Quantitative assessment Ki-67 score for prediction of response to neoadjuvant chemotherapy in breast cancer

Effect of Melatonin Administration on Nerve Regeneration after Recurrent Laryngeal Nerve Injury

Recurrent Laryngeal Nerve (RLN) injury is a complication in neck surgery. The aim of this study is to evaluate the effect of primary suture repair with melatonin treatment on nerve regeneration after RLN damage. After the RLN damage, nerve repair was performed in the first and fourth groups. The third and fourth groups were given intraperitoneal melatonin therapy daily for six weeks. EMG was applied to all subjects and vocal cord movements were evaluated endoscopically. At the end of the sixth week, all subjects were sacrificed, and their larynx were examinedhistologically. Vocal cord paralysis (VCP) was observed in all subjects after RLN damage. In the sixth week, improvement was observed in the first and fourth group who underwent nerve repair, whereas none in the second and third group, who did not undergo nerve repair, improved. With EMG, the highest MUP was in the fourth group. Histologically, an increase in Schwann cells, a decrease in axon damage, and cytoplasmic vacuolization were in the fourth group. Myelin protein zero and Ki-67 staining were the most in the fourth group. In our study, laryngoscopic, electrophysiological and histopathological findings show that melatonin contributes to nerve healing but this could not translate into functional recovery.

The Expression Profiles of lncRNAs Are Associated with Neoadjuvant Chemotherapy Resistance in Locally Advanced, Luminal B-Type Breast Cancer

lncRNAs are noncoding transcripts with tissue and cancer specificity. Particularly, in breast cancer, lncRNAs exhibit subtype-specific expression; they are particularly upregulated in luminal tumors. However, no gene signature-based laboratory tests have been developed for luminal breast cancer identification or the differential diagnosis of luminal tumors, since no luminal A- or B-specific genes have been identified. Particularly, luminal B patients are of clinical interest, since they have the most variable response to neoadjuvant treatment; thus, it is necessary to develop diagnostic and predictive biomarkers for these patients to optimize treatment decision-making and improve treatment quality. In this study, we analyzed the lncRNA expression profiles of breast cancer cell lines and patient tumor samples from RNA-Seq data to identify an lncRNA signature specific for luminal phenotypes. We identified an lncRNA signature consisting of LINC01016, GATA3-AS1, MAPT-IT1, and DSCAM-AS1 that exhibits luminal subtype-specific expression; among these lncRNAs, GATA3-AS1 is associated with the presence of residual disease (Wilcoxon test, p < 0.05), which is related to neoadjuvant chemotherapy resistance in luminal B breast cancer patients. Furthermore, analysis of GATA3-AS1 expression using RNA in situ hybridization (RNA ISH) demonstrated that this lncRNA is detectable in histological slides. Similar to estrogen receptors and Ki67, both commonly detected biomarkers, GATA3-AS1 proves to be a suitable predictive biomarker for clinical application in breast cancer laboratory tests.

Evolving Management of Breast Cancer in the Era of Predictive Biomarkers and Precision Medicine

Breast cancer is the most common cancer among women in the world as well as in the United States. Molecular and histological differentiation have helped clinicians optimize treatments with various therapeutics, including hormonal therapy, chemotherapy, immunotherapy, and radiation therapy. Recently, immunotherapy has become the standard of care in locally advanced triple-negative breast cancer and an option across molecular subtypes for tumors with a high tumor mutation burden. Despite the advancements in personalized medicine directing the management of localized and advanced breast cancers, the emergence of resistance to these therapies is the leading cause of death among breast cancer patients. Therefore, there is a critical need to identify and validate predictive biomarkers to direct treatment selection, identify potential responders, and detect emerging resistance to standard therapies. Areas of active scientific and clinical research include novel personalized and predictive biomarkers incorporating tumor microenvironment, tumor immune profiling, molecular characterization, and histopathological differentiation to predict response and the potential emergence of resistance.

Assessment of the Predictive Role of Ki-67 in Breast Cancer Patients' Responses to Neoadjuvant Chemotherapy

OBJECTIVE

Neoadjuvant chemotherapy (NAC) in breast cancer (BC) is being considered for a broader range of cases, including locally advanced tumors and situations where downstaging could reduce extensive surgery. Several trials have explored predictive markers of pathological complete response (pCR). The role of Ki-67 as a predictor of pCR has been demonstrated in studies. However, the cut-off remains vague, given the lack of standardization of measurement methods. The aim of our study was to evaluate the predictive value of Ki-67 in response to NAC and to identify the cut-off values that exhibit the strongest correlation with best response.

MATERIALS AND METHODS

This retrospective study included 187 patients who had undergone surgery following NAC for BC at the CHU Souss Massa of Agadir between January 2020 and January 2023. Logistic regression was used to assess the correlation between Ki-67 and patients' characteristics. Optimal Ki-67 cutoff was identified by receiver operating characteristic curve. Kaplan-Meier curves were used to assess disease-free survival (DFS), and survival comparisons were assessed with the log-rank test.

RESULTS

The median age was 51.8±10.7 years and 51.4% of tumors were smaller than 5 cm. Node invasion was found in 55.4%. Luminal B subtype was found in 49.7%, followed by human epidermal growth factor receptor-2 (HER-2)-positive in 27.4%, triple-negative in 14.3% and Luminal A in 8.6%. pCR occurred in 40% of patients overall. Subgroup analysis revealed a significant association between pCR and tumor size (p<0.001), lymph node involvement (p<0.001), grade 2 (p<0.001), vascular invasion (p<0.001), and positive HER-2 status (p = 0.022). In statistical analysis, pathological responses were improved in patients with Ki-67 >35% (p<0.001). DFS was 98.8% at 12 months. No statistical difference was found in DFS according to Ki-67 values and pCR status.

CONCLUSION

Our results indicate that Ki-67 is a predictive marker for response in the neoadjuvant setting in BC patients. Our study showed that a Ki-67 cut-off >35% predicts a better pCR rate in response to NAC. However, this cutoff value remains controversial due to the absence of a standard method of measurement, with inter- and intra-observer variability. It would be necessary to validate this cutoff in other studies.

Prediction of Receptor Status in Radiomics: Recent Advances in Breast Cancer Research

Breast cancer is a multifactorial heterogeneous disease and the leading cause of cancer-related deaths in women; its diagnosis and treatment require clinical sensitivity and a comprehensive disciplinary research approach. The expression of different receptors on tumor cells not only provides the basis for molecular typing of breast cancer but also has a decisive role in the diagnosis, treatment, and prognosis of breast cancer. To date, immunohistochemistry (IHC), which uses invasive histological sampling, has been extensively used in clinical practice to analyze the status of receptors and to make an accurate diagnosis of breast cancer. As an invasive assay, IHC can provide important biological information on tumors at a single point in time, but cannot predict future changes (due to treatment or tumor mutations) without additional invasive procedures. These issues highlight the need to develop a non-invasive method for predicting receptor status. The emerging field of radiomics may offer a non-invasive approach to identification of receptor status without requiring biopsy. In this paper, we present a review of the latest research results in radiomics for predicting the status of breast cancer receptors, with potential important clinical applications.

Predicting abiraterone efficacy in advanced prostate cancer: Insights from marker of proliferation Ki67

BACKGROUND

KI67 is a well-known biomarker reflecting cell proliferation. We aim to elucidate the predictive role of KI67 in the efficacy of abiraterone for patients with advanced prostate cancer (PCa).

METHODS

Clinicopathological data of 152 men with metastatic PCa, who received abiraterone therapy were retrospectively collected. The KI67 positivity was examined by immunohistochemistry using the prostate biopsy specimen. The predictive value of KI67 on the therapeutic efficacy of abiraterone was explored using Kaplan-Meier curve and Cox regression analysis. The endpoints included prostate-specific antigen (PSA) progression-free survival (PSA-PFS), radiographic PFS (rPFS), and overall survival (OS).

RESULTS

In total, 85/152 (55.9%) and 67/152 (44.1%) cases, respectively, received abiraterone at metastatic hormone-sensitive (mHSPC) and castration-resistant PCa (mCRPC) stage. The median KI67 positivity was 20% (interquartile range: 10%-30%). Overall, KI67 rate was not correlated with PSA response. Notably, an elevated KI67-positive rate strongly correlated with unfavorable abiraterone efficacy, with KI67 ≥ 30% and KI67 ≥ 20% identified as the optimal cutoffs for prognosis differentiation in mHSPC (median PSA-PFS: 11.43 Mo vs. 26.43 Mo, p < 0.001; median rPFS: 16.63 Mo vs. 31.90 Mo, p = 0.003; median OS: 21.77 Mo vs. not reach, p = 0.005) and mCRPC (median PSA-PFS: 7.17 Mo vs. 12.20 Mo, p = 0.029; median rPFS: 11.67 Mo vs. 16.47 Mo, p = 0.012; median OS: 21.67 Mo vs. not reach, p = 0.073) patients, respectively. Multivariate analysis supported the independent predictive value of KI67 on abiraterone efficacy. In subgroup analysis, an elevated KI67 expression was consistently associated with unfavorable outcomes in the majority of subgroups. Furthermore, data from another cohort of 79 PCa patients with RNA information showed that those with KI67 RNA levels above the median had a significantly shorter OS than those below the median (17.71 vs. 30.72 Mo, p = 0.035).

CONCLUSIONS

This study highlights KI67 positivity in prostate biopsy as a strong predictor of abiraterone efficacy in advanced PCa. These insights will assist clinicians in anticipating clinical outcomes and refining treatment decisions for PCa patients.

Impact of Neoadjuvant Chemotherapy (NAC) on Biomarker Expression in Breast Cancer

Background and Objectives: This study aimed to explore biomarker change after NAC (neoadjuvant chemotherapy) and to investigate biomarker expression as a prognostic factor in patients with residual disease (RD) after NAC. Materials and Methods: We retrospectively evaluated 104 patients with invasive breast cancer, who underwent NAC and surgery at Pusan National University Hospital from 2015 to July 2022. The expression of the biomarker was assessed, and the overall survival (OS) and disease-free survival (DFS) were investigated. Results: After NAC, 24 patients (23.1%) out of 104 total patients had a pathological complete response (pCR). We found that changes in at least one biomarker were observed in 41 patients (51.2%), among 80 patients with RD. In patients with RD after NAC (n = 80), a subtype change was identified in 20 patients (25.0%). Any kind of change in the HER2 status was present 19 (23.7%) patients. The hormone receptor (HR)+/HER2+ subtype was significantly associated with better disease-free survival (DFS) (HR, 0.13; 95% CI, 0.02-0.99; p = 0.049). No change in p53 was associated with better DFS, and negative-to-positive change in p53 expression after NAC was correlated with worse DFS (p < 0.001). Negative-to-positive change in p53 was an independent, worse DFS factor in the multivariate analysis (HR,18.44; 95% CI, 1.86-182.97; p = 0.013). Conclusions: Biomarker change and subtype change after NAC were not infrequent, which can affect the further treatment strategy after surgery. The expression change of p53 might have a prognostic role. Overall, we suggest that the re-evaluation of biomarkers after NAC can provide a prognostic role and is needed for the best decision to be made on further treatment.

Response-guided neoadjuvant sacituzumab govitecan for localized triple-negative breast cancer: results from the NeoSTAR trial

BACKGROUND

Sacituzumab govitecan (SG), a novel antibody-drug conjugate (ADC) targeting TROP2, is approved for pre-treated metastatic triple-negative breast cancer (mTNBC). We conducted an investigator-initiated clinical trial evaluating neoadjuvant (NA) SG (NCT04230109), and report primary results.

PATIENTS AND METHODS

Participants with early-stage TNBC received NA SG for four cycles. The primary objective was to assess pathological complete response (pCR) rate in breast and lymph nodes (ypT0/isN0) to SG. Secondary objectives included overall response rate (ORR), safety, event-free survival (EFS), and predictive biomarkers. A response-guided approach was utilized, and subsequent systemic therapy decisions were at the discretion of the treating physician.

RESULTS

From July 2020 to August 2021, 50 participants were enrolled (median age = 48.5 years; 13 clinical stage I disease, 26 stage II, 11 stage III). Forty-nine (98%) completed four cycles of SG. Overall, the pCR rate with SG alone was 30% [n = 15, 95% confidence interval (CI) 18% to 45%]. The ORR per RECIST V1.1 after SG alone was 64% (n = 32/50, 95% CI 77% to 98%). Higher Ki-67 and tumor-infiltrating lymphocytes (TILs) were predictive of pCR to SG (P = 0.007 for Ki-67 and 0.002 for TILs), while baseline TROP2 expression was not (P = 0.440). Common adverse events were nausea (82%), fatigue (76%), alopecia (76%), neutropenia (44%), and rash (48%). With a median follow-up time of 18.9 months (95% CI 16.3-21.9 months), the 2-year EFS for all participants was 95%. Among participants with a pCR with SG (n = 15), the 2-year EFS was 100%.

CONCLUSIONS

In the first NA trial with an ADC in localized TNBC, SG demonstrated single-agent efficacy and feasibility of response-guided escalation/de-escalation. Further research on optimal duration of SG as well as NA combination strategies, including immunotherapy, are needed.

A phenol amine molecule from Salinivenus iranica acts as the inhibitor of cancer stem cells in breast cancer cell lines

In recent years, the anticancer properties of metabolites from halophilic microorganisms have received a lot of attention. Twenty-nine halophilic bacterial strains were selected from a culture collection to test the effects of their supernatant metabolites on stem cell-like properties of six human cancer cell lines. Human fibroblasts were used as normal control. Sphere and colony formation assay were done to assess the stem cell-like properties. invasion and migration assay, and tumor development in mice model were done to assess the anti-tumorigenesis effect in vitro and in vivo. The metabolites from Salinivenus iranica demonstrated the most potent cytotoxic effect on breast cancer cell lines (IC50 = 100 µg/mL) among all strains, with no effect on normal cells. In MDA-MB-231 cells, the supernatant metabolites enhanced both early and late apoptosis (approximately 9.5% and 48.8%, respectively) and decreased the sphere and colony formation ability of breast cancer cells. Furthermore, after intratumor injection of metabolites, tumors developed in the mice models reduced dramatically, associated with increased pro-apoptotic caspase-3 expression. The purified cytotoxic molecule, a phenol amine with a molecular weight of 1961.73 Dalton (IC50 = 1 µg/mL), downregulated pluripotency gene SRY-Box Transcription Factor 2 (SOX-2) expression in breast cancer cells which is associated with resistance to conventional anticancer treatment. In conclusion, we suggested that the phenol amine molecule from Salinivenus iranica could be a potential anti-breast cancer component.

Adherence Disparities and Utilization Trends of Oncotype Dx Assay: A National Cancer Database Study

INTRODUCTION

Oncotype Dx (ODX) is a genetic assay that analyzes tumor recurrence risk and provides chemotherapy recommendations for T1-T2 stage, hormone receptor-positive, human epidermal growth factor receptor-negative, and nodal-negative breast cancer patients. Despite its established validity, the utilization of this assay is suboptimal. The study aims to evaluate factors that are associated with adherence rate with the testing guidelines and examine changes in utilization trends.

METHODS

This is a retrospective study, utilizing data from the National Cancer Database from 2010 to 2017. Patients who met the ODX testing guidelines were first evaluated for testing adherence. Secondly, all patients who underwent ODX testing were assessed to evaluate the trend in ODX utilization.

RESULTS

A total of 429,648 patients met the criteria for ODX, and 43.4% of this population underwent testing. Advanced age, racial minorities, low-income status, well-differentiated tumor grade, uninsured status, and treatment at community cancer centers were associated with a decreased likelihood of receiving ODX in eligible patients. Additionally, a notable amount of testing was performed on patients who did not meet the ODX testing criteria. Among the 295,326 patients that underwent ODX testing, 16.6% of patients were node-positive and 1.8% had T3 or T4 stage tumors.

CONCLUSIONS

A considerable number of patients who were eligible for ODX did not receive it, indicating potential barriers to care and disparities in breast cancer treatment. ODX usage has been expanded to broader patient populations, indicating more research is needed to validate the effectiveness of the assay in these patient groups.

Relationship between NUDT21 mediated alternative polyadenylation process and tumor

Alternative polyadenylation (APA) is a molecular process that generates diversity at the 3' end of RNA polymerase II transcripts from over 60% of human genes. APA and microRNA regulation are both mechanisms of post-transcriptional regulation of gene expression. As a key molecular mechanism, Alternative polyadenylation often results in mRNA isoforms with the same coding sequence but different lengths of 3' UTRs, while microRNAs regulate gene expression by binding to specific mRNA 3' UTRs. Nudix Hydrolase 21 (NUDT21) is a crucial mediator involved in alternative polyadenylation (APA). Different studies have reported a dual role of NUDT21 in cancer (both oncogenic and tumor suppressor). The present review focuses on the functions of APA, miRNA and their interaction and roles in development of different types of tumors.NUDT21 mediated 3' UTR-APA changes can be used to generate specific signatures that can be used as potential biomarkers in development and disease. Due to the emerging role of NUDT21 as a regulator of the aforementioned RNA processing events, modulation of NUDT21 levels may be a novel viable therapeutic approach.

The Effect of Black Cohosh on Ki67 expression and Tumor Volume: A Pilot Study of Ductal Carcinoma in Situ Patients

BACKGROUND

Black cohosh (BC) (Cimicifuga racemosa) may prevent and treat breast cancer through anti-proliferative, pro-apoptotic, anti-estrogenic, and anti-inflammatory effects. This study sought to evaluate the effect of BC on tumor cellular proliferation, measured by Ki67 expression, in a pre-operative window trial of ductal carcinoma in situ (DCIS) patients.

METHODS

Patients were treated pre-operatively for 2 to 6 weeks with BC extract. Eligible subjects were those who had DCIS on core biopsy. Ki67 was measured using automated quantitative immunofluorescence (AQUA) pre/post-operatively. Ki67, tumor volume, and hormone changes were assessed with 2-sided Wilcoxon signed-rank tests, α = .05.

RESULTS

Thirty-one patients were treated for an average of 24.5 days (median 25; range 15-36). Ki67 decreased non-significantly (n = 26; P = .20; median pre-treatment 1280, post-treatment 859; range pre-treatment 175-7438, post-treatment 162-3370). Tumor volume, estradiol, and FSH did not change significantly. No grade 3 or 4 adverse events were reported.

CONCLUSIONS

BC use showed no significant impact on cellular proliferation, tumor volume, or invasive disease upgrade rates in DCIS patients. It was well-tolerated, with no observed significant toxicities. Further study is needed to elucidate BC's role in breast cancer treatment and prevention.ClinicalTrials.gov Identifier: NCT01628536https://clinicaltrials.gov/ct2/show/NCT01628536.

Ultrasound Strain Elastography and Contrast-Enhanced Ultrasound in Predicting the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: A Nomogram Integrating Ki-67 and Ultrasound Features

OBJECTIVES

To explore whether conventional elastography and contrast-enhanced ultrasound (CEUS) combined with histopathology can monitor the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer (BC), and develop a Nomogram prediction model monitoring response to NAC.

METHODS

From February 2010 to November 2015, 91 BC patients who received NAC were recruited. The maximum diameter, stiffness, and CEUS features were assessed. Core biopsy, surgical pathology immunophenotype, and Miller-Payne (MP) evaluation were documented. Univariate and multivariate analysis was performed using receiver operating characteristic (ROC) analysis and logistic regression analysis.

RESULTS

There were 37 cases showing pathological complete response (pCR) and 54 of non-pCR. The changes of maximal diameter were correlated with MP (P < .05). The sensitivity (SEN), specificity (SPE), and area under the ROC curve (AUC) of baseline size predicting pCR were 57.40%, 70.30%, and 0.64 (P = .024). Baseline Ki-67 index of pCR group is significantly higher than that of non-pCR group (P = .029), and the ROC analysis of baseline Ki-67 indicates the SEN, SPE, and AUC of 51.70%, 78.00%, and 0.638 (P = .050). When combined with size, CEUS features, stiffness, and Ki-67 of baseline, the ROC curve shows good performance with SEN, SPE, and AUC of 70.00%, 76.19%, 0.821 (P = .004). Incorporating the change of characteristics into multivariate regression analysis, the results demonstrate excellent performance (SEN 100.00%, SPE 95.24%, AUC 0.986, P = .000).

CONCLUSIONS

The change of the maximum size was correlated with MP score, which can provide reference to predict efficacy of NAC and evaluate residual lesions. When combining with elastography, CEUS, and Ki-67, better performance in predicting pathological response was shown.

Comparison of endocrine therapy and chemotherapy as different systemic treatment modes for metastatic luminal HER2-negative breast cancer patients —A retrospective study

Purpose

The purpose of this study was to evaluate endocrine therapy and chemotherapy for first-line, maintenance, and second-line treatment of hormone receptor-positive HER-2-negative metastatic breast cancer (HR+HER-2-MBC) and the relationship between different treatment options and survival.

Patients and methods

The patients included in this study were all diagnosed with metastatic breast cancer (MBC) at Shandong Cancer Hospital from January 2013 to June 2017. Of the 951 patients with MBC, 307 patients with HR+HER-2-MBC were included in the analysis. The progression-free survival (PFS) and overall survival (OS) of the various treatment modes were evaluated using Kaplan–Meier analysis and the log-rank test. Because of the imbalance in data, we used the synthetic minority oversampling technique (SMOTE) algorithm to oversample the data to increase the balanced amount of data.

Results

This retrospective study included 307 patients with HR+HER-2-MBC; 246 patients (80.13%) and 61 patients (19.87%) were treated with first-line chemotherapy and first-line endocrine therapy, respectively. First-line endocrine therapy was better than first-line chemotherapy in terms of PFS and OS. After adjusting for known prognostic factors, patients receiving first-line chemotherapy had poorer PFS and OS outcomes than patients receiving first-line endocrine therapy. In terms of maintenance treatment, the endocrine therapy-endocrine therapy maintenance mode achieved the best prognosis, followed by the chemotherapy-endocrine therapy maintenance mode and chemotherapy-chemotherapy maintenance mode, and the no-maintenance mode has resulted in the worst prognosis. In terms of first-line/second-line treatment, the endocrine therapy/endocrine therapy mode achieved the best prognosis, while the chemotherapy/chemotherapy mode resulted in the worst prognosis. The chemotherapy/endocrine therapy mode achieved a better prognosis than the endocrine therapy/chemotherapy mode. There were no significant differences in the KI-67 index (&lt;15%/15-30%/≥30%) among the patients receiving first-line treatment modes, maintenance treatment modes, and first-line/second-line treatment modes. There was no statistical evidence in this study to support that the KI-67 index affected survival. However, in the first-line/second-line model, after SMOTE, we could see that KI-67 ≥ 30% had a poor prognosis.

Conclusions

Different treatment modes for HR+HER-2-MBC were analyzed. Endocrine therapy achieved better PFS and OS outcomes than chemotherapy. Endocrine therapy should be the first choice for first-line, maintenance, and second-line treatment of HR+HER-2-MBC.

Treatment Response Prediction Using Ultrasound-Based Pre-, Post-Early, and Delta Radiomics in Neoadjuvant Chemotherapy in Breast Cancer

Objective

To develop and validate a radiomics nomogram based on pre-treatment, early treatment ultrasound (US) radiomics features combined with clinical characteristics for early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer.

Method

A total of 217 patients with histological results of breast cancer receiving four to eight cycles of NAC before surgery from January 2018 to December 2020 were enrolled. Patients from the study population were randomly separated into a training set (n = 152) and a validation set (n = 65) at a ratio of 7:3. A total of 788 radiomics features were extracted from each region of interest in the US image at pre-treatment baseline (radiomic signature, RS1), early treatment (after completion of two cycles of NAC, RS2) and delta radiomics (calculated between the pre-treatment and post-treatment features, Delta RS). The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The predictive nomogram was built based on the radiomics signature combined with clinicopathological risk factors. Discrimination, calibration, and prediction performance were further evaluated in the validation set.

Results

Of the 217 breast masses, 127 (58.5%) were responsive to NAC and 90 (41.5%) were non-responsive. Following feature selection, nine features in RS1, 11 features in RS2, and eight features in Delta RS remained. With multivariate analysis, the RS1, RS2, Delta RS, and Ki-67 expression were independently associated with breast NAC response. However, the performance of the Delta RS (AUC_{Delta RS} = 0.743) was not higher than RS1 (AUC_RS1 = 0.722, P_{Delta vs RS1} = 0.086) and RS2 (AUC_RS2 = 0.811, P_{Delta vs RS2 =}0.173) with the Delong test. The nomogram incorporating RS1, RS2, and Ki-67 expression showed better predictive ability for NAC response with an area under the curve (AUC) of 0.866 in validation cohorts than either the single RS1 (AUC 0.725) or RS2 (AUC 0.793) or Ki-67 (AUC 0.643).

Conclusion

The nomogram incorporating pre-treatment and early-treatment US radiomics features and Ki-67 expression showed good performance in terms of NAC response in breast cancer, thereby providing valuable information for individual treatment and timely adjustment of chemotherapy regimens.

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.

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study

PURPOSE

To non-invasively evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics.

METHODS

A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual- and population-level clustering. Handcrafted radiomics and deep learning-based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs.

RESULTS

Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429-0.727] vs. 0.559 [95% CI, 0.405-0.705], p-value: 0.920; deep RSs: 0.670 [95% CI, 0.516-0.802] vs. 0.551 [95% CI, 0.397-0.698], p-value: 0.776). The fusion RSs, combining handcrafted and deep learning-based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714-0.900) and 0.792 (95% CI, 0.647-0.897) for the internal and external validation cohorts, respectively.

CONCLUSIONS

The subregional radiomics approach can accurately predict the Ki-67 level based on DBT data; thus, it may be used as a potential non-invasive tool for preoperative treatment planning in BC.

Development of a novel nomogram-based online tool to predict axillary status after neoadjuvant chemotherapy in cN+ breast cancer: A multicentre study on 1,950 patients

BACKGROUND

Type of axillary surgery in breast cancer (BC) patients who convert from cN + to ycN0 after neoadjuvant chemotherapy (NAC) is still debated. The aim of the present study was to develop and validate a preoperative predictive nomogram to select those patients with a low risk of residual axillary disease after NAC, in whom axillary surgery could be minimized.

PATIENTS AND METHODS

1950 clinically node-positive BC patients from 11 Breast Units, treated by NAC and subsequent surgery, were included from 2005 to 2020. Patients were divided in two groups: those who achieved nodal pCR vs. those with residual nodal disease after NAC. The cohort was divided into training and validation set with a geographic separation criterion. The outcome was to identify independent predictors of axillary pathologic complete response (pCR).

RESULTS

Independent predictive factors associated to nodal pCR were axillary clinical complete response (cCR) after NAC (OR 3.11, p < 0.0001), ER-/HER2+ (OR 3.26, p < 0.0001) or ER+/HER2+ (OR 2.26, p = 0.0002) or ER-/HER2- (OR 1.89, p = 0.009) BC, breast cCR (OR 2.48, p < 0.0001), Ki67 > 14% (OR 0.52, p = 0.0005), and tumor grading G2 (OR 0.35, p = 0.002) or G3 (OR 0.29, p = 0.0003). The nomogram showed a sensitivity of 71% and a specificity of 73% (AUC 0.77, 95%CI 0.75-0.80). After external validation the accuracy of the nomogram was confirmed.

CONCLUSION

The accuracy makes this freely-available, nomogram-based online tool useful to predict nodal pCR after NAC, translating the concept of tailored axillary surgery also in this setting of patients.

Comparing Biomarkers for Predicting Pathological Responses to Neoadjuvant Therapy in HER2-Positive Breast Cancer: A Systematic Review and Meta-Analysis

INTRODUCTION

The predictive strength and accuracy of some biomarkers for the pathological complete response (pCR) to neoadjuvant therapy for HER2-positive breast cancer remain unclear. This study aimed to compare the accuracy of the HER2-enriched subtype and the presence of PIK3CA mutations, namely, TILs, HRs, and Ki-67, in predicting the pCR to HER2-positive breast cancer therapy.

METHODS

We screened studies that included pCR predicted by one of the following biomarkers: the HER2-enriched subtype and the presence of PIK3CA mutations, TILs, HRs, or Ki-67. We then calculated the pooled sensitivity, specificity, positive and negative predictive values (PPVs and NPVs, respectively), and positive and negative likelihood ratios (LRs). Summary receiver operating characteristic (SROC) curves and areas under the curve (AUCs) were used to estimate the diagnostic accuracy.

RESULTS

The pooled estimates of sensitivity and specificity for the HER2-enriched subtype and the presence of PIK3CA mutations, namely, TILs, HRs, and Ki-67, were 0.66 and 0.62, 0.85 and 0.27, 0.49 and 0.61, 0.54 and 0.64, and 0.68 and 0.51, respectively. The AUC of the HER2-enriched subtype was significantly higher (0.71) than those for the presence of TILs (0.59, p = 0.003), HRs (0.65, p = 0.003), and Ki-67 (0.62, p = 0.005). The AUC of the HER2-enriched subtype had a tendency to be higher than that of the presence of PIK3CA mutations (0.58, p = 0.220). Moreover, it had relatively high PPV (0.58) and LR+ (1.77), similar NPV (0.73), and low LR- (0.54) compared with the other four biomarkers.

CONCLUSIONS

The HER2-enriched subtype has a moderate breast cancer diagnostic accuracy, which is better than those of the presence of PIK3CA mutations, TILs, HRs, and Ki-67.

Ki-67 as a Prognostic Biomarker in Invasive Breast Cancer

The advent of molecular medicine has transformed breast cancer management. Breast cancer is now recognised as a heterogenous disease with varied morphology, molecular features, tumour behaviour, and response to therapeutic strategies. These parameters are underpinned by a combination of genomic and immunohistochemical tumour factors, with estrogen receptor (ER) status, progesterone receptor (PgR) status, human epidermal growth factor receptor-2 (HER2) status, Ki-67 proliferation indices, and multigene panels all playing a contributive role in the substratification, prognostication and personalization of treatment modalities for each case. The expression of Ki-67 is strongly linked to tumour cell proliferation and growth and is routinely evaluated as a proliferation marker. This review will discuss the clinical utility, current pitfalls, and promising strategies to augment Ki-67 proliferation indices in future breast oncology.

CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study

Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning-based prediction models were then built from this and compared each other. The final radiomic signature was built using logistic regression in the primary cohort, and then tested in a validation cohort. Finally, we compared the efficacy of the radiomic signature to the clinical model and the radiomic-clinical nomogram. Among the 125 patients, 89 were classified as having PD-L1 positive expression. However, there was no significant difference in PD-L1 expression levels determined by clinical characteristics (P = 0.109-0.955). Upon selecting 9 radiomic features, we found that the logistic regression-based prediction model performed the best (AUC = 0.96, P < 0.001). In the external cohort, our radiomic signature showed an AUC of 0.85, which outperformed both the clinical model (AUC = 0.38, P < 0.001) and the radiomics-nomogram model (AUC = 0.61, P < 0.001). Our CT-based hand-crafted radiomic signature model can effectively predict PD-L1 expression levels, providing a noninvasive means of better understanding PD-L1 expression in patients with NSCLC.

A new tool for technical standardization of the Ki67 immunohistochemical assay

Ki67, a nuclear proliferation-related protein, is heavily used in anatomic pathology but has not become a companion diagnostic or a standard-of-care biomarker due to analytic variability in both assay protocols and interpretation. The International Ki67 Working Group in breast cancer has published and has ongoing efforts in the standardization of the interpretation of Ki67, but they have not yet assessed technical issues of assay production representing multiple sources of variation, including antibody clones, antibody formats, staining platforms, and operators. The goal of this work is to address these issues with a new standardization tool. We have developed a cell line microarray system in which mixes of human Karpas 299 or Jurkat cells (Ki67⁺) with Sf9 (Spodoptera frugiperda) (Ki67^-) cells are present in incremental standardized ratios. To validate the tool, six different antibodies, including both ready-to-use and concentrate formats from six vendors, were used to measure Ki67 proliferation indices using IHC protocols for manual (bench-top) and automated platforms. The assays were performed by three different laboratories at Yale and analyzed using two image analysis software packages, including QuPath and Visiopharm. Results showed statistically significant differences in Ki67 reactivity between each antibody clone. However, subsets of Ki67 assays using three clones performed in three different labs show no significant differences. This work shows the need for analytic standardization of the Ki67 assay and provides a new tool to do so. We show here how a cell line standardization system can be used to normalize the staining variability in proliferation indices between different antibody clones in a triple negative breast cancer cohort. We believe that this cell line standardization array has the potential to improve reproducibility among Ki67 assays and laboratories, which is critical for establishing Ki67 as a standard-of-care assay.

A panel of tumor biomarkers to predict complete pathological response to neo-adjuvant treatment in Locally Advanced Rectal Cancer

Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients is related to a favorable prognosis. The identification of early biomarkers predictive of pathological complete response would help optimize the multimodality management of the patients. A panel of 11 tumor-related proteins was investigated by immunohistochemistry in the pretreatment biopsy of a group of locally advanced rectal cancer patients to identify early biomarkers of pathological complete response to neoadjuvant chemoradiotherapy. A mono-institutional retrospective cohort of 95 stage II/III locally advanced rectal cancer patients treated with neoadjuvant chemoradiotherapy and surgery was selected based on clinical–pathological characteristics and the availability of a pretreatment tumor biopsy. Eleven selected protein marker expression (MLH1, GLUT1, Ki67, CA-IX, CXCR4, COX2, CXCL12, HIF1α, VEGF, CD44, and RAD51) was investigated. The optimal cutoff values were calculated by receiver operating characteristic curve analysis. Classification and regression tree analysis was performed to investigate the biomarker interaction. Patients presenting either Ki-67 or HIF1α or RAD51 below the cutoff value, or CXCR4 or COX2 above the cutoff value, were more likely to get a pathological complete response. Classification and regression tree analysis identified three groups of patients resulting from the combination of Ki-67 and CXCR4 expression. Patients with high expression of Ki-67 had the lowest chance to get a pathological complete response (18%), as compared to patients with low expression of both Ki-67 and CXCR4 (29%), and patients with low Ki-67 and high CXCR4 expression (70%). Pretreatment Ki-67, CXCR4, COX2, HIF1α, and RAD51 in tumor biopsies are associated with pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. A combined evaluation of Ki-67 and CXCR4 would increase their predictive potential. If validated, their optimal cutoff could be used to select patients for a tailored multimodality treatment.

HER2 Testing Characteristics Can Predict Residual Cancer Burden following Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer

Objectives

The response to HER2-targeted neoadjuvant chemotherapy (NAC) in HER2-positive (+) breast cancer can be quantified using residual cancer burden (RCB) pathologic evaluation to predict relapse free/overall survival. However, more information is needed to characterize the relationship between patterns of HER2 testing results and response to NAC. We evaluated clinicopathologic characteristics associated with RCB categories in HER2+ patients who underwent HER2-directed NAC.

Methods

A retrospective chart review was conducted with Stage I-III HER2+ breast cancer cases following NAC and surgical resection. HER2 immunohistochemistry (IHC) staining and fluorescence in situ hybridization (FISH), histologic/clinical characteristics, hormone receptor status, and RCB scores (RCB-0, RCB-I, RCB-II, and RCB-III) were evaluated.

Results

64/151 (42.4%) patients with HER2+ disease had pathologic complete response (pCR). Tumors with suboptimal response (RCB-II and RCB-III) were more likely to demonstrate less than 100% HER2 IHC 3+ staining (p < 0.0001), lower HER2 FISH copies (p < 0.0001), and lower HER2/CEP17 ratios (p = 0.0015) compared to RCB-I and RCB-II responses. Estrogen receptor classification using ≥10% versus ≥1% staining showed greater association with higher RCB categories.

Conclusions

HER2+ characteristics show differing response to therapy despite all being categorized as positive; tumors with less than 100% IHC 3+ staining, lower HER2 FISH copies, and lower HER2/CEP17 ratios resulted in higher RCB scores.

Neoadjuvant Chemotherapy of Triple-Negative Breast Cancer: Evaluation of Early Clinical Response, Pathological Complete Response Rates, and Addition of Platinum Salts Benefit Based on Real-World Evidence

Simple Summary

Neoadjuvant chemotherapy (NACT) is the standard treatment for early-stage triple-negative breast cancer (TNBC). Achieving pathological complete response (pCR) is considered an essential prognostic factor with favorable long-term outcomes. The administration of NACT regimens with platinum salts is associated with a higher pCR rate. However, with unclear treatment guidelines and at the expense of a higher incidence of adverse events. Identifying patients and circumstances in which the benefits of platinum NACT outweigh inconveniences is still an ongoing challenge. Considering early clinical response (ECR) after the initial standard NACT cycles together with other suitable predictors could be useful to decide about the administration of platinum salts in clinical practice. The results of this large single institutional retrospective study of consecutive patients showed the significant role of adding platinum salts in older patients with high-proliferative early responded tumors and persisted lymph nodes involvement regardless of BRCA1/2 status.

Abstract

Pathological complete response (pCR) achievement is undoubtedly the essential goal of neoadjuvant therapy for breast cancer, directly affecting survival endpoints. This retrospective study of 237 triple-negative breast cancer (TNBC) patients with a median follow-up of 36 months evaluated the role of adding platinum salts into standard neoadjuvant chemotherapy (NACT). After the initial four standard NACT cycles, early clinical response (ECR) was assessed and used to identify tumors and patients generally sensitive to NACT. BRCA1/2 mutation, smaller unifocal tumors, and Ki-67 ≥ 65% were independent predictors of ECR. The total pCR rate was 41%, the achievement of pCR was strongly associated with ECR (OR = 15.1, p < 0.001). According to multivariable analysis, the significant benefit of platinum NACT was observed in early responders ≥45 years, Ki-67 ≥ 65% and persisted lymph node involvement regardless of BRCA1/2 status. Early responders with pCR had a longer time to death (HR = 0.28, p < 0.001) and relapse (HR = 0.26, p < 0.001). The pCR was achieved in only 7% of non-responders. However, platinum salts favored non-responders’ survival outcomes without statistical significance. Toxicity was significantly often observed in patients with platinum NACT (p = 0.003) but not for grade 3/4 (p = 0.155). These results based on real-world evidence point to the usability of ECR in NACT management, especially focusing on the benefit of platinum salts.

Challenges in Adjuvant Therapy for Premenopausal Women Diagnosed With Luminal Breast Cancers

More than 90% of women with newly diagnosed breast cancer present with stage I to III disease and, with optimal multidisciplinary therapy, are likely to survive their disease. Of these patients, 70% are hormone receptor-positive and candidates for adjuvant endocrine therapy. The adoption of cumulatively better adjuvant treatments contributed to improved outcomes in patients with hormone receptor-positive, early-stage breast cancer. Premenopausal women with hormone receptor-positive breast cancer often present with complex disease and have inferior survival outcomes compared with their postmenopausal counterparts. Risk stratification strategies, including classic clinicopathologic features and newer gene expression assays, can assist in treatment decisions, including adjuvant chemotherapy use and type or duration of endocrine therapy. Gene expression assays may help identify patients who can safely forgo chemotherapy, although to a lesser extent among premenopausal patients, in whom they may play a role only in node-negative disease. Patients at lower risk of recurrence can be adequately treated with tamoxifen alone, whereas higher-risk patients benefit from ovarian function suppression with tamoxifen or an aromatase inhibitor. The role of adding newer therapies such as CDK4/6 inhibitors to adjuvant endocrine therapy is not yet clear. Breast cancer treatments are associated with several side effects, with major impact on patients' quality of life and treatment adherence, particularly in premenopausal women for whom these side effects may be more prominent as the result of the abrupt decrease in estrogen concentrations. Personalized management of treatment side effects, addressing patients' concerns, and health promotion should be an integral part of the care of premenopausal women diagnosed with luminal breast cancers.

Preoperative Prediction of Ki-67 Status in Breast Cancer with Multiparametric MRI Using Transfer Learning

RATIONALE AND OBJECTIVES

Ki-67 is one of the most important biomarkers of breast cancer traditionally measured invasively via immunohistochemistry. In this study, deep learning based radiomics models were established for preoperative prediction of Ki-67 status using multiparametric magnetic resonance imaging (mp-MRI).

MATERIALS AND METHODS

Total of 328 eligible patients were retrospectively reviewed [training dataset (n = 230) and a temporal validation dataset (n = 98)]. Deep learning imaging features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (T1+C). Transfer learning techniques constructed four feature sets based on the individual three MR sequences and their combination (i.e., mp-MRI). Multilayer perceptron classifiers were trained for final prediction of Ki-67 status. Mann-Whitney U test compared the predictive performance of individual models.

RESULTS

The area under curve (AUC) of models based on T2WI,T1+C,DWI and mp-MRI were 0.727, 0.873, 0.674, and 0.888 in the training dataset, respectively, and 0.706, 0.829, 0.643, and 0.875 in the validation dataset, respectively. The predictive performance of mp-MRI classification model in the AUC value was significantly better than that of the individual sequence model (all p< 0.01).

CONCLUSION

In clinical practice, a noninvasive approach to improve the performance of radiomics in preoperative prediction of Ki-67 status can be provided by extracting breast cancer specific structural and functional features from mp-MRI images obtained from conventional scanning sequences using the advanced deep learning methods. This could further personalize medicine and computer aided diagnosis.

Omission of Chemotherapy in HR+/HER2- Early Invasive Breast Cancer Based on Combined 6-IHC Score?

PURPOSE

Current methods of judging whether HR+/HER2- breast cancer (BC) require adjuvant therapy, such as Ki67 and multigene prognostic tests, cannot balance accuracy with the price most patients can afford.

METHODS

A retrospective analysis of 330 HR+/HER2- BC patients was conducted. Six BC-related genes (Cathepsin L2, MMP11, CyclinB1, Aurora A, Survivin, and Ki67) were screened using univariate and multivariate COX regression, and correlate clinical follow-up with immunohistochemical expression (designated as 6-IHC). All the included patients were divided randomly at a 7:3 ratio into training and testing cohorts. The cutoff prognosis index (PI) of 6-IHC was determined by multivariate Cox risk regression analysis after calculating the PI of each patient in training cohort and confirmed in testing cohort. Kaplan-Meier (KM) method was used to analyze Disease-free survival (DFS) and overall survival (OS). Six-IHC score and other factors associated with survival benefit of adjuvant chemotherapy were compared with Ki67 index.

RESULTS

The receiver operating characteristic curve analysis showed that the patients can be divided into 6-IHC score "High" and "Low" risk groups. The 8-year DFS and OS of the KM curves showed that chemotherapy did not significantly improve the DFS in the 6-IHC score "Low" risk group (P= 0.830), but significantly improved the DFS in the 6-IHC score "High" risk group (P = 0.012).

CONCLUSIONS

Combined 6-IHC score could be a reliable tool in predicting cancer-specific recurrences and survival in HR+/HER2-breast cancer patients, with additional advantages over using immunohistochemical expression of Ki67.

Analysis of CK5/6 and EGFR and Its Effect on Prognosis of Triple Negative Breast Cancer

Background

Triple-negative breast cancer (TNBC) is considered to be higher grade, more aggressive and have a poorer prognosis than other types of breast cancer. Discover biomarkers in TNBC for risk stratification and treatments that improve prognosis are in dire need.

Methods

Clinical data of 195 patients with triple negative breast cancer confirmed by pathological examination and received neoadjuvant chemotherapy (NAC) were collected. The expression levels of EGFR and CK5/6 were measured before and after NAC, and the relationship between EGFR and CK5/6 expression and its effect on prognosis of chemotherapy was analyzed.

Results

The overall response rate (ORR) was 86.2% and the pathological complete remission rate (pCR) was 29.2%. Univariate and multivariate logistic regression analysis showed that cT (clinical Tumor stages) stage was an independent factor affecting chemotherapy outcome. Multivariate Cox regression analysis showed pCR, chemotherapy effect, ypT, ypN, histological grades, and post- NAC expression of CK5/6 significantly affected prognosis. The prognosis of CK5/6-positive patients after NAC was worse than that of CK5/6-negative patients (p=0.036). Changes in CK5/6 and EGFR expression did not significantly affect the effect of chemotherapy, but changes from positive to negative expression of these two markers are associated with a tendency to improve prognosis.

Conclusion

For late-stage triple negative breast cancer patients receiving NAC, patients who achieved pCR had a better prognosis than those with non- pCR. Patients with the change in expression of EGFR and CK5/6 from positive to negative after neoadjuvant chemotherapy predicted a better prognosis than the change from negative to positive group.

A comparative study between Ki67 positive versus Ki67 negative females with breast cancer: Cross sectional study

Introduction

The prognosis of breast cancer depends on several clinical and pathological parameters most importantly the clinical stage, other factors predicting the outcome are hormone receptors like estrogen and progesterone receptors. Expression of Ki67 also have been shown to affect the outcome.

Patients and methods

This retrospective study included 278 female patients diagnosed and operated for breast cancer. Patients were grouped into 2 groups according to the expression of Ki67 to those with positive and those with negative expression. Both groups were compared for differences.

Results

The mean age was 48.61 years and the right breast was the commonest affected side, the mean tumor size was 34 mm, 70% had axillary LN involvement, 50% had intermediate tumor grade, and 85.6% had no recurrence. Most patients had stage IIA, IIB, and IIIA, 67.6% had positive expression of Ki67 and had a significant correlation with the tumor grade, tumor necrosis, and ER expression (P values 0.001, 0.047, and 0.002) respectively, while the correlation was negative with recurrence, axillary LN involvement, TNM stage, site of the tumor, age, tumor size, PR and HER-2 receptor (P values 0.476, 0.971, 0.509, 0.405, 0.122, 0.994, 0.892, and 0.418) respectively.

Conclusion

Most patients with breast cancer have positive expression of Ki67 which has a positive correlation with tumor grade, the presense of necrosis inside the tumor and estrogene receptor status. This marker is directly related with higher degrees of tumor agressiveness and may be useful in modulating different treatment modalities.

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Breast cancer patients have great variability in the biological behavior and cancer aggressiveness.

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Ki67 expression have been shown to affect the outcome of breast cancer patients.

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The gene coding for Ki67 is located on the long arm of chromosome number 10.

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Ki67 is directly related with higher degrees of tumor agressiveness.

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Ki67 may be useful in modulating different treatment modalities.

Breast cancer, screening and diagnostic tools: All you need to know

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

Management of hormone receptor-positive, HER2-negative early breast cancer

The majority of breast cancers are diagnosed at an early stage and are hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative. Significant advances have been made in the management of early stage HR-positive, HER2-negative breast cancer, resulting in improved survival outcomes. In this review, we discuss important factors to consider in the management of this disease. In particular, we discuss the role of adjuvant endocrine therapy, specific endocrine therapy agents, the duration of adjuvant endocrine therapy, treatment-related side effects, and the role of genomic assays and other biomarkers when considering treatment recommendations for individuals with HR-positive, HER2-negative early breast cancer. Finally, we address emerging data to individualize therapeutic decision-making and provide future considerations.

Sequential immunohistochemistry and virtual image reconstruction using a single slide for quantitative KI67 measurement in breast cancer

Objective

Ki67 is a prognostic and predictive marker in breast cancer (BC). However, manual scoring (MS) by visual assessment suffers from high inter-observer variability which limits its clinical use. Here, we developed a new digital image analysis (DIA) workflow, named KiQuant for automated scoring of Ki67 and investigated its equivalence with standard pathologist's assessment.

Methods

Sequential immunohistochemistry of Ki67 and cytokeratin, for precise tumor cell recognition, were performed in the same section of 5 tissue microarrays containing 329 tumor cores from different breast cancer subtypes. Slides were digitalized and subjected to DIA and MS for Ki67 assessment. The intraclass correlation coefficient (ICC) and Bland-Altman plot were used to evaluate inter-observer reproducibility. The Kaplan-Meier analysis was used to determine the prognostic potential.

Results

KiQuant showed an excellent correlation with MS (ICC:0.905,95%CI:0.878–0.926) with satisfactory inter-run (ICC:0.917,95%CI:0.884–0.942) and inter-antibody reproducibilities (ICC:0.886,95%CI:0.820–0.929). The distance between KiQuant and MS increased with the magnitude of Ki67 measurement and positively correlated with analyzed tumor area and breast cancer subtype. Agreement rates between KiQuant and MS within the clinically relevant 14% and 30% cut-off points ranged from 33% to 44% with modest interobserver reproducibility below the 20% cut-off (0.606, 95%CI:0.467–0.727). High Ki67 by KiQuant correlated with worse outcome in all BC and in the luminal subtype (P = 0.028 and P = 0.043, respectively). For MS, the association with survival was significant only in 1 out of 3 observers.

Conclusions

KiQuant represents an easy and accurate methodology for Ki67 measurement providing a step toward utilizing Ki67 in the clinical setting.

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Automated Ki67 scoring workflow improved reproducibility.

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Sequential immunohistochemistry in the same section for precise cell recognition.

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Use of a tumor mask for automatic tumor region selection.

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Outperform pathologist-based Ki67 scoring in prognostic prediction.

Artificial intelligence as the next step towards precision pathology

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy requires accurate biomarker assessment. The appearance of digital image analysis holds promise to improve both the volume and precision of histomorphological evaluation. Recently, machine learning, and particularly deep learning, has enabled rapid advances in computational pathology. The integration of machine learning into routine care will be a milestone for the healthcare sector in the next decade, and histopathology is right at the centre of this revolution. Examples of potential high-value machine learning applications include both model-based assessment of routine diagnostic features in pathology, and the ability to extract and identify novel features that provide insights into a disease. Recent groundbreaking results have demonstrated that applications of machine learning methods in pathology significantly improves metastases detection in lymph nodes, Ki67 scoring in breast cancer, Gleason grading in prostate cancer and tumour-infiltrating lymphocyte (TIL) scoring in melanoma. Furthermore, deep learning models have also been demonstrated to be able to predict status of some molecular markers in lung, prostate, gastric and colorectal cancer based on standard HE slides. Moreover, prognostic (survival outcomes) deep neural network models based on digitized HE slides have been demonstrated in several diseases, including lung cancer, melanoma and glioma. In this review, we aim to present and summarize the latest developments in digital image analysis and in the application of artificial intelligence in diagnostic pathology.

Advances in quantitative immunohistochemistry and their contribution to breast cancer

Introduction: Automated image analysis provides an objective, quantitative, and reproducible method of measurement of biomarkers. Image quantification is particularly well suited for the analysis of tissue microarrays which has played a major pivotal role in the rapid assessment of molecular biomarkers. Data acquired from grinding up bulk tissue samples miss spatial information regarding cellular localization; therefore, methods that allow for spatial cell phenotyping at high resolution have proven to be valuable in many biomarker discovery assays. Here, we focus our attention on breast cancer as an example of a tumor type that has benefited from quantitative biomarker studies using tissue microarray format.Areas covered: The history of immunofluorescence and immunohistochemistry and the current status of these techniques, including multiplexing technologies (spectral and non-spectral) and image analysis software will be addressed. Finally, we will turn our attention to studies that have provided proof-of-principle evidence that have been impacted from the use of these techniques.Expert opinion: Assessment of prognostic and predictive biomarkers on tissue sections and TMA using Quantitative immunohistochemistry is an important advancement in the investigation of biologic markers. The challenges in standardization of quantitative technologies for accurate assessment are required for adoption into routine clinical practice.

Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics

OBJECTIVE

Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic analysis.

METHODS

We jointly predicted Ki-67 and tumor grade with a multitask learning framework by separately utilizing radiomics from tumor MRI series. Additionally, we showed how multitask learning models (MTLs) could be extended to combined radiomics from the MRI series for a better prediction based on the assumption that features from different sources of images share common patterns while providing complementary information. Tumor radiomic analysis was performed with morphological, statistical and textural features extracted on the DWI and dynamic contrast-enhanced MRI (DCE-MRI) series of the precontrast and subtraction images, respectively.

RESULTS

Joint prediction of Ki-67 status and tumor grade on MR images using the MTL achieved performance improvements over that of single-task-based predictive models. Similarly, for the prediction tasks of Ki-67 and tumor grade, the MTL for combined precontrast and apparent diffusion coefficient (ADC) images achieved AUCs of 0.811 and 0.816, which were significantly better than that of the single-task- based model with p values of 0.005 and 0.017, respectively.

CONCLUSION

Mapping MRI radiomics to two related clinical indicators improves prediction performance for both Ki-67 expression level and tumor grade.

SIGNIFICANCE

Joint prediction of indicators by multitask learning that combines correlations of MRI radiomics is important for optimal tumor therapy and treatment because clinical decisions are made by integrating multiple clinical indicators.

Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study

Ki67 expression has been a valuable prognostic variable in breast cancer, but has not seen broad adoption due to lack of standardization between institutions. Automation could represent a solution. Here we investigate the reproducibility of Ki67 measurement between three image analysis platforms with supervised classifiers performed by the same operator, by multiple operators, and finally we compare their accuracy in prognostic potential. Two breast cancer patient cohorts were used for this study. The standardization was done with the 30 cases of ER+ breast cancer that were used in phase 3 of International Ki67 in Breast Cancer Working Group initiatives where blocks were centrally cut and stained for Ki67. The outcome cohort was from 149 breast cancer cases from the Yale Pathology archives. A tissue microarray was built from representative tissue blocks with median follow-up of 120 months. The Mib-1 antibody (Dako) was used to detect Ki67 (dilution 1:100). HALO (IndicaLab), QuantCenter (3DHistech), and QuPath (open source software) digital image analysis (DIA) platforms were used to evaluate Ki67 expression. Intraclass correlation coefficient (ICC) was used to measure reproducibility. Between-DIA platform reproducibility was excellent (ICC: 0.933, CI: 0.879-0.966). Excellent reproducibility was found between all DIA platforms and the reference standard Ki67 values of Spectrum Webscope (QuPath-Spectrum Webscope ICC: 0.970, CI: 0.936-0.986; HALO-Spectrum Webscope ICC: 0.968, CI: 0.933-0.985; QuantCenter-Spectrum Webscope ICC: 0.964, CI: 0.919-0.983). All platforms showed excellent intra-DIA reproducibility (QuPath ICC: 0.992, CI: 0.986-0.996; HALO ICC: 0.972, CI: 0.924-0.988; QuantCenter ICC: 0.978, CI: 0.932-0.991). Comparing each DIA against outcome, the hazard ratios were similar. The inter-operator reproducibility was particularly high (ICC: 0.962-0.995). Our results showed outstanding reproducibility both within and between-DIA platforms, including one freely available DIA platform (QuPath). We also found the platforms essentially indistinguishable with respect to prediction of breast cancer patient outcome. Results justify multi-institutional DIA studies to assess clinical utility.

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Time series analysis of neoadjuvant chemotherapy and bevacizumab-treated breast carcinomas reveals a systemic shift in genomic aberrations

Background

Chemotherapeutic agents such as anthracyclines and taxanes are commonly used in the neoadjuvant setting. Bevacizumab is an antibody which binds to vascular endothelial growth factor A (VEGFA) and inhibits its receptor interaction, thus obstructing the formation of new blood vessels.

Methods

A phase II randomized clinical trial of 123 patients with Her2-negative breast cancer was conducted, with patients treated with neoadjuvant chemotherapy (fluorouracil (5FU)/epirubicin/cyclophosphamide (FEC) and taxane), with or without bevacizumab. Serial biopsies were obtained at time of diagnosis, after 12 weeks of treatment with FEC ± bevacizumab, and after 25 weeks of treatment with taxane ± bevacizumab. A time course study was designed to investigate the genomic landscape at the three time points when tumor DNA alterations, tumor percentage, genomic instability, and tumor clonality were assessed. Substantial differences were observed with some tumors changing mainly between diagnosis and at 12 weeks, others between 12 and 25 weeks, and still others changing in both time periods.

Results

In both treatment arms, good responders (GR) and non-responders (NR) displayed significant difference in genomic instability index (GII) at time of diagnosis. In the combination arm, copy number alterations at 25 loci at the time of diagnosis were significantly different between the GR and NR. An inverse aberration pattern was also observed between the two extreme response groups at 6p22-p12 for patients in the combination arm. Signs of subclonal reduction were observed, with some aberrations disappearing and others being retained during treatment. Increase in subclonal amplification was observed at 6p21.1, a locus which contains the VEGFA gene for the protein which are targeted by the study drug bevacizumab. Of the 13 pre-treatment samples that had a gain at VEGFA, 12 were responders. Significant decrease of frequency of subclones carrying gains at 17q21.32-q22 was observed at 12 weeks, with the peak occurring at TMEM100, an ALK1 receptor signaling-dependent gene essential for vasculogenesis. This implies that cells bearing amplifications of VEGFA and TMEM100 are particularly sensitive to this treatment regime.

Conclusions

Taken together, these results suggest that heterogeneity and subclonal architecture influence the response to targeted treatment in combination with chemotherapy, with possible implications for clinical decision-making and monitoring of treatment efficacy.

Trial registration

NCT00773695. Registered 15 October 2008

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0601-y) contains supplementary material, which is available to authorized users.

New Robust and Reproducible Stereological IHC Ki67 Breast Cancer Proliferative Assessment to Replace Traditional Biased Labeling Index

There is a pressing need for an objective decision tool to guide therapy for breast cancer patients that are estrogen receptor positive and HER2/neu negative. This subset of patients contains a mixture of luminal A and B tumors with good and bad outcomes, respectively. The 2 main current tools are on the basis of immunohistochemistry (IHC) or gene expression, both of which rely on the expression of distinct molecular groups that reflect hormone receptors, HER2/neu status, and most importantly, proliferation. Despite the success of a proprietary molecular test, definitive superiority of any method has not yet been demonstrated. Ki67 IHC scoring assessments have been shown to be poorly reproducible, whereas molecular testing is costly with a longer turnaround time. This work proposes an objective Ki67 index using image analysis that addresses the existing methodological issues of Ki67 quantitation using IHC on paraffin-embedded tissue. Intrinsic bias related to numerical assessment performed on IHC is discussed as well as the sampling issue related to the “peel effect” of tiny objects within a thin section. A new nonbiased stereological parameter (V_V) based on the Cavalieri method is suggested for use on a double-stained Ki67/cytokeratin IHC slide. The assessment is performed with open-source ImageJ software with interobserver concordance between 3 pathologists being high at 93.5%. Furthermore, V_V was found to be a superior method to predict an outcome in a small subset of breast cancer patients when compared with other image analysis methods being used to determine the Ki67 labeling index. Calibration methodology is also discussed to further this IHC approach.

Correlation between DCE-MRI radiomics features and Ki-67 expression in invasive breast cancer

The aim of the present study was to investigate the association between Ki-67 expression and radiomics features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with invasive breast cancer. A total of 53 cases with low-Ki-67 expression (Ki-67 proliferation index <14%) and 106 cases with high-Ki-67 expression (Ki-67 proliferation index >14%) were investigated. A systematic approach was applied that focused on the automated segmentation of lesions and extraction of radiomics features. For each lesion 5 morphology, 4 gray-scale histogram and 6 texture features were obtained, and statistical analyzes were performed to assess the differences in these features between the low- and high-Ki-67 expressions. One morphology metric (area), 3 gray-scale histogram indexes (standard deviation, skewness and kurtosis) and 3 texture features (contrast, homogeneity and inverse differential moment) demonstrated a significant difference (P<0.05), with low-Ki-67 expression lesions tending to be smaller, clearer and heterogeneous when compared with the high-Ki-67 expressed cases. These results may provide a noninvasive means to better understand the proliferation of breast cancer.

Breast cancer Ki67 expression prediction by DCE-MRI radiomics features

AIM

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer.

MATERIALS AND METHODS

This institutional review board-approved retrospective study comprised 377 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low-versus high- Ki67 expression was performed. A set of 56 quantitative radiomics features, including morphological, greyscale statistic, and texture features, were extracted from the segmented lesion area. Three machine learning classification methods, including naive Bayes, k-nearest neighbour and support vector machine, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULES

The model that used naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Three most predictive features, i.e., contrast, entropy and line likeness, were selected by the LASSO method and showed a statistical significance (p<0.05) in the classification.

CONCLUSION

The present study showed that quantitative radiomics imaging features of breast tumour extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

An assessment of neuronal calcium sensor-1 and response to neoadjuvant chemotherapy in breast cancer patients

Neuronal calcium sensor-1 (NCS-1) has been identified as a binding partner of the taxane, paclitaxel. Our previous study showed that overexpression of NCS-1 increased the efficacy of paclitaxel in vitro, but was associated with poor clinical outcome. Here, we determine if NCS-1 expression is associated with pathological complete response (pCR) to taxane-based neoadjuvant chemotherapy in 105 pre-treatment breast cancer biopsies. Elevated expression of NCS-1 was found to be positively associated with pCR. These results suggest that NCS-1 may be a predictive biomarker for response to taxane-based neoadjuvant chemotherapy in breast cancer.

Spotlight on the utility of the Oncotype DX® breast cancer assay

The Oncotype DX^® assay was developed to address the need for optimizing the selection of adjuvant systemic therapy for patients with estrogen receptor (ER)-positive, lymph node-negative breast cancer. It has ushered in the era of genomic-based personalized cancer care for ER-positive primary breast cancer and is now widely utilized in various parts of the world. Together with several other genomic assays, Oncotype DX has been incorporated into clinical practice guidelines on biomarker use to guide treatment decisions. The Oncotype DX result is presented as the recurrence score which is a continuous score that predicts the risk of distant disease recurrence. The assay, which provides information on clinicopathological factors, has been validated for use in the prognostication and prediction of degree of adjuvant chemotherapy benefit in both lymph node-positive and lymph node-negative early breast cancers. Clinical studies have consistently shown that the Oncotype DX has a significant impact on decision making in adjuvant therapy recommendations and appears to be cost-effective in diverse health care settings. In this article, we provide an overview of the validation and clinical impact studies for the Oncotype DX assay. We also discuss its potential use in the neoadjuvant setting, as well as the more recent prospective validation trials, and the economic and utility implications of studies that use a lower cutoff score to define low-risk disease.

Aberrant Ki-67 expression through 3'UTR alternative polyadenylation in breast cancers

Ki‐67 (MKI67) is a marker of cellular proliferation of cancer. Here, we show that Ki‐67 is post‐transcriptionally regulated through alternative polyadenylation (APA) and microRNAs in breast cancer. We show that shortening of the Ki‐67 3′UTR results in the loss of the binding sites for the suppressive miRNAs and thus renders the transcript with a shortened 3′UTR insusceptible to miRNA‐mediated suppression. This APA‐mediated shortening of the Ki‐67 3′UTR contributes to increased mRNA stability and enhanced translational efficiency. In summary, our results not only highlight the post‐transcriptional regulation of Ki‐67 involving APA and microRNAs but also suggest that Ki‐67 3′UTR disruption could serve as a molecular marker in breast cancer.

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer

Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.

Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer

Digital image analysis (DIA) is becoming central to the quantitative evaluation of tissue biomarkers for discovery, diagnosis and therapeutic selection for the delivery of precision medicine. In this study, automated DIA using a new purpose-built software platform (QuPath) is applied to a cohort of 293 breast cancer patients to score five biomarkers in tissue microarrays (TMAs): ER, PR, HER2, Ki67 and p53. This software is able to measure IHC expression following fully automated tumor recognition in the same immunohistochemical (IHC)-stained tissue section, as part of a rapid workflow to ensure objectivity and accelerate biomarker analysis. The digital scores produced by QuPath were compared with manual scores by a pathologist and shown to have a good level of concordance in all cases (Cohen's κ>0.6), and almost perfect agreement for the clinically relevant biomarkers ER, PR and HER2 (κ>0.86). To assess prognostic value, cutoff thresholds could be applied to both manual and automated scores using the QuPath software, and survival analysis performed for 5-year overall survival. DIA was shown to be capable of replicating the statistically significant stratification of patients achieved using manual scoring across all biomarkers (P<0.01, log-rank test). Furthermore, the image analysis scores were shown to consistently lead to statistical significance across a wide range of potential cutoff thresholds, indicating the robustness of the method, and identify sub-populations of cases exhibiting different expression patterns within the p53 and Ki67 data sets that warrant further investigation. These findings have demonstrated QuPath's suitability for fast, reproducible, high-throughput TMA analysis across a range of important biomarkers. This was achieved using our tumor recognition algorithms for IHC-stained sections, trained interactively without the need for any additional tumor recognition markers, for example, cytokeratin, to obtain greater insight into the relationship between biomarker expression and clinical outcome applicable to a range of cancer types.

Expressed Gene Fusions as Frequent Drivers of Poor Outcomes in Hormone Receptor-Positive Breast Cancer

We sought to uncover genetic drivers of hormone receptor-positive (HR⁺) breast cancer, using a targeted next-generation sequencing approach for detecting expressed gene rearrangements without prior knowledge of the fusion partners. We identified intergenic fusions involving driver genes, including PIK3CA, AKT3, RAF1, and ESR1, in 14% (24/173) of unselected patients with advanced HR⁺ breast cancer. FISH confirmed the corresponding chromosomal rearrangements in both primary and metastatic tumors. Expression of novel kinase fusions in nontransformed cells deregulates phosphoprotein signaling, cell proliferation, and survival in three-dimensional culture, whereas expression in HR⁺ breast cancer models modulates estrogen-dependent growth and confers hormonal therapy resistance in vitro and in vivo Strikingly, shorter overall survival was observed in patients with rearrangement-positive versus rearrangement-negative tumors. Correspondingly, fusions were uncommon (<5%) among 300 patients presenting with primary HR⁺ breast cancer. Collectively, our findings identify expressed gene fusions as frequent and potentially actionable drivers in HR⁺ breast cancer.Significance: By using a powerful clinical molecular diagnostic assay, we identified expressed intergenic fusions as frequent contributors to treatment resistance and poor survival in advanced HR⁺ breast cancer. The prevalence and biological and prognostic significance of these alterations suggests that their detection may alter clinical management and bring to light new therapeutic opportunities. Cancer Discov; 8(3); 336-53. ©2017 AACR.See related commentary by Natrajan et al., p. 272See related article by Liu et al., p. 354This article is highlighted in the In This Issue feature, p. 253.

DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers

BACKGROUND

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.

PURPOSE

To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

STUDY TYPE

Retrospective study.

POPULATION

Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression.

FIELD STRENGTH/SEQUENCE

T₁ -weighted 3.0T DCE-MR images.

ASSESSMENT

Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed.

STATISTICAL TESTING

Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor.

RESULTS

In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59).

DATA CONCLUSION

Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.