCLC and control serum samples. Group-discriminating mass peaks were targeted for tandem mass spectrometry peptide/protein identification, and yielded a lung cancer phenotype. Bioinformatic analysis revealed a novel lymphocyte adhesion pathway involved with early-stage lung cancer.

The introduction of electromagnetic navigation bronchoscopy for the diagnosis of small pulmonary peripheral lesions in an Asian population

Background

Electromagnetic navigation bronchoscopy (ENB) is emerging as a useful new technique for diagnosing small pulmonary peripheral lesions (SPPLs). However, the accuracy and efficiency of ENB have not been investigated in Asian populations where the differential diagnoses for SPPLs may be different. To analyze this question, this study included patients who received diagnostic ENB followed by surgery for the excision of SPPLs.

Methods

Consecutive patients referred to the Department of Thoracic Surgery, Shanghai Pulmonary Hospital (Tongji University), between May 2014 and April 2015 were recruited. ENB was used to obtain biopsy tissue and make a diagnosis, which was then confirmed by histopathological examination.

Results

The ENB was performed on 84 SPPLs of 78 patients in the study, with four patients having more than one SPPL. It successfully reached and biopsied 81 lesions. The average ENB navigation time was 10.8 minutes (range, 0.5-52 minutes). No mortality occurred, with only two complications (one bleeding and one pneumothorax). The mean diameter of the biopsied SPPLs was 19.0 mm (range, 5.0-30.0 mm). The distance from the sensor probe to the focus was 8.0 mm (range, 1-16 mm). ENB diagnosis had identical results with histopathology examination in 81 lesions (37 lung cancer and 41 non-lung cancer). The sensitivity of ENB was 92.9% (78 out of 84 lesions) in this study.

Conclusions

These data suggested that ENB was an accurate and efficient procedure to sample and diagnose SPPLs in the Asian population. It appeared that ENB had a high percentage of successful results in both navigating and aiding in the diagnosis of SPPLs in the Asian population.

An Algorithmic Approach for Assessment of Mediastinal Lesions Using Conventional Transbronchial Needle Aspiration and Endoscopic Ultrasonography in a Single Procedure

BACKGROUND

In the era of endobronchial/esophageal ultrasound (EBUS-TBNA/EUS-FNA), many centers forgo conventional transbronchial needle aspiration (C-TBNA) in favour of EBUS-TBNA/EUS-FNA despite no conclusive evidence showing better yields with EBUS-TBNA/EUS-FNA.

OBJECTIVES

Assess the feasibility of an algorithmic approach for mediastinal sampling beginning with C-TBNA utilizing rapid onsite cytologic evaluation.

METHODS

Descriptive analysis of 92 consecutive patients referred for adenopathy that underwent C-TBNA and subsequent EBUS-TBNA/EUS-FNA if C-TBNA was negative or nondiagnostic.

RESULTS

92 procedures were analyzed. In 50 (54.3%) of cases, C-TBNA alone was sufficient. EBUS-TBNA was performed after C-TBNA in 27 (29.3%) of cases and EUS-FNA in 33 (35.9%) of cases. The yield was 92.9% for C-TBNA, 92.5% for EBUS-TBNA, and 89.7% for EUS-FNA. There were no statistically significant differences in yields by LN station (P = 0.51), the relationship between yield and LN size (P = 0.37), or time difference in procedures following the algorithm compared to EBUS/EUS only procedures (33.7 minutes versus 32.4 minutes on average [95% CI for difference: -9.1 to 11.7], P = 0.80).

CONCLUSIONS

An algorithmic approach to assess the mediastinum using C-TBNA initially is feasible without sacrificing yield or procedure times. C-TBNA was sufficient for diagnosis in 54.3% of cases and can be efficiently taught in an IP training program.

ERCC1 Expression-Based Randomized Phase II Study of Gemcitabine/Cisplatin Versus Irinotecan/Cisplatin in Patients with Advanced Non-small Cell Lung Cancer

Purpose

We evaluated the clinical utility of excision repair cross-complementation group 1 (ERCC1) expression as a predictive biomarker for platinum-based chemotherapy in advanced non-small cell lung cancer (NSCLC).

Materials and Methods

Eligible patients were randomly assigned to the GP (gemcitabine 1,250 mg/m² on days 1 and 8, and cisplatin 75 mg/m² on day 1 every 3 weeks) or IP (irinotecan 65 mg/m² and cisplatin 30 mg/m² on days 1 and 8 every 3 weeks) arm. The primary goal of this study was to compare the response rate (RR) of the GP and IP arms according to the ERCC1 expression level.

Results

A total of 279 patients were randomly assigned to the GP (n=139) and IP (n=140) arms, among which 63% were ERCC1-positive and 268 patients were assessable for the RR. The GP and IP arms did not differ significantly with respect to the RR (29.8% vs. 27.0%, respectively; p=0.082), median progression-free survival (PFS; 4.5 months vs. 3.9 months, respectively; p=0.117), and overall survival (OS; 16.5 months vs. 16.7 months, respectively; p=0.313). When comparing the efficacy between the ERCC1-positive and ERCC1-negative groups, there was no significant difference in the RR (GP, 28.2% vs. 32.6%, respectively, p=0.509; IP, 30.2% vs. 21.6%, respectively, p=0.536), median PFS (GP, 4.6 months vs. 5.0 months, respectively, p=0.506; IP, 3.9 months vs. 3.7 months, respectively, p=0.748), or median OS (GP, 18.6 months vs. 11.9 months, respectively, p=0.070; IP, 17.5 months vs. 14.0 months, respectively, p=0.821).

Conclusion

Immunohistochemical analysis of the ERCC1 expression level did not differentiate the efficacy of platinum-based chemotherapy in advanced NSCLC.

Diagnosing lung carcinomas with optical coherence tomography

RATIONALE

Lung carcinoma diagnosis on tissue biopsy can be challenging because of insufficient tumor and lack of architectural information. Optical coherence tomography (OCT) is a high-resolution imaging modality that visualizes tissue microarchitecture in volumes orders of magnitude larger than biopsy. It has been proposed that OCT could potentially replace tissue biopsy.

OBJECTIVES

We aim to determine whether OCT could replace histology in diagnosing lung carcinomas. We develop and validate OCT interpretation criteria for common primary lung carcinomas: adenocarcinoma, squamous cell carcinoma (SCC), and poorly differentiated carcinoma.

METHODS

A total of 82 ex vivo tumor samples were included in a blinded assessment with 3 independent readers. Readers were trained on the OCT criteria, and applied these criteria to diagnose adenocarcinoma, SCC, or poorly differentiated carcinoma in an OCT validation dataset. After a 7-month period, the readers repeated the training and validation dataset interpretation. An independent pathologist reviewed corresponding histology.

MEASUREMENTS AND MAIN RESULTS

The average accuracy achieved by the readers was 82.6% (range, 73.7-94.7%). The sensitivity and specificity for adenocarcinoma were 80.3% (65.7-91.4%) and 88.6% (80.5-97.6%), respectively. The sensitivity and specificity for SCC were 83.3% (70.0-100.0%) and 87.0% (75.0-96.5%), respectively. The sensitivity and specificity for poorly differentiated carcinoma were 85.7% (81.0-95.2%) and 97.6% (92.9-100.0%), respectively.

CONCLUSIONS

Although these results are encouraging, they indicate that OCT cannot replace histology in the diagnosis of lung carcinomas. However, OCT has potential to aid in diagnosing lung carcinomas as a complement to tissue biopsy, particularly when insufficient tissue is available for pathology assessment.

Effects of a Multidisciplinary Approach to Improve Volume of Diagnostic Material in CT-Guided Lung Biopsies

BACKGROUND

Recent publications have emphasized the importance of a multidisciplinary strategy for maximum conservation and utilization of lung biopsy material for advanced testing, which may determine therapy. This paper quantifies the effect of a multidisciplinary strategy implemented to optimize and increase tissue volume in CT-guided transthoracic needle core lung biopsies. The strategy was three-pronged: (1) once there was confidence diagnostic tissue had been obtained and if safe for the patient, additional biopsy passes were performed to further increase volume of biopsy material, (2) biopsy material was placed in multiple cassettes for processing, and (3) all tissue ribbons were conserved when cutting blocks in the histology laboratory. This study quantifies the effects of strategies #1 and #2.

DESIGN

This retrospective analysis comparing CT-guided lung biopsies from 2007 and 2012 (before and after multidisciplinary approach implementation) was performed at a single institution. Patient medical records were reviewed and main variables analyzed include biopsy sample size, radiologist, number of blocks submitted, diagnosis, and complications. The biopsy sample size measured was considered to be directly proportional to tissue volume in the block.

RESULTS

Biopsy sample size increased 2.5 fold with the average total biopsy sample size increasing from 1.0 cm (0.9-1.1 cm) in 2007 to 2.5 cm (2.3-2.8 cm) in 2012 (P<0.0001). The improvement was statistically significant for each individual radiologist. During the same time, the rate of pneumothorax requiring chest tube placement decreased from 15% to 7% (P = 0.065). No other major complications were identified. The proportion of tumor within the biopsy material was similar at 28% (23%-33%) and 35% (30%-40%) for 2007 and 2012, respectively. The number of cases with at least two blocks available for testing increased from 10.7% to 96.4% (P<0.0001).

CONCLUSIONS

The effect of this multidisciplinary strategy to CT-guided lung biopsies was effective in significantly increasing tissue volume and number of blocks available for advanced diagnostic testing.

Cribriform adenocarcinoma of the lung: clinicopathologic, immunohistochemical, and molecular analysis of 15 cases of a distinctive morphologic subtype of lung adenocarcinoma

Lung adenocarcinoma is characterized by marked heterogeneity and may be composed of an admixture of histologic growth patterns, including acinar, papillary, solid, and lepidic (bronchioloalveolar). Tumors displaying a prominent or predominant cribriform architecture are rare and most often confused for metastases from other organs. We report the clinical, histologic, immunohistochemical, and molecular features in 15 primary lung adenocarcinomas with a predominant cribriform histology. All patients were adults between 30 and 80 years of age (median: 64), and all but one reported a history of heavy cigarette smoking. All cases showed a predominant (>70%) cribriform architecture that resembled a variety of tumors arising in other organs, including breast, prostate, ovary, pancreas, uterus, colon, and thyroid. Immunohistochemical stains showed a phenotype consistent with a primary lung tumor (ie, TTF1+/CK7+), with negative results for other markers. Molecular analysis in six cases showed that none harbored an EGFR-activating mutation. KRAS mutation was detected in one case, and an ALK1 and ROS1 gene rearrangement were each detected in an additional two cases. Cribriform adenocarcinomas of the lung represent a distinctive histologic subtype of lung cancer that may be morphologically difficult to differentiate from metastases with a predominant cribriform architecture.

Adenosquamous carcinoma

Adenosquamous carcinoma is an unusual and aggressive form of non-small cell lung carcinoma. Although extensively studied, there is persistent uncertainty with regard to its histogenesis and clinical and histopathologic features, related to the inherent heterogeneity of lung carcinoma. This review will attempt a reappraisal of the definition and diagnostic criteria and address problem areas and practical issues in the pathologic evaluation of this neoplasm.

Primary pulmonary non-small cell carcinomas: the College of American Pathologists Interlaboratory Comparison Program confirms a significant trend toward subcategorization based upon fine-needle aspiration cytomorphology alone

Context.-Subtyping of non-small cell lung carcinomas (NSCLCs) is necessary for optimal patient management with specific diagnoses triggering specific molecular tests and affecting therapy. Objective.-To assess the accuracy of the participants of the College of American Pathologists Interlaboratory Comparison Program in diagnosing and subtyping NSCLC fine-needle aspiration (FNA) slides, based on morphology alone, considering preparation and participant type and trends over time. Design.-The performance of program participants was reviewed for the 5-year period spanning 2007-2011. Lung FNA challenges with reference diagnoses of adenocarcinoma and squamous cell carcinoma (SCC) were evaluated for diagnostic concordance by using a nonlinear mixed model analysis. Results.-There were 10 493 pathologist and 6378 cytotechnologist responses with concordance rates of 97.4% and 97.9% for malignancy, respectively. Overall concordance rates for subcategorization were 54.6% for adenocarcinoma and 74.9% for SCC. For the exact reference diagnoses, pathologists performed better for adenocarcinoma and cytotechnologists performed better for SCC. Accurate subcategorization of adenocarcinomas significantly increased over time with 31.5% of adenocarcinomas classified as NSCLC in 2007 and 25.5% of adenocarcinomas classified as NSCLC in 2011 (P < .001). In comparing preparation types, modified Giemsa-stained smears showed the lowest overall concordance (46.8%). Modified Giemsa-stained smears with SCCs were the least likely to be accurately subcategorized (36.4%). Conclusions.-Participants are proficient at interpreting NSCLCs as malignant by FNA but are less successful at subcategorization with cytomorphology alone. During the study period, a statistically significant trend was confirmed toward greater accuracy of subcategorization of adenocarcinomas, suggesting that participants are cognizant of the impact that more specific cytomorphologic interpretations have in directing molecular triage and therapy.

Identification of subgroup patients with stage IIIB/IV non-small cell lung cancer at higher risk for brain metastases

PURPOSE

Brain metastases (BM), a common occurrence in non-small cell lung cancer (NSCLC), usually lead to a poor prognosis. Recently, the selection of treatment modalities for BM has modestly improved patient survival and quality of life. Treatment choice is largely based on the number of BM, the presence of BM-related symptoms, and performance status. Therefore, early BM detection is crucial. In this study, we aimed to elucidate the factors associated with BM and identify subgroups of patients at higher risk for BM.

METHODS AND PATIENTS

The medical records of 596 consecutive patients with stage I-IV NSCLC were reviewed between January 2006 and November 2011. A multivariate logistic regression (MLR) model was used to identify factors associated with BM.

RESULTS

Among 482 eligible stage IIIB/IV NSCLC patients, 173 (36%) experienced BM during their disease course. On MLR analysis, female gender, age < 60 years and adenocarcinoma were associated with BM (OR = 1.71, 95% CI = 1.06-2.75, P = 0.028; OR = 2.11, 95% CI = 1.38-3.22, P = 0.001; and OR = 2.39, 95% CI = 1.16-4.92, P = 0.018, respectively). The actuarial incidence of BM varied widely from 14% to 59% in different subgroups; younger patients with adenocarcinoma tended to experience BM more than older patients with squamous cell carcinoma (OR = 6.88, 95% CI = 2.97-15.94, P < 0.001). Furthermore, the incidence of BM correlated closely with survival after NSCLC diagnosis, and it was 42%, 54% and 64% in patients who survived more than 3, 12 and 24 months, respectively. Notably, the number of BM, the size of the largest BM and the proportion of multiple BM, defined as more than 4 metastatic tumors in brain, were significantly different in NSCLC patients with and without BM-related symptoms or signs (4.0 ± 2.1 vs 2.7 ± 1.9, P < 0.001; 2.6 ± 1.5 vs 1.3 ± 1.0 CM, P < 0.001, and 50% vs 21%, P < 0.001, respectively).

CONCLUSION

We found that subgroups of NSCLC patients characterized by younger age, female gender and adenocarcinoma are at higher risks for BM. These findings might be helpful to detect BM earlier and facilitate the design of clinical trials aiming at their prevention.

Suitability of computed tomography-guided biopsy specimens for subtyping and genotyping of non-small-cell lung cancer

INTRODUCTION

Recent advances in the treatment of NSCLC highlight the importance of distinguishing NSCLC subtypes and genotypes. We aimed to determine whether histological specimens obtained from computed tomography (CT)-guided biopsy are suitable for specific subtyping and epidermal growth factor receptor (EGFR) analyses of NSCLC.

PATIENTS AND METHODS

The clinicohistological data of 332 consecutive patients undergoing 352 CT-guided biopsies for lung lesions between January 2007 and December 2011 were retrospectively analyzed. Additionally, NSCLC specimens were examined for the suitability of EGFR mutational testing.

RESULTS

Of 209 specimens diagnosed as NSCLC, 197 (94.3%) were specifically subtyped into adenocarcinoma (n = 164; 78.5%), squamous cell carcinoma (n = 27; 12.9%) and other subtypes (n = 6; 2.9%). The rate of NSCLC not otherwise specified (NOS) was 5.7%, and the diagnosis of NSCLC-NOS was significantly associated with the poor differentiation of cancer (adjusted odds ratio, 6.17; 95% confidence interval, 1.62-23.55; P = .008). Of 134 histological tumor specimens submitted for EGFR molecular testing, 132 (98.5%) were suitable for analyses, and 130 of them (98.5%) showed conclusive results, revealing 59.8% (n = 79) with EGFR exon mutation(s). The sensitivity, specificity, and positive and negative predictive values of CT-guided biopsy in patients with malignancy were 92.2%, 100%, 100%, and 74.1%, respectively. Six percent (n = 21) of total lung biopsies led to pneumothorax requiring chest drainage, and no procedure-related fatality was observed.

CONCLUSION

Small tumor specimens obtained with CT-guided needle lung biopsy are suitable for specific subtyping and EGFR analyses of NSCLC, thus providing critical information for personalized therapy.

Fibroblast growth factor receptor 1 (FGFR1) copy number is an independent prognostic factor in non-small cell lung cancer

Fibroblast growth factor receptor 1 (FGFR1) is an oncogene that can potentially be targeted by tyrosine kinase inhibitors. We aimed to investigate the prevalence and prognostic significance of alterations in FGFR1 copy number in non-small cell lung cancer (NSCLC). FGFR1 status was evaluated by chromogenic silver in situ hybridisation (ISH) in tissue microarray sections from a retrospective cohort of 304 surgically resected NSCLCs and results were correlated with the clinicopathological features and overall survival. High FGFR1 gene copy number (amplification or high-level polysomy) was significantly more frequent in squamous cell carcinomas (SCC) (24.8%) and large cell carcinomas (LCC) (25%) compared to adenocarcinomas (11.3%) (p = 0.01 and p = 0.03 respectively). Among NSCLC there was no significant correlation between FGFR1-positive status and other clinicopathological features including age, gender, smoking history, tumour size, lymph node status, stage, grade, vascular, lymphatic or perineural invasion. FGFR1-positive patients showed a tendency to longer overall survival in univariate analysis (p = 0.14). Multivariate survival analysis using Cox regression model confirmed FGFR1-positive patients had a significant reduction in the risk of death compared to FGFR1-negative patients (HR 0.6; p = 0.02). High FGFR1 gene copy number is a common finding in SCC and LCC and is an independent favourable prognostic factor.

Toward Personalized Medicine of Lung Cancer

We evaluated clinical parameters, histomorphology, and thyroid transcription factor 1 (TTF-1) immunoreactivity in 40 epidermal growth factor receptor (EGFR) mutation– and anaplastic lymphoma kinase (ALK) rearrangement–negative invasive pulmonary adenocarcinomas. Tumors were histomorphologically quantitated by a pulmonary pathologist and TTF-1 immunohistochemistry applied. EGFR mutation and ALK rearrangement status was determined with polymerase chain reaction/DNA sequencing and fluorescence in situ hybridization, respectively. Treatment response was related to type of treatment ( P < .005) and clinical stage ( P = .001). EGFR mutation– and ALK rearrangement–negative pulmonary adenocarcinomas containing papillary/micropapillary histology showed greater morphologic heterogeneity ( P < .001), greater TTF-1 immunoreactivity ( P = .004), and were more common in treatment responders ( P < .05). These findings support that patients with pulmonary adenocarcinomas that are subject to nontargeted therapies may respond to treatment as a function of tumor cell differentiation with TTF-1 as a potential biomarker of this response.

Volumetric optical frequency domain imaging of pulmonary pathology with precise correlation to histopathology

BACKGROUND

Lung cancer is the leading cause of cancer-related mortality. Radiology and bronchoscopy techniques do not have the necessary resolution to evaluate lung lesions on the microscopic scale, which is critical for diagnosis. Bronchial biopsy specimens can be limited by sampling error and small size. Optical frequency domain imaging (OFDI) provides volumetric views of tissue microstructure at near-histologic resolution and may be useful for evaluating pulmonary lesions to increase diagnostic accuracy. Bronchoscopic OFDI has been evaluated in vivo, but a lack of correlated histopathology has limited the ability to develop accurate image interpretation criteria.

METHODS

We performed OFDI through two approaches (airway-centered and parenchymal imaging) in 22 ex vivo lung specimens, using tissue dye to precisely correlate imaging and histology.

RESULTS

OFDI of normal airway allowed visualization of epithelium, lamina propria, cartilage, and alveolar attachments. Carcinomas exhibited architectural disarray, loss of normal airway and alveolar structure, and rapid light attenuation. Squamous cell carcinomas showed nested architecture. Atypical glandular formation was appreciated in adenocarcinomas, and uniform trabecular gland formation was seen in salivary gland carcinomas. Mucinous adenocarcinomas showed alveolar wall thickening with intraalveolar mucin. Interstitial fibrosis was visualized as signal-dense tissue, with an interstitial distribution in mild interstitial fibrotic disease and a diffuse subpleural pattern with cystic space formation in usual interstitial pneumonitis.

CONCLUSIONS

To our knowledge, this study is the first demonstration of volumetric OFDI with precise correlation to histopathology in lung pathology. We anticipate that OFDI may play a role in assessing airway and parenchymal pathology, providing fresh insights into the volumetric features of pulmonary disease.

In vivo optical coherence tomography: the role of the pathologist

Optical coherence tomography (OCT) is a nondestructive, high-resolution imaging modality, providing cross-sectional, architectural images at near histologic resolutions, with penetration depths up to a few millimeters. Optical frequency domain imaging is a second-generation OCT technology that has equally high resolution with significantly increased image acquisition speeds and allows for large area, high-resolution tissue assessments. These features make OCT and optical frequency domain imaging ideal imaging techniques for surface and endoscopic imaging, specifically when tissue is unsafe to obtain and/or suffers from biopsy sampling error. This review focuses on the clinical impact of OCT in coronary, esophageal, and pulmonary imaging and the role of the pathologist in interpreting high-resolution OCT images as a complement to standard tissue pathology.

Comparative analyses of overall survival in patients with anaplastic lymphoma kinase-positive and matched wild-type advanced nonsmall cell lung cancer

BACKGROUND

The purpose of this study was to investigate the overall survival (OS) of patients with advanced ALK-positive nonsmall cell lung cancer (NSCLC) who were managed in the pre-ALK inhibitor era and to compare their survival with that of a matched case cohort of ALK wild-type (WT) patients.

METHODS

Data from 1166 patients who had stage IIIB/IV NSCLC with nonsquamous histology were collected from the NSCLC database of Seoul National University Hospital between 2003 and 2009. ALK fluorescence in situ hybridization (FISH) was used to analyze 262 patients who either had the WT epidermal growth factor receptor (EGFR) or were nonresponders to previous EGFR tyrosine kinase inhibitor (TKI) therapy. Overall survival (OS) was compared between 3 groups: 1) ALK-positive patients, 2) EGFR mutation-positive patients, and 3) ALK-WT/EGFR-WT patients. Progression-free survival (PFS) after first-line chemotherapy and EGFR TKIs also was analyzed.

RESULTS

Twenty-three patients were ALK-positive according to FISH analysis and did not receive ALK inhibitors during follow-up. The median OS for ALK-positive patients, EGFR mutation-positive patients, and WT/WT patients was 12.2 months, 29.6 months, and 19.3 months, respectively (vs EGFR mutation-positive patients, P = .001; vs WT/WT, P = .127). The PFS after first-line chemotherapy for the 3 groups was not different. However, the PFS for patients who received EGFR TKIs was shorter in ALK-positive patients compared with the other 2 groups (vs EGFR mutation-positive patients, P < .001; vs WT/WT, P < .021).

CONCLUSIONS

In the pre-ALK inhibitor era, ALK-positive patients experienced the shortest survival, although it did not differ statistically from that of WT/WT patients. Although their responses to platinum-based chemotherapy were not different from comparator groups, ALK-positive patients were even more resistant to EGFR TKI treatment than WT/WT patients.

Ancillary testing in lung cancer diagnosis

The pathologic diagnosis of lung cancer historically has relied primarily on morphologic features of tumors in histologic sections. With the emergence of new targeted therapies, the pathologist is called upon increasingly to provide not only accurate typing of lung cancers, but also to provide prognostic and predictive information, based on a growing number of ancillary tests, that may have significant impact on patient management. This review provides an overview of ancillary tests currently used in the pathologic diagnosis of lung cancer, with a focus on immunohistochemistry and molecular diagnostics.

Distinguishing non-small cell lung adenocarcinoma patients from squamous cell carcinoma patients and control individuals using serum profiling

Goals of this study were to analyze the ability of mass spectrometry serum profiling to distinguish non-small cell lung adenocarcinoma from squamous cell carcinoma patients and healthy controls. Sera were obtained from 19 adenocarcinoma patients, 24 squamous cell carcinoma patients, and 21 controls. Identifications of significant mass-to-charge ratio (m/z) peak differences between these groups were performed using t-tests. A "leave one out" cross-validation procedure yielded discriminatory lung adenocarcinoma versus squamous cell carcinoma p and ROC curve values of <.0001 and 0.92, respectively. Test sensitivity and specificity were 84% and 79%, respectively. This approach could aid in lung cancer diagnosis and sub-typing.

Course introduction and selection of immunohistochemical staining panels: principles and importance of incorporating clinical information. The 5th annual retreat for applied immunohistochemistry and molecular pathology january 30th-february 2, 2011, coral gables, Florida

Among the core principles in the practice of immunohistochemistry is the use of carefully chosen marker panels. Choosing an appropriate panel of antibodies is predicated on a sound differential diagnosis that is based on detailed examination of hematoxylin and eosin-stained slides. The panel should contain antibodies designed to be immunoreactive in the most likely disease(s) in the differential as well as selected negative markers. In addition, the importance of detailed historical and clinical information in constructing the differential diagnosis and panel selection cannot be understated. Two cases from the Case Presentation sessions of the 5th Annual Retreat for Applied Immunohistochemistry and Molecular Pathology are summarized to illustrate these points. The first case is that of metastatic well-differentiated neuroendocrine tumor (carcinoid) tumor presenting as a breast mass. The second is that of a squamous cell carcinoma of the lung mimicking a tumor with admixed glandular differentiation by entrapment and disruption of bronchial glands. Application of a select immunohistochemistry panel in light of the differential diagnosis and importance of making a specific diagnosis are discussed.

Evaluation and treatment of patients with non-small cell lung cancer

Lung cancer is the most common cause of cancer-related death in the United States; however, recent clinical advances may change this outcome. New data on low-dose computed tomography for lung cancer screening, and technologic advances in surgery and radiation, have improved outcomes for those with early-stage disease. Identification of driver mutations in lung cancer has led to the development of molecular targeted therapy to improve survival of subsets of patients with metastatic disease. These advances now allow for treatment of many patients with lung cancer with comorbidities or poor performance status who would have had limited options in the past.

Whole cancer genome sequencing by next-generation methods

Traditional approaches to sequence analysis are widely used to guide therapy for patients with lung and colorectal cancer and for patients with melanoma, sarcomas (eg, gastrointestinal stromal tumor), and subtypes of leukemia and lymphoma. The next-generation sequencing (NGS) approach holds a number of potential advantages over traditional methods, including the ability to fully sequence large numbers of genes (hundreds to thousands) in a single test and simultaneously detect deletions, insertions, copy number alterations, translocations, and exome-wide base substitutions (including known "hot-spot mutations") in all known cancer-related genes. Adoption of clinical NGS testing will place significant demands on laboratory infrastructure and will require extensive computational expertise and a deep knowledge of cancer medicine and biology to generate truly useful "clinically actionable" reports. It is anticipated that continuing advances in NGS technology will lower the overall cost, speed the turnaround time, increase the breadth of genome sequencing, detect epigenetic markers and other important genomic parameters, and become applicable to smaller and smaller specimens, including circulating tumor cells and circulating free DNA in plasma.

Comprehensive next-generation cancer genome sequencing in the era of targeted therapy and personalized oncology

DNA sequence analysis has become a significant laboratory test in oncology, permitting treatment to become increasingly personalized for both solid tumors and hematologic malignancies. Traditional approaches to sequence analysis, including Sanger sequencing, pyrosequencing and allele-specific PCR, are now widely used to guide therapy for patients diagnosed with lung and colorectal cancer as well as for melanoma, sarcomas (e.g., gastrointestinal stromal tumors) and subtypes of leukemia and lymphoma. Traditional sequence analysis has been limited in bandwidth and throughput and as a result, has been focused exclusively on testing the most common aberrations in key genes or fully sequencing single genes. The massively parallel or next-generation sequencing (NGS) approach to DNA analysis holds a number of potential advantages over the traditional methods, including the ability to fully sequence large numbers of genes (hundreds to thousands) in a single test. Furthermore, NGS can simultaneously detect deletions, insertions, copy number alterations, translocations and exome-wide base substitutions (including known hot-spot mutations) in all known cancer-related genes. However, significant challenges, particularly with respect to demands on expertise and infrastructure, will have to be overcome to translate NGS to the bedside of the cancer patient. Extensive computational expertise is required to bring NGS into clinical context, and a deep knowledge of cancer medicine and cancer biology will be required to generate truly useful, so-called 'clinically actionable' reports for clinicians. While NGS is on the cusp of being launched as a clinical test, it may be expected that the near future will continue to bring major advances in the technology that will lower the overall cost, speed up the turnaround time, increase the breadth of genome sequencing, and detect epigenetic markers and other important genomic parameters, while becoming applicable to smaller and smaller specimens, including circulating tumor cells and circulating free DNA in plasma.

Molecular Characteristics of ERCC1-Negative versus ERCC1-Positive Tumors in Resected NSCLC

PURPOSE

Excision repair cross-complementation group 1 (ERCC1) is a protein involved in repair of DNA platinum adducts and stalled DNA replication forks. We and others have previously shown the influence of ERCC1 expression upon survival rates and benefit of cisplatin-based chemotherapy in patients with resected non-small-cell lung cancer (NSCLC). However, little is known about the molecular characteristics of ERCC1-positive and ERCC1-negative tumors.

EXPERIMENTAL DESIGN

We took advantage of a cohort of 91 patients with resected NSCLC, for which we had matched frozen and paraffin-embedded samples to explore the comparative molecular portraits of ERCC1-positive and ERCC1-negative tumors of NSCLC. We carried out a global molecular analysis including assessment of ERCC1 expression levels by using both immunohistochemistry (IHC) and quantitative reverse transcriptase PCR (qRT-PCR), genomic instability, global gene and miRNA expression, and sequencing of selected key genes involved in lung carcinogenesis.

RESULTS

ERCC1 protein and mRNA expression were significantly correlated. However, we observed several cases with clear discrepancies. We noted that ERCC1-negative tumors had a higher rate of genomic abnormalities versus ERCC1-positive tumors. ERCC1-positive tumors seemed to share a common DNA damage response (DDR) phenotype with the overexpression of seven genes linked to DDR. The miRNA expression analysis identified miR-375 as significantly underexpressed in ERCC1-positive tumors.

CONCLUSIONS

Our data show inconsistencies in ERCC1 expression between IHC and qRT-PCR readouts. Furthermore, ERCC1 status is not linked to specific mutational patterns or frequencies. Finally, ERCC1-negative tumors have a high rate of genomic aberrations that could consequently influence prognosis in patients with resected NSCLC.