Background: The American Joint Commission Cancer (AJCC) Cancer Staging Manual 8th edition introduced a breast cancer (BC) Prognostic Stage (PS) that combines tumour grade, oestrogen (ER), progesterone (PgR), and human epidermal growth factor-2 (HER2) receptor status with Anatomic TNM Stage (AS). In a further modification, patients with early BC and an Oncotype DX® Recurrence Score (RS) < 11 are assigned to PS 1A irrespective of grade and size up to 5 cm. This study profiles the impact of these changes on staging in patients with early BC and RS < 11. Methods: A total of 127 patients, with primary BC and RS < 11, were identified from a consecutive series of 729 patients with ER-positive, HER2-negative, lymph node-negative, primary BC whose tumours were tested using the Oncotype DX® 21 multigene assay. Each patient was assigned AS, PS, and RS-modified PS, and staging categories were compared. Results: Applying AS, 100 patients were stage IA and 27 IIA. Applying PS, 89 were stage IA, 33 IB, 4 IIA, and 1 IIB. All patients were IA according to RS-modified PS. Comparing PS to AS, 26.7% of patients (n = 34) changed stage, 9.4% (n = 12) to a higher and 17.3% (n = 22) to a lower stage. RS-modified PS versus AS resulted in downstaging in 21.3% (n = 27). Comparing PS modified by RS to PS alone, 29.9% (n = 38) were downstaged. Conclusion: Application of PS and RS-modified PS results in tumour downstaging in approximately 20% of patients with early BC. Upstaging was observed in 9% of patients when staged according to PS and was primarily due to the impact of high histological grade.

Objective: The purpose of this study is to investigate the expression of cancer-associated fibroblast (CAF)-related proteins and their implication in ductal carcinoma in situ (DCIS). Methods: We constructed a tissue microarray of 223 cases of DCIS and examined immunohistochemical staining for the 7 CAF-related proteins. We classified DCIS into luminal type, human epidermal growth factor receptor-2 (HER-2) type, and triple negative breast cancer (TNBC) according to the immunohistochemical results for estrogen receptor, progesterone receptor, and HER-2. We also classified DCIS into desmoplastic, normal-like, and inflammatory type according to stromal histology. Results: There were significant differences in the expression of S100A4, podoplanin, prolyl 4-hydroxylase subunit alpha 3, NG2, and PDGFRα in stromal cells of DCIS when classified according to molecular subtype. The expression rate of all CAF-related proteins in stromal cells was higher in the HER-2 type and TNBC than in the luminal type (p < 0.001). When classified according to stromal subtype, there were significant differences in the expression of all CAF-related proteins in stromal cells, with the inflammatory stromal type showing higher expression of CAF-related proteins than other stromal types. Conclusion: The expression of CAF-related proteins in stromal cells of DCIS varies according to molecular subtype and stromal type.

Objective: This study was designed to evaluate the prevalence of CTNNB1 (β-catenin) mutations in cases of papillary thyroid carcinoma with desmoid-type fibromatosis (PTC-DTF) expressing aberrant nuclear and cytoplasmic immunoreactivity for β-catenin. Methods: Eight cases of PTC-DTF were available for this study. Immunohistochemistry for β-catenin and BRAF^V600E was performed. CTNNB1 and BRAFV600E mutations were also evaluated by direct sequencing. Results: For β-catenin, although we could demonstrate aberrant nuclear and cytoplasmic immunoreactivity in DTF components in all cases, suggesting activated Wnt signaling, direct sequencing revealed a missense mutation, c.121A>G (p.T41A), in exon 3 in only one case, and no mutations in exons 3, 4, and 5 in the other cases. In the BRAF^V600E analyses, immunohistochemistry revealed positive staining in the carcinoma cells but not DTF components of all cases. These findings were subsequently validated by direct sequencing. Conclusion: This study suggests the significance of the BRAFV600E mutation and activation of Wnt signaling pathway in the carcinoma cells and DTF components, respectively. We believe that the CTNNB1 mutations are not the major factor behind β-catenin translocation indicating Wnt pathway activation. Further study is required to evaluate whether molecular abnormalities other than the CTNNB1 mutation cause activation of Wnt signaling in DTF components of PTC-DTF.

This review highlights the main changes in the revised 2016 WHO Classification of Myeloid Neoplasms (published in 2017) that impact the diagnosis and management of patients with myelodysplastic syndrome (MDS). The revision was based on data accumulated since the 2008 WHO classification of MDS, much of which relates to new molecular genetic information about these neoplasms. The new information has led to some reorganization of the MDS disease categories, including a broadening of the subset of cases classified as MDS with ring sideroblasts, many of which have mutations in the spliceosome gene SF3B1. Other revisions have refined the definitions of some disease categories to improve disease risk stratification. The revised categories in the new classification ensure that MDS patients receive risk-adapted therapies based on the most recently available data.

Hypereosinophilia (HE) is defined as a persistent elevated eosinophil count of ≥1.5 × 10⁹/L. HE can be one of the dominant manifestations of a hematopoietic myeloid neoplasm or secondary/reactive to an underlying medical condition. If a cause of HE and its associated tissue/organ damage is not determined, the condition is considered to be idiopathic hypereosinophilic syndrome (HES). The work-up of HE can be challenging due to a broad range of causes of HE that can be either reactive or neoplastic. In recent years, with the advent of molecular genetic testing and the introduction of targeted therapy in the management of these patients, there is a growing interest in better characterization of these diseases. Using a multimodality approach and following a proper ­algorithm, a diagnosis can be made in a large proportion of patients. In idiopathic HES, myeloid neoplasm associated ­somatic mutations as evidence of clonality are reported in ­20–25% patients; however, the mutation data should be ­interpreted cautiously considering the prevalence of clonal hematopoiesis of indeterminate potential (CHIP). Bone marrow morphology has been shown to have important value in the identification of a true myeloid neoplasm in these disorders. A genome-wide study may be needed to understand the “idiopathic” cases that would ultimately lead to better patient care.

Objectives: Ankylosing spondylitis (AS) is a chronic, progressive immune-mediated inflammatory disease, driven primarily by Th1 and Th17 cells. Anti-TNF therapies are successfully used in AS to achieve and maintain remission. However, their influence on the composition of T-cell subsets is not clear. We aimed to characterize the changes in the T-cell repertoire after a long-term anti-TNF treatment in AS patients. Methods: Twenty-two AS patients under long-term anti-TNF therapy were evaluated (15 anti-TNF responders and 7 nonresponders). A wide range of cell subtypes was analyzed with flow cytometry and compared with therapy-naïve and short-term data too. Results: Key findings include decreased proportions of naïve CD4 and CD8 cells, increased frequencies of Th1 and Th17 cells and higher Th1/Th2 ratios in the long-term anti-TNF-treated patients (responders, nonresponders and total), which was found to be significant not only when compared with healthy controls, but also with therapy-naïve and short-term anti-TNF-treated AS patients. We noted several alterations within the various activated T-cell subsets – increase in CD4HLADR cells in responders, in CD8HLADR cells in the whole AS group and in responders, and in CD4CD25 cells in responders, and decrease in CD4CD69 cell percentages in long-term treated patients – becoming evident only after long-term anti-TNF therapy. Conclusions: This study provides a comprehensive assessment of the impact of anti-TNF therapy on the T-cell repertoire in AS. Changes in T-cell phenotype seem to develop progressively during therapy, even in inactive disease, and reflect an ongoing effector T-cell differentiation and activation, along with the parallel compensatory increase in regulatory T cells.

Objective: Spheroid colony formation is a useful method of cancer stem cell (CSC) characterization. We previously showed that the IQ motif containing the GTPase-activating protein 3 gene (IQGAP3) is upregulated in spheroid body-forming gastric cancer (GC) cells compared with parental cells. We investigated IQGAP3 expression in GC. Methods: IQGAP3 protein expression was analyzed by immunohistochemistry in 165 GC cases. RNA interference was used to inhibit IQGAP3 expression in GC cell lines. Results: In non neoplastic gastric mucosa, weak staining of IQGAP3 was observed in the foveolar epithelium, while GC tissue showed stronger, more extensive staining. Of the 165 GC cases, 34 (21%) were positive for IQGAP3 expression. GC cases positive for IQGAP3 were found more frequently in stage II/III/IV cases than in stage I cases. Univariate and multivariate analyses demonstrated that IQGAP3 expression is an independent prognostic classifier of GC patients. Both the number and size of the spheres formed by MKN-74 cells were significantly reduced by knockdown of IQGAP3. The phosphorylation of Akt and Erk1/2 was inhibited by knockdown of IQGAP3. Conclusion: These results suggest that IQGAP3 plays an important role in gastric CSCs. The location of IQGAP3 on the cell membrane makes it a potential therapeutic target for GC.

Interrogation of tissue informs on patient management through delivery of a diagnosis together with associated clinically relevant data. The diagnostic pathologist will usually evaluate the morphological appearances of a tissue sample and, occasionally, the pattern of expression of a limited number of biomarkers. Recent developments in sequencing technology mean that DNA and RNA from tissue samples can now be interrogated in great detail. These new technologies, collectively known as next-generation sequencing (NGS), generate huge amounts of data which can be used to support patient management. In order to maximize the utility of tissue interrogation, the molecular data need to be interpreted and integrated with the morphological data. However, in order to interpret the molecular data, the pathologist must understand the utility and the limitations of NGS data. In this review, the principles behind NGS technologies are described. In addition, the caveats in the interpretation of the data are discussed, and a scheme is presented to “classify” the types of data which are generated. Finally, a glossary of new terminology is included to help pathologists become familiar with the lexicon of NGS-derived molecular data.