Phospho-histone-H3 immunostaining for pulmonary carcinoids: impact on clinical appraisal, interobserver correlation, and diagnostic processing efficiency

Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms' performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance. We found that the annotators' object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.

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.

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.

Improving fine needle aspiration to predict the tumor biological aggressiveness in pancreatic neuroendocrine tumors using Ki-67 proliferation index, phosphorylated histone H3 (PHH3), and BCL-2

INTRODUCTION

Surgery is the only known cure for sporadic pancreatic neuroendocrine tumors (PNETs). Therefore, the prediction of the PNETs biological aggressiveness evaluated on endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) has a significant impact on clinical management. The proliferation rate of Ki-67 in PNETs can help to predict the biological aggressiveness of the tumor. In addition, there is a relatively new proliferation marker called phosphorylated histone H3 (PHH3) that can identify and quantify dividing cells in tissue samples, which is a marker highly specific to mitotic figures. Other markers such as BCL-2 also contribute to tumorigenesis and may be involved in the differentiation of neuroendocrine cells.

MATERIALS AND METHODS

A retrospective observational study was performed on patients undergoing surveillance for PNETs from January 2010 to May 2021. Data collection included the patients' age, sex, tumor location, tumor size in the surgical specimen, and tumor grade in FNA. The 2019 World Health Organization (WHO) classification guideline was followed to diagnose PNETs, including grade and stage. Immunohistochemical stainings for Ki-67, PHH3 and BCL-2 in PNETs were performed.

RESULTS

After excluding cell blocks containing fewer than 100 tumor cells, 44 patients with EUS-FNA and surgical resection specimens were included in this study. There were 19 cases of G1 PNETs, 20 cases of G2 PNETs, and 5 cases of G3 PNETs. The grade assigned based on the Ki-67 index was higher and more sensitive than that based on the mitotic count using H&E slides in some cases of G2 and G3 PNETs. However, there was no significant difference between the mitotic count using PHH3-positive tumor cells and the Ki-67 index to grade PNETs. All grade 1 tumors (19 cases) on surgical resection specimens were correctly graded on FNA (100 % concordance rate). Within the 20 G2 PNETs, 15 cases of grade 2 on surgical resection specimens were graded correctly on FNA based on the Ki-67 index only. Five cases of grade 2 PNETs on surgical resection specimens were graded as grade 1 on FNA when using only the Ki-67 index. Three of five grade 3 tumors on surgical resection specimens were graded as grade 2 on FNA based on the Ki-67 index only. Using only FNA Ki-67 to predict PNET tumor grade, the concordance (accuracy) rate was 81.8 % in total. However, all these eight cases (5 cases of G2 PNETs and 3 cases of G3 PNETs) were graded correctly by using the Ki-67 index plus mitotic rate (using PHH3 IHC stains). Four of 18 (22.2 %) patients with PNETs were positive for BCL-2 stain. In these 4 cases positive for BCL-2 stains, 3 cases were G2 PNETs and one case was G3 PNETs.

CONCLUSION

Grade and the proliferative rate in EUS-FNA can be used to predict the tumor grade in surgical resection specimens. However, when using only FNA Ki-67 to predict PNET tumor grade, about 18 % of cases were downgraded by one level. To solve the problem, immunohistochemical staining for BCL-2 and especially PHH3 would be helpful. Our results demonstrated that the mitotic count using PHH3 IHC stains not only improved the accuracy and precision of PNET grading in the surgical resection specimens, but also could reliably be used in routine scoring of mitotic figures of FNA specimens.

Protein Phosphatase 1 Subunit PPP1R14B Stabilizes STMN1 to Promote Progression and Paclitaxel Resistance in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) represents the most lethal subtype of breast cancer due to its aggressive clinical features and the lack of effective therapeutic targets. To identify novel approaches for targeting TNBC, we examined the role of protein phosphatases in TNBC progression and chemoresistance. Protein phosphatase 1 regulatory subunit 14B (PPP1R14B), a poorly defined member of the protein phosphatase 1 regulatory subunits, was aberrantly upregulated in TNBC tissues and predicted poor prognosis. PPP1R14B was degraded mainly through the ubiquitin-proteasome pathway. RPS27A recruited deubiquitinase USP9X to deubiquitinate and stabilize PPP1R14B, resulting in overexpression of PPP1R14B in TNBC tissues. Gain- and loss-of-function assays demonstrated that PPP1R14B promoted TNBC cell proliferation, colony formation, migration, invasion, and resistance to paclitaxel in vitro. PPP1R14B also induced xenograft tumor growth, lung metastasis, and paclitaxel resistance in vivo. Mechanistic investigations revealed that PPP1R14B maintained phosphorylation and stability of oncoprotein stathmin 1 (STMN1), a microtubule-destabilizing phosphoprotein critically involved in cancer progression and paclitaxel resistance, which was dependent on PP1 catalytic subunits α and γ. Importantly, the tumor-suppressive effects of PPP1R14B deficiency could be partially rescued by ectopic expression of wild-type but not phosphorylation-deficient STMN1. Moreover, PPP1R14B decreased STMN1-mediated α-tubulin acetylation, microtubule stability, and promoted cell-cycle progression, leading to resistance of TNBC cells to paclitaxel. Collectively, these findings uncover a functional and mechanistic role of PPP1R14B in TNBC progression and paclitaxel resistance, indicating PPP1R14B is a potential therapeutic target for TNBC.

SIGNIFICANCE

PPP1R14B upregulation induced by RPS27A/USP9X in TNBC increases STMN1 activity, leading to cancer progression and paclitaxel resistance.