Specific Genomic Alterations in High-Grade Pulmonary Neuroendocrine Tumours with Carcinoid Morphology

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.

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.

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.

Updates in histopathological classification and tissue biomarkers of digestive neuroendocrine neoplasms: What the clinician should know

Histopathological classifications of neuroendocrine neoplasms (NEN) change regularly and the latest WHO classification published in 2022, which concerns all NEN in the body, attempts to standardize classifications in the different locations. Differentiation and proliferation mainly assessed by Ki-67 index are still the cornerstone of those classifications. However, many markers are now used for diagnostic (to check neuroendocrine differentiation, to identify the site of origin of a metastasis, to help separating high-grade neuroendocrine tumors/NET and neuroendocrine carcinoma/NEC), prognostic or theranostic purposes. NENs are often heterogeneous and this can lead to difficulties in classifications, biomarker and prognostic assessment. These different points are discussed successively in this review, insisting especially on the frequent digestive, gastro-entero-pancreatic (GEP) localizations.

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.

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.

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.

High rate of Ki-67 increase in entero-pancreatic NET relapses after surgery with curative intent

Neuroendocrine neoplasms (NENs) present with advanced disease at diagnosis in up to 28% of cases, precluding the possibility of curative-intent surgery. Histopathological heterogeneity of this disease can be observed inter-individually as well as intra-individually during disease course. The present study aimed to assess the frequency of Ki-67 change after radical surgery, in a series of patients with radically resected entero-pancreatic neuroendocrine tumors (EP-NETs). We present the analysis of a multicenter, retrospective, series of EP-NETs G1-G2 recurring after radical resection and with histological re-evaluation at disease recurrence (DR). The primary endpoint was the description of Ki-67 change at DR compared to time of surgery. The secondary endpoint was assessment of recurrence-free survival (RFS) rates. In total, 47 patients had a second histological evaluation and could be included in the present study. Median Ki-67 at surgery was 3% (range 1-15%) but, at DR, a significant increase in the value was observed (7%, range 1-30%; p < .01) and involved 66.0% of cases, with a corresponding increase in tumor grading in 34.0% (p = .05). Median RFS of the overall population was 40 months, and was worse when Ki-67 increased at DR vs. stable Ki-67 value (36 vs. 61 months, respectively; p = .02). In conclusion, in more than half of the cases with relapse after radical surgery, a higher proliferative index with a potentially more aggressive potential was observed. Therefore, histological reassessment should be considered on DR because tailored therapeutic strategies may be required for these patients.

Spatiotemporal heterogeneity and clinical challenge of pancreatic neuroendocrine tumors

During the course of pancreatic neuroendocrine tumors (NETs), they generally become more heterogeneous with individual cells exhibiting distinct molecular fingerprints. This heterogeneity manifests itself through an unequal distribution of genetically-variant, tumor cell subpopulations within disease locations (i.e., spatial heterogeneity) or changes in the genomic landscape over time (i.e., temporal heterogeneity); these characteristics complicate clinical diagnosis and treatment. Effective, feasible tumor heterogeneity detection and eradication methods are essential to overcome the clinical challenges of pancreatic NETs. This review explores the molecular fingerprints of pancreatic NETs and the spectrum of tumoral heterogeneity. We then describe the challenges of assessing heterogeneity by liquid biopsies and imaging modalities and the therapeutic challenges for pancreatic NETs. In general, navigating these challenges, refining approaches for translational research, and ultimately improving patient care are available once we have a better understanding of intratumoral spatiotemporal heterogeneity.

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.

An update on the development of concepts, diagnostic criteria, and challenging issues for neuroendocrine neoplasms across different digestive organs

Digestive neuroendocrine neoplasms (NENs) are a group of heterogeneous neoplasms found throughout the digestive tract, with different behaviour and genetic background. In the last few years, nomenclature and WHO/UICC classifications of digestive NENs have changed, and molecular classifications have emerged, especially in pancreatic locations. Increasing patho-molecular details are needed to diagnose the different categories of NEN, including the use of helpful immunohistochemical markers. In this review, we address these topics in three successive chapters. We first briefly review recent updates in classifications, discuss important grading and proliferating issues and advances in the molecular understanding of NEN. Then, we provide an update on diagnosis, including the most important differential diagnoses of NEN, with a focus on high-grade neoplasms and mixed tumours. Finally, we highlight a variety of currently used and next-generation predictive and prognostic biomarkers as well as biomarkers of tumour origin and describe some site specificities of gastrointestinal NEN. We specifically focus on biomarkers available to pathologists with the potential to change the way patients with NEN are diagnosed and treated.

The 2021 WHO Classification of Lung Tumors: Impact of advances since 2015

The 2021 World Health Organisation (WHO) Classification of Thoracic Tumours was published earlier this year, with classification of lung tumors being one of the chapters. The principles remain those of using morphology first, supported by immunohistochemistry and then molecular techniques. In 2015, there was particular emphasis on using immunohistochemistry to make classification more accurate. In 2021, there is greater emphasis throughout the book on advances in molecular pathology across all tumor types. Major features within this edition are 1) broader emphasis on genetic testing than in the 2015 WHO Classification, 2) a chapter entirely dedicated to the classification of small diagnostic samples, 3) continued recommendation to document percentages of histological patterns in invasive non-mucinous adenocarcinomas, with utilization of these features to apply a formal grading system, as well as using only invasive size for T-factor size determination in part lepidic non-mucinous lung adenocarcinomas as recommended by the 8^th Edition TNM Classification, 4) recognition of spread through airspaces (STAS) as a histological feature with prognostic significance, 5) moving lymphoepithelial carcinoma to squamous cell carcinomas, 6) update on evolving concepts in lung neuroendocrine neoplasm classification, 7) recognition of bronchiolar adenoma/ciliated muconodular papillary tumor (BA/CMPT) as a new entity within the adenoma subgroup, 8) recognition of thoracic SMARCA4-deficient undifferentiated tumor, and 9) inclusion of essential and desirable diagnostic criteria for each tumor.

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

Simple Summary

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome, particularly for the intermediate domains of adenocarcinomas and large-cell neuroendocrine carcinomas. Moreover, subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. The aim of this study was to design and evaluate an objective and reproducible approach to the grading of lung NENs, potentially extendable to other NENs, by exploring a completely new perspective of interpreting the well-recognised proliferation marker Ki-67. We designed an automated pipeline to harvest quantitative information from the spatial distribution of Ki-67-positive cells, analysing its heterogeneity in the entire extent of tumour tissue—which currently represents the main weakness of Ki-67—and employed machine learning techniques to predict prognosis based on this information. Demonstrating the efficacy of the proposed framework would hint at a possible path for the future of grading and classification of NENs.

Abstract

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.

Classification of neuroendocrine neoplasms: lights and shadows

Neuroendocrine neoplasms (NENs) are a heterogeneous group of neoplastic proliferations showing different morphological features, immunophenotype, molecular background, clinical presentation, and outcome. They can virtually originate in every organ of the human body and their classification is not uniform among different sites. Indeed, as they have historically been classified according to the organ in which they primarily arise, the different nomenclature that has resulted have created some confusion among pathologists and clinicians. Although a uniform terminology to classify neuroendocrine neoplasms arising in different systems has recently been proposed by WHO/IARC, some issues remain unsolved or need to be clarified. In this review, we discuss the lights and shadows of the current WHO classifications used to define and characterize NENs of the pituitary gland, lung, breast and those of the head and neck region, and digestive and urogenital systems.

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.

Arid1a-Plagl1-Hh signaling is indispensable for differentiation-associated cell cycle arrest of tooth root progenitors

Chromatin remodelers often show broad expression patterns in multiple cell types yet can elicit cell-specific effects in development and diseases. Arid1a binds DNA and regulates gene expression during tissue development and homeostasis. However, it is unclear how Arid1a achieves its functional specificity in regulating progenitor cells. Using the tooth root as a model, we show that loss of Arid1a impairs the differentiation-associated cell cycle arrest of tooth root progenitors through Hedgehog (Hh) signaling regulation, leading to shortened roots. Our data suggest that Plagl1, as a co-factor, endows Arid1a with its cell-type/spatial functional specificity. Furthermore, we show that loss of Arid1a leads to increased expression of Arid1b, which is also indispensable for odontoblast differentiation but is not involved in regulation of Hh signaling. This study expands our knowledge of the intricate interactions among chromatin remodelers, transcription factors, and signaling molecules during progenitor cell fate determination and lineage commitment.

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.

Molecular Pathology of Well-Differentiated Pulmonary and Thymic Neuroendocrine Tumors: What Do Pathologists Need to Know?

Thoracic (pulmonary and thymic) neuroendocrine tumors are well-differentiated epithelial neuroendocrine neoplasms that are classified into typical and atypical carcinoid tumors based on mitotic index cut offs and presence or absence of necrosis. This classification scheme is of great prognostic value but designed for surgical specimens, only. Deep molecular characterization of thoracic neuroendocrine tumors highlighted their difference with neuroendocrine carcinomas. Neuroendocrine tumors of the lung are characterized by a low mutational burden, and a high prevalence of mutations in chromatin remodeling and histone modification-related genes, whereas mutations in genes frequently altered in neuroendocrine carcinomas are rare. Molecular profiling divided thymic neuroendocrine tumors into three clusters with distinct clinical outcomes and characterized by a different average of copy number instability. Moreover, integrated histopathological, molecular and clinical evidence supports the existence of a grey zone category between neuroendocrine tumors (carcinoid tumors) and neuroendocrine carcinomas. Indeed, cases with well differentiated morphology but mitotic/Ki-67 indexes close to neuroendocrine carcinomas have been increasingly recognized. These are characterized by specific molecular profiles and have an aggressive clinical behavior. Finally, thoracic neuroendocrine tumors may arise in the background of genetic susceptibility, being MEN1 syndrome the well-defined familial form. However, pathologists should be aware of rarer germline variants that are associated with the concurrence of neuroendocrine tumors of the lung or their precursors (such as DIPNECH) with other neoplasms, including but not limited to breast carcinomas. Therefore, genetic counseling for all young patients with thoracic neuroendocrine neoplasia and/or any patient with pathological evidence of neuroendocrine cell hyperplasia-to-neoplasia progression sequence or multifocal disease should be considered.

Biomarkers of Response to Etoposide-Platinum Chemotherapy in Patients with Grade 3 Neuroendocrine Neoplasms

Etoposide-platinum (EP) chemotherapy has long been the reference treatment for grade 3 neuroendocrine neoplasms (G3 NEN). However, G3 NEN are heterogeneous, including well-differentiated tumors (NET) and poorly differentiated large (LCNEC) or small (SCNEC) cell carcinomas, whose response to EP chemotherapy varies considerably. Our aim was to evaluate predictive biomarkers for the response to EP chemotherapy in G3 NEN. We retrospectively studied 89 patients with lung (42%) and digestive (58%) G3 NEN treated by EP chemotherapy between 2006 and 2020. All cases were centrally reviewed for cytomorphology/Ki-67 and immunohistochemistry of retinoblastoma protein (Rb)/p53/p16, analyzed using a semi-quantitative score. The absence of Rb staining (Rbinap) or the absence of very intense p53 staining (p53inap) were considered inappropriate. Rb staining was also studied as a quantitative marker, the best threshold being determined by ROC curve. Intense p16 staining (p16high) also suggested cell cycle dysregulation. Our primary endpoint was the objective response rate (ORR). We included 10 G3 NET, 31 LCNEC and 48 SCNEC, which showed ORR of 20%, 32% and 75%, respectively (NET vs. NEC, p = 0.040; LCNEC vs. SCNEC, p < 0.001). The ORR was significantly higher in NEN presenting with Rbinap (63% vs. 42%, p = 0.025) and p16high (66% vs. 35%, p = 0.006). Rb < 150 optimally identified responders (AUC = 0.657, p < 0.001). The ORR was 67% in Rb < 150 (vs. 25%, p = 0.005). On multivariate analysis, only Rb < 150 was independently associated with ORR (OR 4.16, 95% CI 1.11-15.53, p = 0.034). We confirm the heterogeneity of the response to EP treatment in G3 NEN. Rb < 150 was the best predictive biomarker for the response to EP, and p53 immunostaining had no additional value.

The key roles of the lysine acetyltransferases KAT6A and KAT6B in physiology and pathology

Histone modifications and more specifically ε-lysine acylations are key epigenetic regulators that control chromatin structure and gene transcription, thereby impacting on various important cellular processes and phenotypes. Furthermore, lysine acetylation of many non-histone proteins is involved in key cellular processes including transcription, DNA damage repair, metabolism, cellular proliferation, mitosis, signal transduction, protein folding, and autophagy. Acetylation affects protein functions through multiple mechanisms including regulation of protein stability, enzymatic activity, subcellular localization, crosstalk with other post-translational modifications as well as regulation of protein-protein and protein-DNA interactions. The paralogous lysine acetyltransferases KAT6A and KAT6B which belong to the MYST family of acetyltransferases, were first discovered approximately 25 years ago. KAT6 acetyltransferases acylate both histone H3 and non-histone proteins. In this respect, KAT6 acetyltransferases play key roles in regulation of transcription, various developmental processes, maintenance of hematopoietic and neural stem cells, regulation of hematopoietic cell differentiation, cell cycle progression as well as mitosis. In the current review, we discuss the physiological functions of the acetyltransferases KAT6A and KAT6B as well as their functions under pathological conditions of aberrant expression, leading to several developmental syndromes and cancer. Importantly, both upregulation and downregulation of KAT6 proteins was shown to play a role in cancer formation, progression, and therapy resistance, suggesting that they can act as oncogenes or tumor suppressors. We also describe reciprocal regulation of expression between KAT6 proteins and several microRNAs as well as their involvement in cancer formation, progression and resistance to therapy.