Pathologic Assessment and Staging of Multiple Non-Small Cell Lung Carcinomas: A Paradigm Shift with the Emerging Role of Molecular Methods.

Non-small cell lung carcinomas (NSCLCs) commonly present as 2 or more separate tumors. Biologically, this encompasses 2 distinct processes: separate primary lung carcinomas (SPLCs), representing independently arising tumors, and intrapulmonary metastases (IPMs), representing intrapulmonary spread of a single tumor. The advent of computed tomography imaging has substantially increased the detection of multifocal NSCLCs. The strategies and approaches for distinguishing between SPLCs and IPMs have evolved significantly over the years. Recently, genomic sequencing of somatic mutations has been widely adopted to identify targetable alterations in NSCLC. These molecular techniques have enabled pathologists to reliably discern clonal relationships among multiple NSCLCs in clinical practice. However, a standardized approach to evaluating and staging multiple NSCLCs using molecular methods is still lacking. Here, we reviewed the historical context and provided an update on the growing applications of genomic testing as a clinically relevant benchmark for determining clonal relationships in multiple NSCLCs, a practice we have designated "comparative molecular profiling." We examined the strengths and limitations of the morphology-based distinction of SPLCs vs IPMs and highlighted pivotal clinical and pathologic insights that have emerged from studying multiple NSCLCs using genomic approaches as a gold standard. Lastly, we suggest a practical approach for evaluating multiple NSCLCs in the clinical setting, considering the varying availability of molecular techniques.

Genomic Staging of Multifocal Lung Squamous Cell Carcinomas Is Independent of the Comprehensive Morphologic Assessment.

INTRODUCTION: Morphologic and molecular data for staging of multifocal lung squamous cell carcinomas (LSCCs) are limited. In this study, whole exome sequencing (WES) was used as the gold standard to determine whether multifocal LSCC represented separate primary lung cancers (SPLCs) or intrapulmonary metastases (IPMs). Genomic profiles were compared with the comprehensive morphologic assessment. METHODS: WES was performed on 20 tumor pairs of multifocal LSCC and matched normal lymph nodes using the Illumina NovaSeq6000 S4-Xp (Illumina, San Diego, CA). WES clonal and subclonal analysis data were compared with histologic assessment by 16 thoracic pathologists. In addition, the immune gene profiling of the study cases was characterized by the HTG EdgeSeq Precision Immuno-Oncology Panel. RESULTS: By WES data, 11 cases were classified as SPLC and seven cases as IPM. Two cases were technically suboptimal. Analysis revealed marked genomic and immunogenic heterogeneity, but immune gene expression profiles highly correlated with mutation profiles. Tumors classified as IPM have a large number of shared mutations (ranging from 33.5% to 80.7%). The agreement between individual morphologic assessments for each case and WES was 58.3%. One case was unanimously interpreted morphologically as IPM and was in agreement with WES. In a further 17 cases, the number of pathologists whose morphologic interpretation was in agreement with WES ranged from two (one case) to 15 pathologists (one case) per case. Pathologists showed a fair interobserver agreement in the morphologic staging of multiple LSCCs, with an overall kappa of 0.232. CONCLUSIONS: Staging of multifocal LSCC based on morphologic assessment is unreliable. Comprehensive genomic analyses should be adopted for the staging of multifocal LSCC.

Evaluation of an Integrated Spectroscopy and Classification Platform for Point-of-Care Core Needle Biopsy Assessment: Performance Characteristics from Ex Vivo Renal Mass Biopsies.

PURPOSE: To evaluate a transmission optical spectroscopy instrument for rapid ex vivo assessment of core needle cancer biopsies (CNBs) at the point of care. MATERIALS AND METHODS: CNBs from surgically resected renal tumors and nontumor regions were scanned on their sampling trays with a custom spectroscopy instrument. After extracting principal spectral components, machine learning was used to train logistic regression, support vector machines, and random decision forest (RF) classifiers on 80% of randomized and stratified data. The algorithms were evaluated on the remaining 20% of the data set held out during training. Binary classification (tumor/nontumor) was performed based on a decision threshold. Multinomial classification was also performed to differentiate between the subtypes of renal cell carcinoma (RCC) and account for potential confounding effects from fat, blood, and necrotic tissue. Classifiers were compared based on sensitivity, specificity, and positive predictive value (PPV) relative to a histopathologic standard. RESULTS: A total of 545 CNBs from 102 patients were analyzed, yielding 5,583 spectra after outlier exclusion. At the individual spectra level, the best performing algorithm was RF with sensitivities of 96% and 92% and specificities of 90% and 89%, for the binary and multiclass analyses, respectively. At the full CNB level, RF algorithm also showed the highest sensitivity and specificity (93% and 91%, respectively). For RCC subtypes, the highest sensitivity and PPV were attained for clear cell (93.5%) and chromophobe (98.2%) subtypes, respectively. CONCLUSIONS: Ex vivo spectroscopy imaging paired with machine learning can accurately characterize renal mass CNB at the time of tissue acquisition.

Cytology assessment can predict survival for patients with metastatic pancreatic neuroendocrine neoplasms.

BACKGROUND: Histological features and Ki-67 index have known usefulness in predicting prognosis and guiding therapy among patients with metastatic pancreatic neuroendocrine neoplasms. Fine-needle aspiration may offer advantages for Ki-67 assessment because the technique obtains highly cellular, well-preserved specimens with the potential for broader tumor sampling. In the current study, the authors evaluated concordance for grade and differentiation between concurrent core needle biopsy and cytology preparations. Cytological features and grade then were correlated with survival. METHODS: Differentiation, grade by Ki-67 index, and correlation of these features with survival were compared between concurrent core needle biopsy and cytology specimens from 44 patients with metastatic pancreatic neuroendocrine neoplasms. RESULTS: Differentiation by cytology smear resulted in 38 cases of well (86%) and 6 cases of poor (14%) differentiation. Agreement for differentiation between smear and cell block, smear and core needle biopsy, and cell block and core needle biopsy was 88%, 94%, and 83%, respectively, and agreement for grade was 68%, 54%, and 22%, respectively. Cytology differentiation and cytology grade were found to be strong predictors of outcome with respective hazard ratios of 8.3 (95% confidence interval [95% CI], 3.1-22.1; P<.001) and 1.9 (95% CI, 1.2-2.9) for each ascending grade. The median disease-specific survival cytology projections were 121 months (95% CI, 57-185 months [estimated]) for grade 1, 45 months (95% CI, 29-87 months) for grade 2, and 19 months (95% CI, 1-44 months) for grade 3, with median survivals of 45 months and 3 months, respectively, for patients with well-differentiated and poorly differentiated neuroendocrine tumors (P<.001). CONCLUSIONS: Grading of pancreatic neuroendocrine neoplasms on cytology may not correlate exactly with concurrent core needle biopsy, but cytology differentiation and grade are predictive of survival based on stage-adjusted analysis. Cancer Cytopathol 2017;125:188-196. © 2016 American Cancer Society.