Molecular studies of lung neuroendocrine neoplasms uncover new concepts and entities

Clinical management of typical and atypical carcinoids/neuroendocrine tumors in ENETS centres of excellence (CoE): Survey from the ENETS lung NET task force

Lung carcinoid tumours are neuroendocrine neoplasms originating from the bronchopulmonary tract's neuroendocrine cells, accounting for only 1%-3% of all lung cancers but 30% of all neuroendocrine tumours. The incidence of lung carcinoids, both typical and atypical, has been increasing over the years due to improved diagnostic methods and increased awareness among clinicians and pathologists. The most recent WHO classification includes a subgroup of lung carcinoids with atypical morphology and higher mitotic count and/or Ki67 labelling index. Despite appropriate surgery, the 5-year survival rate for atypical carcinoids barely exceeds 50%-70%. The role of adjuvant therapy in lung carcinoids is not well-defined, and clinical decisions are generally based on the presence of high-risk features. Long-term follow-up is essential to monitor for recurrence, although the optimal follow-up protocol remains unclear. To address the lack of consensus in clinical management decisions, the European Neuroendocrine Tumor Society (ENETS) initiated a survey among 20 expert centres. The survey identified varied opinions on approaches to imaging, surgery, use of adjuvant therapy, and follow-up protocols. Notably, the absence of dedicated multidisciplinary lung neuroendocrine tumour boards in some centres was evident. Experts agreed on the need for a prospective adjuvant trial in high-risk patients, emphasizing the feasibility of such a study. In conclusion, the study highlights the need for a more uniform adoption of existing guidelines in the management of lung carcinoid tumours and emphasizes the importance of international collaboration to advance research and patient care. Close collaboration between healthcare providers and patients is vital for effective long-term surveillance and management of these rare tumours.

Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms

BACKGROUND

Organoids are 3-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types.

RESULTS

We have generated the first multi-omic dataset (whole-genome sequencing [WGS] and RNA-sequencing [RNA-seq]) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2) and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique and the raw and processed data as well as all scripts for genomic analyses to ensure an optimal reuse of the data. In addition, we report gene expression data and somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random forest classifier to detect variants in tumor-only RNA-seq. We also report all histopathological images used for medical diagnosis: hematoxylin and eosin-stained slides, brightfield images, and immunohistochemistry images of protein markers of clinical relevance.

CONCLUSIONS

This dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases.

Altered splicing machinery in lung carcinoids unveils NOVA1, PRPF8 and SRSF10 as novel candidates to understand tumor biology and expand biomarker discovery

BACKGROUND

Lung neuroendocrine neoplasms (LungNENs) comprise a heterogeneous group of tumors ranging from indolent lesions with good prognosis to highly aggressive cancers. Carcinoids are the rarest LungNENs, display low to intermediate malignancy and may be surgically managed, but show resistance to radiotherapy/chemotherapy in case of metastasis. Molecular profiling is providing new information to understand lung carcinoids, but its clinical value is still limited. Altered alternative splicing is emerging as a novel cancer hallmark unveiling a highly informative layer.

METHODS

We primarily examined the status of the splicing machinery in lung carcinoids, by assessing the expression profile of the core spliceosome components and selected splicing factors in a cohort of 25 carcinoids using a microfluidic array. Results were validated in an external set of 51 samples. Dysregulation of splicing variants was further explored in silico in a separate set of 18 atypical carcinoids. Selected altered factors were tested by immunohistochemistry, their associations with clinical features were assessed and their putative functional roles were evaluated in vitro in two lung carcinoid-derived cell lines.

RESULTS

The expression profile of the splicing machinery was profoundly dysregulated. Clustering and classification analyses highlighted five splicing factors: NOVA1, SRSF1, SRSF10, SRSF9 and PRPF8. Anatomopathological analysis showed protein differences in the presence of NOVA1, PRPF8 and SRSF10 in tumor versus non-tumor tissue. Expression levels of each of these factors were differentially related to distinct number and profiles of splicing events, and were associated to both common and disparate functional pathways. Accordingly, modulating the expression of NOVA1, PRPF8 and SRSF10 in vitro predictably influenced cell proliferation and colony formation, supporting their functional relevance and potential as actionable targets.

CONCLUSIONS

These results provide primary evidence for dysregulation of the splicing machinery in lung carcinoids and suggest a plausible functional role and therapeutic targetability of NOVA1, PRPF8 and SRSF10.

Spotlight on Small-Cell Lung Cancer and Other Lung Neuroendocrine Neoplasms

Lung neuroendocrine neoplasms (NENs) encompass a spectrum of neoplasms that are subdivided into the well-differentiated neuroendocrine tumors comprising the low- and intermediate-grade typical and atypical carcinoids, respectively, and the poorly differentiated, high-grade neuroendocrine carcinomas including large-cell neuroendocrine carcinomas and small-cell lung carcinoma (SCLC). Here, we review the current morphological and molecular classifications of the NENs on the basis of the updated WHO Classification of Thoracic Tumors and discuss the emerging subclassifications on the basis of molecular profiling and the potential therapeutic implications. We focus on the efforts in subtyping SCLC, a particularly aggressive tumor with few treatment options, and the recent advances in therapy with the adoption of immune checkpoint inhibitors in the frontline setting for patients with extensive-stage SCLC. We further highlight the promising immunotherapy strategies in SCLC that are currently under investigation.

Metastatic Neuroendocrine Tumors to Parotid Gland: Where Do They Come From?

Background. Metastasis to parotid gland occurs mostly from skin cancers of the head and neck region. Metastatic neuroendocrine tumors to the parotid gland are rare and not well studied so far. In this study, we undertook a retrospective review of secondary neuroendocrine tumors of the parotid diagnosed in our institution. The most common entities, primary tumor locations, their clinical presentations and histopathological features were analyzed. Methods. Cases of secondary neuroendocrine tumors to parotid diagnosed from August 1995 to Jan 2021 were retrieved from our institution's pathology databases, and their clinicopathological features were reviewed. Results. About 29% (120 of 420 cases) of parotid neoplasms were malignant, including 70 cases of parotid primary malignant tumors and 50 cases of metastases to parotid glands. Among metastatic tumors to the parotid glands, squamous cell carcinoma and melanoma of the head and neck region together accounted for about 78% of the cases. Only 11 of 50 metastatic tumors to the parotid were neuroendocrine carcinomas (22%). The most common primaries were poorly differentiated neuroendocrine carcinoma of lung (5 of 11 cases), including 4 cases of small cell carcinoma and one case of large cell neuroendocrine carcinoma. The second most common secondary tumor was Merkel cell carcinoma (4 of 11 cases, 36%) including one case of direct invasion from overlying skin. Medullary carcinoma of the thyroid comprised the rest of the metastases to the parotid (2 of 11 cases, 18%). Conclusion. Our results show that metastatic neuroendocrine carcinomas to the parotid gland account for about 22% of all metastatic tumors to the organ. Lung is the most common primary neuroendocrine carcinoma location. It is not uncommon for metastatic neuroendocrine carcinoma to present as the first sign of systemic clinical manifestation. Merkel cell carcinoma is the second most common entity that metastasizes to parotid, followed by medullary carcinoma of the thyroid.

Acquired ALK Resistance Mutations Identified from Liquid Biopsy in an ALK-Rearranged Squamous Cell Lung Cancer Patient Treated with Sequential ALK TKI Therapy: A Case Report

Anaplastic lymphoma kinase (ALK) rearrangement is extremely rare in lung squamous cell carcinoma (LSCC), and it remains controversial as to whether LSCC patients with ALK rearrangement can benefit from ALK tyrosine kinase inhibitors (TKIs). Here, we report an LSCC patient with ALK rearrangement who was treated with sequential ALK TKI therapies and experienced a clinical benefit of 35 months. Although the use of ALK TKIs showed clinical benefits, targeted next-generation sequencing (NGS) for dynamic monitoring of circulating tumor DNA (ctDNA) from patient plasma revealed the accumulation of ALK resistance mutations, which could provide valuable information in designing the treatment strategy. Our study highlights the importance of dynamic monitoring of ctDNA using NGS to discover tumor evolution to guide treatment decision-making and provides meaningful insights into the potential treatment options for ALK-positive LSCC patients.

Clinical-Pathologic Challenges in the Classification of Pulmonary Neuroendocrine Neoplasms and Targets on the Horizon for Future Clinical Practice

Diagnosing a pulmonary neuroendocrine neoplasm (NEN) may be difficult, challenging clinical decision making. In this review, the following key clinical and pathologic issues and informative molecular markers are being discussed: (1) What is the preferred outcome parameter for curatively resected low-grade NENs (carcinoid), for example, overall survival or recurrence-free interval? (2) Does the WHO classification combined with a Ki-67 proliferation index and molecular markers, such as OTP and CD44, offer improved prognostication in low-grade NENs? (3) What is the value of a typical versus atypical carcinoid diagnosis on a biopsy specimen in local and metastatic disease? Diagnosis is difficult in biopsy specimens and recent observations of an increased mitotic rate in metastatic carcinoid from typical to atypical and high-grade NEN can further complicate diagnosis. (4) What is the (ir)relevance of morphologically separating large cell neuroendocrine carcinoma (LCNEC) SCLC and the value of molecular markers (RB1 gene and pRb protein or transcription factors NEUROD1, ASCL1, POU2F3, or YAP1 [NAPY]) to predict systemic treatment outcome? (5) Are additional diagnostic criteria required to accurately separate LCNEC from NSCLC in biopsy specimens? Neuroendocrine morphology can be absent owing to limited sample size leading to missed LCNEC diagnoses. Evaluation of genomic studies on LCNEC and marker studies have identified that a combination of napsin A and neuroendocrine markers could be helpful. Hence, to improve clinical practice, we should consider to adjust our NEN classification incorporating prognostic and predictive markers applicable on biopsy specimens to inform a treatment outcome-driven classification.

Large cell neuroendocrine carcinoma arising from the anterior mediastinum

Anterior mediastinal large cell neuroendocrine carcinomas (LCNECs) are extremely rare, extremely aggressive malignancies that carry a dismal prognosis. We discuss a woman aged 60 years who presented with a 2-month history of recurrent severe constant epigastric pain. Abdominal examination revealed massive hepatomegaly and a CT scan of the liver confirmed coarse liver lesions. Histology from a liver biopsy was consistent with a large cell (non-small cell) neuroendocrine carcinoma. A CT scan of the chest showed a large anterior mediastinal mass unrelated to the lung, suggesting that the anterior mediastinum was the primary origin of the tumour. The patient was planned to receive platinum/etoposide chemotherapy for a metastatic mediastinal large cell neuroendocrine carcinoma. Unfortunately, her health deteriorated, and she was unfit to undergo any further treatment. She was treated palliatively and died 2 months after the diagnosis.

Recent advances and current controversies in lung neuroendocrine neoplasms✰

In the lung, neuroendocrine tumors (NETs), namely typical and atypical carcinoids, and neuroendocrine carcinomas (NECs), grouping small cell carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC), make up for distinct tumor entities according to epidemiological, genetic, pathologic and clinical data. The proper classification is essential in clinical practice for diagnosis, prognosis and therapy purposes. Through an extensive literature survey, three perspectives on lung NENs have been revised: i) criteria and terminology on biopsy or cytology samples of primaries or metastases; ii) carcinoids with elevated mitotic counts and/or Ki-67 proliferation rates; iii) relevance of molecular landscape to identify new tumor entities and therapeutic targets. Furthermore, a dispute about lung NEN development has been raised according to emerging molecular models. We herein provide a pathology update on practical topics in the setting of lung NENs according to the current classification (recent advances). We have also reappraised the development of these tumors by modeling risk factors and natural history of disease (recent controversies). Combining recent advances and controversies may help clarify our biological understanding of lung NENs and give practical information for the clinical decision-making process.

Genomics of High-Grade Neuroendocrine Neoplasms: Well-Differentiated Neuroendocrine Tumor with High-Grade Features (G3 NET) and Neuroendocrine Carcinomas (NEC) of Various Anatomic Sites

High-grade neuroendocrine neoplasms (HG-NENs) are clinically aggressive diseases, the classification of which has recently been redefined. They now include both poorly differentiated NENs (neuroendocrine carcinoma, NECs) and high proliferating well-differentiated NENs (called grade 3 neuroendocrine tumors, G3 NETs, in the digestive system). In the last decade, the "molecular revolution" that has affected all fields of medical oncology has also shed light in the understanding of HG NENs heterogeneity and has provided new diagnostic and therapeutic tools, useful in the management of these malignancies. Considering the kaleidoscopic aspects of HG NENs in various anatomical sites, this review systematically addresses the genomic landscape of such neoplasm throughout the more common thoracic and digestive locations, as well as it will consider other rare but not exceptional primary sites, including the skin, the head and neck, and the urogenital system. The revision of the available literature will then be oriented to understand the translational relevance of molecular data, by analyzing conceptual issues, clinicopathological correlations, and unmet needs in this field.

Clinical implications of lung neuroendocrine neoplasm classification

INTRODUCTION

Neuroendocrine neoplasms of the lung (Lung NENs) encompass NE tumors (NETs), which are in turn split into typical and atypical carcinoids, and NE carcinomas (NECs), which group together small-cell carcinoma and large-cell NE carcinoma. This classification is the current basis for orienting the daily practice of these patients, with diagnostic, prognostic, and predictive inferences.

AREAS COVERED

The clinical implications of lung NEN classification are addressed according to three converging perspectives, which were dissected through an extensive literature overview: (1) how to put intratumor heterogeneity into the context of the current classification; (2) how to contextualize immunohistochemistry markers to improve diagnosis, prognosis, and therapy prediction; and (3) how to use immuno-oncology strategies for life-threatening NECs, which still account for 90% or more of lung NENs.

EXPERT OPINION

We provide practical insights to account for intratumor heterogeneity, practice the choice of immunohistochemistry markers, and emphasize once again the added value of immuno-oncology in the setting of personalized medicine of lung NENs.

Neuroendocrine Lung Cancer Mouse Models: An Overview

Simple Summary

Neuroendocrine lung tumors are a heterogeneous group of malignancies that share a common neuroendocrine nature. They range from low- and intermediate-grade typical and atypical carcinoma, to the highly malignant large cell neuroendocrine lung carcinoma and small cell carcinoma, with marked differences in incidences and prognosis. This review delineates the current knowledge of the genetic landscape of the human tumors, its influence in the development of genetically engineered mouse models (GEMMs) and the molecular imaging tools available to detect and monitor these diseases. While small cell lung carcinoma is one of the diseases best represented by GEMMs, there is a worrying lack of animal models for the other members of the group, these being understudied diseases. Regardless of the incidence and material available, they all are in urgent need of effective therapies.

Abstract

Neuroendocrine lung tumors comprise a range of malignancies that extend from benign tumorlets to the most prevalent and aggressive Small Cell Lung Carcinoma (SCLC). They also include low-grade Typical Carcinoids (TC), intermediate-grade Atypical Carcinoids (AC) and high-grade Large Cell Neuroendocrine Carcinoma (LCNEC). Optimal treatment options have not been adequately established: surgical resection when possible is the choice for AC and TC, and for SCLC chemotherapy and very recently, immune checkpoint inhibitors. Some mouse models have been generated based on the molecular alterations identified in genomic analyses of human tumors. With the exception of SCLC, there is a limited availability of (preclinical) models making their development an unmet need for the understanding of the molecular mechanisms underlying these diseases. For SCLC, these models are crucial for translational research and novel drug testing, given the paucity of human material from surgery. The lack of early detection systems for lung cancer point them out as suitable frameworks for the identification of biomarkers at the initial stages of tumor development and for testing molecular imaging methods based on somatostatin receptors. Here, we review the relevant models reported to date, their impact on the understanding of the biology of the tumor subtypes and their relationships, as well as the effect of the analyses of the genetic landscape of the human tumors and molecular imaging tools in their development.

A molecular map of lung neuroendocrine neoplasms

BACKGROUND

Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids).

RESULTS

To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories.

CONCLUSIONS

We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.