Invasive lobular carcinoma of the breast: assessment of proliferative activity using automated Ki-67 immunostaining

Artificial intelligence in breast cancer histopathology

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.

Development and validation of an extended Cox prognostic model for patients with ER/PR+ and HER2- breast cancer: a retrospective cohort study

Background

The purpose of this study was to explore a new estrogen receptor (ER) and/or progesterone receptor (PR)+ and human epidermal growth factor receptor 2 (HER2)− breast cancer prognostic model, called the extended Cox prognostic model, for determining the cutoff values for multiple continuous prognostic factors and their interaction via the new model concept and variable selection method.

Methods

A total of 335 patients with ER/PR+ and HER2− breast cancer were enrolled for the final analysis. The primary endpoint was breast cancer-specific mortality (BCSM). Prognostic factors (histological grade, histological type, stage, T, N, lymphovascular invasion (LVI), P53, Ki67, ER, PR, and age) were included in this study. The four continuous variables (Ki67, ER, PR, and age) were partitioned into a series of binary variables that were fitted in the multivariate Cox analysis. A smoothly clipped absolute deviation (SCAD) variable selection method was used. Model performance was expressed in discrimination and calibration.

Results

We developed an extended Cox model with a time threshold of 164-week (more than 3 years) postoperation and developed a user-friendly nomogram based on our extended Cox model to facilitate clinical application. We found that the cutoff values for PR, Ki67, and age were 20%, 60%, and 41–55 years, respectively. There was an interaction between age and PR for patients aged ≥ 41 years and PR ≥ 20% at 164-week postoperation: the older the patients with ER/PR+, HER2−, and PR ≥ 20% were, the lower the survival and more likely to recur and metastasize exceeding 164 weeks (more than 3 years) after surgery.

Conclusions

Our study offers guidance on the prognosis of patients with ER/PR+ and HER2− breast cancer in China. The new concept can inform modeling and the determination of cutoff values of prognostic factors in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02790-0.

E-Cadherin-Deficient Cells Are Sensitive to the Multikinase Inhibitor Dasatinib

The CDH1 gene, encoding the cell adhesion protein E-cadherin, is one of the most frequently mutated genes in gastric cancer and inactivating germline CDH1 mutations are responsible for the cancer syndrome hereditary diffuse gastric cancer (HDGC). CDH1-deficient gastric cancers exhibit high AKT serine/threonine kinase 3 (AKT3) expression, but specific drugs against this AKT isoform are not available. We therefore used two publicly available datasets to identify AKT3-associated genes which could be used to indirectly target AKT3. Reactome analysis identified an enrichment of extracellular matrix remodelling genes in AKT3-high gastric cancers. Of the 51 genes that were significantly correlated with AKT3 (but not AKT1), discoidin domain receptor tyrosine kinase 2 (DDR2) showed the strongest positive association. Treatment of isogenic human cells and mouse gastric and mammary organoids with dasatinib, a small molecule inhibitor of multiple kinases including SRC, BCR-ABL and DDR2, preferentially slowed the growth and induced apoptosis of E-cadherin-deficient cells. Dasatinib treatment also preferentially slowed the growth of gastric and mammary organoids harbouring both Cdh1 and Tp53 mutations. In organoid models, dasatinib treatment was associated with decreased phosphorylation of total AKT, with a stronger effect seen in Cdh1-deficient organoids. Treatment with combinations of dasatinib and an inhibitor of AKT, MK2206, enhanced the effect of dasatinib in breast MCF10A cells. In conclusion, targeting the DDR2-SRC-AKT3 axis with dasatinib represents a promising approach for the chemoprevention and chemotherapy of gastric and breast cancers lacking E-cadherin.

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.

Lobular Breast Cancer: Histomorphology and Different Concepts of a Special Spectrum of Tumors

Simple Summary

Invasive lobular breast cancer (ILC) is a special type of breast cancer (BC) that was first described in 1941. The diagnosis of ILC is made by microscopy of tumor specimens, which reveals a distinct morphology. This review recapitulates the developments in the microscopic assessment of ILC from 1941 until today. We discuss different concepts of ILC, provide an overview on ILC variants, and highlight advances which have contributed to a better understanding of ILC as a special histologic spectrum of tumors.

Abstract

Invasive lobular breast cancer (ILC) is the most common special histological type of breast cancer (BC). This review recapitulates developments in the histomorphologic assessment of ILC from its beginnings with the seminal work of Foote and Stewart, which was published in 1941, until today. We discuss different concepts of ILC and their implications. These concepts include (i) BC arising from mammary lobules, (ii) BC growing in dissociated cells and single files, and (iii) BC defined as a morpho-molecular spectrum of tumors with distinct histological and molecular characteristics related to impaired cell adhesion. This review also provides a comprehensive overview of ILC variants, their histomorphology, and differential diagnosis. Furthermore, this review highlights recent advances which have contributed to a better understanding of the histomorphology of ILC, such as the role of the basal lamina component laminin, the molecular specificities of triple-negative ILC, and E-cadherin to P-cadherin expression switching as the molecular determinant of tubular elements in CDH1-deficient ILC. Last but not least, we provide a detailed account of the tumor microenvironment in ILC, including tumor infiltrating lymphocyte (TIL) levels, which are comparatively low in ILC compared to other BCs, but correlate with clinical outcome. The distinct histomorphology of ILC clearly reflects a special tumor biology. In the clinic, special treatment strategies have been established for triple-negative, HER2-positive, and ER-positive BC. Treatment specialization for patients diagnosed with ILC is just in its beginnings. Accordingly, ILC deserves greater attention as a special tumor entity in BC diagnostics, patient care, and cancer research.

Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study

AIMS

The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)-empowered microscope to improve Ki67 scoring concordance.

METHODS AND RESULTS

We developed an AI-empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67-stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI-empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI-empowered microscope.

CONCLUSION

The AI-empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment.