Interlaboratory variability of MIB1 staining in well-differentiated pancreatic neuroendocrine tumors

Molecular characteristics of early-onset pancreatic ductal adenocarcinoma

The median age of patients with pancreatic ductal adenocarcinoma (PDAC) at diagnosis is 71 years; however, around 10% present with early-onset pancreatic cancer (EOPC), i.e., before age 50. The molecular mechanisms underlying such an early onset are unknown. We assessed the role of common PDAC drivers (KRAS, TP53, CDKN2A and SMAD4) and determined their mutational status and protein expression in 90 formalin-fixed, paraffin-embedded tissues, including multiple primary and matched metastases, from 37 EOPC patients. KRAS was mutated in 88% of patients; p53 was altered in 94%, and p16 and SMAD4 were lost in 86% and 71% of patients, respectively. Meta-synthesis showed a higher rate of p53 alterations in EOPC than in late-onset PDAC (94% vs. 69%, P = 0.0009) and significantly higher loss of SMAD4 (71% vs. 44%, P = 0.0025). The majority of EOPC patients accumulated aberrations in all four drivers; in addition, high tumour heterogeneity was observed across all tissues. The cumulative effect of an exceptionally high rate of alterations in all common PDAC driver genes combined with high tumour heterogeneity suggests an important mechanism underlying the early onset of PDAC.

Exploration of Digital Image Analysis for Ki67 Quantification in the Grading of Medullary Thyroid Carcinoma: A Pilot Study with 85 Cases

BACKGROUND

Although uncommon, medullary thyroid carcinoma (MTC) accounts for a significant proportion of thyroid cancer deaths. Recent studies have validated the two-tier International Medullary Thyroid Carcinoma Grading System (IMTCGS) to predict clinical outcomes. A 5% Ki67 proliferative index (Ki67PI) cut-off separates low-grade from high-grade MTC. In this study, we compared digital image analysis (DIA) to manual counting (MC) for determining the Ki67PI in a MTC cohort, and explored the challenges encountered.

METHODS

Available slides from 85 MTCs were reviewed by two pathologists. The Ki67PI was documented by immunohistochemistry for each case, scanned with the Aperio® slide scanner at 40× magnification, and quantified using the QuPath® DIA platform. The same hotspots were screenshot, printed in color, and blindly counted. For each case, over 500 MTC cells were counted. Each MTC was graded using IMTCGS criteria.

RESULTS

In our MTC cohort (n = 85), 84.7 and 15.3% were low- and high-grade with the IMTCGS. In the entire cohort, QuPath® DIA performed well (R2 = 0.9891) but appeared to undercall compared to MC. QuPath® performed better in high-grade cases (R2 = 0.99) compared to low-grade cases (R2 = 0.7071). Overall, Ki67PI determined with either MC or DIA did not affect IMTCGS grade. Encountered DIA challenges include optimizing cell detection, overlapping nuclei, and tissue artifacts. Encountered MC challenges include background staining, morphologic overlap with normal elements, and counting time.

CONCLUSION

Our study highlights the utility of DIA in quantifying Ki67PI for MTC and can serve as an adjunct for grading in conjunction with the other criteria of mitotic activity and necrosis.

Diagnostic, Prognostic, and Predictive Role of Ki67 Proliferative Index in Neuroendocrine and Endocrine Neoplasms: Past, Present, and Future

The introduction of Ki67 immunohistochemistry in the work-up of neuroendocrine neoplasms (NENs) has opened a new approach for their diagnosis and prognostic evaluation. Since the first demonstration of the prognostic role of Ki67 proliferative index in pancreatic NENs in 1996, several studies have been performed to explore its prognostic, diagnostic, and predictive role in other neuroendocrine and endocrine neoplasms. A large amount of information is now available and published results globally indicate that Ki67 proliferative index is useful to this scope, although some differences exist in relation to tumor site and type. In gut and pancreatic NENs, the Ki67 proliferative index has a well-documented and accepted diagnostic and prognostic role and its evaluation is mandatory in their diagnostic work-up. In the lung, the Ki67 index is recommended for the diagnosis of NENs on biopsy specimens, but its diagnostic role in surgical specimens still remains to be officially accepted, although its prognostic role is now well documented. In other organs, such as the pituitary, parathyroid, thyroid (follicular cell-derived neoplasms), and adrenal medulla, the Ki67 index does not play a diagnostic role and its prognostic value still remains a controversial issue. In medullary thyroid carcinoma, the Ki67 labelling index is used to define the tumor grade together with other morphological parameters, while in the adrenal cortical carcinoma, it is useful to select patients to treated with mitotane therapy. In the present review, the most important information on the diagnostic, prognostic, and predictive role of Ki67 proliferative index is presented discussing the current knowledge. In addition, technical issues related to the evaluation of Ki67 proliferative index and the future perspectives of the application of Ki67 immunostaining in endocrine and neuroendocrine neoplasms is discussed.

Grading of Medullary Thyroid Carcinoma: an Interobserver Reproducibility Study

Grade, based on proliferative activity and tumor necrosis, has recently been shown to be prognostic in medullary thyroid carcinoma (MTC) in multivariate analysis. The aim of this study was to evaluate the interobserver reproducibility of assessed grade in MTC. Three groups (each group included one resident/fellow and one attending pathologist) independently evaluated a cohort of 44 sporadic MTC. For each case, all available tumor slides were reviewed, and mitotic count and the presence of tumor necrosis were recorded. Ki-67 was performed, and the Ki-67 proliferative index was determined in the area of highest proliferative activity. Tumors were graded according to the recently published International Medullary Thyroid Carcinoma Grading System (IMTCGS). Kappa statistics were calculated for each individual criterion (mitotic count, Ki-67 proliferative index, and necrosis) and for assigned IMTCGS grade. For our cohort of 44 MTCs, the kappa statistic for mitotic count, Ki-67 proliferative index, and necrosis was 0.68, 0.86, and 0.89, respectively. The kappa statistic for assigned IMTCGS grade was 0.87. Our findings indicate that there was a strong level of agreement for assessment of grade in our cohort of MTC, indicating that grade as assessed by the IMTCGS is not only prognostic but also reproducible.

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.

Treatment of advanced gastroenteropancreatic neuroendocrine neoplasia, are we on the way to personalised medicine?

Gastroenteropancreatic neuroendocrine neoplasia (GEPNEN) comprises clinically as well as prognostically diverse tumour entities often diagnosed at late stage. Current classification provides a uniform terminology and a Ki67-based grading system, thereby facilitating management. Advances in the study of genomic and epigenetic landscapes have amplified knowledge of tumour biology and enhanced identification of prognostic and potentially predictive treatment subgroups. Translation of this genomic and mechanistic biology into advanced GEPNEN management is limited. 'Targeted' treatments such as somatostatin analogues, peptide receptor radiotherapy, tyrosine kinase inhibitors and mammalian target of rapamycin inhibitors are treatment options but predictive tools are lacking. The inability to identify clonal heterogeneity and define critical oncoregulatory pathways prior to therapy, restrict therapeutic efficacy as does the inability to monitor disease status in real time. Chemotherapy in the poor prognosis NEN G3 group, though associated with acceptable response rates, only leads to short-term tumour control and their molecular biology requires delineation to provide new and more specific treatment options.The future requires an exploration of the NEN tumour genome, its microenvironment and an identification of critical oncologic checkpoints for precise drug targeting. In the advance to personalised medical treatment of patients with GEPNEN, clinical trials need to be based on mechanistic and multidimensional characterisation of each tumour in order to identify the therapeutic agent effective for the individual tumour.This review surveys advances in NEN research and delineates the current status of translation with a view to laying the basis for a genome-based personalised medicine management of advanced GEPNEN.

TERT Promoter Mutation Analysis to Distinguish Glioma From Gliosis

Among the most challenging diagnostic issues in surgical neuropathology is the distinction between scant infiltration by diffuse gliomas and reactive gliosis. The best documented ancillary marker to establish a definitive diagnosis of glioma in this setting is the identification of hotspot mutations in the isocitrate dehydrogenase 1 and 2 (IDH1/IDH2) genes, which is limited, however, by the low prevalence of these mutations in gliomas of elderly adults. Since telomerase reverse transcriptase (TERT) promoter mutations are present in the vast majority of IDH-wildtype diffuse gliomas, we hypothesized that combined analysis of IDH and TERT might overcome these limitations. For this purpose, we analyzed a series of non-neoplastic and neoplastic CNS samples for the prevalence of TERT hotspot mutations. TERT mutations were identified in none out of 58 (0%) reactive gliosis samples, and in 91 out of 117 (78%) IDH-wildtype gliomas. Based on a series of 200 consecutive diffuse gliomas, we found that IDH mutation analysis alone had a sensitivity of 28% (63% and 12%, respectively, in patients below and above age of 50) for detection of gliomas, whereas a combined analysis of IDH and TERT was 85% sensitive (87% and 84%, respectively, below and above age of 50). In sum, our findings suggest that TERT promoter mutation analysis contributes favorably to a molecular panel in cases equivocal for glioma versus gliosis on morphological grounds, especially in patients above age of 50, in which IDH analysis alone performs poorly.

Comparison of different anti-Ki67 antibody clones and hot-spot sizes for assessing proliferative index and grading in pancreatic neuroendocrine tumours using manual and image analysis

AIMS

Ki67 proliferative index (PI) is essential for grading gastroenteric and pancreatic neuroendocrine tumours (GEP NETs). Analytical and preanalytical variables can affect Ki67 PI. In contrast to counting methodology, until now little attention has focused on the question of clone equivalence and the effect of hot-spot size on Ki67 PI in GEP NETs. Using manual counting and image analysis, this study compared the Ki67 PI achieved using MM1, K2 and 30-9 to MIB1, a clone which has been validated for, and is referenced in, guidelines relating to assessment of Ki67 PI in GEP NETs.

METHODS AND RESULTS

Forty-two pancreatic NETs were each immunohistochemically stained for the anti-Ki67 clones MIB1, MM1, K2 and 30-9. Ki67 PI was calculated manually and by image analysis, the latter using three different hot-spot sizes. In manual comparisons using single hot-spot high-power fields, non-MIB1 clones overestimated Ki67 PI compared to MIB1, resulting in grading discordances. Image analysis shows good agreement with manual Ki67 PI but a tendency to overestimate absolute Ki67 PI. Increasing the size of tumour hot-spot from 500 to 2000 cells resulted in a decrease in Ki67 PI.

CONCLUSION

Different anti-Ki67 clones do not produce equivalent PIs in GEP NETs, and clone selection may therefore affect patient care. Increasing the hot-spot size decreases the Ki67 PI. Greater standardisation in terms of antibody clone selection and hot-spot size is required for grading GEP NETs. Image analysis is an effective tool for assisting Ki67 assessment and allows easier standardisation of the size of the tumour hot-spot.

Ki67 Quantitative Interpretation: Insights using Image Analysis

Background:

Proliferation markers, especially Ki67, are increasingly important in diagnosis and prognosis. The best method for calculating Ki67 is still the subject of debate.

Materials and Methods:

We evaluated an image analysis tool for quantitative interpretation of Ki67 in neuroendocrine tumors and compared it to manual counts. We expanded a primary digital pathology platform to include the Leica Biosystems image analysis nuclear algorithm. Slides were digitized using a Leica Aperio AT2 Scanner and accessed through the Cerner CoPath LIS interfaced with Aperio eSlideManager through Aperio ImageScope. Selected regions of interest (ROIs) were manually defined and annotated to include tumor cells only; they were then analyzed with the algorithm and by four pathologists counting on printed images. After validation, the algorithm was used to examine the impact of the size and number of areas selected as ROIs.

Results:

The algorithm provided reproducible results that were obtained within seconds, compared to up to 55 min of manual counting that varied between users. Benefits of image analysis identified by users included accuracy, time savings, and ease of viewing. Access to the algorithm allowed rapid comparisons of Ki67 counts in ROIs that varied in numbers of cells and selection of fields, the outputs demonstrated that the results vary around defined cutoffs that provide tumor grade depending on the number of cells and ROIs counted.

Conclusions:

Digital image analysis provides accurate and reproducible quantitative data faster than manual counts. However, access to this tool allows multiple analyses of a single sample to use variable numbers of cells and selection of variable ROIs that can alter the result in clinically significant ways. This study highlights the potential risk of hard cutoffs of continuous variables and indicates that standardization of number of cells and number of regions selected for analysis should be incorporated into guidelines for Ki67 calculations.

Fit-for-Purpose Immunohistochemical Biomarkers

There are two aspects of immunohistochemistry (IHC) that are relevant to practicing pathologist: (1) understanding of IHC biomarker panels that are suitable for diagnostic, prognostic and predictive testing, and (2) understanding of IHC quality assurance (QA), which makes sure that the tests in these panels work as they should. The two aspects are closely linked together and call for collaborative approach between pathologists and IHC laboratory technologists as both need to be involved in developing and maintaining IHC biomarkers that are "fit-for-purpose". This article reviews the most current IHC QA concepts that are imminently material to practicing pathologists with emphasis on challenges that are specific to endocrine pathology.

Ki67 labeling index: assessment and prognostic role in gastroenteropancreatic neuroendocrine neoplasms

In 1983, a monoclonal antibody, Ki67, was generated, that labeled the nuclei of proliferating non-neoplastic and neoplastic cells. The name Ki67 derived from the city of Kiel (Ki) where the antibody was produced in the university department of pathology and refers to the number of the original clone (67). Systematic assessment of the proliferative activity of tumors using Ki67 started in the 1990s, when Ki67, which only worked on frozen tissue, was complemented by the antibody MIB-1 that also worked in formalin-fixed tissues. Pancreatic neuroendocrine neoplasms (PanNENs) were the first endocrine tumors whose proliferative activity was assessed with Ki67. This approach was so successful that Ki67 was included as prognostic marker in the 2000 and 2004 WHO classifications of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). In 2010, the WHO classification of GEP-NENs introduced a three-tiered grading, originally proposed by ENETS in 2006 that was mainly based on the Ki67 index. As it has subsequently been shown that the Ki67 index is the most reliable factor in the prognostic evaluation of GEP-NENs, especially of PanNENs, the 2017 WHO classification of PanNENs requires its use and strongly recommends exact assessment of the proportion Ki67-labeled cells as basis for the calculation of the Ki67 index. Problems in assessing the Ki67 index include intertumoral and intratumoral staining heterogeneity and counting methods. Despite such problems, the Ki67 index has emerged as indispensable for the prognostic and therapeutic stratification of the majority of GEP-NENs and can barely be replaced by counting mitoses. In future, however, it can be anticipated that the Ki67 cut-offs experience refinement in relation to the type of tumor, its location, and its response to therapy. It is also possible that the prognostic risk of an individual tumor is calculated for each Ki67 unit and not for an "a priori" fixed Ki67 class.

Grade Assignment by Ki-67 Proliferative Index, Mitotic Count, and Phosphohistone H3 Count in Surgically Resected Gastrointestinal and Pancreatic Neuroendocrine Tumors

OBJECTIVES

The aim of this study was to evaluate the concordance in grade assignment for gastroenteropancreatic neuroendocrine tumors using mitotic count (MC), Ki-67 proliferative index (KPI), and phosphohistone H3 count (PHH3C).

METHODS

Resected gastroenteropancreatic neuroendocrine tumors were graded based on MC, KPI, and PHH3C. Concordance was determined using a weighted κ statistic. Median survival across each grade category was determined using Kaplan-Meier methods.

RESULTS

Of the 110 patients, the majority had gastrointestinal primaries and grade 1 or 2 tumors. Rates of discordance in grade assignment were 29% of cases for KPI versus MC (κW = 0.26), 32% for PHH3C versus MC (κW = 0.34), and 32% for PHH3C versus KPI (κW = 0.37). There was fair agreement between grading by KPI and MC. Relative to grade by KPI and MC, PHH3C tended to upgrade tumors. The proportion alive at 3 and 5 years was not significantly different for patients with grade 1 versus grade 2 tumors.

CONCLUSIONS

The concordance between KPI and MC was fair. Phosphohistone H3 count tended to upgrade tumors using the cutoffs established by MC. Grade 1 and grade 2 tumors were associated with similar survival regardless of grading method. The overall relevance of the current cutoff values used in grading neuroendocrine tumors may need to be revisited.

Synaptophysin-Ki67 double stain: a novel technique that improves interobserver agreement in the grading of well-differentiated gastrointestinal neuroendocrine tumors

A common problem in the assessment of Ki67 proliferative index in well-differentiated gastrointestinal neuroendocrine tumors is distinguishing tumor from non-tumor. This is because background stromal lymphocytes, entrapped non-neoplastic glands, and the delicate vascular network characteristic of neuroendocrine tumors frequently contain a subset of proliferating cells. Furthermore, in small biopsies, crush and cautery artifact can alter the morphologic appearance of tumor cells, making the Ki67 proliferative index more difficult to assess. To address these issues, we developed a synaptophysin-Ki67 double stain using a commercially available immunohistochemistry kit, allowing simultaneous visualization of tumor and proliferating nuclei. To test this method, three gastrointestinal pathologists individually graded 50 gastrointestinal neuroendocrine tumors first using synaptophysin-Ki67 double-stained slides and then, after a washout period, using Ki67-only stained slides (along with routine hematoxylin- and eosin-stained slides). Interobserver agreement on Ki67 proliferative index was moderate using the Ki67-only stained slides (intraclass correlation 0.51, 95% confidence interval: 0.35-0.66) and improved using the synaptophysin-Ki67 double stain (intraclass correlation 0.79, 95% confidence interval: 0.69-0.86). Similarly, interobserver agreement on tumor grade was fair with Ki67-only stained slides (κ=0.39, P<0.001) and improved with the double stain (κ=0.58, P<0.001). Analysis of individual pathologists' scores revealed that fewer total number of tumor cells counted correlated with higher grade designation and appeared to contribute to grade discordance. In tumors cited as particularly challenging to assess by the pathologists, three of four tumors were grade discordant with the Ki67-only stain, whereas all four tumors were grade concordant with the synaptophysin-Ki67 stain. The synaptophysin-Ki67 double stain is the first technique to address specifically the histomorphologic challenges of evaluating Ki67 proliferative index in well-differentiated gastrointestinal neuroendocrine tumors. Although further validation is needed, this study provides evidence that the synaptophysin-Ki67 double stain can improve interobserver agreement.

Sporadic Gastric Well-Differentiated Neuroendocrine Tumors Have a Higher Ki-67 Proliferative Index

Well-differentiated neuroendocrine tumor (WDNET) of the stomach can arise in three distinct clinical settings: (1) in association with autoimmune atrophic gastritis, (2) in association with multiple neuroendocrine neoplasia type I (MEN I) or Zollinger-Ellison syndrome (ZES), or (3) sporadic. The Ki-67 proliferative index (PI) in gastric WDNETs in these three distinct clinical settings has not been evaluated in detail. Forty-five gastric WNETs underwent polypectomy (n = 4), endoscopic mucosal resection (n = 12), and surgical resection (n = 29) between 1994 and 2015 were included. H&E slides from each case were reviewed, and Ki-67 immunostain was performed on one representative tumor block. Ki-67 PI was determined by quantitative Aperio image analysis software in areas of strongest nuclear labeling ("hot spots"), and correlated with underlying clinical and pathological features. Twenty-one patients were male and 24 female with a median age of 57 years (range, 30-80 years). Tumors were classified as type I (n = 17), type II (n = 6), and type III (n = 22) WDNETs. Types II and III showed more advanced TNM stage compared to type I (p = 0.02, overall). WHO grade based on Ki-67 PI was higher in type III WDNETs [grade 1 (G1), n = 3; grade 2 (G2), n = 15; and grade 3 (G3), n = 4] than in type I WDNETs [G1, n = 5; G2, n = 12] and in type II WDNETs [G1, n = 2; G2, n = 4] (p = 0.050, overall). Ki-67 PI was significantly higher in type III WDNETs (mean ± SD = 13.0 ± 13.3 %) than in non-sporadic (type I and II) WDNETs (mean ± SD = 5.3 ± 3.3 %; p = 0.015). There was no difference in Ki-67 PI between type I WDNETs (mean ± SD = 5.2 ± 3.5 %) and type II WDNETs (mean ± SD = 5.6 ± 3.1%; p = 0.817). Higher Ki-67 PI was associated with higher tumor T stage (p = 0.003) and also tended to be associated with lymph node metastasis (p = 0.071). In the Kaplan-Meier survival analysis, type I was associated with a significantly longer disease-free survival (DFS) time compared to type II (p = 0.018) or III (0.010). Also, the WHO G3 group had a significantly shorter DFS time than the WHO G1 (p = 0.020) or G2 (p = 0.007) group. Gastric WDNET is a heterogeneous disease entity encompassing three clinical subtypes-type I, type II, and type III-having their own distinct clinicopathologic characteristics and prognosis. Our results showed that sporadic (type III) WDNET had a significantly higher Ki-67 PI than non-sporadic cases (type I or II); increased PI was associated with higher tumor stage. We also described four type III cases of morphologically WD gastric NET with WHO grade 3 on the basis of Ki-67 PI.

The ENETS/WHO grading system for neuroendocrine neoplasms of the gastroenteropancreatic system: a review of the current state, limitations and proposals for modifications

The understanding of neuroendocrine neoplasms has evolved significantly since their initial descriptions in the 1800s to early 1900s. In the gastroenteropancreatic system, this group of malignant tumors is subdivided into well and poorly differentiated neuroendocrine neoplasms based on morphologic, proliferative and biologic differences. However, it has become increasingly apparent that well-differentiated neuroendocrine tumors are not a homogeneous group. Attempting to better predict outcome of these tumors has been the motivation behind numerous proposed classification systems, the evolution of which culminated with the currently used system, the ENETS/WHO classification. Herein, we review the genesis of this classification system and some of its shortcomings. In addition, we discuss some of the most recent proposals that suggest modifications to the current system.

Histopathology of NET: Current concepts and new developments

The diagnosis of neuroendocrine tumors is based on their histopathologic appearance and immunohistochemical profile. With the WHO 2010 classification formal staging and grading was introduced for gastro-entero-pancreatic NET, however, the nomenclature for lung neuroendocrine tumors still relies on the carcinoid term. In this review we also focus on the situation of neuroendocrine carcinoma of unknown primary, tissue biomarkers and actual controversies in the histopathology of NEN.