Search Results (8)

Background: Several targeted drugs have been recently approved for the treatment of PIK3CA-mutated hormone receptor-positive (HR+)/HER2-negative (HER2−) breast cancer (BC). This study aimed at a comprehensive evaluation of the spectrum of PIK3CA alterations in Russian BC patients. Methods: The tumor material from 1872 patients with ER+/HER2− BC was tested by a combination of PCR-based methods. Results: Mutations were detected in 693/1872 (37%) cases, including 46 BC with two PIK3CA lesions. The three most common substitutions (E542K, E545K, and H1047R) were identified in 542/693 (78%) PIK3CA-mutated cases, while as many as 5.5–12% of identified mutations were not potentially detectable by common commercial kits. The study included patients of Slavic and non-Slavic ethnicities residing in regions with different climate conditions, however, these factors did not influence the distribution of PIK3CA mutations. The presence of PIK3CA variants was associated with older patient age at diagnosis (p = 0.0002), smaller tumor size (p = 0.005), lower grade (p = 0.005), Ki67 <20% (p = 0.0001) and progesterone receptor-positive status (p = 0.002) at the initial disease diagnosis, and fewer distant metastases at the time of the detection of BC spread (p = 0.0001). In a subgroup of 413 BC patients who received adjuvant tamoxifen or aromatase inhibitors, PIK3CA mutations were not associated with resistance to either type of treatment. Conclusions: The results of this study highlight the need to extend the PIK3CA testing beyond the hotspot regions of this gene. Although PIK3CA alterations contribute to the pathogenesis of HR+/HER2− BC and represent a target for several novel drugs, they are not intrinsically associated with unfavorable clinical characteristics of this subtype of cancer disease. Full article

Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30–40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. Methods: Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67⁺, and pH3⁺ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. Results: Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67⁺, and pH3⁺ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. Conclusions: Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC. Full article

Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort. Full article

The clinical evolution of solitary fibrous tumors (SFTs) is often uncertain and several risk stratification systems (RSS) have been proposed. The Demicco et al. RSS is the most frequently implemented. In this study we aim to validate two alternative RSS (Sugita et al. and G-Score) using results for the Demicco RSS from a previous study of 97 SFTs. In addition, we aim to determine whether reclassified cases had any distinctive molecular features. As the Sugita et al. system substitutes mitotic count with Ki-67 index we also investigated whether Ki-67 results for tissue microarrays are comparable to those obtained using whole tissue sections. In the present study we detected that many cases classified by Demicco RSS as low-risk were reclassified as intermediate risk using the new system (G-score RSS). Kaplan-Meier survival plots for G-Score RSS showed that the low-risk and intermediate-risk SFTs had a similar evolution that contrasted with the more aggressive high-risk group. Moreover, the similar evolution in both low and intermediate-risk groups occurred despite the G-score system being stricter in classifying low-risk tumors. We observed that Sugita RSS does not provide any better risk stratification in comparison with the Demicco RSS, and testing both RSS in our series produced similar Kaplan-Meier survival data. We found some discordant results when comparing whole sections and the corresponding tissue microarrays samples, finding the hotspot areas easier to locate in whole sections. Forty-one SFTs with initial low-risk assigned by the Demicco RSS were reclassified as intermediate-risk by G-score finding both TP53 and HTER mutations in four cases, only HTER mutation in 11 cases, and only TP53 mutation in 2 cases. All six cases of SFT classified as high-risk by both the Demicco and G-score RSS suffered recurrence/metastasis, and half showed both TP53 and HTER mutations. Five SFTs were categorized as low-risk by both Demicco and G-score, of which 4 cases revealed HTER mutation. Regarding the outcome of these 5 patients, two were lost to follow-up, and one of the remaining three patients suffered recurrence. We believe that although the presence of both TP53 and HTER mutations may confer or be related to poor evolution, the isolated presence of HTER mutation alone would not necessarily be related to poor outcome. The G-score RSS more accurately identified low-risk patients than the other two risk models evaluated in the present series. Late recurrence/metastasis may occasionally be observed even in low-risk SFTs categorized by stricter classification systems such as the G-score RSS. These findings support the possibility that additional, as yet unknown factors may influence the clinical evolution of SFTs. In conclusion, given the possibility of late recurrence, long-term follow-up is recommended for all SFT patients, even in cases classified as low risk by the stricter G-score system. An integration of clinical, radiological, pathological, and molecular findings may improve SFT risk stratification and better predict patient outcome. Full article

Melanoma tumors are the most heterogeneous of all tumor types. Tumor heterogeneity results in difficulties in diagnosis and is a frequent cause of failure in treatment. Novel techniques enable accurate examination of the tumor cells, considering their heterogeneity. The study aimed to determine the somatic variations among high and low proliferating compartments of melanoma tumors. In this study, 12 archival formalin-fixed paraffin-embedded samples of previously untreated primary cutaneous melanoma were stained with Ki-67 antibody. High and low proliferating compartments from four melanoma tumors were dissected using laser-capture microdissection. DNA was isolated and analyzed quantitatively and qualitatively. Libraries for amplicon-based next-generation sequencing (NGS) were prepared using NEBNext Direct Cancer HotSpot Panel. NGS detected 206 variants in 42 genes in melanoma samples. Most of them were located within exons (135, 66%) and were predominantly non-synonymous single nucleotide variants (99, 73.3%). The analysis showed significant differences in mutational profiles between high and low proliferation compartments of melanoma tumors. Moreover, a significantly higher percentage of variants were detected only in high proliferation compartments (39%) compared to low proliferation regions (16%, p < 0.05). Our results suggest a significant functional role of genetic heterogeneity in melanoma. Full article

In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and whole slide images (WSI), representing a diverse multi-centre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to several innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images—and it was posed as a detection problem to mimic pathologists’ workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumor cells, which reduces false positives from non-tumor cells without needing the usual pre-requisite tumor segmentation step for Ki67 quantification. Thirdly, the concept of learning background regions through weak supervision is introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain “significant” activity. Full article

Ki67 hot-spot detection and its evaluation in invasive breast cancer regions play a significant role in routine medical practice. The quantification of cellular proliferation assessed by Ki67 immunohistochemistry is an established prognostic and predictive biomarker that determines the choice of therapeutic protocols. In this paper, we present three deep learning-based approaches to automatically detect and quantify Ki67 hot-spot areas by means of the Ki67 labeling index. To this end, a dataset composed of 100 whole slide images (WSIs) belonging to 50 breast cancer cases (Ki67 and H&E WSI pairs) was used. Three methods based on CNN classification were proposed and compared to create the tumor proliferation map. The best results were obtained by applying the CNN to the mutual information acquired from the color deconvolution of both the Ki67 marker and the H&E WSIs. The overall accuracy of this approach was 95%. The agreement between the automatic Ki67 scoring and the manual analysis is promising with a Spearman’s $\rho$ correlation of 0.92. The results illustrate the suitability of this CNN-based approach for detecting hot-spots areas of invasive breast cancer in WSI. Full article

Background and objectives: B-lymphoma Mo-MLV insertion region 1 (Bmi-1) is a stem cell factor that is overexpressed in various human cancer tissues. It has been implicated in cancer cell proliferation, cell invasion, distant metastasis, and chemosensitivity, and is associated with patient survival. Several reports have also identified Bmi-1 protein overexpression in endometrial carcinoma; however, the relationship between Bmi-1 expression and its significance as a clinicopathological parameter is still insufficiently understood. Accordingly, the present study aimed to clarify whether immunohistochemical staining for Bmi-1 in human endometrial carcinoma and normal endometrial tissues can be used as a prognostic and cell proliferation marker. Materials and Methods: Bmi-1 expression was assessed in endometrioid carcinoma (grade 1–3) and normal endometrial tissues (in the proliferative and secretory phases) by immunohistochemistry; protein expression was evaluated using the nuclear labeling index (%) in the hot spot. Furthermore, we examined other independent prognostic and proliferation markers, including the protein levels of Ki-67, p53, and cyclin A utilizing semi-serial sections of endometrial carcinoma tissues. Results: The expression of the Bmi-1 protein was significantly higher in all grades of endometrial carcinoma than in the secretory phase of normal tissues. Moreover, Bmi-1 levels tended to be higher in G2 and G3 tissues than in G1 tissue, without reaching significance. Bmi-1 expression showed no notable differences among International Federation of Gynecology and Obstetrics (FIGO) stages in endometrial carcinoma. Furthermore, we observed a significant positive relationship between Bmi-1 and Ki-67, cyclin A, or p53 by Spearman’s rank correlation test, implying that high Bmi-1 expression can be an independent prognostic marker in endometrial carcinoma. Conclusions: Our study suggests that Bmi-1 levels in endometrial carcinoma tissues may be useful as a reliable proliferation and prognostic biomarker. Recently, the promise of anti-Bmi-1 strategies for the treatment of endometrial carcinoma has been detected. Our results provide fundamental data regarding this anti-Bmi-1 strategy. Full article