Faculty Bibliography

Identification of biomarkers for early detection of lung cancer (LC) is important, in turn leading to more effective treatment and reduction of mortality. Serological proteome analysis (SERPA) was used to identify proteins around 34 kD as ECH1 and HNRNPA2B1, which had been recognized by serum autoantibody from 25 LC patients. In the validation study, including 90 sera from LC patients and 89 sera from normal individuals, autoantibody to ECH1 achieved an area under the curve (AUC) of 0.799 with sensitivity of 62.2% and specificity of 95.5% in discriminating LC from normal individuals, and showed negative correlation with tumor size (rs = -0.256, p = 0.023). Autoantibody to HNRNPA2B1 performed an AUC of 0.874 with sensitivity of 72.2% and specificity of 95.5%, and showed negative correlation with lymph node metastasis (rs = -0.279, p = 0.012). By using longitudinal preclinical samples, autoantibody to ECH1 showed an AUC of 0.763 with sensitivity of 60.0% and specificity of 89.3% in distinguishing early stage LC from matched normal controls, and elevated autoantibody levels could be detected greater than 2 y before LC diagnosis. ECH1 and HNRNPA2B1 are autoantigens that elicit autoimmune responses in LC and their autoantibody can be the potential biomarkers for the early detection of LC.

INTRODUCTION: Current T component for malignant pleural mesothelioma (MPM) has been predominantly informed by surgical datasets and consensus. The International Association for the Study of Lung Cancer undertook revision of the 7th Edition staging system for MPM with the goal of developing recommendations for the 8th edition. METHODS: Data elements including detailed T descriptors were developed by consensus. Tumor thickness at three pleural levels was also recorded. An electronic data capture system was established to facilitate data submission. RESULTS: 3,519 cases were submitted to the database. Of those eligible for T component analysis, 509 cases had only clinical staging; 836 cases had only surgical staging; and 642 cases had both available. Survival was examined for T categories according to the current 7th edition staging system. There was clear separation between all clinically staged categories except T1a vs. T1b (HR 0.99, p=0.95) and T3 vs. T4 (HR 1.22, P=0.09), although numbers of T4 cases were small. Pathological staging failed to demonstrate a survival difference between adjacent categories with the exception of T3 vs. T4. Performance improved with collapse of T1a and T1b into a single T1 category; no current descriptors were shifted or eliminated. Tumour thickness and nodular or rind-like morphology were significantly associated with survival. CONCLUSIONS: A recommendation to collapse both clinical and pathological T1a and T1b into a T1 classification will be made for the 8th edition staging system. Simple measurement of pleural thickness has prognostic significance and should be examined further with a view to incorporation into future staging.

For nearly 40 years, there was no generally accepted staging system for malignant pleural mesothelioma (MPM). In 1994, members of the International Mesothelioma Interest Group (IMIG), in collaboration with the International Association for the Study of Lung Cancer (IASLC), proposed a tumor, node and metastasis (TNM) staging system based on analyses of outcomes in retrospective surgical series and small clinical trials. Subsequently accepted by the American Joint Commission on Cancer (AJCC) and the Union for International Cancer Control (UICC) for the 6th editions of their staging manuals, this system has since been the international staging standard. However, it has significant limitations, particularly with respect to clinical staging and to the categories for lymph node (N) staging. Here we provide an overview of the development of the IASLC MPM staging database which was designed to address these limitations through the development of a large international dataset. Analyses of this database, described in papers linked to this overview, are being used to inform revisions in the 8th editions of the AJCC and UICC staging systems.

INTRODUCTION: Nodal categories for malignant pleural mesothelioma (MPM) are derived from the lung cancer staging system and have not been adequately validated. The International Association for the Study of Lung Cancer (IASLC) developed a multinational database to generate evidence-based recommendations to inform the 8th edition of the tumor, node metastasis classification of MPM. METHODS: Data from 29 centers were entered prospectively (n=1,566) or by transfer of retrospective data (n=1,953). Survival according to the 7th edition N categories was evaluated using Kaplan-Meier (KM) survival curves and Cox proportional hazards regression analysis. Survival was measured from the date of diagnosis. RESULTS: There were 2,432 analyzable cases: 1,603 had clinical (c) staging; 1,614 had pathologic (p) staging; and 785 had both. For clinically staged tumors there was no separation in KM curves between cN0, cN1 or cN2 (cN1 vs. cN0 HR 1.06, p=0.77 and cN2 vs. cN1 HR 1.04, p=0.85). For pathologically staged tumors, patients with pN1 or pN2 tumors had worse survival than those with pN0 tumors (HR 1.51, p<0.0001) but no survival difference was noted between those with pN1 and pN2 (HR 0.99, p=0.99). Patients with both pN1 and pN2 nodal involvement had poorer survival than those with pN2 only (HR 1.60, p=0.007) or pN0 (HR 1.62, p<0.0001). CONCLUSIONS: A recommendation to collapse both clinical and pathological N1 and N2 categories into a single N category comprising ipsilateral, intrathoracic nodal metastases (N1) will be made for the 8th edition staging system. Nodes previously categorized as N3 will be reclassified as N2.

INTRODUCTION: The metastasis (M) component and TNM stage groupings for malignant pleural mesothelioma (MPM) have been empiric. The International Association for the Study of Lung Cancer developed a multinational database to propose evidence-based revisions for the 8th editions of the tumor, node and metastases (TNM) classification of MPM. METHODS: Data from 29 centers were submitted either electronically or by transfer of existing institutional databases. The M component, as it currently stands was validated by confirming sufficient discrimination (by Kaplan Meier) with respect to overall survival (OS) between the clincal (c)M0 and cM1 categories. Candidate stage groups were developed using a recursive partitioning and amalgamation (RPA) algorithm applied to all cM0 cases. RESULTS: Of 3,519 submitted cases, 2,414 were analyzable and 84 cases were cM1. Median OS for cM1 was 9.7 months versus 13.4 months (p=.0013) for the locally advanced (T4 or N3) cM0 cases, supporting inclusion of only cM1 in the stage IV group. Exploratory analyses suggest a possible difference in OS for single versus multiple site cM1. RPA generated survival tree on the OS outcomes restricted to cM0 with newly proposed (8th edition) T and N components, indicates that optimal stage groupings for the 8th edition will be: stage IA (T1N0), stage IB (T2-3N0), stage II (T1-2N1), stage IIIA (T3N1), stage IIIB (T1-3N2 or any T4), and stage IV (any M1). CONCLUSIONS: This first evidence-based revision of the TNM classification for MPM leads to substantial changes in the T and N components and the stage groupings.

We used a custom-made comparative genomic hybridization array (aCGH; average probe interval 254 bp) to screen 33 malignant mesothelioma (MM) biopsies for somatic copy number loss throughout the 3p21 region (10.7 Mb) that harbors 251 genes, including BRCA1 (breast cancer 1)-associated protein 1 (BAP1), the most commonly mutated gene in MM. We identified frequent minute biallelic deletions (<3 kb) in 46 of 251 genes: four were cancer-associated genes: SETD2 (SET domain-containing protein 2) (7 of 33), BAP1 (8 of 33), PBRM1 (polybromo 1) (3 of 33), and SMARCC1 (switch/sucrose nonfermentable- SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily c, member 1) (2 of 33). These four genes were further investigated by targeted next-generation sequencing (tNGS), which revealed sequence-level mutations causing biallelic inactivation. Combined high-density aCGH and tNGS revealed biallelic gene inactivation in SETD2 (9 of 33, 27%), BAP1 (16 of 33, 48%), PBRM1 (5 of 33, 15%), and SMARCC1 (2 of 33, 6%). The incidence of genetic alterations detected is much higher than reported in the literature because minute deletions are not detected by NGS or commercial aCGH. Many of these minute deletions were not contiguous, but rather alternated with segments showing oscillating copy number changes along the 3p21 region. In summary, we found that in MM: (i) multiple minute simultaneous biallelic deletions are frequent in chromosome 3p21, where they occur as distinct events involving multiple genes; (ii) in addition to BAP1, mutations of SETD2, PBRM1, and SMARCC1 are frequent in MM; and (iii) our results suggest that high-density aCGH combined with tNGS provides a more precise estimate of the frequency and types of genes inactivated in human cancer than approaches based exclusively on NGS strategy.

BACKGROUND: Standard imaging modalities are inaccurate in staging malignant pleural mesothelioma (MPM). Single-institution studies suggest that volumetric computed tomography (CT) is more accurate but labor intensive. We established a multicenter network to test interobserver variability, accuracy (relative to pathologic stage), and the prognostic significance of semiautomated volumetric CT. METHODS: Six institutions electronically submitted to an established multicenter database clinical and pathologic data for patients with MPM who had operations. Institutional radiologists reviewed preoperative CT scans for quality and then submitted by electronic network (AG Mednet, www.agmednet.com) to the biostatistical center. Two reference radiologists blinded to clinical data performed semiautomated tumor volume calculations using Vitrea Enterprise 6.0 software (Vital Images, Minnetonka, MN) and then submitted readings to the biostatistical center. Study end points included feasibility of the network, interobserver variability for volumetric CT, correlation of tumor volume to pTN stages, and overall survival (OS). RESULTS: Of 164 patients, the CT scans for 129 were analyzable and read by reference radiologists. Most tumors were less than 500 cm3. A small bias was observed between readers because one provided consistently larger measurements than the other (mean difference, 47.9; p = .0027), but for 80%, the absolute difference was 200 cm3 or less. Spearman correlation between readers was 0.822. Volume correlated with pTN stages and OS, best defined by three groups with average volumes of 91.2, 245.3, and 511.3 cm3 associated with median OS of 37, 18, and 8 months, respectively. CONCLUSIONS: For the first time, a multicenter network was established and initial correlations of tumor volume with pTN stages and OS are shown. A larger multicenter international study is planned to confirm the results and refine correlations.

The differential diagnosis between pleural malignant mesothelioma (MM) and lung cancer is often challenging. Immunohistochemical (IHC) stains used to distinguish these malignancies include markers that are most often positive in MM and less frequently positive in carcinomas, and vice versa. However, in about 10-20% of the cases, the IHC results can be confusing and inconclusive, and novel markers are sought to increase the diagnostic accuracy. We stained 45 non-small cell lung cancer samples (32 adenocarcinomas and 13 squamous cell carcinomas) with a monoclonal antibody for BRCA1-associated protein 1 (BAP1) and also with an IHC panel we routinely use to help differentiate MM from carcinomas, which include, calretinin, Wilms Tumor 1, cytokeratin 5, podoplanin D2-40, pankeratin CAM5.2, thyroid transcription factor 1, Napsin-A, and p63. Nuclear BAP1 expression was also analyzed in 35 MM biopsies. All 45 non-small cell lung cancer biopsies stained positive for nuclear BAP1, whereas 22/35 (63%) MM biopsies lacked nuclear BAP1 staining, consistent with previous data. Lack of BAP1 nuclear staining was associated with MM (two-tailed Fisher's Exact Test, P = 5.4 x 10-11). Focal BAP1 staining was observed in a subset of samples, suggesting polyclonality. Diagnostic accuracy of other classical IHC markers was in agreement with previous studies. Our study indicated that absence of nuclear BAP1 stain helps differentiate MM from lung carcinomas. We suggest that BAP1 staining should be added to the IHC panel that is currently used to distinguish these malignancies.

Purpose To identify the ability of computer-derived three-dimensional (3D) computed tomographic (CT) segmentation techniques to help differentiate lung adenocarcinoma subtypes. Materials and Methods This study had institutional research board approval and was HIPAA compliant. Pathologically classified resected lung adenocarcinomas (n = 23) with thin-section CT data were identified. Two readers independently placed over-inclusive volumes around nodules from which automated computer measurements were generated: mass (total mass) and volume (total volume) of the nodule and of any solid portion, in addition to the solid percentage of the nodule volume (percentage solid volume) or mass (percentage solid mass). Interobserver agreement and differences in measurements among pathologic entities were evaluated by using t tests. A multinomial logistic regression model was used to differentiate the probability of three diagnoses: invasive non-lepidic-predominant adenocarcinoma (INV), lepidic-predominant adenocarcinoma (LPA), and adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Results Mean percentage solid volume of INV was 35.4% (95% confidence interval [CI]: 26.2%, 44.5%)-higher than the 14.5% (95% CI: 10.3%, 18.7%) for LPA (P = .002). Mean percentage solid volume of AIS/MIA was 8.2% (95% CI: 2.7%, 13.7%) and had a trend toward being lower than that for LPA (P = .051). Accuracy of the model based on total volume and percentage solid volume was 73.2%; accuracy of the model based on total mass and percentage solid mass was 75.6%. Conclusion Computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma. (c) RSNA, 2016.

OBJECTIVES: Autoantibodies against tumor-associated antigens (TAAs) identified in patients with advanced lung cancer may be detected in subjects with early lung cancer or even predate the diagnosis. The purpose of this study is to address the temporal relationship between lung cancer development and serum autoantibody response. MATERIALS AND METHODS: Two cohorts of patients with newly diagnosed lung cancer were included. The first cohort included 90 sera from patients with lung cancer (Stages I-III) and 89 normal control sera. In the second cohort, 93 serial serum samples from 25 patients with CT-scan screen-detected stage I lung cancer were collected before the diagnosis of lung cancer (average 32 months) and 56 controls were matched on age, gender, and smoking. Autoantibody levels were measured by immunoassay. RESULTS: Measurement of autoantibodies against seven TAAs (14-3-3zeta, c-Myc, MDM2, NPM1, p16, p53 and cyclin B1) individually could discriminate lung cancer patients from normal individuals in the first cohort and the area under curve (AUC) was 0.863 based on a panel of seven autoantibodies, with sensitivity of 68.9% and specificity of 79.5%. Autoantibodies in serial pre-diagnostic serum samples against the same panel of seven TAAs were detected prior to lung cancer diagnosis with sensitivity of 76.0% and specificity of 73.2% (AUC) (95%CI): 0.885 (0.797-0.973)). Elevated autoantibody levels could be detected greater than four years prior to lung cancer diagnosis. CONCLUSION: A panel of seven TAAs may enhance the early detection of lung cancer, consistent with a humoral immune response to TAAs that can be detected months to years prior to the diagnosis.