Interlaboratory variability of Ki67 staining in breast cancer

Peer-to-peer validation of Ki-67 scoring in a pathology quality circle as a tool to assess interobserver variability: are we better than we thought?

Ki-67, a nuclear protein expressed in all stages of cellular proliferation, is a valuable tool to assess tumor proliferation and has been linked to more aggressive tumor behavior. However, interlaboratory staining heterogeneity and inter-observer variability challenge its reproducibility. Round Robin tests are a suitable tool to standardize and harmonize immunohistochemical and molecular analyses in histopathology. The study investigates the interrater and interlaboratory reproducibility of Ki-67-scoring using both manual and automated approaches. Unstained TMA slides comprising diverse tumor types (breast cancer, neuroendocrine tumors, lymphomas, and head and neck squamous cell carcinoma) were distributed to six pathology laboratories, each employing their routine staining protocols. Manual and automated scoring methods were applied, and interrater and interlaboratory agreement assessed using intraclass correlation coefficients (ICC). The results highlight good-to-excellent reliability overall, with automated scoring demonstrating higher consistency (ICC 0.955) than manual scoring (ICC 0.871). Results were more variable when looking at the individual entities. Reliability remained good for lymphomas (ICC 0.878) and breast cancer (ICC 0.784) and was poor in well-differentiated neuroendocrine tumors (ICC 0.354). This study clearly advocates standardized practices and training to ensure consistency in Ki-67-assessment, and it demonstrates that this can be achieved in a peer-to-peer approach in local quality-circles.

The REMAR (Rhein-Main-Registry) real-world study: prospective evaluation of the 21-gene breast recurrence score® assay in addition to Ki-67 for adjuvant treatment decisions in early-stage breast cancer

PURPOSE

Ki-67 is recommended by international/national guidelines for risk stratification in early breast cancer (EBC), particularly for defining "intermediate risk," despite inter-laboratory/inter-observer variability and cutoff uncertainty. We investigated Ki-67 (> 10%- < 40%, determined locally) as a prognostic marker for intermediate/high risk in EBC, pN0-1 patients.

METHODS

This prospective, non-interventional, real-world study included females ≥ 18 years, with pN0/pN1mi/pN1, HR+ , HER2-negative EBC, and locally determined Ki-67 ranging 10%-40%. The primary outcome was changes in treatment recommendations after disclosing the Oncotype DX Breast Recurrence Score®(RS) assay result.

RESULTS

The analysis included 567 patients (median age, 57 [range, 29-83] years; 70%/1%/29%/ with pN0/pN1mi/pN1 disease; 81% and 19% with RS results 0-25 and 26-100, respectively). The correlations between local and central Ki-67, local Ki-67, and the RS, and central Ki-67 and the RS results were weak (r = 0.35, r = 0.3, and r = 0.46, respectively), and discrepancies were noted in both directions (e.g., local Ki-67 was lower or higher than central Ki-67). After disclosing the RS, treatment recommendations changed for 190 patients (34%). Changes were observed in pN0 and pN1mi/pN1 patients and in patients with centrally determined Ki-67 ≤ 10% and > 10%. Treatment changes were aligned with RS results (adding chemotherapy for patients with higher RS results, omitting it for lower RS results), and their net result was 8% reduction in adjuvant chemotherapy use (from 32% pre-RS results to 24% post-RS results).

CONCLUSION

The Oncotype DX® assay is a tool for individualizing treatments that adds to classic treatment decision factors. The RS result and Ki-67 are not interchangeable, and Ki-67, as well as nodal status, should not be used as gatekeepers for testing eligibility, to avoid under and overtreatment.

Flow cytometric analysis for Ki67 assessment in formalin-fixed paraffin-embedded breast cancer tissue

BACKGROUND

Pathologists commonly employ the Ki67 immunohistochemistry labelling index (LI) when deciding appropriate therapeutic strategies for patients with breast cancer. However, despite several attempts at standardizing the Ki67 LI, inter-observer and inter-laboratory bias remain problematic. We developed a flow cytometric assay that employed tissue dissociation, enzymatic treatment and a gating process to analyse Ki67 in formalin-fixed paraffin-embedded (FFPE) breast cancer tissue.

RESULTS

We demonstrated that mechanical homogenizations combined with thrombin treatment can be used to recover efficiently intact single-cell nuclei from FFPE breast cancer tissue. Ki67 in the recovered cell nuclei retained reactivity against the MIB-1 antibody, which has been widely used in clinical settings. Additionally, since the method did not alter the nucleoskeletal structure of tissues, the nuclei of cancer cells can be enriched in data analysis based on differences in size and complexity of nuclei of lymphocytes and normal mammary cells. In a clinical study using the developed protocol, Ki67 positivity was correlated with the Ki67 LI obtained by hot spot analysis by a pathologist in Japan (rho = 0.756, P < 0.0001). The number of cancer cell nuclei subjected to the analysis in our assay was more than twice the number routinely checked by pathologists in clinical settings.

CONCLUSIONS

The findings of this study showed the application of this new flow cytometry method could potentially be used to standardize Ki67 assessments in breast cancer.

Prediction of response to neoadjuvant chemotherapy by MammaTyper® across breast cancer subtypes: A retrospective cross-sectional study

BACKGROUND

Neoadjuvant chemotherapy (NACT) is widely used in the treatment of triple-negative and HER2-positive breast cancer (BC), but its use in estrogen receptor (ER) and/or progesterone receptor (PR) positive/HER2-negative BC is questioned because of the low pathologic complete response (pCR) rates. This retrospective study assessed the mRNA-based MammaTyper® assay's capability of predicting pCR with NACT, and ER, PR, Ki67, and HER2 status at immunohistochemical (IHC) through transcriptomics.

METHODS

Diagnostic biopsies from 76 BC patients treated at the Cremona Hospital between 2012-2018 were analyzed. Relative mRNA expression levels of ERBB2, ESR1, PGR, and MKI67 were measured using the MammaTyper® kit and integrated into a pCR score. Predicting capability of pCR and standard IHC biomarkers could be assessed with ROC curves in 75 and 76 patients, respectively.

RESULTS

Overall, 68.0% patients obtained a MammaTyper® high-score and 32.0% a MammaTyper® low-score. Among high-score patients, 62.7% achieved pCR, compared to 16.7% in the low-score group (p = 0.0003). The binary MammaTyper® score showed good prediction of pCR in the overall cohort (area under curve [AUC] = 0.756) and in HR+/HER2-negative cases (AUC = 0.774). In cases with residual disease, the continuous MammaTyper® score correlated moderately with residual tumor size and decrease in tumor size. MammaTyper® showed substantial agreement with IHC for ESR1/ER and ERBB2/HER2, and moderate agreement for PGR/PR and MKI67/Ki67.

CONCLUSION

Overall, MammaTyper® pCR score may serve as a standardized tool for predicting NACT response in HR+/HER2-negative BC, potentially guiding treatment strategies. Additionally, it could provide a more standardized and reproducible assessment of ER, PR, HER2, and Ki67 status.

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.

Prognostic predictive value of Ki-67 in stage I-II triple-negative breast cancer

Aim: Our research aimed to determine an optimal cutoff value and investigate the prognostic predictive function of Ki-67. Materials & methods: We retrospectively enrolled 1146 patients diagnosed with stage I-II triple-negative breast cancer. Disease-free and overall survival were analyzed using the Kaplan-Meier method and the Cox regression model. Results: We classified Ki-67 >45% as the high group (n = 716). A Ki-67 level of >45% was associated with poorer disease-free survival (p = 0.039) and overall survival (p = 0.029). Lymph node stage, neoadjuvant chemotherapy, and radiotherapy were independent predictive variables of prognosis. Conclusion: Triple-negative breast cancer may be further subcategorized according to the Ki-67 level. Neoadjuvant chemotherapy and postoperative radiotherapy can improve the prognosis of early triple-negative breast cancer.

Analysis of the Correlation and Prognostic Significance of Tertiary Lymphoid Structures in Breast Cancer: A Radiomics-Clinical Integration Approach

BACKGROUND

Tertiary lymphoid structures (TLSs) are potential prognostic indicators. Radiomics may help reduce unnecessary invasive operations.

PURPOSE

To analyze the association between TLSs and prognosis, and to establish a nomogram model to evaluate the expression of TLSs in breast cancer (BC) patients.

STUDY TYPE

Retrospective.

POPULATION

Two hundred forty-two patients with localized primary BC (confirmed by surgery) were divided into BC + TLS group (N = 122) and BC - TLS group (N = 120).

FIELD STRENGTH/SEQUENCE

3.0T; Caipirinha-Dixon-TWIST-volume interpolated breath-hold sequence for dynamic contrast-enhanced (DCE) MRI and inversion-recovery turbo spin echo sequence for T2-weighted imaging (T2WI).

ASSESSMENT

Three models for differentiating BC + TLS and BC - TLS were developed: 1) a clinical model, 2) a radiomics signature model, and 3) a combined clinical and radiomics (nomogram) model. The overall survival (OS), distant metastasis-free survival (DMFS), and disease-free survival (DFS) were compared to evaluate the prognostic value of TLSs.

STATISTICAL TESTS

LASSO algorithm and ANOVA were used to select highly correlated features. Clinical relevant variables were identified by multivariable logistic regression. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), and through decision curve analysis (DCA). The Kaplan-Meier method was used to calculate the survival rate.

RESULTS

The radiomics signature model (training: AUC 0.766; test: AUC 0.749) and the nomogram model (training: AUC 0.820; test: AUC 0.749) showed better validation performance than the clinical model. DCA showed that the nomogram model had a higher net benefit than the other models. The median follow-up time was 52 months. While there was no significant difference in 3-year OS (P = 0.22) between BC + TLS and BC - TLS patients, there were significant differences in 3-year DFS and 3-year DMFS between the two groups.

DATA CONCLUSION

The nomogram model performs well in distinguishing the presence or absence of TLS. BC + TLS patients had higher long-term disease control rates and better prognoses than those without TLS.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Comprehensive and Accurate Molecular Profiling of Breast Cancer through mRNA Expression of ESR1, PGR, ERBB2, MKI67, and a Novel Proliferation Signature

BACKGROUND

An accurate status determination of breast cancer biomarkers (ER, PR, HER2, Ki67) is crucial for guiding patient management. The "gold standard" for assessing these biomarkers in FFPE tissue is IHC, which faces challenges in standardization and exhibits substantial variability. In this study, we compare the concordance of a new commercial RT-qPCR kit with IHC in determining BC biomarker status.

METHODS

The performance was evaluated using 634 FFPE specimens, which underwent histological analysis in accordance with standard of care methods. HER2 2+ tumors were referred to ISH testing. An immunoreactive score of ≥2/12 was considered positive for ER/PR and 20% staining was used as a cut-off for Ki67 high/low score. RT-qPCR and results calling were performed according to the manufacturer's instructions.

RESULTS

High concordance with IHC was seen for all markers (93.2% for ER, 87.1% for PR, 93.9% for HER2, 77.9% for Ki67 and 80.1% for proliferative signature (assessed against Ki67 IHC)).

CONCLUSIONS

By assessing the concordance with the results obtained through IHC, we sought to demonstrate the reliability and utility of the kit for precise BC subtyping. Our findings suggest that the kit provides a highly precise and accurate quantitative assessment of BC biomarkers.

Breast cancer molecular subtype and relationship with clinicopathological profiles among Vietnamese women: A retrospective study

BACKGROUND

Molecular subtypes play an important role in predicting prognosis and guiding treatment for breast cancer. Having a better knowledge of ethnic molecular features is essential.

OBJECTIVES

Determining the distribution of various breast cancer molecular subtypes and investigating the relationship between these subtypes and clinicopathological features.

METHODS

Retrospective data was collected from Hanoi National Cancer Hospital and Bach Mai Hospital that included 274 women diagnosed with invasive breast cancer between January 2017 and June 2019. Patients were categorized into five subtypes according to the 2015 St. Gallen molecular classification. The variables analyzed were molecular subtypes and tumor-related characteristics. To evaluate the relationship between these subtypes and clinicopathological features, a Chi-squared test and Fisher exact test were performed.

RESULTS

The most prominent subtype was Luminal A (33.2%), followed by Luminal B/Her2- (19.7%) and Luminal B/Her2 + (17.5%), then HER2 overexpression (16.4%), whereas triple negative was the least popular subtype (13.1%). Particularly, 33.9% of all patients, including the Luminal B/Her2 + and the HER2 overexpressing groups, were Her2 positive. There was a statistically significant difference between molecular subtypes and histological type (p = 0.01), tumor grade (p < 0.001), but it was independent of age, tumor size, lymph node metastasis, and lymphovascular invasion.

CONCLUSIONS

In contrast to the triple negative variant, the Luminal A variant is the most common among Vietnamese women. The rate of positive tests for HER2 was rather high. These subtypes were closely related to tumor grade and histopathological type. Understanding the molecular subtypes and their relation to clinicopathological features helps clinicians with patient treatment, and prognosis. The application of the 2015 St. Gallen molecular classification should be recommended for use in clinical practice.

Clinical applications of graph neural networks in computational histopathology: A review

Pathological examination is the optimal approach for diagnosing cancer, and with the advancement of digital imaging technologies, it has spurred the emergence of computational histopathology. The objective of computational histopathology is to assist in clinical tasks through image processing and analysis techniques. In the early stages, the technique involved analyzing histopathology images by extracting mathematical features, but the performance of these models was unsatisfactory. With the development of artificial intelligence (AI) technologies, traditional machine learning methods were applied in this field. Although the performance of the models improved, there were issues such as poor model generalization and tedious manual feature extraction. Subsequently, the introduction of deep learning techniques effectively addressed these problems. However, models based on traditional convolutional architectures could not adequately capture the contextual information and deep biological features in histopathology images. Due to the special structure of graphs, they are highly suitable for feature extraction in tissue histopathology images and have achieved promising performance in numerous studies. In this article, we review existing graph-based methods in computational histopathology and propose a novel and more comprehensive graph construction approach. Additionally, we categorize the methods and techniques in computational histopathology according to different learning paradigms. We summarize the common clinical applications of graph-based methods in computational histopathology. Furthermore, we discuss the core concepts in this field and highlight the current challenges and future research directions.

Ki67 and breast cancer mortality in women with invasive breast cancer

BACKGROUND

The percentage of cells staining positive for Ki67 is sometimes used for decision-making in patients with early invasive breast cancer (IBC). However, there is uncertainty regarding the most appropriate Ki67 cut points and the influence of interlaboratory measurement variability. We examined the relationship between breast cancer mortality and Ki67 both before and after accounting for interlaboratory variability and 8 patient and tumor characteristics.

METHODS

A multicenter cohort study of women with early IBC diagnosed during 2009-2016 in more than 20 NHS hospitals in England and followed until December 31, 2020.

RESULTS

Ki67 was strongly prognostic of breast cancer mortality in 8212 women with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative early IBC (Ptrend < .001). This relationship remained strong after adjustment for patient and tumor characteristics (Ptrend < .001). Standardization for interlaboratory variability did little to alter these results. For women with Ki67 scores of 0%-5%, 6%-10%, 11%-19%, and 20%-29% the corresponding 8-year adjusted cumulative breast cancer mortality risks were 3.3% (95% confidence interval [CI] = 2.8% to 4.0%), 3.7% (95% CI = 3.0% to 4.4%), 3.4% (95% CI = 2.8% to 4.1%), and 3.4% (95% CI = 2.8% to 4.1%), whereas for women with Ki67 scores of 30%-39% and 40%-100%, these risks were higher, at 5.1% (95% CI = 4.3% to 6.2%) and 7.7% (95% CI = 6.6% to 9.1) (Ptrend < .001). Similar results were obtained when the adjusted analysis was repeated with omission of pathological information about tumor size and nodal involvement, which would not be available preoperatively for patients being considered for neoadjuvant therapy.

CONCLUSION

Our findings confirm the prognostic value of Ki67 scores of 30% or more in women with ER-positive, HER2-negative early IBC, irrespective of interlaboratory variability. These results also suggest that Ki67 may be useful to aid decision-making in the neoadjuvant setting.

A Systematic Review and Pooled Analysis of Hypothesized versus Observed Effect Sizes in Early Phase Cancer Prevention Clinical Trials

Early phase cancer prevention trials are designed to demonstrate safety, tolerability, feasibility, and signals of efficacy of preventive agents. Yet it is often observed that many trials fail to detect intervention effects. We conducted a systematic review and pooled analyses of recently completed early phase chemoprevention trials to gain in depth insight on the failure of detecting efficacy signals by comparing hypothesized effect sizes to the corresponding observed effect sizes.Single- or multi-arm efficacy chemoprevention trials conducted under the phase 0/I/II Cancer Prevention Clinical Trials Program of the Division of Cancer Prevention, NCI between 2003 and 2019 were evaluated. A total of 59 chemoprevention trials were reviewed. Twenty-four studies were efficacy or biomarker trials with complete information on hypothesized and observed effect sizes and included in this analysis. The majority of the trials (n = 18) were multi-arm randomized studies of which 15 trials were blinded. The pooled estimate of the observed to hypothesized effect size ratio was 0.57 (95% confidence interval: 0.42-0.73, P < 0.001) based on a random-effects model. There were no significant differences detected in the ratio of observed to hypothesized effect sizes when conducting various subgroup analyses.The results demonstrate that the majority of early phase cancer chemoprevention trials have substantially smaller observed effect sizes than hypothesized effect sizes. Sample size calculations for early phase chemoprevention trials need to balance the potential detectable effect sizes with realistic and cost-effective accrual of study populations, thereby, detecting only intervention effects large enough to justify subsequent large-scale confirmatory trials.

PREVENTION RELEVANCE

The results of this systematic review and pooled analyses demonstrate that for early chemoprevention trials, there are substantial differences between hypothesized and observed effect sizes, regardless of study characteristics. The conduct of early phase chemoprevention trial requires careful planning of study design, primary endpoint, and sample size determination.

Subtypes of high-grade breast ductal carcinoma in situ (DCIS): incidence and potential clinical impact

OBJECTIVE

The purpose of this study was to investigate and classify the molecular subtypes of high-grade ductal carcinoma in situ (DCIS) and identify possible high-risk subtypes. The heterogenicity of DCIS with variable clinical and histopathological presentations has been recognized. Nevertheless, only histopathological grading and diameter are currently implemented in clinical decision-making following the diagnosis of DCIS. The molecular subtypes of DCIS and their IHC surrogate markers have not been defined in conventional treatment guidelines and recommendations. We applied the definitions of molecular subtypes according to the IHC surrogate markers defined for IBC and subclassified high-grade DCIS, accordingly.

METHODS

Histopathological specimens were collected, revised, and regraded from 494 patients diagnosed with DCIS between 1996 and 2018. Other in situ and papillary lesions observed in breast biopsies were excluded from this study. 357 high-grade DCIS cases were submitted to IHC analysis. The markers investigated were ER, PR, HER2, and Ki67.

RESULTS

45 cases were classified as grade 1, 19 as grade 2, and 430 as grade 3. Sixty patients with high-grade DCIS had an additional invasive component in the surgical specimen. Thirty-three patients were diagnosed with recurrent DCIS or invasive cancer (minimum one year after their primary DCIS diagnosis). The proportions of luminal A and luminal B HER2-negative subtypes varied depending on whether 2011 or 2013 St. Gallen Consensus Conference guidelines were adopted. Luminal A was the most prevalent subtype, according to both classifications. The luminal B HER2-positive subtype was found in 22.1% of cases, HER2-enriched subtype in 21.8%, and TPN subtype in 5.6%. There were strong indications that HER2-enriched subtype was significantly more frequent among DCIS with invasive component (p = 0.0169).

CONCLUSIONS

High-grade DCIS exhibits all the molecular subtypes previously identified in IBC, but with a somewhat different distribution in our cohort. HER2-enriched subtype is substantially related to the presence of an invasive component in DCIS; consequently, it is regarded as a high-risk entity.

Single-cell protein profiling defines cell populations associated with triple-negative breast cancer aggressiveness

Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer that lacks targeted therapy. TNBC manifests characteristic, extensive intratumoral heterogeneity that promotes disease progression and influences drug response. Single-cell techniques in combination with next-generation computation provide an unprecedented opportunity to identify molecular events with therapeutic potential. Here, we describe the generation of a comprehensive mass cytometry panel for multiparametric detection of 23 phenotypic markers and 13 signaling molecules. This single-cell proteomic approach allowed us to explore the landscape of TNBC heterogeneity, with particular emphasis on the tumor microenvironment. We prospectively profiled freshly resected tumors from 26 TNBC patients. These tumors contained phenotypically distinct subpopulations of cancer and stromal cells that were associated with the patient's clinical status at the time of surgery. We further classified the epithelial-mesenchymal plasticity of tumor cells, and molecularly defined phenotypically diverse populations of tumor-associated stroma. Furthermore, in a retrospective tissue-microarray TNBC cohort, we showed that the level of CD97 at the time of surgery has prognostic potential.

The evaluation of tumorigenicity and characterization of colonies in a soft agar colony formation assay using polymerase chain reaction

In regenerative medicine, the tumorigenic potency of cells in cellular therapy products (CTPs) is a major concern for their application to patients. This study presents a method-the soft agar colony formation assay using polymerase chain reaction (PCR)-to evaluate tumorigenicity. MRC-5 cells, contaminated with HeLa cells, were cultured for up to 4 weeks in soft agar medium. Cell-proliferation-related mRNAs, Ki-67 and cyclin B, could be detected in 0.01% of HeLa cells after 5 days of culture, whereas cyclin-dependent kinase 1 (CDK1) could be detected after 2 weeks. On the other hand, CDK2, proliferating cell nuclear antigen (PCNA), and minichromosome maintenance protein 7 (MCM7) were not useful to detect HeLa cells even after 4 weeks of culture. The cancer stem cell (CSC) markers, aldehyde dehydrogenase 1 (ALDH1) and CD133 in 0.01% of HeLa cells, could be detected 2 and 4 weeks after culture, respectively. However, another CSC marker CD44 was not useful because its expression was also detected in MRC-5 cells alone. This study suggests that the application of the PCR method to the soft agar colony formation assay could evaluate not only the tumorigenic potency in the short-term but also characterize the colonies, eventually improving the safety of CTPs.

Evaluation of a continuous-time random-walk diffusion model for the differentiation of malignant and benign breast lesions and its association with Ki-67 expression

The purpose of the current study was to evaluate the performance of a continuous-time random-walk (CTRW) diffusion model for differentiating malignant and benign breast lesions and to consider the potential association between CTRW parameters and the Ki-67 expression. Sixty-four patients (46.2 ± 11.4 years) with breast lesions (29 malignant and 35 benign) were evaluated with the CTRW model, intravoxel incoherent motion model, and diffusion-weighted imaging. Echo planar diffusion-weighted imaging was conducted using 13 b-values (0-3000 s/mm² ). Three CTRW model parameters, including an anomalous diffusion coefficient D_m , and two parameters related to temporal and spatial diffusion heterogeneity, α and β, respectively, were obtained, and had MRI b-values of 0-3000 s/mm² . Receiver operating characteristic (ROC) analysis was conducted to determine the sensitivity, specificity, and diagnostic accuracy of CTRW parameters for differentiating malignant from benign breast lesions. In malignant breast lesions, the CTRW parameters D_m , α, and β were significantly lower than the corresponding parameters of benign breast lesions. In the malignant breast lesion group, the CTRW parameter D_m was significantly lower in high Ki-67 expression than in low Ki-67 expression. In ROC analysis, the combination of CTRW parameters (D_m , α, β) demonstrated the highest area under the curve value (0.985) and diagnostic accuracy (94.23%) in differentiating malignant and benign breast lesions. The CTRW model effectively differentiated malignant from benign breast lesions. The CTRW diffusion model offers a new way for noninvasive assessment of breast malignancy and better understanding of the proliferation of malignant lesions.

Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations on compiling test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help pathologists and regulatory agencies verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.

The Role of MicroRNAs in HER2-Positive Breast Cancer: Where We Are and Future Prospective

Simple Summary

Breast cancer is the most diagnosed malignancy in woman worldwide and, despite the availability of new innovative therapies, it remains the first cause of death for tumor in woman. 20% of all breast cancer cases are HER2 positive, meaning that they are characterized by an aberrant expression of the growth factor receptor HER2. This receptor is involved in survival and proliferation mechanisms, conferring to this breast cancer subtype a particular aggressiveness. The introduction of anti-HER2 agents, such as trastuzumab, in the clinical practice, significantly improved the prognosis. However, a great portion of patients is not responsive to this therapy. Thus, cancer research is working to provide new tools to better manage HER2 positive breast cancers, such as biomarkers and therapeutic approaches. MicroRNAs could be used for these purposes. They are small molecules involved in almost all biological processes, including cancer promoting pathways. Researchers consider microRNAs as promising clinical tools because they are easily detectable and stable in both tissues and blood samples, and an increasing body of evidence supports their potential use as targets of therapy, prognostic and predictive biomarkers, or therapeutic agents. This review sums up the most recent scientific publications about microRNAs in HER2 positive breast cancer.

Abstract

Breast cancer that highly expresses human epidermal growth factor receptor 2 (HER2+) represents one of the major breast cancer subtypes, and was associated with a poor prognosis until the introduction of HER2-targeted therapies such as trastuzumab. Unfortunately, up to 30% of patients with HER2+ localized breast cancer continue to relapse, despite treatment. MicroRNAs (miRNAs) are small (approximately 20 nucleotides long) non-coding regulatory oligonucleotides. They function as post-transcriptional regulators of gene expression, binding complementarily to a target mRNA and leading to the arrest of translation or mRNA degradation. In the last two decades, translational research has focused on these small molecules because of their highly differentiated expression patterns in blood and tumor tissue, as well as their potential biological function. In cancer research, they have become pivotal for the thorough understanding of oncogenic biological processes. They might also provide an efficient approach to early monitoring of tumor progression or response to therapy. Indeed, changes in their expression patterns can represent a flag for deeper biological changes. In this review, we sum up the recent literature regarding miRNAs in HER2+ breast cancer, taking into account their potential as powerful prognostic and predictive biomarkers, as well as therapeutic tools.

Identification of alanine aminotransferase 1 interaction network via iTRAQ-based proteomics in alternating migration, invasion, proliferation and apoptosis of HepG2 cells

OBJECTIVE

To investigate the mechanism of alanine aminotransferase 1 (ALT1) in the progression of HCC, the differentially expressed proteins (DEPs) in the ALT1 interaction network were identified by targeted proteomic analysis.

METHODS

Wound healing and transwell assays were conducted to assess the effect of ALT1 on cellular migration and invasion. Cell Counting Kit-8 (CCK-8), colony formation, and flow cytometry assays were performed to identify alterations in proliferation and apoptosis. After coimmunoprecipitation processing, mass spectrometry with iso-baric tags for relative and absolute quantitation was utilized to explore the protein interactions in ALT1 knockdown HepG2 cells.

RESULTS

The results showed that ALT1 knockdown inhibits the migration, invasion, proliferation of HepG2 cells, and promotes apoptosis. A total of 116 DEPs were identified and the bioinformatics analysis suggested that the ALT1-interacting proteins were primarily associated with cellular and metabolic processes. Knockdown of ALT1 in HepG2 cells reduced the expression of Ki67 and epithelial cell adhesion molecule (EP-CAM), while the expression of apoptosis-stimulating protein 2 of p53 (ASPP2) was increased significantly. Suppression of the ALT1 and EP-CAM expression contributed to alterations in epithelial-mesenchymal transition (EMT) -associated markers and matrix metalloproteinases (MMPs). Additionally, inhibition of ALT1 and Ki67 also decreased the expression of apoptosis and proliferation factors. Furthermore, inhibition of ALT1 and ASPP2 also changed the expression of P53, which may be the signaling pathway by which ALT regulates these biological behaviors.

CONCLUSIONS

This study indicated that the ALT1 protein interaction network is associated with the biological behaviors of HepG2 cells via the p53 signaling pathway.

Comparison of Genomic Profiling Data with Clinical Parameters: Implications for Breast Cancer Prognosis

Simple Summary

Around 20 years ago, genomic profiling of breast carcinomas identified tumor subtypes with clinical implications and opened the door for a better understanding of breast cancer biology. The commercialization of multigene tests had a significant impact on clinical practice, and yet, controversy exists as to which methodology is best to inform the choice of therapy and existing recommendations are inconsistent and often driven by cost-effectiveness. Here we report data from a cohort of breast cancer patients in which pathological and molecular subtyping are directly compared in a clinical setting. The findings show that some patients with genomic low-risk tumors could receive unnecessary systemic therapy if only following the classical clinical parameters, while others could remain under-treated. This study suggests that to design precise treatment regimens for patients with early breast cancer, the conventional clinicopathological classification should be complemented with the robust prognostic information provided by molecular subtyping.

Abstract

Precise prognosis is crucial for selection of adjuvant therapy in breast cancer. Molecular subtyping is increasingly used to complement immunohistochemical and pathological classification and to predict recurrence. This study compares both outcomes in a clinical setting. Molecular subtyping (MammaPrint^®, TargetPrint^®, and BluePrint^®) and pathological classification data were compared in a cohort of 143 breast cancer patients. High risk clinical factors were defined by a value of the proliferation factor Ki67 equal or higher than 14% and/or high histological grade. The results from molecular classification were considered as reference. Core needle biopsies were found to be comparable to surgery samples for molecular classification. Discrepancies were found between molecular and pathological subtyping of the samples, including misclassification of HER2-positive tumors and the identification of a significant percentage of genomic high risk T1N0 tumors. In addition, 20% of clinical low-risk tumors showed genomic high risk, while clinical high-risk samples included 42% of cases with genomic low risk. According to pathological subtyping, a considerable number of breast cancer patients would not receive the appropriate systemic therapy. Our findings support the need to determine the molecular subtype of invasive breast tumors to improve breast cancer management.

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

Biomarker Dynamics and Long-Term Treatment Outcomes in Breast Cancer Patients with Residual Cancer Burden after Neoadjuvant Therapy

A residual cancer burden after neoadjuvant therapy (NAT) for breast cancer (BC) is associated with worse treatment outcomes compared to patients who achieved pathologic complete remission. This single-institutional retrospective study of 767 consecutive patients, including 468 patients with assessable residual cancer burden (aRCB) after NAT, with a median follow-up of 36 months, evaluated the biomarkers assessed before NAT from a biopsy and after NAT from a surgical specimen, their dynamics, and effect on long-term outcomes in specific breast cancer subtypes. The leading focus was on proliferation index Ki-67, which was significantly altered by NAT in all BC subtypes (p < 0.001 for HER2 positive and luminal A/B HER2 negative and p = 0.001 for TNBC). Multivariable analysis showed pre-NAT and post-NAT Ki-67 as independent predictors of survival outcomes for luminal A/B HER2 negative subtype. For TNBC, post-NAT Ki-67 was significant alone, and, for HER2 positive, the only borderline association of pre-NAT Ki-67 was observed in relation to the overall survival. Steroid and HER2 receptors were re-assessed just in a portion of the patients with aRCB. The concordance of both assessments was 92.9% for ER status, 80.1% for PR, and 92.2% for HER2. In conclusion, these real-world data of a consecutive cohort confirmed the importance of biomarkers assessment in patients with aRCB, and the need to consider specific BC subtypes when interpreting their influence on prognosis.

Association of Ki-67 Change Pattern After Core Needle Biopsy and Prognosis in HR+/HER2− Early Breast Cancer Patients

Background

To investigate the association of Ki-67 change pattern after core needle biopsy (CNB) and prognosis in HR+/HER2− early breast cancer patients.

Method

Eligible patients were categorized into three groups: Low group, Elevation group, and High group. Chi-square test and logistic regression analysis were used to compare the clinic-pathological characteristics. Kaplan–Meier method was used to estimate the rates of recurrence-free interval (RFI) and breast cancer-specific survival (BCSS), which were compared via the Log-rank test. Cox proportional hazard analysis was performed to investigate independent prognostic factors.

Results

A total of 2,858 patients were included: 1,179 (41.3%), 482 (16.9%), and 1,197 (41.8%) patients were classified into the low, elevation, and high groups, respectively. Age, tumor size, histological grade, lymph-vascular invasion (LVI), and ER level status were associated with Ki-67 change pattern after CNB. With a median follow-up of 53.6 months, the estimated 5-year RFI rates for the low group, elevation, and high groups were 96.4%, 95.3% and 90.9%, respectively (P < 0.001). And 5-year BCSS rates were 99.3%, 98.3% and 96.8%, respectively (P = 0.001). Compared with patients in the low group, patients in the high group had significantly worse RFI (hazard ratio [HR] 1.71, 95% confidence interval [CI] 1.16–2.54) in multivariate analysis.

Conclusions

Ki-67 change after CNB was associated with prognosis in HR+/HER2− early breast cancer. Patients with Ki-67 high or elevation after CNB had an inferior disease outcome, indicating the necessity of re-evaluating Ki-67 on surgical specimens after CNB.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in ER (+) and/or PR (+) and HER2 (-) Breast Cancer

Background: Although intrinsic molecular subtype has been widely used, there remains great clinical heterogeneity of prognosis in the estrogen receptor (ER)- and/or progesterone receptor (PR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC).

Methods: The transcriptome expression data of messenger RNA (mRNA) were downloaded from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Gene Expression Omnibus (GEO) databases. Immune-related genes were acquired from the ImmPort database and additional literature search. Univariate Cox, LASSO regression, and multivariate Cox regression were used to screen prognostic immune-related genes and establish the risk signature. The correlation between the risk signature and clinical characteristics, the abundances of immune cells within the tumor microenvironment, and cancer phenotypes were further assessed.

Results: Of note, 102 immune-related prognostic genes were identified in the METABRIC dataset by univariate Cox analysis. Consecutively, 7 immune-related genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1, and CCL19) were selected to establish the risk signature by LASSO regression and multivariate Cox analysis. Its performance was further verified in TCGA and GSE21653 datasets. Multivariate Cox analysis showed that the risk signature was an independent prognostic factor. The 7-gene signature showed a significant correlation with intrinsic molecular subtypes and 70-gene signature. Furthermore, the CD4⁺ memory T cells were significantly higher in the low-risk group while a significantly higher proportion of M0-type macrophages was found in the high-risk group in both METABRIC and TCGA cohorts, which may have an influence on the prognosis. Furthermore, we found that the low-risk group may be associated with the immune-related pathway and the high-risk group was with the cell cycle-related pathway, which also showed an impact on the prognosis.

Conclusion: These seven immune-related gene risk signatures provided an effective method for prognostic stratification in ER (+) and/or PR (+) and HER2 (−) BC.

Visual and digital assessment of Ki-67 in breast cancer tissue - a comparison of methods

Background

In breast cancer (BC) Ki-67 cut-off levels, counting methods and inter- and intraobserver variation are still unresolved. To reduce inter-laboratory differences, it has been proposed that cut-off levels for Ki-67 should be determined based on the in-house median of 500 counted tumour cell nuclei. Digital image analysis (DIA) has been proposed as a means to standardize assessment of Ki-67 staining in tumour tissue. In this study we compared digital and visual assessment (VA) of Ki-67 protein expression levels in full-face sections from a consecutive series of BCs. The aim was to identify the number of tumour cells necessary to count in order to reflect the growth potential of a given tumour in both methods, as measured by tumour grade, mitotic count and patient outcome.

Methods

A series of whole sections from 248 invasive carcinomas of no special type were immunohistochemically stained for Ki-67 and then assessed by VA and DIA. Five 100-cell increments were counted in hot spot areas using both VA and DIA. The median numbers of Ki-67 positive tumour cells were used to calculate cut-off levels for Low, Intermediate and High Ki-67 protein expression in both methods.

Results

We found that the percentage of Ki-67 positive tumour cells was higher in DIA compared to VA (medians after 500 tumour cells counted were 22.3% for VA and 30% for DIA). While the median Ki-67% values remained largely unchanged across the 100-cell increments for VA, median values were highest in the first 1-200 cells counted using DIA. We also found that the DIA100 High group identified the largest proportion of histopathological grade 3 tumours 70/101 (69.3%).

Conclusions

We show that assessment of Ki-67 in breast tumours using DIA identifies a greater proportion of cases with high Ki-67 levels compared to VA of the same tumours. Furthermore, we show that diagnostic cut-off levels should be calibrated appropriately on the introduction of new methodology.

Standardizing Pathologic Evaluation of Breast Carcinoma After Neoadjuvant Chemotherapy

CONTEXT.—

Neoadjuvant systemic therapy refers to the use of systemic agent(s) for malignancy prior to surgical treatment and has recently emerged as an option for most breast cancer patients eligible for adjuvant systemic therapy. Consequently, treated breast carcinomas have become routine specimens in pathology practices. A standard protocol has not yet been universally adopted for the evaluation and reporting of these specimens. The American Joint Committee on Cancer staging system recognizes the challenges in staging breast carcinomas after neoadjuvant treatment and provides important data points but does not currently provide detailed guidance in estimating the residual tumor burden in the breast and lymph nodes. The Residual Cancer Burden system is the only Web-based system that quantifies treatment response as a continuous variable using residual tumor burden in the breast and the lymph nodes.

OBJECTIVE.—

To provide clarifications and guidance for evaluation and reporting of postneoadjuvant breast specimens, discuss issues with the current staging and reporting systems, and provide specific suggestions for future modifications to the American Joint Committee on Cancer system and the Residual Cancer Burden calculator.

DATA SOURCES.—

English-language literature on the subject and the data from the I-SPY 2, a multicenter, adaptive randomization phase 2 neoadjuvant platform trial for early-stage, high-risk breast cancer patients.

CONCLUSIONS.—

This article highlights challenges in the pathologic evaluation and reporting of treated breast carcinomas and provides recommendations and clarifications for pathologists and clinicians. It also provides specific recommendations for staging and discusses future directions.

Low correlation between Ki67 assessed by qRT-PCR in Oncotype Dx score and Ki67 assessed by Immunohistochemistry

Breast cancers expressing high levels of Ki67 are associated with poor outcomes. Oncotype DX test was designed for ER+/HER2- early-stage breast cancers to help adjuvant chemotherapy decision by providing a Recurrent Score (RS). RS measures the expression of 21 specific genes from tumor tissue, including Ki67. The primary aim of this study was to assess the agreement between Ki67_RNA obtained with Oncotype DX RS and Ki67_IHC. Other objectives were to analyze the association between the event free survival (EFS) and the expression level of Ki67_RNA; and association between RS and Ki67_RNA. Herein, we report a low agreement of 0.288 by Pearson correlation coefficient test between Ki67_IHC and Ki67_RNA in a cohort of 98 patients with early ER+/HER2- breast cancers. Moreover, Ki67_RNA^high tumors were significantly associated with the occurrence of events (p = 0.03). On the other hand, we did not find any association between Ki67_IHC and EFS (p = 0.26). We observed a low agreement between expression level of Ki67_RNA and Ki67 protein labelling by IHC. Unlike Ki67_IHC and independently of the RS, Ki67_RNA could have a prognostic value. It would be interesting to better assess the prognosis and predictive value of Ki67_RNA measured by qRT-PCR. The Ki67_RNA in medical routine could be a good support in countries where Oncotype DX is not accessible.

XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses

BACKGROUND

The malignant probability of MRI BiRADS 4 breast lesions ranges from 2% to 95%, leading to unnecessary biopsies. The purpose of this study was to construct an optimal XGboost prediction model through a combination of DKI independently or jointly with other MR imaging features and clinical characterization, which was expected to reduce false positive rate of MRI BiRADS 4 masses and improve the diagnosis efficiency of breast cancer.

METHODS

120 patients with 158 breast lesions were enrolled. DKI, Diffusion-weighted Imaging (DWI), Proton Magnetic Resonance Spectroscopy (¹H-MRS) and Dynamic Contrast-Enhanced MRI (DCE-MRI) were performed on a 3.0-T scanner. Wilcoxon signed-rank test and χ2 test were used to compare patient's clinical characteristics, mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), total choline (tCho) peak, extravascular extracellular volume fraction (V_e), flux rate constant (K_ep) and volume transfer constant (K^trans). ROC curve analysis was used to analyze the diagnostic performances of the imaging parameters. Spearman correlation analysis was performed to evaluate the associations of imaging parameters with prognostic factors and breast cancer molecular subtypes. The Least Absolute Shrinkage and Selectionator operator (lasso) and the area under the curve (AUC) of imaging parameters were used to select discriminative features for differentiating the breast benign lesions from malignant ones. Finally, an XGboost prediction model was constructed based on the discriminative features and its diagnostic efficiency was verified in BiRADS 4 masses.

RESULTS

MK derived from DKI performed better for differentiating between malignant and benign lesions than ADC, MD, tCho, K_ep and K^trans (p < 0.05). Also, MK was shown to be more strongly correlated with histological grade, Ki-67 expression and lymph node status. MD, MK, age, shape and menstrual status were selected to be the optimized feature subsets to construct an XGboost model, which exhibited superior diagnostic ability for breast cancer characterization and an improved evaluation of suspicious breast tumors in MRI BiRADS 4.

CONCLUSIONS

DKI is promising for breast cancer diagnosis and prognostic factor assessment. An optimized XGboost model that included DKI, age, shape and menstrual status is effective in improving the diagnostic accuracy of BiRADS 4 masses.

Analysis of Ki-67 expression in women with breast cancer: Comparative evaluation of two different methodologies by immunophenotyping

The Ki-67 antigen is a nuclear protein with proven prognostic value in different neoplasms and recognizes the predictive value in breast cancer (BC). No consensus exists on the ideal cutoff point. In this study, Ki-67 expression was evaluated in samples of BC by flow cytometry (FC) and compared with immunohistochemical (IHC) examination. For this, the BC tissue samples were sectioned, macerated, filtered, and marked with anti-Ki-67 FITC and anti-CD45 V500 antibodies. We selected the neoplastic cells according to CD45 expression and size and internal complexity (FSC × SSC) using the Infinicity 1.7 software. Lymphocytes were negative control. We compared the results with IHC analyses carried out in parallel and independently. The expression of Ki-67 was evaluated in both methodologies through Bland-Altman analysis. Among the 44 samples analyzed, only three showed bias higher than the established confidence interval (mean bias 2.1%, p = 0.62), with no significant difference for the perfect mean bias (0%). Therefore, one can state that FC provides results equivalent to IHC analysis and possibly analyzes more cells simultaneously. The results obtained in this study show the absence of observational bias through software analysis in a larger number of tumor cell populations. We can conclude that FC may be a promising alternative method for investigating Ki-67 in solid tumours.

Prognostic value of residual cancer burden and Miller-Payne system after neoadjuvant chemotherapy for breast cancer

BACKGROUND

To verify the feasibility of using the residual cancer burden (RCB) index to stratify prognosis of patients after neoadjuvant chemotherapy (NAC) and to compare RCB with the Miller-Payne system.

METHODS

We retrospectively analyzed clinicopathological data of patients receiving treatment between January 1, 2010 and December 31, 2018. Kaplan-Meier curves were used to compare the survival outcomes and estimate disease-free survival (DFS) and disease-specific survival (DSS). Harrell's concordance index (C-index) was used to evaluate the predictive accuracy of RCB and Miller-Payne system.

RESULTS

A total of 423 female patients with complete data were included in the analysis, with a median follow-up time of 58.5 months (range, 7-126 months); 84 patients experienced recurrence, and 48 experienced breast cancer related death. RCB index and the Miller-Payne system were associated with prognosis in the whole cohort. Patients who achieved RCB-I had similar survival outcomes as those with pathological complete response (pCR, RCB-0). In whole cohort, for the RCB index and the Miller-Payne system, respectively, C-indexes for DFS were 0.73 and 0.64, for DSS were 0.74 and 0.64. The average RCB score was different among three subtypes (F=9.335, P<0.001).

CONCLUSIONS

The RCB index and the Miller-Payne system can stratify survival outcome of patients after NAC, and RCB had a superior prediction accuracy, especially for triple-negative breast cancer (TNBC). New cut-off value should be sought in order to improve prediction accuracy.

Combined Quantification of 18F-FDG and 68Ga-DOTATATE PET/CT for Prognosis in High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms

RATIONALE AND OBJECTIVES

High-grade gastroenteropancreatic neuroendocrine neoplasms (G3 GEP-NENs) are pathologically classified into well differentiated neuroendocrine tumors (G3 NETs) and poorly differentiated neuroendocrine carcinomas (G3 NECs). Using a novel parameter, we examined the prognostic value of ¹⁸F-FDG and ⁶⁸Ga-DOTATATE PET/CT quantification in comparison to pathologic assessment in G3 GEP-NENs.

MATERIALS AND METHODS

A total of 31 patients with G3 GEP-NENs were reviewed. For each patient, the SUVmax on ¹⁸F-FDG and ⁶⁸Ga-DOTATATE PET/CT were used to calculate the FDG-DOTATATE-Z (FDZ) score: a continuous parameter that increases with ⁶⁸Ga-DOTATATE uptake and decreases with ¹⁸F-FDG uptake. The variation in the FDZ score with respect to pathologic variables was examined. Kaplan-Meier and Cox regression analyses were performed to evaluate the effect of FDZ score on overall survival. An external cohort of 21 patients was used for validation.

RESULTS

The FDZ score was significantly higher in G3 NETs compared to G3 NECs (p<0.001), and was inversely correlated with Ki67 index (R²=0.33, p<0.001). Patients in the FDZ>0.05 group showed significantly longer survival compared to those in the FDZ≤0.05 group, with median of 34.9 vs. 12.0 months (p<0.001). On univariate regression, FDZ>0.05 (p=0.005), well differentiated disease (p=0.044), and lower Ki67 index (p=0.042) were predictors of survival. On multivariate regression, only FDZ>0.05 could independently predict longer survival with HR=0.16 (p=0.018), which was reproduced in the external validation cohort.

CONCLUSION

Combined quantification of ¹⁸F-FDG and ⁶⁸Ga-DOTATATE PET/CT into a novel parameter, the FDZ score, reflects the pathologic characteristics of G3 GEP-NENs and is a prognostic indicator of overall survival independent of differentiation.

Evaluation of ER, PR and HER2 markers by flow cytometry for breast cancer diagnosis and prognosis

BACKGROUND AND AIMS

Laboratory diagnosis of breast cancer (BC) is done by morphological analysis and immunohistochemistry (IHC). However, this methodology still has some limitations. The aim of this study is to validate flow cytometry (FC) immunophenotyping to investigate diagnostic and prognostic markers of BC.

METHODS

Tumor samples from surgical specimens of patients previously diagnosed with BC, were first sliced and then macerated together with PBS. Then, sample was filtered and the single cell suspension obtained was labeled with antibodies against estrogen (ERα), progesterone (PR) and HER2 receptors and CD45. The results were compared, in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), with reference methods.

RESULTS

Results obtained comparing FC with reference methods were: ERα detection (sensitivity: 75%; specificity: 90%; PPV: 96.7%; NPV: 47.4%); PR detection (sensitivity: 72%; specificity: 70%; PPV: 79.3%; NPV: 60.8%); HER2 detection (sensitivity: 80%; specificity: 90.2%; PPV: 66.7%; NPV: 94.9%).

CONCLUSION

The results obtained show the capacity of this methodology on BC markers differentiation. FC, together with morphological analysis and IHC can overcome individual limitations of each methodology and provide reliable results on a faster and efficient manner, resulting in improvements on BC diagnosis and prognosis.

Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group

Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast Cancer Working Group (IKWG) for Ki67 scoring and to evaluate the prognostic potential of this platform in an independent cohort. Four algorithms were independently built by four pathologists based on our study cohort using an open-source digital image analysis (DIA) platform (QuPath) following the detailed guideline of the IKWG. The algorithms were applied on an ER+ breast cancer study cohort of 157 patients with 15 years of follow-up. The reference Ki67 score was obtained by a DIA algorithm trained on a subset of the study cohort. Intraclass correlation coefficient (ICC) was used to measure reproducibility. High interobserver reliability was reached with an ICC of 0.938 (CI: 0.920-0.952) among the algorithms and the reference standard. Comparing each machine-read score against relapse-free survival, the hazard ratios were similar (2.593-4.165) and showed independent prognostic potential (p ≤ 0.018, for all comparisons). In conclusion, we demonstrate high reproducibility and independent prognostic potential using the IKWG DIA instructions to score Ki67 in breast cancer. A prospective study is needed to assess the clinical utility of the IKWG DIA Ki67 instructions.

Breast Cancer and p16: Role in Proliferation, Malignant Transformation and Progression

The definition of new molecular biomarkers could provide a more reliable approach in predicting the prognosis of invasive breast cancers (IBC). The aim of this study is to analyze the expression of p16 protein in IBC, as well as its participation in malignant transformation. The study included 147 patients diagnosed with IBC. The presence of non-invasive lesions (NIL) was noted in each IBC and surrounding tissue. p16 expression was determined by reading the percentage of nuclear and/or cytoplasmic expression in epithelial cells of IBC and NIL, but also in stromal fibroblasts. Results showed that expression of p16 increases with the progression of cytological changes in the epithelium; it is significantly higher in IBC compared to NIL (p < 0.0005). Cytoplasmic p16 expression is more prevalent in IBC (76.6%), as opposed to nuclear staining, which is characteristic of most NIL (21.1%). There is a difference in p16 expression between different molecular subtypes of IBC (p = 0.025). In the group of p16 positive tumors, pronounced mononuclear infiltrates (p = 0.047) and increased expression of p16 in stromal fibroblasts (p = 0.044) were noted. In conclusion, p16 protein plays an important role in proliferation, malignant transformation, as well as in progression from NIL to IBC.

Correlation of 68Ga-DOTATATE uptake on PET/CT with pathologic features of cellular proliferation in neuroendocrine neoplasms

OBJECTIVE

68Ga-DOTATATE positron emission tomography/computed tomography (PET/CT) is a useful tool for diagnosing and staging neuroendocrine neoplasms (NEN). Unlike other PET tracers like FDG, the meaningfulness and use of standardized uptake values (SUVs) of 68Ga-DOTATATE is not well-established. This study aimed to determine if a correlation exists between intensity of 68Ga-DOTATATE uptake and markers of cellular proliferation.

METHODS

This retrospective study included 79 patients with positive 68Ga-DOTATATE PET/CT and Ki-67 and/or mitotic index (MI) available on pathology report. SUVmax of the most intense lesion and the most intense organ-matched lesion were determined. Demographics and pathology results for Ki-67 and MI were collected from the electronic medical record. Correlations and trends for correlations of SUVmax to Ki-67 and MI were performed using Kruskal-Wallis and Cuzick trend tests.

RESULTS

A trend for an association between SUVmax and Ki-67 grade was found; median SUVmax of Ki-67 < 3%, 3-20%, and > 20% was 35.2, 31.8, and 12.8 (p = 0.077), respectively. There was also a trend between SUVmax and Ki-67 categories in organ-matched lesions (p = 0.08). The median organ-matched SUVmax of MI < 2, 2-20, and > 20 lesions was 34.2, 18, and 21.7, respectively, (Cuzick trend test p = 0.066). The median SUVmax for small bowel, pancreatic, and other primary locations was 27.6, 46.9, and 9.3 (p < 0.01), respectively.

CONCLUSIONS

The association between 68Ga-DOTATATE SUVmax, histologic grade, and primary site of NEN demonstrates its potential use for prognostication, or potentially as a surrogate for histologic grading when biopsy is not possible.

An Image Analysis Solution For Quantification and Determination of Immunohistochemistry Staining Reproducibility

Supplemental Digital Content is available in the text.

With immunohistochemical (IHC) staining increasingly being used to guide clinical decisions, variability in staining quality and reproducibility are becoming essential factors in generating diagnoses using IHC tissue preparations. The current study tested a method to track and quantify the interrun, intrarun, and intersite variability of IHC staining intensity. Our hypothesis was that staining precision between laboratory sites, staining runs, and individual slides may be verified quantitatively, efficiently and effectively utilizing algorithm-based, automated image analysis. To investigate this premise, we tested the consistency of IHC staining in 40 routinely processed (formalin-fixed, paraffin-embedded) human tissues using 10 common antibiomarker antibodies on 2 Dako Omnis instruments at 2 locations (Carpinteria, CA: 30 m above sea level and Longmont, CO: 1500 m above sea level) programmed with identical, default settings and sample pretreatments. Digital images of IHC-labeled sections produced by a whole slide scanner were analyzed by a simple commercially available algorithm and compared with a board-certified veterinary pathologist’s semiquantitative scoring of staining intensity. The image analysis output correlated well with pathology scores but had increased sensitivity for discriminating subtle variations and providing reproducible digital quantification across sites as well as within and among staining runs at the same site. Taken together, our data indicate that digital image analysis offers an objective and quantifiable means of verifying IHC staining parameters as a part of laboratory quality assurance systems.

Grading of invasive breast carcinoma: the way forward

Histologic grading has been a simple and inexpensive method to assess tumor behavior and prognosis of invasive breast cancer grading, thereby identifying patients at risk for adverse outcomes, who may be eligible for (neo)adjuvant therapies. Histologic grading needs to be performed accurately, on properly fixed specimens, and by adequately trained dedicated pathologists that take the time to diligently follow the protocol methodology. In this paper, we review the history of histologic grading, describe the basics of grading, review prognostic value and reproducibility issues, compare performance of grading to gene expression profiles, and discuss how to move forward to improve reproducibility of grading by training, feedback and artificial intelligence algorithms, and special stains to better recognize mitoses. We conclude that histologic grading, when adequately carried out, remains to be of important prognostic value in breast cancer patients.

Developing a clinical-pathologic model to predict genomic risk of recurrence in patients with hormone receptor positive, human epidermal growth factor receptor-2 negative, node negative breast cancer

INTRODUCTION

Patients with hormone receptor (HR)-positive, human epidermal growth factor receptor-2 (HER2)-negative, node negative (NN) breast cancer may be offered a gene expression profiling (GEP) test to determine recurrence risk and benefit of adjuvant chemotherapy. We developed a clinical-pathologic (CP) model to predict genomic recurrence risk and examined its performance characteristics.

METHODS

Patients diagnosed with HR-positive, HER2-negative, NN breast cancer with a tumour size < 30 mm and who underwent a GEP test [OncotypeDX or Prosigna] in Alberta from October 2017 through March 2019 were identified. Patients were classified as low or high genomic risk. Multivariable logistic regression analysis was performed to examine the associations of CP factors with genomic risk. A CP model was developed using coefficients of regression and sensitivity analyses were performed.

RESULTS

A total of 366 patients were eligible (135 were tested using OncotypeDX and 231 with Prosigna). Of these, 64 (17.5%) patients were classified as high genomic risk. On multivariable logistic regression, tumour size > 20 mm (odds ratio [OR], 3.58; 95% confidence interval [CI], 1.84-6.98; P<0.001), low expression of progesterone receptor (OR, 3.46; 95% CI, 1.76-6.82; P<0.001), and histological grade III (OR, 7.24; 95% CI, 3.82-13.70; P<0.001) predicted high genomic risk. A CP model using these variables was developed to provide a score of 0-4. A CP cut-point of 0, identified 56% of genomic low risk patients with a specificity of 98.4%.

CONCLUSIONS

A CP model could be used to narrow the population of breast cancer patients undergoing GEP testing.

Prognostic role of serum thymidine kinase 1 kinetics during neoadjuvant chemotherapy for early breast cancer

Background

Emerging data support the use of thymidine kinase 1 (TK1) activity as a prognostic marker and for monitoring of response in breast cancer (BC). The long-term prognostic value of TK1 kinetics during neoadjuvant chemotherapy is unclear, which this study aimed to elucidate.

Methods

Material from patients enrolled to the single-arm prospective PROMIX trial of neoadjuvant epirubicin, docetaxel and bevacizumab for early BC was used. Ki67 in baseline biopsies was assessed both centrally and by automated digital imaging analysis. TK1 activity was measured from blood samples obtained at baseline and following two cycles of chemotherapy. The associations of TK1 and its kinetics as well as Ki67 with event-free survival and overall survival (OS) were evaluated using multivariable Cox regression models.

Results

Central Ki67 counting had excellent correlation with the results of digital image analysis (r = 0.814), but not with the diagnostic samples (r = 0.234), while it was independently prognostic for worse OS [adjusted hazard ratio (HR_adj) = 2.72, 95% confidence interval (CI) 1.19-6.21, P = 0.02]. Greater increase in TK1 activity after two cycles of chemotherapy resulted in improved event-free survival (HR_adj = 0.50, 95% CI 0.26-0.97, P = 0.04) and OS (HR_adj = 0.46, 95% CI 0.95, P = 0.04). There was significant interaction between the prognostic value of TK1 kinetics and Ki67 (p_interaction 0.04).

Conclusion

Serial measurement of serum TK1 activity during neoadjuvant chemotherapy provides long-term prognostic information in BC patients. The ease of obtaining serial samples for TK1 assessment motivates further evaluation in larger studies.

•

This is a correlative analysis of a prospective phase II study on neoadjuvant chemotherapy for breast cancer.

•

Serial measurement of serum TK1 activity during treatment provides independent long-term prognostic information.

•

We demonstrate the validity and clinical utility of both central and automated image analysis-based Ki67 assessment.

•

Finally, we explore the biologic correlations between TK1 and Ki67.

Determination of breast cancer prognosis after neoadjuvant chemotherapy: comparison of Residual Cancer Burden (RCB) and Neo-Bioscore

Background

To compare RCB (Residual Cancer Burden) and Neo-Bioscore in terms of prognostic performance and see if adding pathological variables improve these scores.

Methods

We analysed 750 female patients with invasive breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) at Institut Curie between 2002 and 2012. Scores were compared in global population and by BC subtype using Akaike information criterion (AIC), C-Index (concordance index), calibration curves and after adding lymphovascular invasion (LVI) and pre-/post-NAC TILs levels.

Results

RCB and Neo-Bioscore were significantly associated to disease-free and overall survival in global population and for triple-negative BC. RCB had the lowest AICs in every BC subtype, corresponding to a better prognostic performance. In global population, C-Index values were poor for RCB (0.66; CI [0.61–0.71]) and fair for Neo-Bioscore (0.70; CI [0.65–0.75]). Scores were well calibrated in global population, but RCB yielded better prognostic performances in each BC subtype. Concordance between the two scores was poor. Adding LVI and TILs improved the performance of both scores.

Conclusions

Although RCB and Neo-Bioscore had similar prognostic performances, RCB showed better performance in BC subtypes, especially in luminal and TNBC. By generating fewer prognostic categories, RCB enables an easier use in everyday clinical practice.

UK NEQAS ICC & ISH Ki-67 Data Reveal Differences in Performance of Primary Antibody Clones

We examined data from 374 laboratories staining for Ki-67 as part of external quality assessment over 8 runs between 2013 and 2017 (total data sets=2601). One of 5 primary antibodies was used for 94.8% of submissions, with MIB-1 (Agilent Dako) comprising 58.8% of the total. Examining assessment score as a continuous variable showed the 30-9 (Ventana) and K2 (Leica Biosystems) clones were associated with the highest mean scores (17.0; 95% confidence interval, 16.8-17.2 and 16.3; 95% confidence interval, 15.9-16.6, respectively). Stain quality was not significantly different between them. Both were associated with significantly better staining compared with MIB-1 (Agilent Dako), MM1 (Leica Biosystems), and SP6 from various suppliers (P<0.05). Similarly, categorical assessment of "Good" versus "Not good" staining quality showed that the 30-9 and K2 clones were both significantly associated with "Good" staining (both P<0.001). Other methodological parameters were examined for significant primary antibody-specific effects; none were seen for 30-9, K2, or SP6. The MM1 clone was more likely to be associated with good quality staining when it was used with Leica Biosystems sourced antigen retrieval, detection, and platform, all statistically significant at P<0.01. MIB-1 was more likely to be associated with good quality staining results when it was used with Agilent Dako antigen retrieval, detection, and staining platforms (P<0.0001), and less likely at the same significance level when used with Leica Biosystems reagents and equipment. The data presented here show the importance of not just primary antibody choice but also matching that choice to other methodological factors.

68 Ga-DOTATATE Positron Emission Tomography-Computed Tomography Quantification Predicts Response to Somatostatin Analog Therapy in Gastroenteropancreatic Neuroendocrine Tumors

BACKGROUND

Somatostatin analogs (SSAs) are the frontline antitumor therapy in advanced well-differentiated gastroenteropancreatic neuroendocrine tumors (GEP-NETs). A subset of patients demonstrate early disease progression on SSA therapy, yet the currently known predictors for treatment failure lack specificity to affect therapeutic decision. SSAs target tumor somatostatin receptors, the level of which can be quantitatively assessed with 68 Ga-DOTATATE positron emission tomography-computed tomography (PET/CT). We investigated the ability of 68 Ga-DOTATATE PET/CT to predict response to SSA therapy.

MATERIALS AND METHODS

The records of 108 consecutive patients with well-differentiated grade 1-2 GEP-NETs on SSA monotherapy who received 68 Ga-DOTATATE PET/CT scans were retrospectively reviewed to obtain baseline characteristics, 68 Ga-DOTATATE maximum standardized uptake value (SUVmax), and progression-free survival (PFS) data. The optimal SUVmax cutoff for patient stratification was obtained with receiver operating characteristic curve analysis. PFS in the high versus low SUVmax groups was compared with Kaplan-Meier survival analysis. The effects of baseline characteristics and SUVmax on PFS were examined with univariate and multivariate Cox regression.

RESULTS

68 Ga-DOTATATE SUVmax predicted therapeutic failure with sensitivity and specificity of 39% and 98%, respectively. SUVmax of <18.35 was associated with shorter PFS, which was reproduced in the subgroup analysis of SSA-naïve patients. Low SUVmax was the only predictor of early treatment failure (hazard ratio, 6.85) in multivariate analysis, as well as in the subgroup analysis of grade 2 GEP-NETs.

CONCLUSION

Low SUVmax on 68 Ga-DOTATATE PET/CT independently predicts early failure on SSA monotherapy in patients with well-differentiated grade 1-2 GEP-NET. Patients with lack of expected benefit from SSA therapy can be readily identified using routine 68 Ga-DOTATATE PET/CT with very high specificity.

IMPLICATIONS FOR PRACTICE

Based on 68 Ga-DOTATATE positron emission tomography-computed tomography imaging, clinicians can better inform patients on the expected benefit of somatostatin analog therapy for gastroenteropancreatic neuroendocrine tumors, especially when access to the therapy is difficult, and offer proactive discussion on alternative management options.

Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group

Ki67 immunohistochemistry (IHC), commonly used as a proliferation marker in breast cancer, has limited value for treatment decisions due to questionable analytical validity. The International Ki67 in Breast Cancer Working Group (IKWG) consensus meeting, held in October 2019, assessed the current evidence for Ki67 IHC analytical validity and clinical utility in breast cancer, including the series of scoring studies the IKWG conducted on centrally stained tissues. Consensus observations and recommendations are: 1) as for estrogen receptor and HER2 testing, preanalytical handling considerations are critical; 2) a standardized visual scoring method has been established and is recommended for adoption; 3) participation in and evaluation of quality assurance and quality control programs is recommended to maintain analytical validity; and 4) the IKWG accepted that Ki67 IHC as a prognostic marker in breast cancer has clinical validity but concluded that clinical utility is evident only for prognosis estimation in anatomically favorable estrogen receptor–positive and HER2-negative patients to identify those who do not need adjuvant chemotherapy. In this T1-2, N0-1 patient group, the IKWG consensus is that Ki67 5% or less, or 30% or more, can be used to estimate prognosis. In conclusion, analytical validity of Ki67 IHC can be reached with careful attention to preanalytical issues and calibrated standardized visual scoring. Currently, clinical utility of Ki67 IHC in breast cancer care remains limited to prognosis assessment in stage I or II breast cancer. Further development of automated scoring might help to overcome some current limitations.

Study of Ki-67 index in the molecular subtypes of breast cancer: Inter-observer variability and automated scoring

Background

Ki-67 index is an important prognostic marker in breast cancer and is also used to differentiate luminal A subtype from luminal B. Inter-observer variations in determining the index and the cut-off value to be considered in distinguishing the two subtypes remain problems in clinical practice.

Methods

MIB-1 immunohistochemistry was done on 200 cases of breast cancer with 50 cases in each molecular subtype. The Ki-67 scoring was done manually by two observers and automated method (using the software ImmunoRatio). The mean value of Ki-67 was calculated in each molecular group and in the entire estrogen receptor and progesterone receptor (ER/PR) positive group. The inter-observer variability between the two observers and the automated method was also assessed.

Results

The mean and median values of Ki-67 of all the 200 cases obtained by manual scoring was 31.13% and 29.65% by observer 1, 28.48% and 27.90% by observer 2, and 38.27% and 35.45% by the automated method. The mean Ki-67 value obtained by manual scoring, in luminal A, luminal B, HER2 enriched and triple negative was 21.07%, 37.19%, 33.72% and 27.27%, respectively. There was significant correlation between the two observers and with the automated scoring.. The mean value of the Ki-67 index in the ER/PR positive group was 29.1%.

Conclusion

The inter-observer correlation and the correlation with the automated scoring system of the Ki-67 index was good. 29.1% was the mean Ki-67 index in the ER/PR positive group and this value was within the acceptable range as per St Galen's recommendation.

Mammographic tumour appearance is related to clinicopathological factors and surrogate molecular breast cancer subtype

Mammographic tumour appearance may provide prognostic useful information. For example, spiculation indicates invasiveness, but also better survival compared to tumours with other appearances. We aimed to study the relationship between mammographic tumour appearance and established clinicopathological factors, including surrogate molecular breast cancer subtypes, in the large Malmö Diet and Cancer Study. A total of 1116 women with invasive breast cancer, diagnosed between 1991 and 2014, were included. Mammographic tumour appearance in relation to status for oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2, histological grade, Ki67 and molecular subtype was analysed using various regression models. All models were adjusted for relevant confounders, including breast density, which can affect mammographic appearance. The results consistently showed that spiculated tumours are indicative of favourable characteristics, as they are more likely to be ER and PR positive, and more often exhibit lower histological grade and lower Ki67 expression. Furthermore, spiculated tumours tend to be of luminal A-like subtype, which is associated with a good prognosis. The establishment of associations between mammographic tumour appearance and clinico­pathological factors may aid in characterizing breast cancer at an earlier stage. This could contribute to more individualized breast cancer treatment in the future.