Large cell neuroendocrine carcinoma with a solitary brain metastasis and low Ki-67: a unique subtype

Genomic landscape and actionable mutations of brain metastases derived from non-small cell lung cancer: A systematic review

Background

Brain metastases derived from non-small cell lung cancer (NSCLC) represent a significant clinical problem. We aim to characterize the genomic landscape of brain metastases derived from NSCLC and assess clinical actionability.

Methods

We searched Embase, MEDLINE, Web of Science, and BIOSIS from inception to 18/19 May 2022. We extracted information on patient demographics, smoking status, genomic data, matched primary NSCLC, and programmed cell death ligand 1 expression.

Results

We found 72 included papers and data on 2346 patients. The most frequently mutated genes from our data were EGFR (n = 559), TP53 (n = 331), KRAS (n = 328), CDKN2A (n = 97), and STK11 (n = 72). Common missense mutations included EGFR L858R (n = 80) and KRAS G12C (n = 17). Brain metastases of ever versus never smokers had differing missense mutations in TP53 and EGFR, except for L858R and T790M in EGFR, which were seen in both subgroups. Of the top 10 frequently mutated genes that had primary NSCLC data, we found 37% of the specific mutations assessed to be discordant between the primary NSCLC and brain metastases.

Conclusions

To our knowledge, this is the first systematic review to describe the genomic landscape of brain metastases derived from NSCLC. These results provide a comprehensive outline of frequently mutated genes and missense mutations that could be clinically actionable. These data also provide evidence of differing genomic landscapes between ever versus never smokers and primary NSCLC compared to the BM. This information could have important consequences for the selection and development of targeted drugs for these patients.

Diagnostic criteria and evolving molecular characterization of pulmonary neuroendocrine carcinomas

Neuroendocrine carcinomas of the lung are currently classified into two categories: small cell lung carcinoma and large cell neuroendocrine carcinoma. Diagnostic criteria for small cell- and large cell neuroendocrine carcinoma are based solely on tumor morphology; however, overlap in histologic and immunophenotypic features between the two types of carcinoma can potentially make their classification challenging. Accurate diagnosis of pulmonary neuroendocrine carcinomas is paramount for patient management, as clinical course and treatment differ between small cell and large cell neuroendocrine carcinoma. Molecular-genetic, transcriptomic, and proteomic data published over the past decade suggest that small cell and large cell neuroendocrine carcinomas are not homogeneous categories but rather comprise multiple groups of distinctive malignancies. Nuances in the susceptibility of small cell lung carcinoma subtypes to different chemotherapeutic regimens and the discovery of targetable mutations in large cell neuroendocrine carcinoma suggest that classification and treatment of neuroendocrine carcinomas may be informed by ancillary molecular and protein expression testing going forward. This review summarizes current diagnostic criteria, prognostic and predictive correlates of classification, and evidence of previously unrecognized subtypes of small cell and large cell neuroendocrine carcinoma.

Risk factors, survival analysis, and nomograms for distant metastasis in patients with primary pulmonary large cell neuroendocrine carcinoma: A population-based study

INTRODUCTION

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rapidly progressive and easily metastatic high-grade lung cancer, with a poor prognosis when distant metastasis (DM) occurs. The aim of our study was to explore risk factors associated with DM in LCNEC patients and to perform survival analysis and to develop a novel nomogram-based predictive model for screening risk populations in clinical practice.

METHODS

The study cohort was derived from the Surveillance, Epidemiology, and End Results database, from which we selected patients with LCNEC between 2004 to 2015 and formed a diagnostic cohort (n = 959) and a prognostic cohort (n = 272). The risk and prognostic factors of DM were screened by univariate and multivariate analyses using logistic and Cox regressions, respectively. Then, we established diagnostic and prognostic nomograms using the data in the training group and validated the accuracy of the nomograms in the validation group. The diagnostic nomogram was evaluated using receiver operating characteristic curves, decision curve analysis curves, and the GiViTI calibration belt. The prognostic nomogram was evaluated using receiver operating characteristic curves, the concordance index, the calibration curve, and decision curve analysis curves. In addition, high- and low-risk groups were classified according to the prognostic monogram formula, and Kaplan-Meier survival analysis was performed.

RESULTS

In the diagnostic cohort, LCNEC close to bronchus, with higher tumor size, and with higher N stage indicated higher likelihood of DM. In the prognostic cohort (patients with LCNEC and DM), men with higher N stage, no surgery, and no chemotherapy had poorer overall survival. Patients in the high-risk group had significantly lower median overall survival than the low-risk group.

CONCLUSION

Two novel established nomograms performed well in predicting DM in patients with LCNEC and in evaluating their prognosis. These nomograms could be used in clinical practice for screening of risk populations and treatment planning.

Molecular Pathology of Pulmonary Large Cell Neuroendocrine Carcinoma: Novel Concepts and Treatments

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is an aggressive neoplasm with poor prognosis. Histologic diagnosis of LCNEC is not always straightforward. In particular, it is challenging to distinguish small cell lung carcinoma (SCLC) or poorly differentiated carcinoma from LCNEC. However, histological classification for LCNEC as well as their therapeutic management has not changed much for decades. Recently, genomic and transcriptomic analyses have revealed different molecular subtypes raising hopes for more personalized treatment. Two main molecular subtypes of LCNEC have been identified by studies using next generation sequencing, namely type I with TP53 and STK11/KEAP1 alterations, alternatively called as non-SCLC type, and type II with TP53 and RB1 alterations, alternatively called as SCLC type. However, there is still no easy way to classify LCNEC subtypes at the actual clinical level. In this review, we have discussed histological diagnosis along with the genomic studies and molecular-based treatment for LCNEC.

Neuroendocrine Carcinomas with Atypical Proliferation Index and Clinical Behavior: A Systematic Review

BACKGROUND

Highly proliferative (G3) neuroendocrine neoplasms are divided into well differentiated tumors (NETs) and poorly differentiated carcinomas (NECs), based on the morphological appearance. This systematic review aims to evaluate the clinicopathological features and the treatment response of the NEC subgroup with a Ki67 labeling index (LI) < 55%.

METHODS

A literature search was performed using MEDLINE, Cochrane Library, and Scopus between December 2019 and April 2020, last update in October 2020. We included studies reporting data on the clinicopathological characteristics, survival, and/or therapy efficacy of patients with NECs, in which the Ki67 LI was specified.

RESULTS

8 papers were included, on a total of 268 NEC affected patients. NECs with a Ki67 LI < 55% have been reported in patients of both sexes, mainly of sixth decade, pancreatic origin, and large-cell morphology. The prevalent treatment choice was chemotherapy, followed by surgery and, in only one study, peptide receptor radionuclide therapy. The subgroup of patients with NEC with a Ki67 LI < 55% showed longer overall survival and progression free survival and higher response rates than the subgroup of patients with a tumor with higher Ki67 LI (≥55%).

CONCLUSIONS

NECs are heterogeneous tumors. The subgroup with a Ki67 LI < 55% has a better prognosis and should be treated and monitored differently from NECs with a Ki67 LI ≥ 55%.

Challenges in Ki-67 assessments in pulmonary large-cell neuroendocrine carcinomas

AIMS

To gather the best available evidence regarding Ki-67% values in large-cell neuroendocrine carcinoma (LCNEC) and determine whether certain cut-off values could serve as a prognostic feature in LCNEC.

METHODS AND RESULTS

Aperio ScanScope AT Turbo, eSlide Manager and ImageScope software (Leica Biosystems) were used to measure Ki-67% in 77 resected LCNEC diagnosed by World Health Organisation (WHO) criteria. Cases were stratified into six classes by 10% Ki-67 increments. Using the Kaplan-Meier method, overall (OS) and disease-free survivals (DFS) were compared by AJCC stage, by six Ki-67% classes and with Ki-67% cut-points ≥20% and ≥40%. Tumours were from 0.9 to 11.5 cm and pathological stages 1-3. The system measured Ki-67% positivity using 4072-44 533 tumour nuclei per case (mean 16610 ± 8039). Ki-67% ranged from 1 to 64% (mean = 26%; median = 26%). Only 16 (21%) tumours had Ki-67% ≥40%. OS ranged from 1 to 298 months (median follow-up = 25 months). DFS ranged from 1 to 276 months (median follow-up = 9 months). OS and DFS differed across AJCC stage (overall log-rank P = 0.038 and P = 0.037). However, neither OS nor DFS significantly correlated with Ki-67% when six or two classes were used with either ≥20% Ki-67 or ≥40% Ki-67 as cut-point. A literature review identified 14 reports meeting our inclusion criteria with ≥10 LCNEC. Reported Ki-67% ranged from 2% to 100%. Problems contributing to variability in Ki-67% measurements are discussed.

CONCLUSION

Our findings caution against a blanket use of 20%, 40% or other Ki-67% cut-points for LCNEC diagnosis or prognostication.