Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids

Pulmonary Cytopathology: Current and Future Impact of Microscopy and Immunohistochemistry

With the advancement of tissue procurement techniques, in-depth knowledge of morphology is crucial for cytopathologists to diagnose neoplastic and nonneoplastic lung diseases optimally. Cytopathologists must also be well versed in immunohistochemistry/immunocytochemistry markers and their interpretation for an accurate diagnosis.

Spread Through Air Spaces in Stage I Pulmonary Large Cell Neuroendocrine Carcinoma

BACKGROUND

Pulmonary large cell neuroendocrine carcinoma (LCNEC) represents an exceptionally aggressive and infrequent variant within the realm of non-small cell lung cancer, necessitating surgical intervention as the primary therapeutic approach. However, the postoperative management strategy for early-stage patients continues to be a subject of intense debate and uncertainty.

METHODS

A retrospective analysis was conducted on a cohort of patients diagnosed with LCNEC who underwent surgical resection at Shanghai Pulmonary Hospital between July 2018 and June 2022. Comprehensive assessments, encompassing univariate and multivariate analyses, were performed to evaluate the prognostic significance of these indicators in patient clinical profiles, overall survival (OS), and disease-free survival (DFS).

RESULTS

A comprehensive screening effort identified 171 patients with LCNEC, with 70 stage I patients meeting the criteria for inclusion in the final cohort. Of these, 11 patients (15.7%) presented with combined LCNEC, and 59 (84.3%) exhibited pure LCNEC. Univariate and multivariate analyses both unveiled that spread through air spaces (STAS) status emerged as an independent prognostic determinant for both DFS (P = .003) and OS (P = .013), whereas histologic subtype independently predicted OS (P = .011). Subgroup survival analyses further underscored that the advantageous effects of postoperative chemotherapy were significantly pronounced exclusively among STAS-positive patients, showcasing a statistically significant enhancement in DFS (P = .047) and OS (P = .018).

CONCLUSIONS

STAS may serve as an adverse prognostic factor in stage I LCNEC patients, potentially offering guidance for postoperative chemotherapy decisions.

An Illustrated Review of the Recent 2019 World Health Organization Classification of Neuroendocrine Neoplasms: A Radiologic and Pathologic Correlation

ABSTRACT

Recent advances in molecular pathology and an improved understanding of the etiology of neuroendocrine neoplasms (NENs) have given rise to an updated World Health Organization classification. Since gastroenteropancreatic NENs (GEP-NENs) are the most common forms of NENs and their incidence has been increasing constantly, they will be the focus of our attention. Here, we review the findings at the foundation of the new classification system, discuss how it impacts imaging research and radiological practice, and illustrate typical and atypical imaging and pathological findings. Gastroenteropancreatic NENs have a highly variable clinical course, which existing classification schemes based on proliferation rate were unable to fully capture. While well- and poorly differentiated NENs both express neuroendocrine markers, they are fundamentally different diseases, which may show similar proliferation rates. Genetic alterations specific to well-differentiated neuroendocrine tumors graded 1 to 3 and poorly differentiated neuroendocrine cancers of small cell and large-cell subtype have been identified. The new tumor classification places new demands and creates opportunities for radiologists to continue providing the clinically most relevant report and on researchers to design projects, which continue to be clinically applicable.

Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

Molecular Classification of Gastrointestinal and Pancreatic Neuroendocrine Neoplasms: Are We Ready for That?

In the last two decades, the increasing availability of technologies for molecular analyses has allowed an insight in the genomic alterations of neuroendocrine neoplasms (NEN) of the gastrointestinal tract and pancreas. This knowledge has confirmed, supported, and informed the pathological classification of NEN, clarifying the differences between neuroendocrine carcinomas (NEC) and neuroendocrine tumors (NET) and helping to define the G3 NET category. At the same time, the identification genomic alterations, in terms of gene mutation, structural abnormalities, and epigenetic changes differentially involved in the pathogenesis of NEC and NET has identified potential molecular targets for precision therapy. This review critically recapitulates the available molecular features of digestive NEC and NET, highlighting their correlates with pathological aspects and clinical characteristics of these neoplasms and revising their role as predictive biomarkers for targeted therapy. In this context, the feasibility and applicability of a molecular classification of gastrointestinal and pancreatic NEN will be explored.

Assessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumours in the lungNENomics project

BACKGROUND

Six thoracic pathologists reviewed 259 lung neuroendocrine tumours (LNETs) from the lungNENomics project, with 171 of them having associated survival data. This cohort presents a unique opportunity to assess the strengths and limitations of current World Health Organization (WHO) classification criteria and to evaluate the utility of emerging markers.

PATIENTS AND METHODS

Patients were diagnosed based on the 2021 WHO criteria, with atypical carcinoids (ACs) defined by the presence of focal necrosis and/or 2-10 mitoses per 2 mm2. We investigated two markers of tumour proliferation: the Ki-67 index and phospho-histone H3 (PHH3) protein expression, quantified by pathologists and automatically via deep learning. Additionally, an unsupervised deep learning algorithm was trained to uncover previously unnoticed morphological features with diagnostic value.

RESULTS

The accuracy in distinguishing typical from ACs is hampered by interobserver variability in mitotic counting and the limitations of morphological criteria in identifying aggressive cases. Our study reveals that different Ki-67 cut-offs can categorise LNETs similarly to current WHO criteria. Counting mitoses in PHH3+ areas does not improve diagnosis, while providing a similar prognostic value to the current criteria. With the advantage of being time efficient, automated assessment of these markers leads to similar conclusions. Lastly, state-of-the-art deep learning modelling does not uncover undisclosed morphological features with diagnostic value.

CONCLUSIONS

This study suggests that the mitotic criteria can be complemented by manual or automated assessment of Ki-67 or PHH3 protein expression, but these markers do not significantly improve the prognostic value of the current classification, as the AC group remains highly unspecific for aggressive cases. Therefore, we may have exhausted the potential of morphological features in classifying and prognosticating LNETs. Our study suggests that it might be time to shift the research focus towards investigating molecular markers that could contribute to a more clinically relevant morpho-molecular classification.

Recent advances and future challenges in the diagnosis of neuroendocrine neoplasms

Neuroendocrine neoplasms (NEN) are a heterogeneous group of malignancies with increasing incidence, whose diagnosis is usually delayed, negatively impacting on patients' prognosis. The latest advances in pathological classifications, biomarker identification and imaging techniques may provide early detection, leading to personalized treatment strategies. In this narrative review the recent developments in diagnosis of NEN are discussed including progresses in pathological classifications, biomarker and imaging. Furthermore, the challenges that lie ahead are investigated. By discussing the limitations of current approaches and addressing potential roadblocks, we hope to guide future research directions in this field. This article is proposed as a valuable resource for clinicians and researchers involved in the management of NEN. Update of pathological classifications and the availability of standardized templates in pathology and radiology represent a substantially improvement in diagnosis and communication among clinicians. Additional immunohistochemistry markers may now enrich pathological classifications, as well as miRNA profiling. New and multi-analytical circulating biomarkers, as liquid biopsy and NETest, are being proposed for diagnosis but their validation and availability should be improved. Radiological imaging strives for precise, non-invasive and less harmful technique to improve safety and quality of life in NEN patient. Nuclear medicine may benefit of somatostatin receptors' antagonists and membrane receptor analogues. Diagnosis in NEN still represents a challenge due to their complex biology and variable presentation. Further advancements are necessary to obtain early and minimally invasive diagnosis to improve patients' outcomes.

Investigating pulmonary neuroendocrine cells in human respiratory diseases with airway models

Despite accounting for only ∼0.5% of the lung epithelium, pulmonary neuroendocrine cells (PNECs) appear to play an outsized role in respiratory health and disease. Increased PNEC numbers have been reported in a variety of respiratory diseases, including chronic obstructive pulmonary disease and asthma. Moreover, PNECs are the primary cell of origin for lung neuroendocrine cancers, which account for 25% of aggressive lung cancers. Recent research has highlighted the crucial roles of PNECs in lung physiology, including in chemosensing, regeneration and immune regulation. Yet, little is known about the direct impact of PNECs on respiratory diseases. In this Review, we summarise the current associations of PNECs with lung pathologies, focusing on how new experimental disease models, such as organoids derived from human pluripotent stem cells or tissue stem cells, can help us to better understand the contribution of PNECs to respiratory diseases.

Application of Artificial Intelligence in Neuroendocrine Lung Cancer Diagnosis and Treatment: A Systematic Review

Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes. In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promising avenues for improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities. Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.

Rachel score: a nomogram model for predicting the prognosis of lung neuroendocrine tumors

BACKGROUND

Lung NET, classified in typical carcinoids (TC) and atypical carcinoids (AC), are highly heterogeneous in their biology and prognosis. The histological subtype and TNM stage are well-established prognostic factors for lung NET. In a previous work by our group, we demonstrated a significant impact of laterality on lung NET survival outcomes.

MATERIALS AND METHODS

We developed a nomogram that integrates relevant prognostic factors to predict lung NET outcomes. By adding the scores for each of the variables included in the model, it was possible to obtain a prognostic score (Rachel score). Wilcoxon non-parametric statistical test was applied among parameters and Harrell's concordance index was used to measure the models' predictive power. To test the discriminatory power and the predictive accuracy of the model, we calculated Gonen and Heller concordance index. Time-dependent ROC curves and their area under the curve (AUC) were used to evaluate the models' predictive performance.

RESULTS

By applying Rachel score, we were able to identify three prognostic groups (specifically, high, medium and low risk). These three groups were associate to well-defined ranges of points according to the obtained nomogram (I: 0-90, II: 91-130; III: > 130 points), providing a useful tool for prognostic stratification. The overall survival (OS) and progression free survival (PFS) Kaplan-Meier curves confirmed significant differences (p < 0.0001) among the three groups identified by Rachel score.

CONCLUSIONS

A prognostic nomogram was developed, incorporating variables with significant impact on lung NET survival. The nomogram showed a satisfactory and stable ability to predict OS and PFS in this population, confirming the heterogeneity beyond the histopathological diagnosis of TC vs AC.

An in-silico analysis reveals further evidence of an aggressive subset of lung carcinoids sharing molecular features of high-grade neuroendocrine neoplasms

Little is known as to whether there may be any pathogenetic link between pulmonary carcinoids and neuroendocrine carcinomas (NECs). A gene signature we previously found to cluster pulmonary carcinoids, large cell neuroendocrine carcinoma (LCNEC) and small cell lung carcinoma (SCLC), and which encompassed MEN1, MYC, MYCL1, RICTOR, RB1, SDHA, SRC and TP53 mutations or copy number variations (CNVs), was used to reclassify an independent cohort of 54 neuroendocrine neoplasms (NENs) [31 typical carcinoids (TC), 11 atypical carcinoids (AC) and 12 SCLC], by means of transcriptome and mutation data. Unsupervised clustering analysis identified two histology-independent clusters, namely CL1 and CL2, where 17/42 (40.5%) carcinoids and all the SCLC samples fell into the latter. CL2 carcinoids affected survival adversely, were enriched in T to G transversions or T > C/C > T transitions in the context of specific mutational signatures, presented with at least 1.5-fold change (FC) increase of gene mutations including TSC2, SMARCA2, SMARCA4, ERBB4 and PTPRZ1, differed for gene expression and showed epigenetic changes in charge of MYC and MTORC1 pathways, cellular senescence, inflammation, high-plasticity cell state and immune system exhaustion. Similar results were also found in two other independent validation sets comprising 101 lung NENs (24 carcinoids, 21 SCLC and 56 LCNEC) and 30 carcinoids, respectively. We herein confirmed an unexpected sharing of molecular traits along the spectrum of lung NENs, with a subset of genomically distinct aggressive carcinoids sharing molecular features of high-grade neuroendocrine neoplasms.

Real-World comprehensive genomic profiling data for diagnostic clarity in pulmonary Large-Cell neuroendocrine carcinoma

BACKGROUND

Pulmonary large-cell neuroendocrine carcinoma (LCNEC) is an uncommon subtype of lung cancer believed to represent a spectrum of tumors sharing characteristics of both small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Other groups have proposed genomic LCNEC subtypes, including small cell-like, non-small cell-like, and carcinoid-like subtypes. The primary goal of this study was to better define the NSCLC-like subtype with comprehensive genomic profiling (CGP).

METHODS

An institutional database was queried to identify tissue specimens (TBx, N = 1,426) and liquid biopsies (LBx, N = 39) submitted for CGP during routine clinical care (8/2014 - 7/2023) with a disease ontology of LCNEC. TBx were profiled with FoundationOne® (F1) or F1CDx, using hybrid-capture technology to detect genomic alterations (GAs).

RESULTS

1,426 LCNEC samples were genomically profiled. The presence of RB1 and TP53 genomic alterations (GAs) were used to define a SCLC-like subtype (n = 557). A carcinoid-like group was defined by the presence of MEN1 mutation in the absence of TP53 GAs (n = 25). The remaining 844 samples were compared to the SCLC-like group and GAs enriched relative to the SCLC-like samples with a false discovery rate (FDR) < 0.0001 were used to define a NSCLC-like group. These NSCLC-like subtype-defining GAs included SMARCA4, KRAS, FGF3/4/19, STK11, CDKN2A/B, MTAP, and CCND1. Under this schema, 530 samples were classified as NSCLC-like and 314 remained unclassified.

CONCLUSIONS

Large-scale CGP can better characterize biologically distinct molecular subtypes in LCNEC. Further studies to define how these molecular subtypes may help inform treatment decisions in this complex and challenging malignancy are warranted.

Prognostic Thresholds of Mitotic Count and Ki-67 Labeling Index for Recurrence and Survival in Lung Atypical Carcinoids

Atypical carcinoid (AC) is a rare neuroendocrine neoplasm of the lung, which exhibits a varying malignant potential. In this study, we aimed to identify the prognostic thresholds of the mitotic count and Ki-67 labeling index for recurrence and survival in AC. We retrospectively reviewed 78 patients who had been radically resected for AC and calculated said thresholds using time-dependent receiver operating characteristic curves and the Youden index. We then dichotomized the patients into groups of above or below these thresholds and estimated the cumulative incidences of the groups using the Aalen-Johansen estimator. We compared the groups using univariable and multivariable Fine-Gray subdistribution hazard models. Our findings show that more patients recurred and died from this disease if their mitotic count exceeded three and four mitoses per 2 mm2, respectively, or if their Ki-67 labeling index exceeded 14% and 11%, respectively. Both thresholds independently predicted survival (p < 0.001 and p = 0.015, respectively). These thresholds may serve as a valuable tool for clinicians and researchers in making treatment plans and predicting outcomes for patients with AC.

Updates on lung neuroendocrine neoplasm classification

Lung neuroendocrine neoplasms (NENs) are a heterogeneous group of pulmonary neoplasms showing different morphological patterns and clinical and biological characteristics. The World Health Organisation (WHO) classification of lung NENs has been recently updated as part of the broader attempt to uniform the classification of NENs. This much-needed update has come at a time when insights from seminal molecular characterisation studies revolutionised our understanding of the biological and pathological architecture of lung NENs, paving the way for the development of novel diagnostic techniques, prognostic factors and therapeutic approaches. In this challenging and rapidly evolving landscape, the relevance of the 2021 WHO classification has been recently questioned, particularly in terms of its morphology-orientated approach and its prognostic implications. Here, we provide a state-of-the-art review on the contemporary understanding of pulmonary NEN morphology and the potential contribution of artificial intelligence, the advances in NEN molecular profiling with their impact on the classification system and, finally, the key current and upcoming prognostic factors.

Clinical impact of mixed pulmonary carcinoma and carcinoid: the driver from their mono-clonal origin

The combination of neuroendocrine/non neuroendocrine lung tumors (CNNELT) mentioned in the last edition of the World Health Organization (WHO) of Thoracic Tumors refers to small cell carcinoma (SCLC) or large cell neuroendocrine carcinoma (LCNEC) mixed with any other non-small cell lung carcinoma (NSCLC). Typical Carcinoid (TC)/Atypical Carcinoid (AC) combined with NSCLC is not included among this category. However, case reports of TC/AC combined with NSCLC have been described. We previously reported 2 cases of lung adenocarcinoma (LUA) mixed with carcinoid sharing mutations in both components supporting the hypothesis of a clonal origin. We extended our analysis to other four cases of mixed NSCLC-carcinoid by performing targeted-DNA and RNA-based NGS analysis in both primary and their paired lymph nodes metastasis. In all cases, LUA and AC components shared at least 1 common mutation (KRAS driver mutation p.Gly12Val in cases 1 and 3, AKAP13-RET fusion in case 2, and missense KRAS driver mutation p.Gly12Ala in case 4, reinforcing the hypothesis of a clonal origin. Moreover, the same mutation was detected in the metastasis constituted only by AC (cases 2 and 4). Although it is a rare malignancy in the lung, mixed LUA and TC/AC could be included among the histotypes for which a deep molecular characterization of both components is needed to identify the presence of potential druggable genetic alterations.

Druggable growth dependencies and tumor evolution analysis in patient-derived organoids of neuroendocrine neoplasms from multiple body sites

Neuroendocrine neoplasms (NENs) comprise well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). Treatment options for patients with NENs are limited, in part due to lack of accurate models. We establish patient-derived tumor organoids (PDTOs) from pulmonary NETs and derive PDTOs from an understudied subtype of NEC, large cell neuroendocrine carcinoma (LCNEC), arising from multiple body sites. PDTOs maintain the gene expression patterns, intra-tumoral heterogeneity, and evolutionary processes of parental tumors. Through hypothesis-driven drug sensitivity analyses, we identify ASCL1 as a potential biomarker for response of LCNEC to treatment with BCL-2 inhibitors. Additionally, we discover a dependency on EGF in pulmonary NET PDTOs. Consistent with these findings, we find that, in an independent cohort, approximately 50% of pulmonary NETs express EGFR. This study identifies an actionable vulnerability for a subset of pulmonary NETs, emphasizing the utility of these PDTO models.

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.

Targeting Replication Stress and Chemotherapy Resistance with a Combination of Sacituzumab Govitecan and Berzosertib: A Phase I Clinical Trial

PURPOSE

Despite promising preclinical studies, toxicities have precluded combinations of chemotherapy and DNA damage response (DDR) inhibitors. We hypothesized that tumor-targeted chemotherapy delivery might enable clinical translation of such combinations.

PATIENTS AND METHODS

In a phase I trial, we combined sacituzumab govitecan, antibody-drug conjugate (ADC) that delivers topoisomerase-1 inhibitor SN-38 to tumors expressing Trop-2, with ataxia telangiectasia and Rad3-related (ATR) inhibitor berzosertib. Twelve patients were enrolled across three dose levels.

RESULTS

Treatment was well tolerated, with improved safety over conventional chemotherapy-based combinations, allowing escalation to the highest dose. No dose-limiting toxicities or clinically relevant ≥grade 4 adverse events occurred. Tumor regressions were observed in 2 patients with neuroendocrine prostate cancer, and a patient with small cell lung cancer transformed from EGFR-mutant non-small cell lung cancer.

CONCLUSIONS

ADC-based delivery of cytotoxic payloads represents a new paradigm to increase efficacy of DDR inhibitors. See related commentary by Berg and Choudhury, p. 3557.

Precision medicine in gastroenteropancreatic neuroendocrine neoplasms: Where are we in 2023?

Precision medicine describes a target-related approach to tailoring diagnosis and treatment of the individual patient. While this personalized approach is revoluzionizing many areas of oncology, it is quite late in the field of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs), in which there are few molecular alterations to be therapeutically targeted. We critically reviewed the current evidence about precision medicine in GEP NENs, focusing on potential clinically relevant actionable targets for GEP NENs, such as the mTOR pathway, MGMT, hypoxia markers, RET, DLL-3, and some general agnostic targets. We analysed the main investigational approaches with solid and liquid biopsies. Furthermore, we reviewed a model of precision medicine more specific for NENs that is the theragnostic use of radionuclides. Overall, currently no true predictive factors for therapy have been validated so far in GEP NENs, and the personalized approach is based more on clinical thinking within a NEN-dedicated multidisciplinary team. However, there is a robust background to suppose that precision medicine, with the theragnostic model will yield new insights in this context soon.

Spatial and temporal heterogeneity of digestive neuroendocrine neoplasms

Neuroendocrine neoplasms (NENs) are initially monoclonal neoplasms that progressively become polyclonal, with very different genotypic and phenotypic characteristics leading to biological differences, including the Ki-67 proliferation index, morphology, or sensitivity to treatments. Whereas inter-patient heterogeneity has been well described, intra-tumor heterogeneity has been little studied. However, NENs present a high degree of heterogeneity, both spatially within the same location or between different lesions, and through time. This can be explained by the emergence of tumor subclones with different behaviors. These subpopulations can be distinguished by the Ki-67 index, but also by the expression of hormonal markers or by differences in the intensity of uptake on metabolic imaging, such as ⁶⁸Ga-somatostatin receptor and Fluorine-18 fluorodeoxyglucose positron emission tomography. As these features are directly related to prognosis, it seems mandatory to move toward a standardized, improved selection of the tumor areas to be studied to be as predictive as possible. The temporal evolution of NENs frequently leads to changes in tumor grade over time, with impact on prognosis and therapeutic decision-making. However, there is no recommendation regarding systematic biopsy of NEN recurrence or progression, and which lesion to sample. This review aims to summarize the current state of knowledge, the main hypotheses, and the main implications regarding intra-tumor spatial and temporal heterogeneity in digestive NENs.

Comparative expression analysis of immune-related markers in surgically resected lung neuroendocrine neoplasms

BACKGROUND

Although immunotherapy has led to a paradigm shift in the treatment of lung cancer, the therapeutic approaches for lung neuroendocrine neoplasms (LNENs) are still limited. Our aim was to explore the immunological landscape and the expression of immune checkpoint markers in LNENs.

METHODS

Surgically removed tumor samples of 26 atypical carcinoid (AC), 30 large cell neuroendocrine carcinoma (LCNEC) and 29 small cell lung cancer (SCLC) patients were included. The immune phenotype of each tumor type was assessed by using a panel of 15 immune-related markers. As these markers are potentially expressed by immune cells and/or tumor cells, they might serve as putative targets for immunotherapy. Expression patterns were measured by immunohistochemistry and correlated with clinicopathological parameters and prognosis.

RESULTS

Unsupervised hierarchical clustering revealed distinct immunologic profiles across tumor types. Specifically, AC tumors were characterized by high tumor cell CD40 expression and low levels of immune infiltrates whereas SCLC samples had a high CD47 and Inducible T Cell Costimulator (ICOS) expression in tumor cells and immune cells, respectively. High CD70 and CD137 expression by tumor cells as well as elevated expression of CD27, Lymphocyte Activation Gene 3 (LAG3), and CD40 by immune cells were characteristic for LCNEC samples. Overall, SCLC and LCNEC tumors had a more immunogenic phenotype than AC samples. High tumor cell CD47 and CD40 expressions were associated with impaired and improved survival outcomes, respectively.

CONCLUSIONS

By providing insights into the widely divergent immunologic profiles of LNENs, our results might serve as a basis for the development of novel immunotherapy-related approaches in these devastating malignancies.

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.

Targeting neuroendocrine tumors with octreotide and lanreotide: Key points for clinical practice from NET specialists

Octreotide and lanreotide are the two somatostatin analogs (SSA) currently available in clinical practice. They have been approved first to control the clinical syndrome (mainly carcinoid syndrome) associated with functioning neuroendocrine tumors (NET) and later for tumor growth control in advanced low/intermediate grade NET. Although evidence regarding their role, especially as antiproliferative therapy, has been increasing over the years some clinical indications remain controversial. Solicited by AIOM (Italian Association of Medical Oncology) a group of clinicians from various specialties, including medical oncology, endocrinology, and gastroenterology, deeply involved in NET for their clinical and research activity, addressed eight open questions, critically reviewing evidence and guidelines and sharing clinical take-home messages. The questions regarded the use of long-acting octreotide and lanreotide in the following settings: functioning and non-functioning NET refractory to label dose, first-line metastatic pulmonary NET, combination with other therapy with an antiproliferative intent, maintenance in NET responding to other therapies, adjuvant treatment, Ki-67-related cut-off, somatostatin receptor imaging, safety, and feasibility. The level of evidence is not absolute for the majority of these clinical contexts, so it is recommended to distinguish routine versus sporadic utilization in very selected cases. Mention of such specific issues by the main European guidelines (ENETS, European Neuroendocrine Tumor Society, and ESMO, European Society for Medical Oncology) was explored and their position reported. However, different clinical decisions on single patients could be made if the case is carefully discussed within a NET-dedicated multidisciplinary team.

The uprise of RNA biology in neuroendocrine neoplasms: altered splicing and RNA species unveil translational opportunities

Neuroendocrine neoplasms (NENs) comprise a highly heterogeneous group of tumors arising from the diffuse neuroendocrine system. NENs mainly originate in gastrointestinal, pancreatic, and pulmonary tissues, and despite being rare, show rising incidence. The molecular mechanisms underlying NEN development are still poorly understood, although recent studies are unveiling their genomic, epigenomic and transcriptomic landscapes. RNA was originally considered as an intermediary between DNA and protein. Today, compelling evidence underscores the regulatory relevance of RNA processing, while new RNA molecules emerge with key functional roles in core cell processes. Indeed, correct functioning of the interrelated complementary processes comprising RNA biology, its processing, transport, and surveillance, is essential to ensure adequate cell homeostasis, and its misfunction is related to cancer at multiple levels. This review is focused on the dysregulation of RNA biology in NENs. In particular, we survey alterations in the splicing process and available information implicating the main RNA species and processes in NENs pathology, including their role as biomarkers, and their functionality and targetability. Understanding how NENs precisely (mis)behave requires a profound knowledge at every layer of their heterogeneity, to help improve NEN management. RNA biology provides a wide spectrum of previously unexplored processes and molecules that open new avenues for NEN detection, classification and treatment. The current molecular biology era is rapidly evolving to facilitate a detailed comprehension of cancer biology and is enabling the arrival of personalized, predictive and precision medicine to rare tumors like NENs.

Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity

Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Neuroendocrine Neoplasms: Genetics and Epigenetics

Neuroendocrine neoplasms (NENs) are a group of rare, heterogeneous tumors of neuroendocrine cell origin, affecting a range of different organs. The clinical management of NENs poses significant challenges, as tumors are often diagnosed at an advanced stage where overall survival remains poor with current treatment regimens. In addition, a host of complex and often unique molecular changes underpin the pathobiology of each NEN subtype. Exploitation of the unique genetic and epigenetic signatures driving each NEN subtype provides an opportunity to enhance the diagnosis, treatment, and monitoring of NEN in an emerging era of individualized medicine.

Ascl1 and OTP tumour expressions are associated with disease-free survival in lung atypical carcinoids

According to World Health Organization guidelines, atypical carcinoids (ACs) are well-differentiated lung neuroendocrine tumours with 2-10 mitoses/2 mm² and/or foci of necrosis (usually punctate). Besides morphological criteria, no further tools in predicting AC clinical outcomes are proposed. The aim of this work was to identify novel factors able to predict AC disease aggressiveness and progression. METHODS AND RESULTS: Three hundred-seventy lung carcinoids were collected and centrally reviewed by two expert pathologists. Morphology and immunohistochemical markers (Ki-67, TTF-1, CD44, OTP, SSTR2A, Ascl1, p53, and Rb1) were studied and correlated with disease-free survival (DFS) and overall survival (OS). Fifty-eight of 370 tumours were defined as AC. Survival analysis showed that patients with Ascl1 + ACs and those with OTP-ACs had a significantly worse DFS than patients with Ascl1-ACs and OTP + ACs, respectively. Combining Ascl1 and OTP expressions, groups were formed reflecting the aggressiveness of disease (P = 0.0005). Ki-67 ≥10% patients had a significantly worse DFS than patients with Ki-67 <10%. At multivariable analysis, Ascl1 (present versus absent, hazard ratio [HR] = 3.42, 95% confidence interval [CI] 1.35-8.65, P = 0.009) and OTP (present versus absent, HR = 0.26, 95% CI 0.10-0.68, P = 0.006) were independently associated with DFS. The prognosis of patients with Ki-67 ≥10% tended to be worse compared to that with Ki-67 <10%. On the contrary, OTP (present versus absent, HR = 0.28, 95% CI 0.09-0.89, P = 0.03), tumour stage (III-IV versus I-II, HR = 4.25, 95% CI 1.42-12.73, P = 0.01) and increasing age (10-year increase, HR = 1.67, 95% CI 1.04-2.68, P = 0.03) were independently associated with OS. CONCLUSION: This retrospective analysis of lung ACs showed that Ascl1 and OTP could be the main prognostic drivers of postoperative recurrence.

A guide to multi-omics data collection and integration for translational medicine

The emerging high-throughput technologies have led to the shift in the design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods. Currently, there is no clear consensus on the best combination of omics to include and the data integration methodologies required for their analysis. This article aims to guide the design of multi-omics studies in the field of translational medicine regarding the types of omics and the integration method to choose. We review articles that perform the integration of multiple omics measurements from patient samples. We identify five objectives in translational medicine applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, and (v) understand regulatory processes. We describe common trends in the selection of omic types combined for different objectives and diseases. To guide the choice of data integration tools, we group them into the scientific objectives they aim to address. We describe the main computational methods adopted to achieve these objectives and present examples of tools. We compare tools based on how they deal with the computational challenges of data integration and comment on how they perform against predefined objective-specific evaluation criteria. Finally, we discuss examples of tools for downstream analysis and further extraction of novel insights from multi-omics datasets.

Morphologic and Molecular Heterogeneity of Cervical Neuroendocrine Neoplasia: A Report of 14 Cases

Neuroendocrine neoplasms (NENs) of the cervix are rare aggressive tumors associated with poor prognosis and only limited treatment options. Although there is some literature on molecular underpinnings of cervical small cell neuroendocrine carcinomas (SCNECs), detailed morphologic and associated molecular characteristics of cervical NENs remains to be elucidated. Herein, 14 NENs (SCNEC: 6, large cell neuroendocrine carcinoma [LCNEC]: 6, neuroendocrine tumor [NET]: 2), including 5 admixed with human papillomavirus (HPV)-associated adenocarcinoma (carcinoma admixed with neuroendocrine carcinoma) were analyzed. All except 3 SCNECs were HPV16/18 positive. TP53 (3) and/or RB1 (4) alterations (3 concurrent) were only seen in SCNECs (4/6) and were enriched in the HPV16/18-negative tumors. The other most common molecular changes in neuroendocrine carcinomas (NECs) overlapping with those reported in the literature for cervical carcinomas involved PI3K/MAPK pathway (4) and MYC (4) and were seen in both SCNECs and LCNECs. In contrast, the 2 NETs lacked any significant alterations. Two LCNECs admixed with adenocarcinoma had enough material to sequence separately each component. In both pathogenic alterations were shared between the 2 components, including ERBB2 amplification in one and an MSH6 mutation with MYC amplification in the other. Overall, these findings suggest that cervical HPV-associated NETs are genomically silent and high-grade NECs (regardless of small or large cell morphology) share molecular pathways with common cervical carcinomas as it has been reported in the endometrium and are different from NECs at other sites. Molecular analysis of these highly malignant neoplasms might inform the clinical management for potential therapeutic targets.

Development and verification of new monoclonal orthopedia homeobox (OTP) specific antibodies for pulmonary carcinoid diagnostics

Background

Orthopedia homeobox (OTP) has shown to be a useful prognostic marker to predict outcome in pulmonary carcinoids, which is also supported by the World Health Organization. However, the discontinuation of the initially used polyclonal antibody and absence of a reliable routinely applicable monoclonal OTP antibody hampers implementation in routine diagnostics. Here, new monoclonal antibodies directed against OTP were developed and verified on formalin-fixed paraffin-embedded tissue of pulmonary neuroendocrine tumors (NETs) for clinical diagnostics.

Methods

OTP specific monoclonal antibodies were produced from mice immunised with a recombinant human OTP protein fragment. Enzyme-linked immunosorbent assay (ELISA) positive hybridomas were evaluated using immunohistochemistry (IHC). Following epitope-mapping and isotyping, purified monoclonal antibodies were validated for IHC in formalin-fixed paraffin-embedded tissues, the optimal dilution was determined, and results were cross validated with the OTP polyclonal antibody (HPA039365, Atlas Antibodies). Staining protocols were optimized on two automated staining platforms and performance was harmonized using a tissue microarray (TMA).

Results

Two clones (CL11222 and CL11225) were selected for purified monoclonal antibody (mAb) production. Intratumor heterogeneity assessment revealed similar performance for both clones. While clone CL11225 displayed a unique epitope compared to those present in the polyclonal antibody, this clone performed most similar to the polyclonal antibody. Cross-platform assessment revealed an excellent agreement for clone CL11225 while clone CL11222 showed somewhat discordant results on Dako.

Conclusions

New monoclonal OTP specific antibodies have been developed and verified on different automated immunohistochemical staining platforms. The OTP specific monoclonal antibodies showed excellent agreement with the often-used polyclonal antibody allowing application in routine diagnostics.

Small Biopsy and Cytology of Pulmonary Neuroendocrine Neoplasms: Brief Overview of Classification, Immunohistochemistry, Molecular Profiles, and World Health Organization Updates

Pulmonary neuroendocrine neoplasms comprise ~20% of all lung tumors. Typical carcinoid, atypical carcinoid, small cell carcinoma, and large cell neuroendocrine carcinoma represent the 4 major distinct subtypes recognized on resections. This review provides a brief overview of the cytomorphologic features and the 2021 World Health Organization classification of these tumor types on small biopsy and cytology specimens. Also discussed are the role of immunohistochemistry in the diagnosis and molecular signatures of pulmonary neuroendocrine tumors.

Survival Benefit of Adjuvant Chemotherapy in Pulmonary Carcinoid Tumors

Pulmonary carcinoid tumors are a rare subtype of neuroendocrine cell tumor found in approximately 1–2% of lung cancers. Management is primarily through surgical resection, with limited benefit of adjuvant therapy in the clinical setting. Genomic profiling is in the nascent stages to molecularly classify these tumors, but there are promising insights for future targeted therapy. A total of 80 abstracts were analyzed for further review with 11 included in our final analysis. Only 4 of the 11 reviewed in depth provided statistical analysis. We evaluated PFS, OS, 1- and 5-year survival as mentioned in the studies. Nodal and KI67 status were also analyzed. Based on the current literature, there is no definitive evidence that adjuvant chemotherapy after resection confers a survival benefit in typical or atypical carcinoids.

Combined Large Cell Neuroendocrine Carcinomas of the Lung: Integrative Molecular Analysis Identifies Subtypes with Potential Therapeutic Implications

Simple Summary

In this manuscript, we offer an integrated molecular analysis of 44 combined large cell neuroendocrine carcinomas (CoLCNECs) in order to deepen the knowledge about these rare histotypes and to clarify their relationship with lung cancers. In the present state of research, molecular studies are still scant, consisting of small and heterogeneous cohorts, and the genomic landscape is poorly characterized. This study shows that CoLCNECs constitute a standalone group of neuroendocrine neoplasm, with three different molecular profiles, two of which overlap with pure LCNEC or adenocarcinoma. CoLCNECs can be considered an independent histologic category with specific genomic and transcriptomic features, different and therefore not comparable to other lung cancers. Indeed, in addition to a histological re-evaluation of lung cancer classification, our study may help to develop a new diagnostic approach for novel and personalized treatments in CoLCNECs.

Abstract

Background: Combined large cell neuroendocrine carcinoma (CoLCNEC) is given by the association of LCNEC with adeno or squamous or any non-neuroendocrine carcinoma. Molecular bases of CoLCNEC pathogenesis are scant and no standardized therapies are defined. Methods: 44 CoLCNECs: 26 with adenocarcinoma (CoADC), 7 with squamous cell carcinoma (CoSQC), 3 with small cell carcinoma (CoSCLC), 4 with atypical carcinoid (CoAC) and 4 napsin-A positive LCNEC (NapA+), were assessed for alterations in 409 genes and transcriptomic profiling of 20,815 genes. Results: Genes altered included TP53 (n = 30), RB1 (n = 14) and KRAS (n = 13). Targetable alterations included six KRAS G12C mutations and ALK-EML4 fusion gene. Comparison of CoLCNEC transcriptomes with 86 lung cancers of pure histology (8 AC, 19 ADC, 19 LCNEC, 11 SCLC and 29 SQC) identified CoLCNEC as a separate entity of neuroendocrine tumours with three different molecular profiles, two of which showed a non-neuroendocrine lineage. Hypomethylation, activation of MAPK signalling and association to immunotherapy signature specifically characterized each of three CoLCNEC molecular clusters. Prognostic stratification was also provided. Conclusions: CoLCNECs are an independent histologic category. Our findings support the extension of routine evaluation of KRAS mutations, fusion genes and immune-related markers to offer new perspectives in the therapeutic management of CoLCNEC.

DLL3 as an Emerging Target for the Treatment of Neuroendocrine Neoplasms

INTRODUCTION

Neuroendocrine neoplasms (NEN) are heterogeneous malignancies that can arise at almost any anatomical site and are classified as biologically distinct well-differentiated neuroendocrine tumors (NET) and poorly differentiated neuroendocrine carcinomas (NEC). Current systemic therapies for advanced disease, including targeted therapies, chemotherapy, and immunotherapy, are associated with limited duration of response. New therapeutic targets are needed. One promising target is delta-like ligand 3 (DLL3), an inhibitory ligand of the Notch receptor whose overexpression on the surface of NEN is associated with tumorigenesis.

METHODS

This article is a narrative review that highlights the role of DLL3 in NEN progression and prognosis, the potential for therapeutic targeting of DLL3, and ongoing studies of DLL3-targeting therapies. Classification, incidence, pathogenesis, and current management of NEN are reviewed to provide biological context and illustrate the unmet clinical needs.

DISCUSSION

DLL3 is overexpressed in many NENs, implicated in tumor progression, and is typically associated with poor clinical outcomes, particularly in patients with NEC. Targeted therapies using DLL3 as a homing beacon for cytotoxic activity mediated via several different mechanisms (eg, antibody-drug conjugates, T-cell engager molecules, CAR-Ts) have shown promising clinical activity in small-cell lung cancer (SCLC). DLL3 may be a clinically actionable target across NEN.

CONCLUSIONS

Current treatment options for NEN do not provide sustained responses. DLL3 is expressed on the cell surface of many NEN types and is associated with poor clinical outcomes. Initial clinical studies targeting DLL3 therapeutically in SCLC have been promising, and additional studies are expanding this approach to the broader group of NEN.

Neuroendocrine tumor G3 of bronchopulmonary origin and its classification

Neuroendocrine tumors (NET) with high proliferative activity (Ki-67 index >20% and/or mitotic counts >2 mm² ) are defined as NET G3 in the 2019 World Health Organization (WHO) classification of digestive system neuroendocrine neoplasms (NENs). NETs G3 occur mostly in the pancreas, colon, rectum, and stomach and only rarely in the small intestine and the appendix. In the bronchopulmonary system, similar tumors have also been recognized and were mostly classified as atypical carcinoid (AC) or large cell neuroendocrine carcinoma. Bronchopulmonary NENs that were classified as NETs G3 are characterized by histological and immunohistochemical similarities with carcinoids/NETs, and a clinical course that is more aggressive than with ACs and similar to that of neuroendocrine carcinomas. The morphomolecular and clinical features of bronchopulmonary neoplasms with a high proliferative activity were reviewed and a future classification system that is applicable for both digestive and bronchopulmonary NETs is proposed.

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.

Differential Orthopedia Homeobox expression in pulmonary carcinoids is associated with changes in DNA methylation

Limited number of tumor types have been examined for Orthopedia Homeobox (OTP) expression. In pulmonary carcinoids, loss of expression is a strong indicator of poor prognosis. Here, we investigated OTP expression in 37 different tumor types, and the association between OTP expression and DNA methylation levels in lung neuroendocrine neoplasms. We analyzed publicly available multi-omics data (whole-exome-, whole-genome-, RNA sequencing and Epic 850K-methylation array) of 58 typical carcinoids, 27 atypical carcinoids, 69 large cell neuroendocrine carcinoma and 51 small cell lung cancer patients and TCGA (The Cancer Genome Atlas) data of 33 tumor types. 850K-methylation analysis was cross-validated using targeted pyrosequencing on 35 carcinoids. We report bimodality of OTP expression in carcinoids (OTPhigh vs OTPlow group, likelihood-ratio test P = 1.5 × 10-2 ), with the OTPhigh group specific to pulmonary carcinoids while absent from all other cohorts analyzed. Significantly different DNA methylation levels were observed between OTPhigh and OTPlow carcinoids in 12/34 OTP infinium probes (FDR < 0.05 and β-value effect size > .2). OTPlow carcinoids harbor high DNA methylation levels as compared to OTPhigh carcinoids. OTPlow carcinoids showed a significantly worse overall survival (log-rank test P = .0052). Gene set enrichment analysis for somatically mutated genes associated with hallmarks of cancer showed robust enrichment of three hallmarks in the OTPlow group, that is, sustaining proliferative signaling, evading growth suppressor and genome instability and mutation. Together our data suggest that high OTP expression is a unique feature of pulmonary carcinoids with a favorable prognosis and that in poor prognostic patients, OTP expression is lost, most likely due to changes in DNA methylation levels.

Prognostic significance of laterality in lung neuroendocrine tumors

PURPOSE

Well-differentiated lung neuroendocrine tumors (Lu-NET) are classified as typical (TC) and atypical (AC) carcinoids, based on mitotic counts and necrosis. However, prognostic factors, other than tumor node metastasis (TNM) stage and the histopathological diagnosis, are still lacking. The current study is aimed to identify potential prognostic factors to better stratify lung NET, thus, improving patients' treatment strategy and follow-up.

METHODS

A multicentric retrospective study, including 300 Lung NET, all surgically removed, from Italian and Spanish Institutions.

RESULTS

Median age 61 years (13-86), 37.7% were males, 25.0% were AC, 42.0% were located in the lung left parenchyma, 80.3% presented a TNM stage I-II. Mitotic count was ≥2 per 10 high-power field (HPF) in 24.7%, necrosis in 13.0%. Median overall survival (OS) was 46.1 months (0.6-323), median progression-free survival (PFS) was 36.0 months (0.3-323). Female sex correlated with a more indolent disease (T1; N0; lower Ki67; lower mitotic count and the absence of necrosis). Left-sided primary tumors were associated with higher mitotic count and necrosis. At Cox-multivariate regression model, age, left-sided tumors, nodal (N) positive status and the diagnosis of AC resulted independent negative prognostic factors for PFS and OS.

CONCLUSIONS

This study highlights that laterality is an independent prognostic factors in Lu-NETs, with left tumors being less frequent but showing a worse prognosis than right ones. A wider spectrum of clinical and pathological prognostic factors, including TNM stage, age and laterality is suggested. These parameters could help clinicians to personalize the management of Lu-NET.

TP53, CDKN2A/P16, and NFE2L2/NRF2 regulate the incidence of pure- and combined-small cell lung cancer in mice

Studies have shown that Nrf2^E79Q/+ is one of the most common mutations found in human tumors. To elucidate how this genetic change contributes to lung cancer, we compared lung tumor development in a genetically-engineered mouse model (GEMM) with dual Trp53/p16 loss, the most common mutations found in human lung tumors, in the presence or absence of Nrf2^E79Q/+. Trp53/p16-deficient mice developed combined-small cell lung cancer (C-SCLC), a mixture of pure-SCLC (P-SCLC) and large cell neuroendocrine carcinoma. Mice possessing the LSL-Nrf2^E79Q mutation showed no difference in the incidence or latency of C-SCLC compared with Nrf2^+/+ mice. However, these tumors did not express NRF2 despite Cre-induced recombination of the LSL-Nrf2^E79Q allele. Trp53/p16-deficient mice also developed P-SCLC, where activation of the NRF2^E79Q mutation associated with a higher incidence of this tumor type. All C-SCLCs and P-SCLCs were positive for NE-markers, NKX1-2 (a lung cancer marker) and negative for P63 (a squamous cell marker), while only P-SCLC expressed NRF2 by immunohistochemistry. Analysis of a consensus NRF2 pathway signature in human NE⁺-lung tumors showed variable activation of NRF2 signaling. Our study characterizes the first GEMM that develops C-SCLC, a poorly-studied human cancer and implicates a role for NRF2 activation in SCLC development.

TGF-β Signaling Pathway-Based Model to Predict the Subtype and Prognosis of Head and Neck Squamous Cell Carcinoma

Background: Although immunotherapy with immune checkpoint therapy has been used to treat head and neck squamous cell carcinoma (HNSCC), response rates and treatment sensitivity remain limited. Recent studies have indicated that transforming growth factor-β (TGF-β) may be an important target for novel cancer immunotherapies.

Materials and methods: We collected genomic profile data from The Cancer Genome Atlas and Gene Expression Omnibus. The least absolute shrinkage and selection operator method and Cox regression were used to establish a prognostic model. Gene set enrichment analysis was applied to explore biological functions. Tracking of indels by decomposition and subclass mapping algorithms were adopted to evaluate immunotherapy efficiency.

Result: We established a seven TGF-β pathway-associated gene signature with good prediction efficiency. The high-risk score subgroup mainly showed enrichment in tumor-associated signaling such as hypoxia and epithelial-mesenchymal transition (EMT) pathways; This subgroup was also associated with tumor progression. The low-risk score subgroup was more sensitive to immunotherapy and the high-risk score subgroup to cisplatin, erlotinib, paclitaxel, and crizotinib.

Conclusion: The TGF-β pathway signature gene model provides a novel perspective for evaluating effectiveness pre-immunotherapy and may guide further studies of precision immuno-oncology.

Suppression of CX3CL1 by miR-497-5p inhibits cell growth and invasion through inactivating the ERK/AKT pathway in NSCLC cells

Non-small cell lung cancer (NSCLC) is the most common lung cancer with a highest mortality rate. MiR-497-5p has been reported as tumor suppressor in many cancers, but the role and mechanism of miR-497-5p in regulating NSCLC progression are still largely unknown in vitro and in vivo. Here, miR-497-5p was significantly downregulated in human NSCLC tissues and cell lines, compared with matched adjacent tissues and normal lung epithelial cell line. Then, miR-497-5p mimic and inhibitor were, respectively, transfected into human NSCLC cells A549 and H460, CCK-8 assay, transwell assay, and flow cytometry were used to detect the capacities of cell proliferation, invasion and apoptosis. MiR-497-5p negatively regulated proliferation and invasion of NSCLC cancer cells. MiR-497-5p was demonstrated to directly bound to 3'-UTR of CX3CL1 mRNA and post-transcriptionally suppressed its expression thus inactivating its downstream oncogenic pathway ERK/AKT. Moreover, transfection with short hairpin RNA (shRNA) against CX3CL1 decreased capacity of cell proliferation and invasion and promoted cell apoptosis in NSCLC cells. In addition, ERK inhibitor U0126 attenuated the promotion effect of miR-497-5p inhibitor on activation of ERK/AKT and cell proliferation and migration. Finally, overexpression of miR-497-5p substantially suppressed activation of the ERK/AKT pathway and tumor growth in tumor-bearing mice in vivo. Taken together, our findings showed that miR-497-5p is downregulated in human NSCLC tissues and cell lines, and it inhibited tumor growth and cell invasion by targeting CX3CL1 gene to inactivate the ERK/AKT pathway in NSCLC cells.

Deep Learning Facilitates Distinguishing Histologic Subtypes of Pulmonary Neuroendocrine Tumors on Digital Whole-Slide Images

Simple Summary

Challenges persist in diagnosing pulmonary neuroendocrine tumors. Our case study shows that deep learning combined with convolutional neural networks has the potential to assist in the diagnosis of pulmonary neuroendocrine tumors from digital whole-slide images.

Abstract

The histological distinction of lung neuroendocrine carcinoma, including small cell lung carcinoma (SCLC), large cell neuroendocrine carcinoma (LCNEC) and atypical carcinoid (AC), can be challenging in some cases, while bearing prognostic and therapeutic significance. To assist pathologists with the differentiation of histologic subtyping, we applied a deep learning classifier equipped with a convolutional neural network (CNN) to recognize lung neuroendocrine neoplasms. Slides of primary lung SCLC, LCNEC and AC were obtained from the Laboratory of Clinical and Experimental Pathology (University Hospital Nice, France). Three thoracic pathologists blindly established gold standard diagnoses. The HALO-AI module (Indica Labs, UK) trained with 18,752 image tiles extracted from 60 slides (SCLC = 20, LCNEC = 20, AC = 20 cases) was then tested on 90 slides (SCLC = 26, LCNEC = 22, AC = 13 and combined SCLC with LCNEC = 4 cases; NSCLC = 25 cases) by F1-score and accuracy. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The tumor maps were false colored and displayed side by side to original hematoxylin and eosin slides with superimposed pathologist annotations. The trained HALO-AI yielded a mean F1-score of 0.99 (95% CI, 0.939–0.999) on the testing set. Our CNN model, providing further larger validation, has the potential to work side by side with the pathologist to accurately differentiate between the different lung neuroendocrine carcinoma in challenging cases.

Clinic and genetic similarity assessments of atypical carcinoid, neuroendocrine neoplasm with atypical carcinoid morphology and elevated mitotic count and large cell neuroendocrine carcinoma

Background

Pulmonary neuroendocrine neoplasms can be divided into typical carcinoid, atypical carcinoid, large cell neuroendocrine carcinoma, and small cell (lung) carcinoma. According to the World Health Organization, these four neoplasms have different characteristics and morphological traits, mitotic counts, and necrotic status. Importantly, “a grey-zone” neoplasm with an atypical carcinoid-like morphology, where the mitotic rate exceeds the criterion of 10 mitoses per 2 mm², have still not been well classified. In clinical practice, the most controversial area is the limit of 11 mitoses to distinguish between atypical carcinoids and large cell neuroendocrine carcinomas.

Methods

Basic and clinical information was obtained from patient medical records. A series of grey-zone patients (n = 8) were selected for exploring their clinicopathological features. In addition, patients with atypical carcinoids (n = 9) and classical large cell neuroendocrine carcinomas (n = 14) were also included to compare their similarity to these neoplasms with respect to tumour morphology and immunohistochemical staining.

Results

We found that these grey-zone tumour sizes varied and affected mainly middle-aged and older men who smoked. Furthermore, similar gene mutations were found in the grey-zone neoplasms and large cell neuroendocrine carcinomas, for the mutated genes of these two are mainly involved in PI3K-Akt signal pathways and Pathways in cancer, including a biallelic alteration of TP53/RB1 and KEAP1.

Conclusions

Our findings indicate that neuroendocrine neoplasm with atypical carcinoid morphology and elevated mitotic counts is more similar to large cell neuroendocrine carcinoma than atypical carcinoid. Furthermore, this study may help improve diagnosing these special cases in clinical practice to avoid misdiagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09391-w.

Lung Neuroendocrine Tumors: How Does Molecular Profiling Help?

PURPOSE OF REVIEW

Lung neuroendocrine tumors (NETs)-typical carcinoids and atypical carcinoids-have unique molecular alterations that are distinct from neuroendocrine carcinomas of the lung and non-small cell lung cancers. Here, we review the role of molecular profiling in the prognosis and treatment of lung NETs.

RECENT FINDINGS

There have been no recently identified molecular prognostic factors for lung NETs and none that have been routinely used to guide management of patients with lung NETs. Previous findings suggest that patients with loss of chromosome 11q may have a worse prognosis along with upregulation of anti-apoptotic pathways (e.g., loss of CD44 and OTP protein expression). Lung NETs rarely harbor driver mutations commonly found in non-small cell lung cancer (NSCLC) or TP53/RB1 mutations found universally in small cell lung cancer. Lung NETs also have low tumor mutation burden and low PD-L1 expression. Everolimus, an mTOR inhibitor and the only FDA approved therapy for unresectable lung NETs, is an effective treatment but the presence of a molecular alteration in the PI3K/AKT/mTOR pathway is not known to predict treatment response. The predominant mutations in lung NETs occur in genes regulating chromatin remodeling and histone modification, with potential targeted therapies emerging in clinical trials. Lung NETs have recurring alterations in genes that regulate the epigenome. Future targeted therapy interfering with epigenetic pathways may hold promise.

The single-cell transcriptional landscape of lung carcinoid tumors

Lung carcinoid tumors, also referred to as pulmonary neuroendocrine tumors or lung carcinoids, are rare neoplasms of the lung with a more favorable prognosis than other subtypes of lung cancer. Still, some patients suffer from relapsed disease and metastatic spread. Several recent single-cell studies have provided detailed insights into the cellular heterogeneity of more common lung cancers, such as adeno- and squamous cell carcinoma. However, the characteristics of lung carcinoids on the single-cell level are yet completely unknown. To study the cellular composition and single-cell gene expression profiles in lung carcinoids, we applied single-cell RNA sequencing to three lung carcinoid tumor samples and normal lung tissue. The single-cell transcriptomes of carcinoid tumor cells reflected intertumoral heterogeneity associated with clinicopathological features, such as tumor necrosis and proliferation index. The immune microenvironment was specifically enriched in non-inflammatory monocyte-derived myeloid cells. Tumor-associated endothelial cells were characterized by distinct gene expression profiles. A spectrum of vascular smooth muscle cells and pericytes predominated the stromal microenvironment. We found a small proportion of myofibroblasts exhibiting features reminiscent of cancer-associated fibroblasts. Stromal and immune cells exhibited potential paracrine interactions which may shape the microenvironment via NOTCH, VEGF, TGFβ and JAK/STAT signaling. Moreover, single-cell gene signatures of pericytes and myofibroblasts demonstrated prognostic value in bulk gene expression data. Here, we provide first comprehensive insights into the cellular composition and single-cell gene expression profiles in lung carcinoids, demonstrating the non-inflammatory and vessel-rich nature of their tumor microenvironment, and outlining relevant intercellular interactions which could serve as future therapeutic targets. This article is protected by copyright. All rights reserved.

Large Cell Neuroendocrine Carcinoma of the Lung: Current Understanding and Challenges

Large cell neuroendocrine carcinoma of the lung (LCNEC) is a rare and highly aggressive type of lung cancer, with a complex biology that shares similarities with both small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). The prognosis of LCNEC is poor, with a median overall survival of 8-12 months. The diagnosis of LCNEC requires the identification of neuroendocrine morphology and the expression of at least one of the neuroendocrine markers (chromogranin A, synaptophysin or CD56). In the last few years, the introduction of next-generation sequencing allowed the identification of molecular subtypes of LCNEC, with prognostic and potential therapeutic implications: one subtype is similar to SCLC (SCLC-like), while the other is similar to NSCLC (NSCLC-like). Because of LCNEC rarity, most evidence comes from small retrospective studies and treatment strategies that are extrapolated from those adopted in patients with SCLC and NSCLC. Nevertheless, limited but promising data about targeted therapies and immune checkpoint inhibitors in patients with LCNEC are emerging. LCNEC clinical management is still controversial and standardized treatment strategies are currently lacking. The aim of this manuscript is to review clinical and molecular data about LCNEC to better understand the optimal management and the potential prognostic and therapeutic implications of molecular subtypes.

Comprehensive Characterization of the Genomic Landscape in Chinese Pulmonary Neuroendocrine Tumors Reveals Prognostic and Therapeutic Markers (CSWOG-1901)

BACKGROUND

Pulmonary neuroendocrine tumors (pNETs) include typical carcinoid (TC), atypical carcinoid (AC), large cell neuroendocrine carcinoma (LCNEC), and small cell lung carcinoma (SCLC). The optimal treatment strategy for each subtype remains elusive, partly due to the lack of comprehensive understanding of their molecular features. We aimed to explore differential genomic signatures in pNET subtypes and identify potential prognostic and therapeutic biomarkers.

METHODS

We investigated genomic profiles of 57 LCNECs, 49 SCLCs, 18 TCs, and 24 ACs by sequencing tumor tissues with a 520-gene panel and explored the associations between genomic features and prognosis.

RESULTS

Both LCNEC and SCLC displayed higher mutation rates for TP53, PRKDC, SPTA1, NOTCH1, NOTCH2, and PTPRD than TC and AC. Small cell lung carcinoma harbored more frequent co-alterations in TP53-RB1, alterations in PIK3CA and SOX2, and mutations in HIF-1, VEGF and Notch pathways. Large cell neuroendocrine carcinoma (12.7 mutations/Mb) and SCLC (11.9 mutations/Mb) showed higher tumor mutational burdens than TC (2.4 mutations/Mb) and AC (7.1 mutations/Mb). 26.3% of LCNECs and 20.8% of ACs harbored alterations in classical non-small cell lung cancer driver genes. The presence of alterations in the homologous recombination pathway predicted longer progression-free survival in advanced LCNEC patients with systemic therapy (P = .005) and longer overall survival (OS) in SCLC patients with resection (P = .011). The presence of alterations in VEGF (P = .048) and estrogen (P = .018) signaling pathways both correlated with better OS in patients with resected SCLC.

CONCLUSION

We performed a comprehensive genomic investigation on 4 pNET subtypes in the Chinese population. Our data revealed distinctive genomic signatures in subtypes and provided new insights into the prognostic and therapeutic stratification of pNETs.

Utility of KI-67 as a prognostic biomarker in pulmonary neuroendocrine neoplasms: a systematic review and meta-analysis

OBJECTIVES

Ki-67, a marker of cellular proliferation, is associated with prognosis across a wide range of tumours, including gastroenteropancreatic neuroendocrine neoplasms (NENs), lymphoma, urothelial tumours and breast carcinomas. Its omission from the classification system of pulmonary NENs is controversial. This systematic review sought to assess whether Ki-67 is a prognostic biomarker in lung NENs and, if feasible, proceed to a meta-analysis.

RESEARCH DESIGN AND METHODS

Medline (Ovid), Embase, Scopus and the Cochrane library were searched for studies published prior to 28 February 2019 and investigating the role of Ki-67 in lung NENs. Eligible studies were those that included more than 20 patients and provided details of survival outcomes, namely, HRs with CIs according to Ki-67 percentage. Studies not available as a full text or without an English manuscript were excluded. This study was prospectively registered with PROSPERO.

RESULTS

Of 11 814 records identified, seven studies met the inclusion criteria. These retrospective studies provided data for 1268 patients (693 TC, 281 AC, 94 large cell neuroendocrine carcinomas and 190 small cell lung carcinomas) and a meta-analysis was carried out to estimate a pooled effect. Random effects analyses demonstrated an association between a high Ki-67 index and poorer overall survival (HR of 2.02, 95% CI 1.16 to 3.52) and recurrence-free survival (HR 1.42; 95% CI 1.01 to 2.00).

CONCLUSION

This meta-analysis provides evidence that high Ki-67 labelling indices are associated with poor clinical outcomes for patients diagnosed with pulmonary NENs. This study is subject to inherent limitations, but it does provide valuable insights regarding the use of the biomarker Ki-67, in a rare tumour.

PROSPERO REGISTRATION NUMBER

CRD42018093389.

Machine learning for multi-omics data integration in cancer

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.