Molecular Testing in Breast Cancer: A Guide to Current Practices

Prognostic factors of patients with human epidermal growth factor receptor 2-positive breast cancer following neoadjuvant therapy: Development and validation of a predictive nomogram

OBJECTIVE

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer exhibits an aggressive phenotype and poor prognosis. The application of neoadjuvant therapy (NAT) in patients with breast cancer can significantly reduce the risks of disease recurrence and improve survival. By integrating different clinicopathological factors, nomograms are valuable tools for prognosis prediction. This study aimed to assess the prognostic value of clinicopathological factors in patients with HER2-positive breast cancer and construct a nomogram for outcome prediction.

METHODS

We retrospectively analyzed the clinicopathological data from 374 patients with breast cancer admitted to the Fourth Hospital of Hebei Medical University between January 2009 and December 2017, who were diagnosed with invasive breast cancer through preoperative core needle biopsy pathology, underwent surgical resection after NAT, and were HER2-positive. Patients were randomly divided into a training and validation set at a ratio of 7:3. Univariate and multivariate survival analyses were performed using Kaplan-Meier and Cox proportional hazards regression models. Results of the multivariate analysis were used to create nomograms predicting 3-, 5-, and 8-year overall survival (OS) rates. Calibration curves were plotted to test concordance between the predicted and actual risks. Harrell C-index and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discriminability of the nomogram prediction model.

RESULTS

All included patients were women, with a mean age of 50 ± 10.4 years (range: 26-72 years). In the training set, both univariate and multivariate analyses identified residual cancer burden (RCB) class, tumor-infiltrating lymphocytes(TILs), and clinical stage as independent prognostic factors for OS, and these factors were combined to construct a nomogram. The calibration curves demonstrated good concordance between the predicted and actual risks, and the C-index of the nomogram was 0.882 (95 % CI 0.863-0.901). The 3-, 5-, and 8-year areas under the ROC curve (AUCs) were 0.909, 0.893, and 0.918, respectively, indicating good accuracy of the nomogram. The calibration curves also demonstrated good concordance in the validation set, with a C-index of 0.850 (95 % CI 0.804-0.896) and 3-, 5-, and 8-year AUCs of 0.909, 0.815, and 0.834, respectively, which also indicated good accuracy.

CONCLUSION

The nomogram prediction model accurately predicted the prognostic status of post-NAT patients with breast cancer and was more accurate than clinical stage and RCB class. Therefore, it can serve as a reliable guide for selecting clinical treatment measures for breast cancer.

Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis

BACKGROUND

Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods.

OBJECTIVE

This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers.

METHODS

A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically.

RESULTS

Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain.

CONCLUSION

Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

A color-based tumor segmentation method for clinical ex vivo breast tissue assessment utilizing a multi-contrast brightfield imaging strategy

We demonstrate an automated two-step tumor segmentation method leveraging color information from brightfield images of fresh core needle biopsies of breast tissue. Three different color spaces (HSV, CIELAB, YCbCr) were explored for the segmentation task. By leveraging white-light and green-light images, we identified two different types of color transformations that could separate adipose from benign and tumor or cancerous tissue. We leveraged these two distinct color transformation methods in a two-step process where adipose tissue segmentation was followed by benign tissue segmentation thereby isolating the malignant region of the biopsy. Our tumor segmentation algorithm and imaging probe could highlight suspicious regions on unprocessed biopsy tissue to guide selection of areas most similar to malignant tissues for tissue pathology whether it be formalin fixed or frozen sections, expedite tissue selection for molecular testing, detect positive tumor margins, or serve an alternative to tissue pathology, in countries where these services are lacking.

The Sensitization of Triple-Negative Breast Cancers to Poly ADP Ribose Polymerase Inhibition Independent of BRCA1/2 Mutation Status by Chemically Modified microRNA-489

Chemoresistance and inefficient therapeutic efficacies in triple-negative breast cancers (TNBCs) are among the major clinical problems in breast cancers. A potential new method to sensitize these tumors to current treatment options is, therefore, urgent and necessary. Our previous studies demonstrated that miR-489 serves as one of the top tumor-suppressing miRs and features downregulated expression in metastatic TNBCs and that the restoration of miR-489 expression in TNBCs effectively inhibits the metastatic potentials of TNBCs both in vitro and in vivo. The chemical modification of miR-489 (CMM489) through the replacement of uracil with 5-FU further enhances the therapeutic potential of miR-489. In the present study, we tested the effects of CMM489 in synergizing DNA damage response (DDR) inhibitors such as PARP inhibitors. CMM489 is particularly effective in sensitizing TNBC cell lines with inherent resistance to PARP inhibitors regardless of BRCA mutation status. One of the anti-cancer mechanisms through which CMM489 synergizes with PARP inhibitors is the blockade of homologous recombination (HR) in TNBC cells upon DNA damage. The results of this study highlight the potential use of CMM489 in combination treatments with PARP inhibitors in TNBCs.

miRNA Expression Profiling in Human Breast Cancer Diagnostics and Therapy

Breast cancer is one of the most commonly diagnosed cancer types worldwide. Regarding molecular characteristics and classification, it is a heterogeneous disease, which makes it more challenging to diagnose. As is commonly known, early detection plays a pivotal role in decreasing mortality and providing a better prognosis for all patients. Different treatment strategies can be adjusted based on tumor progression and molecular characteristics, including personalized therapies. However, dealing with resistance to drugs and recurrence is a challenge. The therapeutic options are limited and can still lead to poor clinical outcomes. This review aims to shed light on the current perspective on the role of miRNAs in breast cancer diagnostics, characteristics, and prognosis. We discuss the potential role of selected non-coding RNAs most commonly associated with breast cancer. These include miR-21, miR-106a, miR-155, miR-141, let-7c, miR-335, miR-126, miR-199a, miR-101, and miR-9, which are perceived as potential biomarkers in breast cancer prognosis, diagnostics, and treatment response monitoring. As miRNAs differ in expression levels in different types of cancer, they may provide novel cancer therapy strategies. However, some limitations regarding dynamic alterations, tissue-specific profiles, and detection methods must also be raised.

Expression Patterns of ER, PR, HER-2/neu and p53 in Association with Nottingham Tumour Grade in Breast Cancer Patients

Objectives

Recent molecular studies show that breast cancer (BC) is a heterogeneous disease, and several molecular changes may accumulate over time to influence treatment response. As a result, employing reliable molecular biomarkers to monitor these modifications may help deliver personalised treatment. However, this may be unrealistic in the resource-limited parts of the world. Thus, this study aimed to investigate the expression pattern of hormone receptors and p53 tumour suppressor using immunohistochemistry (IHC) in BC compared to the traditional tumour grade.

Methods

In total, 205 cases were investigated, and the Modified Bloom-Richardson score system was adopted in grading the tumours. The tissue sections of the cases were stained with specific primary antibodies at dilutions of 1:60 for oestrogen receptors (ER) and progesterone receptors (PR), 1:350 for the human epidermal growth factor (HER-2/neu) and 1:50 for p53.

Results

Invasive ductal carcinoma of no-specific type (n = 190, 92.7%) was predominant and grade II tumour (n = 146, 71.2%) was the most frequent. Hormone receptors ER (n = 127) and PR (n = 145) had 62.0% and 70.7% positive cases, respectively; 34.1% (n = 70) were positive for HER-2/neu, while 76.1% (n = 156) were positive for p53. Significant associations between Nottingham grade and expression patterns of ER (P <0.01), PR (P <0.001), HER-2/neu (P <0.001) and p53 (P = 0.001) were observed.

Conclusion

Nottingham grade had a high degree of concordance with the patterns of expression of hormone receptors, HER-2/neu and p53, suggesting that it may play an important role in connection with the predictive and prognostic biomarkers for BC.

Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status

Purpose

The detection of human epidermal growth factor receptor 2 (HER2) expression status is essential to determining the chemotherapy regimen for breast cancer patients and to improving their prognosis. We developed a deep learning radiomics (DLR) model combining time-frequency domain features of ultrasound (US) video of breast lesions with clinical parameters for predicting HER2 expression status.

Patients and Methods

Data for this research was obtained from 807 breast cancer patients who visited from February 2019 to July 2020. Ultimately, 445 patients were included in the study. Pre-operative breast ultrasound examination videos were collected and split into a training set and a test set. Building a training set of DLR models combining time-frequency domain features and clinical features of ultrasound video of breast lesions based on the training set data to predict HER2 expression status. Test the performance of the model using test set data. The final models integrated with different classifiers are compared, and the best performing model is finally selected.

Results

The best diagnostic performance in predicting HER2 expression status is provided by an Extreme Gradient Boosting (XGBoost)-based time-frequency domain feature classifier combined with a logistic regression (LR)-based clinical parameter classifier of clinical parameters combined DLR, particularly with a high specificity of 0.917. The area under the receiver operating characteristic curve (AUC) for the test cohort was 0.810.

Conclusion

Our study provides a non-invasive imaging biomarker to predict HER2 expression status in breast cancer patients.

Targeting PI3K/AKT/mTOR Pathway in Breast Cancer: From Biology to Clinical Challenges

Breast cancer (BC) is the most common women cancer and cause of cancer death. Despite decades of scientific progress in BC treatments, the clinical benefit of new drugs is modest in several cases. The phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway mutations are frequent in BC (20-40%) and are significant causes of aggressive tumor behavior, as well as treatment resistance. Improving knowledge of the PI3K/AKT/mTOR pathway is an urgent need. This review aims to highlight the central role of PI3K-mTORC1/C2 mutations in the different BC subtypes, in terms of clinical outcomes and treatment efficacy. The broad base of knowledge in tumor biology is a key point for personalized BC therapy in the precision medicine era.

Classic and New Markers in Diagnostics and Classification of Breast Cancer

Simple Summary

With ever-increasing incidence, breast cancer is considered a most diagnosed type of cancer among women worldwide. Breast cancer arises through malignant transformation of ductal or lobular cells in female (or male) breast and the genetic, phenotypic and morphological heterogeneity has an effect on tumour’s behaviour, thereby instigating a need for individual personalized therapy. A traditional assessment of tumour’s characteristics involves a biopsy and histological analysis of a tumour tissue, and in recent years has been accompanied by analysis of molecular biomarkers to enhance the results. In this work we aimed to thoroughly investigate the latest data in this field of study and give a comprehensive review of novel molecular biomarkers of breast cancer and methodologies used to analyse them.

Abstract

Breast cancer remains the most frequently diagnosed form of female’s cancer, and in recent years it has become the most common cause of cancer death in women worldwide. Like many other tumours, breast cancer is a histologically and biologically heterogeneous disease. In recent years, considerable progress has been made in diagnosis, subtyping, and complex treatment of breast cancer with the aim of providing best suited tumour-specific personalized therapy. Traditional methods for breast cancer diagnosis include mammography, MRI, biopsy and histological analysis of tumour tissue in order to determine classical markers such as estrogen and progesterone receptors (ER, PR), cytokeratins (CK5/6, CK14, C19), proliferation index (Ki67) and human epidermal growth factor type 2 receptor (HER2). In recent years, these methods have been supplemented by modern molecular methodologies such as next-generation sequencing, microRNA, in situ hybridization, and RT-qPCR to identify novel molecular biomarkers. MicroRNAs (miR-10b, miR-125b, miR145, miR-21, miR-155, mir-30, let-7, miR-25-3p), altered DNA methylation and mutations of specific genes (p16, BRCA1, RASSF1A, APC, GSTP1), circular RNA (hsa_circ_0072309, hsa_circRNA_0001785), circulating DNA and tumour cells, altered levels of specific proteins (apolipoprotein C-I), lipids, gene polymorphisms or nanoparticle enhanced imaging, all these are promising diagnostic and prognostic tools to disclose any specific features from the multifaceted nature of breast cancer to prepare best suited individualized therapy.

Pathological Diagnosis, Work-Up and Reporting of Breast Cancer 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

This text is based on the recommendations accepted by the 4th Hungarian Consensus Conference on Breast Cancer, modified on the basis of the international consultation and conference within the frames of the Central-Eastern European Academy of Oncology. The recommendations cover non-operative, intraoperative and postoperative diagnostics, determination of prognostic and predictive markers and the content of cytology and histology reports. Furthermore, they address some specific issues such as the current status of multigene molecular markers, the role of pathologists in clinical trials and prerequisites for their involvement, and some remarks about the future.

Sociodemographic disparities in molecular testing for breast cancer

PURPOSE

Molecular testing is a critical component of breast cancer care used to identify the presence of estrogen and/or progesterone receptors (jointly hormone receptors-HRs) and the expression of human epidermal growth factor 2 (HER2) on a tumor. Our objective was to characterize trends and predictors of lack of molecular testing among female breast cancer patients overall and by sociodemographic characteristics.

METHODS

We examined data on female breast cancer patients diagnosed between 2010 and 2016 from Surveillance Epidemiology and End Results-18. Joinpoint regression analyses assessed annual percent change (APC) in lack of ER, PR, or HER2 testing. Multivariable, multilevel logistic regression models identified factors associated with lack of molecular testing.

RESULTS

A nominally lower proportion of rural patients did not receive molecular testing (e.g., 1.8% in rural vs. 2.3% in urban for HER2). For all tests, a higher proportion of Hispanic and non-Hispanic Black women were not tested. Across all characteristics, improvement in testing was noted, although disparities among groups remained. For example, lack of HER2 testing improved from 3.2 to 1.7% in White patients (APC = - 10.05) but was consistently higher in Black patients 3.9 to 2.3% (APC = - 8.21). Multivariable, multilevel models showed that older, non-Hispanic Black, and unpartnered women were at greater odds of not receiving molecular testing.

CONCLUSIONS

While lack of molecular testing of breast cancer patients is relatively rare, racial/ethnic, insurance status, and age-related disparities have been identified. To reduce testing and downstream treatment and outcome disparities, it is imperative for all breast cancer patients to receive molecular testing.

Correlation of ER, PR, and HER2 at protein and mRNA levels in the Asian patients with operable breast cancer

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. The estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are the important biomarkers in the prognosis of breast cancer, and their expression is used to categorize breast cancer into subtypes. We aimed to analyze the concordance among ER, PR, and HER2 expression levels and breast cancer subtyping results obtained by immunohistochemistry (IHC, for protein) and reverse transcriptase-polymerase chain reaction (RT-PCR, for mRNA) and to assess the recurrence-free survival (RFS) of the different subtypes as determined by the two methods. We compared biomarker expression by IHC and RT-PCR in 397 operable breast cancer patients and categorized all patients into luminal, HER2, and triple-negative (TN) subtypes. The concordance of biomarker expression between the two methods was 81.6% (κ = 0.4075) for ER, 87.2% (κ = 0.5647) for PR, and 79.1% (κ = 0.2767) for HER2. The κ-statistic was 0.3624 for the resulting luminal, HER2, and TN subtypes. The probability of 5-year RFS was 0.78 for the luminal subtype versus 0.77 for HER2 and 0.51 for TN, when determined by IHC (P=0.007); and 0.80, 0.71, and 0.61, respectively, when determined by the RT-PCR method (P=0.008). Based on the current evidence, subtyping by RT-PCR performs similar to conventional IHC with regard to the 5-year prognosis. The PCR method may thus provide a complementary means of subtyping when IHC results are ambiguous.

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Molecular Testing in Breast Cancer: Current Status and Future Directions

Molecular testing in breast cancer is a rapidly developing field that is becoming increasingly integral to patient care. This article provides an overview of currently available molecular assays and testing modalities that have prognostic, predictive, and therapeutic value. These include multigene assays for invasive breast cancer (Oncotype DX, MammaPrint, Prosigna, and Breast Cancer Index) and ductal carcinoma in situ (Oncotype DX DCIS and DCISionRT) and companion tests to detect PIK3CA mutations and NTRK fusions. The various assays related to immune checkpoint inhibitors, consisting of immunohistochemistry with anti-programmed death-ligand 1 antibodies SP142 and 22C3 and detection of microsatellite instability, mismatch repair deficiency, and tumor mutational burden are also discussed. Finally, the practical utility and hopeful promise of next-generation sequencing panels and circulating tumor (cell-free) DNA assays are evaluated. This review should serve as a useful and practical reference for practicing pathologists, molecular pathologists, clinicians, and researchers.

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

Background

To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC).

Methods

This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20).

Findings

The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test).

Interpretation

The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients.

Funding

NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.

Paired tumor sequencing and germline testing in breast cancer management: An experience of a single academic center

BACKGROUND

Genetic testing for cancer predisposition is recommended to women with breast cancer who meet the criteria for such testing. After the FDA approvals of the poly ADP ribose polymerase (PARP) inhibitors, olaparib and talazoparib, for treatment of metastatic breast cancer, carrying germline mutations in BRCA1 and BRCA2 genes, the genetic testing result has become critical in their care. With the recent FDA approval of alpelisib for the treatment of PIK3CA-mutated hormone-receptor positive metastatic breast cancer, tumor molecular profiling to identify somatic mutations and potential molecularly targeted agents is increasingly utilized in the treatment of advanced breast cancer.

AIM

Combining germline and somatic sequencing (paired testing) offers an advantage over a single technique approach. Our study evaluates the role of paired testing on the management of breast cancer patients.

METHODS AND RESULTS

Forty-three breast cancer patients treated at Rush University Medical Center underwent paired germline and somatic variant testing in 2015 to 2017. A retrospective chart review was conducted with the analysis of demographic, clinical, and genomic data. Three actionable germline variants were found in the CHEK2 (2) and ATM (1) genes. 95% of tumors had somatic mutations. Seventy-seven percent of tumors had genomic alterations targetable with agents approved for breast cancer and 88% had molecular targets for agents approved for other cancers. Clinical examples of such use are described and potential future directions of tumor and paired testing are discussed.

CONCLUSIONS

Germline variants were present in a relatively small patient group not routinely tested for inherited alterations. Potentially targetable somatic alterations were identified in the majority of breast cancers. Paired testing is a feasible and efficient approach that delivers valuable information for the care of breast cancer patients and eliminates serial testing.

Therapeutic Potential of Chemically Modified miR-489 in Triple-Negative Breast Cancers

Triple-negative breast cancers (TNBCs) lack ER, PR and her2 receptors that are targets of common breast cancer therapies with poor prognosis due to their high rates of metastasis and chemoresistance. Based on our previous studies that epigenetic silencing of a potential metastasis suppressor, arrestin domain-containing 3 (ARRDC3), is linked to the aggressive nature of TNBCs, we identified a sub-group of tumor suppressing miRNAs whose expressions were significantly up-regulated by ARRDC3 over-expression in TNBC cells. Among these tumor suppressing miRs, we found that miR-489 is most anti-proliferative in TNBC cells. miR-489 also blocked DNA damaging responses (DDRs) in TNBC cells. To define the mechanism by which miR-489 inhibits TNBC cell functions, we screened the potential target genes of miR-489 and identified MDC-1 and SUZ-12 as novel target genes of miR-489 in TNBC cells. To further exploit the therapeutic potentials of miR-489 in TNBC models, we chemically modified the guide strand of miR-489 (CMM489) by replacing Uracil with 5-fluorouracil (5-FU) so that tumor suppressor (miR-489) and DNA damaging (5-FU) components are combined into a single agent as a novel drug candidate for TNBCs. Our studies demonstrated that CMM489 shows superior effects over miR-489 or 5-FU in inhibition of TNBC cell proliferation and tumor progression, suggesting its therapeutic efficacy in TNBC models.

A Systematic Review of the Prevalence and Diagnostic Workup of PIK3CA Mutations in HR+/HER2- Metastatic Breast Cancer

PIK3CA mutation frequency varies among breast cancer (BC) subtypes. Recent evidence suggests combination therapy with the PI3K inhibitor (PI3Ki) alpelisib and endocrine therapy (ET) improves response rates and progression-free survival (PFS) in PIK3CA-mutant, hormone receptor positive (HR+) BC versus ET alone; thus, better understanding the clinical and epidemiologic elements of these mutations is warranted. This systematic review characterizes the PIK3CA mutation epidemiology, type of testing approaches (e.g., liquid or tissue tumor biopsy), and stability/concordance (e.g., consistency in results by liquid versus solid tumor sample, by the same method over time) in patients with HR+/HER2- advanced (locally unresectable) or metastatic disease (HR+/HER2- mBC) and explores performance (e.g., pairwise concordance, sensitivity, specificity, or predictive value) of respective mutation findings. A comprehensive search of PubMed/MEDLINE, EMBASE, Cochrane Central, and select conference abstracts (i.e., AACR, ASCO, SABCS, ECCO, and ESMO conferences between 2014 and 2017) identified 39 studies of patients with HR+, HER2- mBC. The median prevalence of PIK3CA mutation was 36% (range: 13.3% to 61.5%); identified testing approaches more commonly used tissue over liquid biopsies and primarily utilized next-generation sequencing (NGS), polymerase chain reaction (PCR), or Sanger sequencing. There was concordance and stability between tissues (range: 70.4% to 94%) based on limited data. Given the clinical benefit of the PI3Ki alpelisib in patients with PIK3CA mutant HR+/HER2- mBC, determination of tumor PIK3CA mutation status is of importance in managing patients with HR+/HER2- mBC. Prevalence of this mutation and utility of test methodologies likely warrants PIK3CA mutation testing in all patients with this breast cancer subtype via definitive assessment of PIK3CA mutational status.

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival

BACKGROUND

Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed.

METHODS

As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes-Basal-like, HER2-enriched, Luminal A, and Luminal B-as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors.

RESULTS

This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm-similar to PAM50-are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns.

CONCLUSIONS

Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.

Multimodal analysis of formalin-fixed and paraffin-embedded tissue by MALDI imaging and fluorescence in situ hybridization for combined genetic and metabolic analysis

Multimodal tissue analyses that combine two or more detection technologies provide synergistic value compared to single methods and are employed increasingly in the field of tissue-based diagnostics and research. Here, we report a technical pipeline that describes a combined approach of HER2/CEP17 fluorescence in situ hybridization (FISH) analysis with MALDI imaging on the very same section of formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE biopsies and a tissue microarray of human gastroesophageal adenocarcinoma were analyzed by MALDI imaging. Subsequently, the very same section was hybridized by HER2/CEP17 FISH. We found that tissue morphology of both, the biopsies and the tissue microarray, was unaffected by MALDI imaging and the HER2 and CEP17 FISH signals were analyzable. In comparison with FISH analysis of samples without MALDI imaging, we observed no difference in terms of fluorescence signal intensity and gene copy number. Our combined approach revealed adenosine monophosphate, measured by MALDI imaging, as a prognostic marker. HER2 amplification, which was detected by FISH, is a stratifier between good and poor patient prognosis. By integrating both stratification parameters on the basis of our combined approach, we were able to strikingly improve the prognostic effect. Combining molecules detected by MALDI imaging with the gene copy number detected by HER2/CEP17 FISH, we found a synergistic effect, which enhances patient prognosis. This study shows that our combined approach allows the detection of genetic and metabolic properties from one very same FFPE tissue section, which are specific for HER2 and hence suitable for prognosis. Furthermore, this synergism might be useful for response prediction in tumors.

Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.

Arrestin Domain Containing 3 Reverses Epithelial to Mesenchymal Transition and Chemo-Resistance of TNBC Cells by Up-Regulating Expression of miR-200b

Our previous studies demonstrated the importance of arrestin domain containing 3 (ARRDC3), a metastasis suppressor, in inhibiting invasive and metastatic potential of triple negative breast cancer (TNBC) in vitro and in vivo. However, little is known about ARRDC3 mediated transcriptional control and its target genes that are implicated in its metastatic suppressing activity. In this study, we used miRNA array and subsequent functional analyses to identify miRNAs whose expression are significantly regulated by ARRDC3 in TNBC cells. We identified miR-200b as a major target gene of ARRDC3. miR-200b played an essential role in mediating ARRDC3 dependent reversal of EMT phenotypes and chemo-resistance to DNA damaging agents in TNBC cells. Expression of miR-200b also increased the expression of ARRDC3 as well in TNBC cells, suggesting a positive feedback loop between these two molecules. In addition, we combined the therapeutic powers of miR-200b and 5-fluorourancil (5-FU) into a single compound (5-FU-miR-200b) to maximize the synergistic effects of these compounds. Chemically modified miR-200b (5-FU-miR-200b mimic) was more effective in inhibiting metastatic potentials of TNBC cells than unmodified miR-200b and does not require transfection reagents, implying its therapeutic potential in TNBC. Our studies showed the importance of therapeutic targeting ARRDC3/miR-200b pathway in TNBC.

Amplified fluorescence imaging of HER2 dimerization on cancer cells by using a co-localization triggered DNA nanoassembly

Convenient and sensitive detection of human epidermal growth factor receptor 2 (HER2) dimerization is highly desirable for molecule subtyping and guiding personalized HER2 targeted therapy of breast cancer. A colocalization-triggered DNA nanoassembly (CtDNA) strategy was developed for amplified imaging of HER2 dimerization. It exploits (a) the advantage of the specificity of aptamer proximity hybridization, and (b) the high sensitivity of hairpin-free nonlinear HCR. The mechanism of step-by-step hairpin-free nonlinear HCR for DNA dendritic nanoassembly was studied by native polyacrylamide gel electrophoresis, atomic force microscopy and fluorometry. The results revealed a high specificity, sensitivity, and excellent controllability of the DNA dendritic nanoassembly. The method was used to identify HER2 homodimers and HER2/HER3 heterodimers in various breast cancer cell lines using fluorescence microscopy. It was then extended to image and quantitatively evaluate HER2 homodimers in clinical formalin-fixed paraffin-embedded breast cancer tissue specimens. This revealed its remarkable accuracy and practicality for clinical diagnostics. Graphical abstract Schematic presentation of amplified imaging of human epidermal growth factor receptor 2 (HER2) dimerization on cancer cell surfaces by using a co-localization triggered DNA nanoassembly (CtDNA).

Integrating clinical, molecular, proteomic and histopathological data within the tissue context: tissunomics

Malignant tumours show a marked degree of morphological, molecular and proteomic heterogeneity. This variability is closely related to microenvironmental factors and the location of the tumour. The activation of genetic alterations is very tissue‐dependent and only few tumours have distinct genetic alterations. Importantly, the activation state of proteins and signaling factors is heterogeneous in the primary tumour and in metastases and recurrences. The molecular diagnosis based only on genetic alterations can lead to treatments with unpredictable responses, depending on the tumour location, such as the tumour response in melanomas versus colon carcinomas with BRAF mutations. Therefore, we understand that the correct evaluation of tumours requires a system that integrates both morphological, molecular and protein information in a clinical and pathological context, where intratumoral heterogeneity can be assessed. Thus, we propose the term ‘tissunomics’, where the diagnosis will be contextualised in each tumour based on the complementation of the pathological, molecular, protein expression, environmental cells and clinical data.

The Potential Use of DCE-MRI Texture Analysis to Predict HER2 2+ Status

Purpose: To evaluate the ability of texture analysis of breast dynamic contrast enhancement-magnetic resonance (DCE-MR) images in differentiating human epidermal growth factor receptor 2 (HER2) 2+ status of breast tumors. Methods: A total of 73 cases were retrospectively selected. HER2 2+ status was confirmed by fluorescence in situ hybridization. For each case, 279 textural features were derived. A student's t-test or Mann-Whitney U test was used to select features with statistically significant differences between HER2 2+ positive and negative groups. A principal component analysis was applied to eliminate feature correlation. Three machine learning classifiers, logistic regression (LR), quadratic discriminant analysis (QDA), and a support vector machine (SVM), were trained and tested using a leave-one-out cross-validation method. The area under a receiver operating characteristic curve (AUC) was measured to assess the classifier's performance. Results: The AUCs for the different classifiers were satisfactory, ranging from 0.808 to 0.865. The classification methods derived with LR and SVM demonstrated similarly high performances, and the accuracy levels were 81.06 and 81.18%, respectively. The AUC for the classifier derived with SVM was the highest (0.865), and a marked specificity (88.90%) was presented. For the classifier with LR, the AUC was 0.851, and the corresponding sensitivity (94.44%) was the highest. Conclusion: The texture analysis for breast DCE-MRI proposed in this study demonstrated potential utility in HER2 2+ status discrimination.

Challenges and Future Trends for Microarray Analysis

The current situation in microarray data analysis and prospects for the future are briefly discussed in this chapter, in which the competition between microarray technologies and high-throughput technologies is considered under a data analysis view. The up-to-date limitations of DNA microarrays are important to forecast challenges and future trends in microarray data analysis; these include data analysis techniques associated with an increasing sample sizes, new feature selection methods, deep learning techniques, covariate significance testing as well as false discovery rate methods, among other procedures for a better interpretability of the results.

Magee Equation Recurrence Score Is Associated With Distal Metastatic Risk in Male Breast Carcinomas: Experience From Two Institutions

OBJECTIVES

To conduct a clinicopathologic study of male breast carcinoma with Magee equation recurrence score (RS).

METHODS

Seventy-four cases of male invasive breast cancers (MBCs) were studied, including 63 hormone receptor (HR)-positive, 10 human epidermal growth factor receptor 2-positive, and one triple-negative breast cancers.

RESULTS

Among 63 HR-positive MBC patients, nine patients developed distal metastasis. Statistical analysis demonstrated distal metastasis was significantly associated with positive lymph node, high Nottingham grade, and increased Magee equation RS. All HR-positive MBCs were further stratified into two categories (low RS <18; intermediate/high RS ≥18). There was a significantly increased risk for distal metastasis in MBCs with intermediate/high Magee equation RS, but no increased risk for lymph node metastasis.

CONCLUSIONS

Our data suggest Magee equation RS may be useful to predict distal metastatic risk in MBC patients.

Robustness of biomarker determination in breast cancer by RT-qPCR: impact of tumor cell content, DCIS and non-neoplastic breast tissue

Background

Tissue heterogeneity in formalin-fixed paraffin-embedded (FFPE) breast cancer specimens may affect the accuracy of reverse transcription quantitative real-time PCR (RT-qPCR). Herein, we tested the impact of tissue heterogeneity of breast cancer specimen on the RT-qPCR-based gene expression assay MammaTyper®.

Methods

MammaTyper® quantifies the mRNA expression of the four biomarkers ERBB2, ESR1, PGR, and MKI67. Based on pre-defined cut-off values, this molecular in vitro diagnostic assay permits binary marker classification and determination of breast cancer subtypes as defined by St Gallen 2013. In this study, we compared data from whole FFPE sections with data obtained in paired RNA samples after enrichment for invasive carcinoma via macro- or laser-capture micro-dissection.

Results

Compared to whole sections, removal of surrounding adipose tissue by macrodissection generated mean absolute 40-ddCq differences of 0.28–0.32 cycles for all four markers, with ≥90% concordant binary classifications. The mean raw marker Cq values in the adipose tissue were delayed by 6 to 7 cycles compared with the tumor-enriched sections, adding a trivial linear fold change of 1.0078 to 1.0156. Comparison of specimens enriched for invasive tumor with whole sections with as few as 20% tumor cell content resulted in mean absolute differences that remained on average below 0.59 Cq. The mean absolute difference between whole sections containing up to 60% ductal carcinoma in situ (DCIS) and specimens after dissection of DCIS was only 0.16–0.25 cycles, although there was a tendency for higher gene expression in DCIS. Observed variations were related to small size of samples and proximity of values to the limit of detection.

Conclusion

Expression of ESR1, PGR, ERBB2 and MKI67 by MammaTyper® is robust in clinical FFPE samples. Assay performance was unaffected by adipose tissue and was stable in samples with as few as 20% tumor cell content and up to 60% DCIS.

Electronic supplementary material

The online version of this article (10.1186/s13000-018-0760-6) contains supplementary material, which is available to authorized users.

Adaptation to chronic exposure to sepantronium bromide (YM155), a prototypical survivin suppressant is due to persistent DNA damage-response in breast cancer cells

Sepantronium bromide (YM155), originally developed against the anti-apoptotic protein survivin, performed exceptionally well in pre-clinical and phase I clinical trials. However, in phase II trials of several cancer types including breast cancer it performed poorly. Additionally, no definitive correlation between survivin level and response to therapy was found. In an attempt to understand the true reason of the late-stage failure of this promising drug, we developed YM155-resistant MCF-7 breast cancer cell line and characterized side-by-side with the drug-naïve parental cell line. Chronic YM155 treatment resulted in downregulation of survivin expression yet triggered cellular responses typical of adaptation to persistent DNA damage. Lowering endogenous antioxidant glutathione level and activity of cell cycle check-point kinase restored YM155 activity. Thus, contrary to its development as a survivin suppressant, YM155 primarily acts as a chemotherapeutic drug causing oxidative stress-mediated DNA damage. Adaptation to long-term exposure to YM155 can be prevented and/or overcome by interfering with detoxification and DNA damage-response pathways. Finally, proteins associated with DNA damage-response pathway will be more appropriate as predictive biomarkers of YM155 in breast tumor cells.

Genome-Based Classification and Therapy of Prostate Cancer

In the past decade, multi-national and multi-center efforts were launched to sequence prostate cancer genomes, transcriptomes, and epigenomes with the aim of discovering the molecular underpinnings of tumorigenesis, cancer progression, and therapy resistance. Multiple biological markers and pathways have been discovered to be tumor drivers, and a molecular classification of prostate cancer is emerging. Here, we highlight crucial findings of these genome-sequencing projects in localized and advanced disease. We recapitulate the utility and limitations of current clinical practices to diagnosis, prognosis, and therapy, and we provide examples of insights generated by the molecular profiling of tumors. Novel treatment concepts based on these molecular alterations are currently being addressed in clinical trials and will lead to an enhanced implementation of precision medicine strategies.

Macrodissection prior to closed system RT-qPCR is not necessary for estrogen receptor and HER2 concordance with IHC/FISH in breast cancer

An on-demand, closed RT-qPCR, the GeneXpert (GX) system, has the potential to provide biomarker information in low-resourced settings and elsewhere. We used this system with a research use only version of the Breast Cancer STRAT4 cartridge that measures the mRNA expression levels of ERBB2, ESR1, PGR, and MKi67. Here we evaluated the impact of non-macrodissected (non m-d) versus macrodissected (m-d) samples using STRAT4 on formalin-fixed, paraffin-embedded (FFPE) core needle biopsies. Two cohorts were assessed: (1) 60 FFPE infiltrating ductal carcinoma (IDCA) cases and (2) 20 FFPE IDCA cases with ductal carcinoma in situ (DCIS) with a range of HER2 expression as determined by clinical immunohistochemistry and fluorescence in situ hybridization (IHC/FISH). We observed about half of the core needle biopsy area as invasive tumor in both IDCA (mean = 51.5%) and IDCA with DCIS (mean = 53.5%) cohorts, but also found the mRNA levels were independent of tumor area. We found excellent agreement of the mRNA transcript level between the paired samples, m-d versus non m-d, for ERBB2, ESR1, PGR, and MKi67 for both the IDCA and IDCA with DCIS cohorts. No significant difference (P > 0.99) was observed when we compared the mRNA transcript level between the paired samples m-d versus non m-d. In addition, we noted a significant concordance (P < 0.001) between RT-qPCR and IHC/FISH for HER2-positivity, ER-positivity, and PR-positivity, independent of specimen dissection. These data suggest that mRNA expression for ERBB2, ESR, and PGR is sufficiently low in surrounding tissue cells such that macrodissection is not required for assessment of key breast cancer mRNA markers and is independent of the amount of input tumor. This approach may be valuable in settings lacking pathology expertise or using specimen types, such as fine-needle aspirates, where it may be challenging to separate non-tumor from tumor tissue.

Immunoexpression of claudins 4 and 7 among invasive breast carcinoma subtypes: A large diagnostic study using tissue microarray

Molecular phenotyping and tissue microarray (TMA) studies have identified distinct invasive breast carcinoma subtypes: Luminal A, luminal B, enriched with overexpressed human epidermal growth factor receptor 2 (HER-2) and triple-negative, i.e., negative for HER-2, as well as for estrogen and progesterone receptor (ER and PR, respectively) expression. These subtypes are useful in clinical management, since they bear distinct prognoses and predictive responses to targeted therapy. However, although molecular profiling provides important prognostic indicators, breast cancer risk stratification remains a challenge in triple-negative cases. What is referred to as claudin-low subtype was identified as a triple-negative subset that is associated with more aggressive tumor behavior and worse prognosis. However, the immunohistochemical expression of claudins has not yet been standardized. Our objective was to verify whether the immunoexpression of claudins 4 and 7 (the main claudins specifically expressed in human breast tissue) in TMA is associated with survival and prognosis in luminal A, HER-2 and triple-negative molecular subtypes. In this diagnostic study, we investigated ER/PR receptor status, HER-2, claudin 4 and 7 expression and stem cell CD44/24 profiles, and verified the association with prognosis and survival outcomes in 803 invasive breast carcinoma cases arranged in four TMAs. Among these, 503 (62.6%) were positive for claudin 4 and 369 (46.0%) for claudin 7. Claudin 4 exhibited the lowest expression in luminal A and triple-negative subtypes, and the highest frequency of expression in HER-2-enriched subtypes, whereas claudin 7 staining was not associated with any subtype. The stem cell phenotype was not associated with subgroups or claudins 4 and 7. Claudin immunoexpression profile was not able to distinguish between patients with better or worse prognosis, and it was not correlated to triple-negative cases. Therefore, it may be concluded that the immunoexpression of claudins 4 and 7, individually or within the usual immunohistochemical context (ER, PR and HER-2), does not provide additional prognostic information on breast cancer subtypes.

High concordance of a closed-system, RT-qPCR breast cancer assay for HER2 mRNA, compared to clinically determined immunohistochemistry, fluorescence in situ hybridization, and quantitative immunofluorescence

Historically, mRNA measurements have been tested on several commercially available platforms, but none have gained broad acceptance for assessment of HER2. An mRNA measurement, as a continuous value, has the potential for use in adjudication of the equivocal category. Here we use a real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) assay in a closed, single-use cartridge, automated system. Multiple cores (1 mm in diameter) were retrospectively collected from 80 formalin-fixed paraffin-embedded (FFPE) tissue blocks with invasive breast cancer seen by Yale Pathology Labs between 1998 and 2011. Tissue cores were processed with a FFPE lysis kit to create lysates that were tested with the automated RT-qPCR assay. Results for IHC and FISH were extracted from the pathology reports and quantitative immunofluorescence (QIF) for each case was measured as previously described. Quality control testing showed that the GX platform RT-qPCR shows no case to case cross contamination on material from routine histology practices. Concordance between RT-qPCR and IHC/FISH was 91.25% (sensitivity=0.87; specificity=0.94; PPV=0.89; NPV=0.92) using a pre-defined delta Ct cut-off (dCt≥-1) for HER2. Concordance (OPA) between RT-qPCR and QIF was 94% (sensitivity=0.90; specificity=0.96; PPV=0.93; NPV=0.94) using dCt≥-1 and a previously defined cut-point for positivity by QIF. In conclusion, the closed system RT-qPCR assay shows >90% concordance with the ASCO/CAP HER2 IHC/FISH scoring. Additionally, the RT-qPCR assay is highly concordant (94%) with the continuous variable HER2 QIF assay, and may better reflect the true continuum of HER2 receptor status in invasive breast cancer. These initial results suggest that fast, closed system molecular assays may have future value for the adjudication of the ASCO/CAP HER2 equivocal category or possibly routine usage in time constrained or low resource settings.

Comparison of risk classification between EndoPredict and MammaPrint in ER-positive/HER2-negative primary invasive breast cancer

PURPOSE

To compare the concordance in risk classification between the EndoPredict and the MammaPrint scores obtained for the same cancer samples on 40 estrogen-receptor positive/HER2-negative breast carcinomas.

METHODS

Formalin-fixed, paraffin-embedded invasive breast carcinoma tissues that were previously analyzed with MammaPrint as part of routine care of the patients, and were classified as high-risk (20 patients) and low-risk (20 patients), were selected to be analyzed by the EndoPredict assay, a second generation gene expression test that combines expression of 8 genes (EP score) with two clinicopathological variables (tumor size and nodal status, EPclin score).

RESULTS

The EP score classified 15 patients as low-risk and 25 patients as high-risk. EPclin re-classified 5 of the 25 EP high-risk patients into low-risk, resulting in a total of 20 high-risk and 20 low-risk tumors. EP score and MammaPrint score were significantly correlated (p = 0.008). Twelve of 20 samples classified as low-risk by MammaPrint were also low-risk by EP score (60%). 17 of 20 MammaPrint high-risk tumors were also high-risk by EP score. The overall concordance between EP score and MammaPrint was 72.5% (κ = 0.45, (95% CI, 0.182 to 0.718)). EPclin score also correlated with MammaPrint results (p = 0.004). Discrepancies between both tests occurred in 10 cases: 5 MammaPrint low-risk patients were classified as EPclin high-risk and 5 high-risk MammaPrint were classified as low-risk by EPclin and overall concordance of 75% (κ = 0.5, (95% CI, 0.232 to 0.768)).

CONCLUSIONS

This pilot study demonstrates a limited concordance between MammaPrint and EndoPredict. Differences in results could be explained by the inclusion of different gene sets in each platform, the use of different methodology, and the inclusion of clinicopathological parameters, such as tumor size and nodal status, in the EndoPredict test.