Nomogram update based on TAILORx clinical trial results - Oncotype DX breast cancer recurrence score can be predicted using clinicopathologic data

How to Tackle Discordance in Adjuvant Chemotherapy Recommendations by Using Oncotype DX Results, in Early-Stage Breast Cancer

BACKGROUND

The use of the Oncotype DX test reduces the rate of adjuvant chemotherapy recommendations. Few in-depth analyses have been performed on this decision-making process.

METHODS

We retrospectively analyzed patient data based on available Oncotype DX test results (RS) irrespective of nodal status at a single center. We collected recommendations from six oncologists, first without RS (pre-RS) and then with RS results (post-RS). We investigated changes in recommendations, agreement between oncologist decisions, and the effect of different National Comprehensive Cancer Network (NCCN) recommendation categories (for, against, and considering chemotherapy).

RESULTS

Data from 201 patients were included in the analysis. Recommendation of chemotherapy decreased by an average of 39.5%. Agreement improved substantially with RS, with a kappa value pre-RS of 0.37 (fair agreement) and post-RS of 0.75 (substantial agreement). Discordance remained substantial in cases where the NCCN recommendations considered chemotherapy only (32%). Pre-RS consensus against chemotherapy predicted low RS results (50 out of 51 patients). Post-RS consensus was highest in the NCCN chemotherapy recommendation group.

CONCLUSIONS

The Oncotype DX test substantially improves decision accuracy in recommending adjuvant chemotherapy. It may be further improved with a consensus decision. In the case of pre-RS consensus against chemotherapy, the test can be spared.

Correlation Between Oncotype Dx Recurrence Score and PREDICT Estimates in Early Breast Cancer: A Single Institution Experience

PURPOSE

Oncotype Dx Recurrence Score (RS) is prognostic and predictive of chemotherapy benefit in women with node-negative and node-positive in hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early breast cancer. Nevertheless, its direct cost may be inhibitive. This study assesses the correlation between the RS and the free online PREDICT tools' estimations of adjuvant chemotherapy benefit.

PATIENTS AND METHODS

A retrospective review of the electronic medical records of 112 patients with tumors tested for the RS and the PREDICT tool was used to estimate survival benefits. The correlation between RS and PREDICT estimations was analyzed using Spearman rank and McNemar tests.

RESULTS

The median age of patients was 53 (95% CI, 50 to 55) years, with most patients having negative axillary lymph nodes (78%). While the absolute value for RS showed significant positive correlations with adjuvant chemotherapy's benefit as estimated by PREDICT, no significant correlations were found between the two methods in the percentage of chemotherapy gain. Notably, discordance rates between 48% and 67% between RS-based risk assignments and those based on PREDICT estimates were significant across the study population and subgroups. Only one disease recurrence and one breast cancer-related death were documented over a median follow-up of 23.5 (95% CI, 19.8 to 27.2) months.

CONCLUSION

Our findings highlight a significant discordance between RS and PREDICT tools in predicting the benefits of adjuvant chemotherapy in patients with HR+, HER2- early breast cancer. While both tools aim to personalize cancer treatment, their discordance varies, suggesting that PREDICT could not substitute RS to predict adjuvant chemotherapy benefits regardless of patient risk classification. Further studies are needed to explore these relationships and optimize precision medicine approaches in breast cancer management.

Development of a nomogram to predict recurrence scores obtained using Oncotype DX in Japanese patients with breast cancer

BACKGROUND

Chemotherapy is crucial for hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer, and its survival benefits may outweigh adverse events. Oncotype DX (ODX) assesses this balance; however, it is expensive. Using nomograms to identify cases requiring ODX may be economically beneficial. We aimed to identify clinicopathological variables that correlated with the recurrence score (RS) and develop a nomogram that predicted the RS.

METHODS

We included 457 patients with estrogen receptor-positive, HER2-negative breast cancer with metastases in fewer than four axillary lymph nodes who underwent surgery and ODX at our hospital between 2007 and 2023. We developed nomograms and internally validated them in 310 patients who underwent surgery between 2007 and 2021 and validated the model's performance in 147 patients who underwent surgery between 2022 and 2023.

RESULTS

Logistic regression analysis revealed that progesterone receptor (PgR) level, histological grade (HG), and Ki67 index independently predicted the RS. A nomogram was developed using these variables to predict the RS (area under the curve [AUC], 0.870; 95% confidence interval [CI], 0.82-0.92). The nomogram was applied to the model validation group (AUC, 0.877; 95% CI, 0.80-0.95). When the sensitivity of the nomogram was 90%, the model was able to identify 52.3% low-RS and 41.2% high-RS cases not requiring ODX.

CONCLUSIONS

This was the first nomogram model developed based on data from a cohort of Japanese women. It may help determine the indications for ODX and the use of nomogram to identify cases requiring ODX may be economically beneficial.

Development and validation of a clinical breast cancer tool for accurate prediction of recurrence

Given high costs of Oncotype DX (ODX) testing, widely used in recurrence risk assessment for early-stage breast cancer, studies have predicted ODX using quantitative clinicopathologic variables. However, such models have incorporated only small cohorts. Using a cohort of patients from the National Cancer Database (NCDB, n = 53,346), we trained machine learning models to predict low-risk (0-25) or high-risk (26-100) ODX using quantitative estrogen receptor (ER)/progesterone receptor (PR)/Ki-67 status, quantitative ER/PR status alone, and no quantitative features. Models were externally validated on a diverse cohort of 970 patients (median follow-up 55 months) for accuracy in ODX prediction and recurrence. Comparing the area under the receiver operating characteristic curve (AUROC) in a held-out set from NCDB, models incorporating quantitative ER/PR (AUROC 0.78, 95% CI 0.77-0.80) and ER/PR/Ki-67 (AUROC 0.81, 95% CI 0.80-0.83) outperformed the non-quantitative model (AUROC 0.70, 95% CI 0.68-0.72). These results were preserved in the validation cohort, where the ER/PR/Ki-67 model (AUROC 0.87, 95% CI 0.81-0.93, p = 0.009) and the ER/PR model (AUROC 0.86, 95% CI 0.80-0.92, p = 0.031) significantly outperformed the non-quantitative model (AUROC 0.80, 95% CI 0.73-0.87). Using a high-sensitivity rule-out threshold, the non-quantitative, quantitative ER/PR and ER/PR/Ki-67 models identified 35%, 30% and 43% of patients as low-risk in the validation cohort. Of these low-risk patients, fewer than 3% had a recurrence at 5 years. These models may help identify patients who can forgo genomic testing and initiate endocrine therapy alone. An online calculator is provided for further study.

Prediction of Oncotype DX Recurrence Score Based on Systematic Evaluation of Ki-67 Scores in Hormone Receptor-Positive Early Breast Cancer

PURPOSE

Oncotype DX (ODX) predicts the risk of recurrence and benefits of adding chemotherapy for patients with estrogen receptor positive (ER+)/human epidermal growth factor receptor 2 negative (HER2-) early-stage breast cancer. We aimed to develop a simplified scoring system using readily available clinicopathological parameters to predict a high-risk ODX recurrence score (RS) while minimizing reproducibility issues regarding Ki-67 index evaluation methods.

METHODS

We enrolled 300 patients with ER+/HER2- early breast cancer, for whom ODX RS data were available in the test set. Using the QuPath image analysis platform, we systematically evaluated the average, hotspot, and hottest spot Ki-67 scores in the test set. Logistic regression analyses were conducted to establish a predictive scoring system for high-risk ODX RS. An independent validation set comprising 117 patients over different periods was established.

RESULTS

Factors such as age ≤ 50 years, invasive ductal carcinoma tumor type, histologic grade 2 or 3, tumor necrosis, progesterone receptor negativity, and a high Roche-analyzed Ki-67 score (> 20) were associated with high-risk ODX RS. These variables were incorporated into our scoring system. The area under the curve of the scoring system was 0.8057. When applied to both the test and validation sets with a cutoff value of 3, the sensitivity of our scoring system was 92%.

CONCLUSION

We successfully developed a scoring system based on the systematic evaluation of Ki-67 scoring methods. We believe that our user-friendly predictive scoring system for high risk ODX RS could help clinicians in identifying patients who may or may require additional ODX testing.

Young Age as a Predictor of Chemotherapy Recommendation and Treatment in Breast Cancer: A National Cancer Database Study

INTRODUCTION

Breast cancer, although the second most common malignancy in women in the United States, is rare in patients under the age of 40 y. However, this young patient population has high recurrence and mortality rates, with chemotherapy frequently used as adjuvant treatment. We aimed to determine whether age is an independent predictor of chemotherapy recommendation and subsequent treatment and the relationship to Oncotype Dx (ODX) recurrence score (RS).

METHODS

The National Cancer Database was retrospectively reviewed from 2010-2016 to identify women with early-stage (pT1-pT3, pN0-pN1mic, M0), hormone receptor positive, human epidermal growth factor receptor 2 negative breast cancer who underwent ODX RS testing.

RESULTS

Of 95,382 patients who met the inclusion criteria, risk groups using the traditional ODX RS cutoffs were 59% low, 33% intermediate, and 8% high. Using Trial Assigning Individualized Options for Treatment RS cutoffs, risk groups were 23% low, 62% intermediate, and 15% high. Chemotherapy recommendation decreased as age at diagnosis increased (P < 0.001). Increasing age was associated with decreased odds of chemotherapy recommendation in univariate models both continuously (odds ratio: 0.98, 95% confidence interval 0.97-0.98; P < 0.001) and categorically by decade (P < 0.001). Age by decade remained an independent prognosticator of chemotherapy recommendation (P < 0.001), adjusted for risk groups.

CONCLUSIONS

Chemotherapy recommendation and treatment differs by age among patients with early-stage hormone receptor positive breast cancer who undergo ODX testing. While molecular profiling has been shown to accurately predict the benefit of chemotherapy, younger age at diagnosis is a risk factor for discordant use of ODX RS for treatment strategies in breast cancer; with patients aged 18-39 disproportionately affected.

A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care

PURPOSE

We reviewed existing personalized, web-based, interactive decision-making tools available to guide breast cancer treatment and survivorship care decisions in clinical settings.

METHODS

The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched PubMed and related databases for interactive web-based decision-making tools developed to support breast cancer treatment and survivorship care from 2013 to 2023. Information on each tool's purpose, target population, data sources, individual and contextual characteristics, outcomes, validation, and usability testing were extracted. We completed a quality assessment for each tool using the International Patient Decision Aid Standard (IPDAS) instrument.

RESULTS

We found 54 tools providing personalized breast cancer outcomes (e.g., recurrence) and treatment recommendations (e.g., chemotherapy) based on individual clinical (e.g., stage), genomic (e.g., 21-gene-recurrence score), behavioral (e.g., smoking), and contextual (e.g., insurance) characteristics. Forty-five tools were validated, and nine had undergone usability testing. However, validation and usability testing included mostly White, educated, and/or insured individuals. The average quality assessment score of the tools was 16 (range: 6-46; potential maximum: 63).

CONCLUSIONS

There was wide variation in the characteristics, quality, validity, and usability of the tools. Future studies should consider diverse populations for tool development and testing.

IMPLICATIONS FOR CANCER SURVIVORS

There are tools available to support personalized breast cancer treatment and survivorship care decisions in clinical settings. It is important for both cancer survivors and physicians to carefully consider the quality, validity, and usability of these tools before using them to guide care decisions.

Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer-The BRAIN Study

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2- breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2- breast cancer.

The role of modern parameters and their relationship with recurrence risk as assessed by Oncotype DX: real-world evidence

Genomic analysis through various platforms is an essential tool for determining prognosis and treatment in a significant subgroup of early-stage breast cancer patients with hormone receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative status. Additionally, combined clinical and pathological characteristics can accurately predict the recurrence score (RS), as demonstrated by the University of Tennessee risk nomogram. In this study, we aimed to identify classical clinical-pathological factors associated with high RS in a local population, including modern parameters such as current abemaciclib treatment recommendations, HER2-low status, different Ki-67 cutoff values, and samples obtained from secondary primary tumours. This is a retrospective single-institution study that analysed a total of 215 tumour samples. Among lymph node-negative patients (n = 179), age, Ki67 values, and progesterone receptor status predicted RS after multivariate analysis. HER2-low status was not associated with RS differences (p = 0.41). Among lymph node-positive patients (n = 36), MonarchE inclusion criteria (15) were not associated with a higher RS (p = 0.61), and HER2-low did not reach statistical significance. However, tumours classified as secondary primaries numerically exhibited a higher RS. Based on these findings from our real-world sample, the mere application of clinical and pathological parameters is insufficient to predict RS outcomes. Modern parameters such as HER2-low status or adjuvant abemaciclib recommendations were not associated with RS differences. Regarding the observation of secondary tumours, more evidence is needed to understand whether prior hormone therapy exposure impacts the biological risk of secondary primary tumours.

Nomogram Update to Predict the High Genomic Risk Breast Cancer by Different Races

PURPOSE

To develop a nomogram to predict the high-risk recurrence score (RS) and to customize the nomogram for different races in early-stage hormone receptor (HoR)-positive, human epidermal growth factor receptor-2 (HER2)-negative breast cancer.

METHODS

Patients diagnosed between 2010 and 2015 were included from the surveillance, epidemiology, and end results oncotype DX database. The nomogram was assessed with a receiver operating characteristic curve to measure the area under the curve (AUC) with a 95% confidence interval (95% CI). The nomogram was developed and internally validated for discrimination and calibration, and then validated in different races.

RESULTS

A total of 48,464 patients were included and randomly assigned to the training cohort (n = 36370, 75.0%) and validation cohort (n = 12,094, 25.0%). Patients in the training cohort were identified to develop the nomogram, including 32,683 (89.9%) White women, 3135 (8.6%) Black women, and 552 (1.5%) Chinese women. Five independent predictive factors for high-risk RS were included to develop the nomogram, including tumor grade, progesterone receptor status, histological subtype, race, and tumor stage. The AUC was 0.696 (95% CI, 0.682-0.710) in the training cohort and 0.700 (95% CI, 0.676-0.724) in the validation cohort. There was no significant difference between the training cohort and the validation cohort. When validating the nomogram classified by race, the AUC was 0.694 (95% CI, 0.682-0.706) for the White cohort, 0.708 (95% CI, 0.673-0.743) for the Black cohort, and 0.653 (95% CI, 0.565-0.741) for the Chinese cohort.

CONCLUSION

The developed nomogram for predicting high-risk RS is available for different races in patients with HoR+/HER2- breast cancer, which could be used as qualified surrogates before ordering the 21-gene RS testing.

A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles

PURPOSE

The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer assay that provides prognostic and predictive breast cancer recurrence information for hormone positive, HER2-negative patients. The aim of this study is to propose a novel methodology to assist physicians in their decision-making.

METHODS

A retrospective study between 2012 and 2020 with 333 cases that underwent an ODX assay from three hospitals in the Bourgogne Franche-Comté region (France) was conducted. Clinical and pathological reports were used to collect the data. A methodology based on distributional random forest was developed to predict the ODX score classes (ODX [Formula: see text] and ODX [Formula: see text]) using 9 clinico-pathological characteristics. This methodology can be used particularly to identify the patients of the training cohort that share similarities with the new patient and to predict an estimate of the distribution of the ODX score.

RESULTS

The mean age of participants is 56.9 years old. We have correctly classified [Formula: see text] of patients in low risk and [Formula: see text] of patients in high risk. The overall accuracy is [Formula: see text]. The proportion of low risk correct predicted value (PPV) is [Formula: see text]. The percentage of high risk correct predicted value (NPV) is approximately [Formula: see text]. The F1-score and the Area Under Curve (AUC) are of 0.87 and 0.759, respectively.

CONCLUSION

The proposed methodology makes it possible to predict the distribution of the ODX score for a patient. This prediction is reinforced by the determination of a family of known patients with follow-up of identical scores. The use of this methodology with the pathologist's expertise on the different histological and immunohistochemical characteristics has a clinical impact to help oncologist in decision-making regarding breast cancer therapy.

Genomic signatures in breast cancer in a real-world setting: Experience in a Brazilian Northeastern Center

OBJECTIVE

We aim to evaluate the indication and use of genomic signatures in breast cancer patients and outcomes who in patients undergoing adjuvant chemotherapy or not.

METHODS

This is a retrospective study of breast cancer patients managed in a private oncology clinic in Teresina, from November 2014 to February 2021. All patients with an indication of genomic signature were included. Clinical and pathological variables, use of genomic signatures, treatment and follow-up were obtained. The nomogram to predict Oncotype DX results (University of Tennessee Medical Center) was also calculated. Clinical risk calculation was based on MINDACT, using the modified version of Adjuvant Online. The genetic signatures performed were: the Oncotype, MammaPrint and EndoPredict.

RESULTS

Fifty (50) female patients were included in the study. The mean age of the participants was 57.1 years. Among the patients receiving a genomic signature (26-52.0%), there was a change in treatment in 8 (30.7%) cases. Chemotherapy was indicated in four patients, It was contraindicated in another four patients. Treatment changed in 30.7% of the tested patients. Chemotherapy was indicated for those who would not receive it before. It was contraindicated in patients who would previously undergo chemotherapy.

Clinicopathological Factors Associated with Oncotype DX Risk Group in Patients with ER+/HER2- Breast Cancer

Oncotype DX (ODX), a 21-gene assay, predicts the recurrence risk in early breast cancer; however, it has high costs and long testing times. We aimed to identify clinicopathological factors that can predict the ODX risk group and serve as alternatives to the ODX test. This retrospective study included 547 estrogen receptor-positive, human epidermal growth factor receptor 2-negative, and lymph node-negative breast cancer patients who underwent ODX testing. Based on the recurrence scores, three ODX risk categories (low: 0-15, intermediate: 16-25, and high: 26-100) were established in patients aged ≤50 years (n = 379), whereas two ODX risk categories (low: 0-25 and high: 26-100) were established in patients aged >50 years (n = 168). Factors selected for analysis included body mass index, menopausal status, type of surgery, and pathological and immunohistochemical features. The ODX risk groups showed significant association with histologic grade (p = 0.0002), progesterone receptor expression (p < 0.0001), Ki-67 (p < 0.0001), and p53 expression (p = 0.023) in patients aged ≤50 years. In patients aged >50 years, tumor size (p = 0.022), Ki-67 (p = 0.001), and p53 expression (p = 0.001) were significantly associated with the risk group. Certain clinicopathological factors can predict the ODX risk group and enable decision-making on adjuvant chemotherapy; these factors differ according to age.

The Story of the Magee Equations: The Ultimate in Applied Immunohistochemistry

Magee equations (MEs) are a set of multivariable models that were developed to estimate the actual Onco type DX (ODX) recurrence score in invasive breast cancer. The equations were derived from standard histopathologic factors and semiquantitative immunohistochemical scores of routinely used biomarkers. The 3 equations use slightly different parameters but provide similar results. ME1 uses Nottingham score, tumor size, and semiquantitative results for estrogen receptor (ER), progesterone receptor, HER2, and Ki-67. ME2 is similar to ME1 but does not require Ki-67. ME3 includes only semiquantitative immunohistochemical expression levels for ER, progesterone receptor, HER2, and Ki-67. Several studies have validated the clinical usefulness of MEs in routine clinical practice. The new cut-off for ODX recurrence score, as reported in the Trial Assigning IndividuaLized Options for Treatment trial, necessitated the development of Magee Decision Algorithm (MDA). MEs, along with mitotic activity score can now be used algorithmically to safely forgo ODX testing. MDA can be used to triage cases for molecular testing and has the potential to save an estimated $300,000 per 100 clinical requests. Another potential use of MEs is in the neoadjuvant setting to appropriately select patients for chemotherapy. Both single and multi-institutional studies have shown that the rate of pathologic complete response (pCR) to neoadjuvant chemotherapy in ER+/HER2-negative patients can be predicted by ME3 scores. The estimated pCR rates are 0%, <5%, 14%, and 35 to 40% for ME3 score <18, 18 to 25, >25 to <31, and 31 or higher, respectively. This information is similar to or better than currently available molecular tests. MEs and MDA provide valuable information in a time-efficient manner and are available free of cost for anyone to use. The latter is certainly important for institutions in resource-poor settings but is also valuable for large institutions and integrated health systems.

Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.83 versus 0.76 in an external validation cohort, p = 0.0005) and can identify a subset of patients with excellent prognoses who may not need further genomic testing.

Prediction of Oncotype DX Recurrence Score Using Clinicopathological Variables in Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer

PURPOSE

Oncotype DX (ODX) is a well-validated multigene assay that is increasingly used in Korean clinical practice. This study aimed to develop a clinicopathological prediction (CPP) model for the ODX recurrence scores (RSs).

METHODS

A total of 297 patients (study group, n = 175; external validation group, n = 122) with estrogen receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative, T1-3N0-1M0 breast cancer, and available ODX test results were included in the study. Risk categorization as determined by ODX RSs concurred with the TAILORx study (low-risk, RS ≤ 25; high-risk, RS > 25). Univariate and multivariate logistic regression analyses were used to assess the relationships between clinicopathological variables and risk stratified by the ODX RSs. A CPP model was constructed based on regression coefficients (β values) for clinicopathological variables significant by multivariate regression analysis.

RESULTS

Progesterone receptor (PR) negativity, high Ki-67 index, and nuclear grade (NG) 3 independently predicted high-risk RS, and these variables were used to construct the CPP model. The C-index, which represented the discriminatory ability of our CPP model for predicting a high-risk RS, was 0.915 (95% confidence interval [CI], 0.859-0.971). When the CPP model was applied to the external validation group, the C-index was 0.926 (95% CI, 0.873-0.978).

CONCLUSION

Our CPP model based on PR, Ki-67 index, and NG could aid in the selection of patients with breast cancer requiring an ODX test.

Clinical Utility of Genomic Recurrence Risk Stratification in Early, Hormone-Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer: Real-World Experience

BACKGROUND

RNA-based genomic risk assessment estimates chemotherapy benefit in patients with hormone-receptor positive (HR+)/Human Epidermal Growth Factor 2-negative (ERBB2-) breast cancer (BC). It is virtually used in all patients with early HR+/ERBB2- BC regardless of clinical recurrence risk.

PATIENTS AND METHODS

We conducted a retrospective chart review of adult patients with early-stage (T1-3; N0; M0) HR+/ERBB2- BC who underwent genomic testing using the Oncotype DX (Exact Sciences) 21-genes assay. Clinicopathologic features were collected to assess the clinical recurrence risk, in terms of clinical risk score (CRS) and using a composite risk score of distant recurrence Regan Risk Score (RRS). CRS and RRS were compared to the genomic risk of recurrence (GRS).

RESULTS

Between January 2015 and December 2020, 517 patients with early-stage disease underwent genomic testing, and clinical data was available for 501 of them. There was statistically significant concordance between the 3 prognostication methods (P < 0.01). Within patients with low CRS (n = 349), 9.17% had a high GRS, compared to 8.93% in patients with low RRS (n = 280). In patients with grade 1 histology (n = 130), 3.85% had a high GRS and 68.46% had tumors > 1 cm, of whom only 4.49% had a high GRS. Tumor size > 1cm did not associate with a high GRS.

CONCLUSION

Genomic testing for patients with grade 1 tumors may be safely omitted, irrespective of size. Our finds call for a better understanding of the need for routine genomic testing in patients with low grade/low clinical risk of recurrence.

Artificial intelligence in anatomical pathology: building a strong foundation for precision medicine

With the convergence of digital pathology (DP) and artificial intelligence (AI), anatomic pathology practice has been experiencing an exciting paradigm shifting. Pathologists will be provided with an augmented ability to improve diagnostic accuracy, efficiency, and consistency. There will be subvisual morphometric features discovered and multiomics data integrated to provide better prognostic and theragnostic information to guide individual patients' management. The perspective for future precision medicine is promising. However, there are many challenges before AI-assisted DP diagnostic workflows can be successfully implemented. Herein, we briefly review some examples of AI application in anatomic pathology with an emphasis on the subspecialty of gastrointestinal pathology and discuss potential challenges for clinical implementation.

Shear-wave elastography-based nomograms predicting 21-gene recurrence score for adjuvant chemotherapy decisions in patients with breast cancer

PURPOSE

To develop and validate nomograms based on shear-wave elastography (SWE) combined with clinicopathologic features for predicting Oncotype DX recurrence score (RS) for use with adjuvant systemic therapy guidelines.

METHODS

In a retrospective study, patients with breast cancer who underwent definitive surgery of the breast between August 2011 and December 2019 were eligible for this study. Those with surgery between August 2011 and March 2019 were assigned to a development set and the rest were assigned to an independent validation set. Clinicopathologic features and SWE elasticity indices were assessed with logistic regression to develop nomograms for predicting RS ≥ 16 and ≥ 26. Analysis of the area under the receiver operating characteristic curve (AUROC) was used to assess the performance of the nomograms.

RESULTS

Of a total 381 women (mean age, 51 ± 9 years), 286 (mean age, 51 ± 9 years) were in the development set and 95 (mean age, 51 ± 9 years) in the validation set. All SWE elasticity indices were independently associated with each RS cutoff (odds ratio, 1.006-1.039 for RS ≥ 16; odds ratio, 1.008-1.076 for RS ≥ 26). Nomograms based on SWE combined with clinicopathologic features were developed and validated for RS ≥ 16 (mean elasticity [AUROC, 0.74; 95% CI: 0.68, 0.80] and maximum elasticity [AUROC, 0.74; 95% CI: 0.69, 0.80]) and for RS ≥ 26 (mean elasticity [AUROC, 0.81; 95% CI: 0.73, 0.89], maximum elasticity [AUROC, 0.82; 95% CI: 0.74, 0.89], and elasticity ratio [AUROC, 0.86; 95% CI: 0.80, 0.93]).

CONCLUSION

Nomograms based on SWE can predict Oncotype DX RS for use in adjuvant systemic therapy decisions.

Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age

PURPOSE

The use of the Oncotype DX recurrence score (RS) to predict chemotherapy benefit in patients with hormone receptor-positive/HER2 negative (HR+/HER2-) breast cancer has recently expanded to include postmenopausal patients with N1 disease. RS availability is limited in resource-poor settings, however, prompting the development of statistical models that predict RS using clinicopathologic features. We sought to assess the performance of our supervised machine learning model in a cohort of patients > 50 years of age with N1 disease.

METHODS

We identified patients > 50 years of age with pT1-2N1 HR+/HER2- breast cancer and applied the statistical model previously developed in a node-negative cohort, which uses age, pathologic tumor size, histology, progesterone receptor expression, lymphovascular invasion, and tumor grade to predict RS. We measured the model's ability to predict RS risk category (low: RS ≤ 25; high: RS > 25).

RESULTS

Our cohort included 401 patients, 60.6% of whom had macrometastases, with a median of 1 positive node. The majority of patients had a low-risk observed RS (85.8%). For predicting RS category, the model had specificity of 97.3%, sensitivity of 31.8%, a negative predictive value of 87.9%, and a positive predictive value of 70.0%.

CONCLUSION

Our model, developed in a cohort of node-negative patients, was highly specific for identifying cN1 patients > 50 years of age with a low RS who could safely avoid chemotherapy. The use of this model for identifying patients in whom genomic testing is unnecessary would help decrease the cost burden in resource-poor settings as reliance on RS for adjuvant treatment recommendations increases.

The current staging and classification systems of breast cancer and their pitfalls: Is it possible to integrate the complexity of this neoplasm into a unified staging system?

Breast cancer is one of the leading causes of cancer death in women worldwide due to its variable aggressiveness and high propensity to develop distant metastases. The staging can be performed clinically or pathologically, generating the stage stratification by the TNM (T - tumor size; N- lymph node metastasis; M - distant organ metastasis) system. However, cancers with virtually identical TNM characteristics can present highly contrasting behaviors due to the divergence of molecular profiles. This review focuses on the histopathological nuances and molecular understanding of breast cancer through the profiling of gene and protein expression, culminating in improvements promoted by the integration of this information into the traditional staging system. As a culminating point, it will highlight predictive statistical tools for genomic risks and decision algorithms as a possible solution to integrate the various systems because they have the potential to reduce the indications for such tests, serving as a funnel in association with staging and previous classification.

A Novel Surrogate Nomogram Capable of Predicting OncotypeDX Recurrence Score©

Background: OncotypeDX Recurrence Score© (RS) is a commercially available 21-gene expression assay which estimates prognosis and guides chemoendocrine prescription in early-stage estrogen-receptor positive, human epidermal growth factor receptor-2-negative (ER+/HER2−) breast cancer. Limitations of RS testing include the cost and turnaround time of several weeks. Aim: Our aim is to develop a user-friendly surrogate nomogram capable of predicting RS. Methods: Multivariable linear regression analyses were performed to determine predictors of RS and RS > 25. Receiver operating characteristic analysis produced an area under the curve (AUC) for each model, with training and test sets were composed of 70.3% (n = 315) and 29.7% (n = 133). A dynamic, user-friendly nomogram was built to predict RS using R (version 4.0.3). Results: 448 consecutive patients who underwent RS testing were included (median age: 58 years). Using multivariable regression analyses, postmenopausal status (β-Coefficient: 0.25, 95% confidence intervals (CIs): 0.03−0.48, p = 0.028), grade 3 disease (β-Coefficient: 0.28, 95% CIs: 0.03−0.52, p = 0.026), and estrogen receptor (ER) score (β-Coefficient: −0.14, 95% CIs: −0.22−−0.06, p = 0.001) all independently predicted RS, with AUC of 0.719. Using multivariable regression analyses, grade 3 disease (odds ratio (OR): 5.67, 95% CIs: 1.32−40.00, p = 0.037), decreased ER score (OR: 1.33, 95% CIs: 1.02−1.66, p = 0.050) and decreased progesterone receptor score (OR: 1.16, 95% CIs: 1.06−1.25, p = 0.002) all independently predicted RS > 25, with AUC of 0.740 for the static and dynamic online nomogram model. Conclusions: This study designed and validated an online user-friendly nomogram from routinely available clinicopathological parameters capable of predicting outcomes of the 21-gene RS expression assay.

Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score

Background

Oncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations.

Methods

We retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features.

Results

The Pearson's correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (p-value = 1.32 × 10-13) and 0.5041 (p-value = 1.15 × 10-12) for the validation sets 1 and 2, respectively. The adjusted R 2 values using Magee features alone are 0.3442 and 0.2167 in the two validation sets, respectively. In contrast, the adjusted R 2 values were enhanced to 0.4431 and 0.2182 when WSI-derived imaging features were jointly used with Magee features.

Conclusion

Our results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.

Supervised machine learning model to predict oncotype DX risk category in patients over age 50

PURPOSE

Routine use of the oncotype DX recurrence score (RS) in patients with early-stage, estrogen receptor-positive, HER2-negative (ER+/HER2-) breast cancer is limited internationally by cost and availability. We created a supervised machine learning model using clinicopathologic variables to predict RS risk category in patients aged over 50 years.

METHODS

From January 2012 to December 2018, we identified patients aged over 50 years with T1-2, ER+/HER2-, node-negative tumors. Clinicopathologic data and RS results were randomly split into training and validation cohorts. A random forest model with 500 trees was developed on the training cohort, using age, pathologic tumor size, histology, progesterone receptor (PR) expression, lymphovascular invasion (LVI), and grade as predictors. We predicted risk category (low: RS ≤ 25, high: RS > 25) using the validation cohort.

RESULTS

Of the 3880 tumors identified, 1293 tumors comprised the validation cohort in patients of median (IQR) age 62 years (56-68) with median (IQR) tumor size 1.2 cm (0.8-1.7). Most tumors were invasive ductal (80.3%) of low-intermediate grade (80.5%) without LVI (80.9%). PR expression was ≤ 20% in 27.3% of tumors. Specificity for identifying RS ≤ 25 was 96.3% (95% CI 95.0-97.4) and the negative predictive value was 92.9% (95% CI 91.2-94.4). Sensitivity and positive predictive value for predicting RS > 25 was lower (48.3 and 65.1%, respectively).

CONCLUSION

Our model was highly specific for identifying eligible patients aged over 50 years for whom chemotherapy can be omitted. Following external validation, it may be used to triage patients for RS testing, if predicted to be high risk, in resource-limited settings.

Practical Consensus Recommendations for Optimizing Risk versus Benefit of Chemotherapy in Patients with HR Positive Her2 Negative Early Breast Cancer in India

Breast cancer is a public health challenge globally as well as in India. Improving outcome and cure requires appropriate biomarker testing to assign risk and plan treatment. Because it is documented that significant ethnic and geographical variations in biological and genetic features exist worldwide, such biomarkers need to be validated and approved by authorities in the region where these are intended to be used. The use of western guidelines, appropriate for the Caucasian population, can lead to inappropriate overtreatment or undertreatment in Asia and India. A virtual meeting of domain experts discussed the published literature, real-world practical experience, and results of opinion poll involving 185 oncologists treating breast cancer across 58 cities of India. They arrived at a practical consensus recommendation statement to guide community oncologists in the management of hormone positive (HR-positive) Her2-negative early breast cancer (EBC). India has a majority (about 50%) of breast cancer patients who are diagnosed in the premenopausal stage (less than 50 years of age). The only currently available predictive test for HR-positive Her2-negative EBC that has been validated in Indian patients is CanAssist Breast. If this test gives a score indicative of low risk (< 15.5), adjuvant chemotherapy will not increase the chance of metastasis-free survival and should not be given. This is applicable even during the ongoing COVID-19 pandemic.

Prognostic performance of the 2017 World Workshop Classification on staging and grading of periodontitis compared with the British Society of Periodontology's implementation

BACKGROUND

The British Society of Periodontology (BSP) implemented a simplified version of the 2017 World Workshop Classification (WWC) on staging and grading of periodontitis, for use in UK clinical practice. The aim of this study was to assess the long-term (>10 years) prognostic capability of BSP's implementation (BSP-i) compared with the 2017 WWC, using periodontal-related tooth loss (TLP) as a disease outcome.

METHODS

Data on medical history, smoking status, and clinical periodontal parameters were retrieved from 270 patients who received non-surgical and surgical periodontal therapy from 1966 to 2007. Each patient received a baseline diagnosis according to the 2017 WWC and the BSP-i guidelines for implementation. Univariate multilevel Cox regression frailty models were performed to analyze the association between variables with TLP. A post-hoc comparison with Bonferroni correction was performed to analyze interclass comparisons. The prognostic performance of both systems was analyzed using Harrell C index.

RESULTS

The prognostic performance of both systems was very similar (0.922 for the 2017 WWC and 0.925 for the BSP-i). The singular prognostic performance of BSP stage was slightly higher than that of 2017 WWC stage (0.9212 versus 0.9188), while the 2017 WWC grade showed a slightly better performance than BSP grade (0.9175 versus 0.9155). BSP-i's extent performed better than the 2017 WWC extent (0.9203 versus 0.9098); however, in the 2017 WWC extent, the class "localized" was associated with a better prognosis than "generalized."

CONCLUSION

The overall prognostic performance of the two systems was excellent, with both systems having a Harrell C index score of >0.92.

Grading of invasive breast carcinoma: the way forward

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

Concordance between results of inexpensive statistical models and multigene signatures in patients with ER+/HER2- early breast cancer

Multigene signatures (MGS) are used to guide adjuvant chemotherapy (aCT) decisions in patients diagnosed with estrogen receptor (ER)-positive HER2-negative early breast cancer. We used results from three MGS (Oncotype DX® (ODX), MammaPrint® (MP) or Prosigna®) and assessed the concordance between high or low risk of recurrence and the predicted risk of recurrence based on statistical models. In addition, we looked at the impact of MGS results on final aCT administration during the multidisciplinary meeting (MDM). We retrospectively included 129 patients with ER-positive HER2-negative early breast cancer for which MGS testing was performed after MDM at University Hospitals Leuven between May 2013 and April 2019 in case there was doubt about aCT recommendation. Tumor tissue was analyzed either by ODX (N = 44), MP (N = 28), or Prosigna® (N = 57). Eight statistical models were computed: Magee equations (ME), Memorial Sloan Kettering simplified risk score (MSK-SRS), Breast Cancer Recurrence Score Estimator (BCRSE), OncotypeDXCalculator (ODXC), new Adjuvant! Online (nAOL), Mymammaprint.com (MyMP), PREDICT, and SiNK. Concordance, negative percent agreement, and positive percent agreement were calculated. Of 129 cases, 53% were MGS low and 47% MGS high risk. Concordances of 100.0% were observed between risk results obtained by ODX and ME. For MP, BCRSE demonstrated the best concordance, and for Prosigna® the average of ME. Concordances of <50.0% were observed between risk results obtained by ODX and nAOL, ODX and MyMP, ODX and SiNK, MP and MSK-SRS, MP and nAOL, MP and MyMP, MP and SiNK, and Prosigna® and ODXC. Integration of MGS results during MDM resulted in change of aCT recommendation in 47% of patients and a 15% relative and 9% absolute reduction. In conclusion, statistical models, especially ME and BCRSE, can be useful in selecting ER-positive HER2-negative early breast cancer patients who may need MGS testing resulting in enhanced cost-effectiveness and reduced delay in therapeutic decision-making.

Clinical Impact of a Novel Model Predictive of Oncotype DX Recurrence Score in Breast Cancer

BACKGROUND/AIM

Oncotype DX recurrence score (RS) for breast cancer is a useful tool for determining chemotherapy indication but it is expensive and time-consuming. We determined whether four immuno-histochemical markers, namely human epidermal growth factor 2 (HER2), estrogen receptor (ER), progesterone receptor (PgR), and Ki-67, are predictive of an RS ≥26 in Japanese patients.

PATIENTS AND METHODS

The study included 95 Japanese patients evaluated for RS. A predictive model was created using logistic regression analysis.

RESULTS

The discriminant function was calculated as follows: p=1/{1+exp [-(4.611+1.2342×HER2-0.0813×ER- 0.0489 ×PgR+0.0857×Ki67)]}. Using a probability of 0.5 as the cutoff, the accuracy, sensitivity, specificity, positive predictive and negative predictive values were 90.5%, 72.2%, 94.8%, 76.4% and 93.5%, respectively.

CONCLUSION

The model had a high negative predictive value in predicting RS ≥26 in Japanese patients, indicating that Oncotype DX testing may be omitted in patients with a negative result according to the predictive model.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Prognostic value of the 21-gene recurrence score for regional recurrence in patients with estrogen receptor-positive breast cancer

PURPOSE

To evaluate the prognostic value of the 21-gene recurrence score (RS) for regional recurrence (RR) in patients with estrogen receptor-positive breast cancer.

METHODS

We reviewed the medical records of 446 patients who underwent 21-gene RS assay after breast-conserving surgery or mastectomy. The high-RS group was defined as patients who were (1) older than 50 years with an RS of 26 or higher, or (2) 50 years or younger with an RS of 16 or higher.

RESULTS

The 5-year rates of local recurrence (LR), RR, and distant metastasis (DM) were 2.2%, 2.7%, and 4.7%, respectively. The 5-year overall survival (OS) rate was 99.1%. Of the patients, 269 (60.3%) had low-RS, while 177 (39.7%) had high-RS. The 5-year OS rate of the high-RS group was significantly lower than that of the low-RS. The 5-year rates of RR and DM in the high-RS group were significantly higher than those in the low-RS group, while the LR rates did not differ significantly. In multivariable analysis, the high-RS group had a significant relationship with increased RR rate (p = 0.037). Patients who had both high-RS and clinical high-risk features had a significantly higher 5-year RR rate (7.9%) compared with other groups.

CONCLUSIONS

High-RS was an independent risk factor for RR. The significantly higher RR rate of patients with both high-RS and clinical high-risk features compared with other groups suggests that this patient group can be a potential candidate for regional nodal irradiation.

Adequacy of early-stage breast cancer systemic adjuvant treatment to Saint Gallen-2013 statement: the MCC-Spain study

The St Gallen Conference endorsed in 2013 a series of recommendations on early breast cancer treatment. The main purpose of this article is to ascertain the clinical factors associated with St Gallen-2013 recommendations accomplishment. A cohort of 1152 breast cancer cases diagnosed with pathological stage < 3 in Spain between 2008 and 2013 was begun and then followed-up until 2017/2018. Data on patient and tumour characteristics were obtained from medical records, as well as their first line treatment. First line treatments were classified in three categories, according on whether they included the main St Gallen-2013 recommendations, more than those recommended or less than those recommended. Multinomial logistic regression models were carried out to identify factors associated with this classification and Weibull regression models were used to find out the relationship between this classification and survival. About half of the patients were treated according to St Gallen recommendations; 21% were treated over what was recommended and 33% received less treatment than recommended. Factors associated with treatment over the recommendations were stage II (relative risk ratio [RRR] = 4.2, 2.9-5.9), cancer positive to either progesterone (RRR = 8.1, 4.4-14.9) or oestrogen receptors (RRR = 5.7, 3.0-11.0). Instead, factors associated with lower probability of treatment over the recommendations were age (RRR = 0.7 each 10 years, 0.6-0.8), poor differentiation (RRR = 0.09, 0.04-0.19), HER2 positive (RRR = 0.46, 0.26-0.81) and triple negative cancer (RRR = 0.03, 0.01-0.11). Patients treated less than what was recommended in St Gallen had cancers in stage 0 (RRR = 21.6, 7.2-64.5), poorly differentiated (RRR = 1.9, 1.2-2.9), HER2 positive (RRR = 3.4, 2.4-4.9) and luminal B-like subtype (RRR = 3.6, 2.6-5.1). Women over 65 years old had a higher probability of being treated less than what was recommended if they had luminal B-like, HER2 or triple negative cancer. Treatment over St Gallen was associated with younger women and less severe cancers, while treatment under St Gallen was associated with older women, more severe cancers and cancers expressing HER2 receptors.

A nomogram to predict the high-risk RS in HR+/HER2-breast cancer patients older than 50 years of age

Background

The 21-gene recurrence score (RS) testing can predict the prognosis for luminal breast cancer patients. Meanwhile, patients > 50 years with RS > 25 have improved survival with adjuvant chemotherapy. The current study aimed to develop a nomogram with routine parameters to predict RS.

Methods

We included patients diagnosed with hormone receptor (HR)-positive, human epidermal growth factor receptor-2 (HER2)-negative who underwent the 21-gene RS testing and aged > 50 years. The primary outcome was high-risk RS (> 25). Univariate and multivariate analyses were performed to identify significant predictors. A predictive nomogram based on logistic model was developed and evaluated with receiver operating characteristic (ROC) curves. The nomogram was internally validated for discrimination and calibration with bootstrapping method, and externally validated in another cohort. We then assessed the nomogram in different subgroups of patients and compared it with several published models.

Results

A total of 1100 patients were included. Five clinicopathological parameters were used as predictors of a high-risk RS, including tumor grade, histologic subtype, ER expression, PR expression, and Ki-67 index. The area under the curve (AUC) was 0.798 (95% CI 0.772–0.825) and optimism adjusted AUC was 0.794 (95% CI 0.781–0.822). External validation demonstrated an AUC value of 0.746 (95% CI 0.685–0.807), which had no significant difference with the training cohort (P = 0.124). Calibration plots indicated that the nomogram-predicted results were well fitted to the actual outcomes in both internal and external validation. The nomogram had better discriminate ability in patients who had tumors > 2 cm (AUC = 0.847, 95% CI 0.804–0.890). When compared with four other existing models, similar AUC was observed between our nomogram and the model constructed by discriminate Lee et al.

Conclusions

We developed a user-friendly nomogram to predict the high-risk RS in luminal breast cancer patients who were older than 50 years of age, which could guide treatment decision making for those who have no access to the 21-gene RS testing.

How Canadian Oncologists Use Oncotype DX for Treatment of Breast Cancer Patients

Background: The literature suggests that medical oncologists differ on how they use the Oncotype DX (ODX) genomic assay for making decisions about systemic therapy in breast cancer patients. Given the emergence of data supporting the use of genomic profiling for the prognosis and predicting benefit of chemotherapy, we surveyed medical oncologists in Canada to assess their usage and perception of the ODX assay. Methods: A 34-item survey was distributed to Canadian medical oncologists via the Canadian Association of Medical Oncologists. Data was collected on physician demographics, ODX usage patterns, and physicians’ perception of the impact clinical and pathologic characteristics make on ODX utilization. Results: Response rate was 20.6% with 47 responses received from 228 survey sent. Forty-five responses were eligible for analysis. Sixty-two percent (28/45) of respondents treated predominantly breast cancer, and 60% (27/45) have been in practice for at least 10 years. The most cited reason for using ODX was to avoid giving patients unnecessary chemotherapy (64%; 29/45). Sixty-seven percent (30/45) deferred making treatment decisions until ODX testing was completed. Factors most strongly impacting ODX utilization included: patient request, medical comorbidities and tumor grade. In clinical scenarios, ODX was more frequently selected for patients aged 40–65 (vs. <40 or >65), grade 2 tumors (vs. grade 1 or 3), and Ki-67 index of 10–20% (vs. <10% or >20%). Conclusions: This survey demonstrated that Canadian medical oncologists are preferentially using ODX to avoid giving patients unnecessary chemotherapy. The utilization of ODX is mainly in patients with intermediate clinical and pathologic features.

The increasing importance of histologic grading in tailoring adjuvant systemic therapy in 30,843 breast cancer patients

Purpose

The large variation in histologic grading of invasive breast cancer (IBC) that has been reported likely influences tailoring adjuvant therapy. The role of grading in therapeutic decision-making in daily practice, was evaluated using the Dutch national guidelines for IBC-management.

Methods

Synoptic reports of IBC resection-specimens, obtained between 2013 and 2016, were extracted from the nationwide Dutch Pathology Registry, and linked to treatment-data from the Netherlands Cancer Registry. The relevance of grading for adjuvant chemotherapy (aCT) was quantified by identifying patients for whom grade was the determinative factor. In addition, the relation between grade and aCT-administration was evaluated by multivariate logistic regression for patients with a guideline-aCT-indication.

Results

30,843 patients were included. Applying the guideline that was valid between 2013 and 2016, grade was the determinative factor for the aCT-indication in 7744 (25.1%) patients, a percentage that even increased according to the current guideline where grade would be decisive for aCT in 10,869 (35.2%) patients. Also in current practice, the indication for adjuvant endocrine therapy (aET) would be based on grade in 9173 (29.7%) patients. Finally, as patients with lower-grade tumors receive aCT significantly less often, grade was also decisive in tailoring aCT de-escalation.

Conclusions

In the largest study published so far we illustrate the increasing importance of histologic grade in tailoring adjuvant systemic breast cancer therapy. Next to playing a key-role in aCT-indication and de-escalation, the role of grading has expanded to the indication for aET. Optimizing histologic grading by pathologists is urgently needed to diminish the risk of worse patient outcome due to non-optimal treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06098-7.

The Role of Oncotype DX® Recurrence Score in Predicting Axillary Response After Neoadjuvant Chemotherapy in Breast Cancer

INTRODUCTION

Oncotype DX^® recurrence score (RS) is well-recognized for guiding decision making in adjuvant chemotherapy; however, the predictive capability of this genomic assay in determining axillary response to neoadjuvant chemotherapy (NCT) has not been established.

METHODS

Using the National Cancer Data Base (NCDB), we identified patients diagnosed with T1-T2, clinically N1/N2, estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER +/HER2 -) invasive ductal carcinoma of the breast between 2010 and 2015. Patients with an Oncotype DX^® RS who received NCT were included. RS was defined as low (< 18), intermediate (18-30), or high (> 30). Unadjusted and adjusted analyses were performed to determine the association between axillary pathologic complete response (pCR) and RS.

RESULTS

This study included a total of 158 women. RS was low in 56 (35.4%) patients, intermediate in 62 (39.2%) patients, and high in 40 (25.3%) patients. The majority of patients presented with clinical N1 disease (89.2%). Axillary pCR was achieved in 23 (14.6%) patients. When stratifying patients with axillary pCR by RS, 11 (47.8%) patients had a high RS, 6 (26.1%) patients had an intermediate RS, and 6 (26.1%) patients had a low RS. Comparing cohorts by RS, 27.5% of patients with high RS tumors had an axillary pCR, compared with only 9.7% in the intermediate RS group, and 10.7% in the low RS group (p = 0.0268).

CONCLUSION

Our findings demonstrate that Oncotype DX^® RS is an independent predictor of axillary pCR in patients with ER +/HER2 - breast cancers receiving NCT. A greater proportion of patients with a high RS achieved axillary pCR. These results support Oncotype DX^® as a tool to improve clinical decision making in axillary management.

Peri-tumoural stroma collagen organization of invasive ductal carcinoma assessed by polarized light microscopy differs between OncotypeDX risk group

A commercially available genomic test, OncotypeDX has emerged as a useful postsurgical treatment guide for early stage breast cancer. Despite widespread clinical adoption, there remain logistical issues with its implementation. Collagenous stromal architecture has been shown to hold prognostic value that may complement OncotypeDX. Polarimetric analysis of breast cancer surgical samples allows for the quantification of collagenous stroma abundance and organization. We examine intratumoural collagen abundance and alignment along the tumor-host interface for 45 human samples of invasive ductal carcinoma categorized as low or higher risk by OncotypeDX. Furthermore, we probe the separatory power of collagen alignment patterns to classify unlabeled samples as low or higher OncotypeDX risk group using a linear discriminant (LD) model. No significant difference in mean collagen abundance was found between the two risk groups. However, collagen alignment along the tumor boundary was found to be significantly lower in higher risk samples. The LD model achieved a 71% total accuracy and 81% sensitivity to higher risk samples. Prognostic information extracted from the stromal morphology has potential to complement OncotypeDX as an easy-to-implement prescreening methodology.

Do 21-Gene Recurrence Score Influence Chemotherapy Decisions in T1bN0 Breast Cancer Patients?

Purpose: Hormone receptor (HR)-positive breast cancer patients with tumor size ≤1.0 cm and negative node have favorable outcomes. The 21-gene Recurrence Score (RS) could predict response to chemotherapy for HR+ breast cancer, but its role in T1bN0 disease is challenging.

Methods: T1bN0 breast cancer patients diagnosed between January 2014 and June 2019 with RS results were included and categorized as Low- (RS < 18), Intermediate- (RS 18–30), or High-risk (RS > 30) groups. Univariate and multivariate analysis were used to assess factors associated with RS distribution and chemotherapy recommendation. Chemotherapy decisions change and patient adherence after 21-gene RS testing were also evaluated.

Results: Among 237 patients with T1bN0 tumors, proportions of Low-, Intermediate-, and High-risk RS were 19.8, 63.3, and 16.9%, respectively. Multivariate analysis found that ER expression (P = 0.011), PR expression (P < 0.001), and Ki-67 index (P = 0.001) were independently associated with RS distribution. Adjuvant chemotherapy was recommended for 31.6% of patients, which was more frequently given to patients with higher tumor grade [Odds ratio (OR) = 2.99 for grade II, OR = 59.19 for grade III, P = 0.006], lymph vascular invasion (OR = 8.22, P = 0.032), Luminal-B subtype (OR = 5.68, P < 0.001), and Intermediate-to High-risk RS (OR = 10.01 for Intermediate-risk, OR = 192.42 for High-risk, P < 0.001). Chemotherapy decision change was found in 18.6% of patients, mainly in those with Intermediate- to High-risk RS tumor with the majority from no-chemotherapy to chemotherapy. The treatment compliance rate after the 21-gene RS testing with MDT was 95.4%.

Conclusion: RS category was related to ER, PR, and Ki-67 expression, which was recognized as an independent factor of chemotherapy recommendation in T1bN0 breast cancer. The 21-gene RS testing would lead to a chemotherapy decision change rate of 18.6% as well as a high treatment adherence, which can be applied in T1bN0 patients.

Unmet Clinical Need: Developing Prognostic Biomarkers and Precision Medicine to Forecast Early Tumor Relapse, Detect Chemo-Resistance and Improve Overall Survival in High-Risk Breast Cancer

Chemo-resistant breast cancer is a major barrier to curative treatment for a significant number of women with breast cancer. Neoadjuvant chemotherapy (NACT) is standard first- line treatment for most women diagnosed with high-risk TNBC, HER2+, and locally advanced ER+ breast cancer. Current clinical prognostic tools evaluate four clinicopathological factors: Tumor size, LN status, pathological stage, and tumor molecular subtype. However, many similarly treated patients with identical residual cancer burden (RCB) following NACT experience distinctly different tumor relapse rates, clinical outcomes and survival. This problem is particularly apparent for incomplete responders with a high-risk RCB classification following NACT. Therefore, there is a pressing need to identify new prognostic and predictive biomarkers, and develop novel curative therapies to augment current standard of care (SOC) treatment regimens to save more lives. Here, we will discuss these unmet needs and clinical challenges that stand in the way of precision medicine and personalized cancer therapy.

Ki-67 index, progesterone receptor expression, histologic grade and tumor size in predicting breast cancer recurrence risk: A consecutive cohort study

Background

The 21‐gene recurrence score (RS) assay has been recommended by major guidelines for treatment decision in hormone receptor (HR)‐positive early breast cancer (EBC). However, the genomic assay is not accessible and affordable worldwide. Alternatively, an increasing number of studies have shown that traditional immunohistochemistry (IHC) can partially or even completely replace the role of the 21‐gene genomic assay. Here, we developed and validated a predictive model (IHC3 model) combining the Ki‐67 index, progesterone receptor (PR) expression, histologic grade, and tumor size to predict the recurrence risk of HR‐positive EBC.

Methods

The data from 389 patients (development set) with HR‐positive, human epidermal growth factor receptor 2‐negative, lymph node non‐metastasized invasive breast cancer were used to construct the IHC3 model based on the Surexam^® 21‐gene RS and the TAILORx clinical trial criteria. An additional 146 patients with the same characteristics constituted the validation set. The predictive accuracy of the IHC3 model was compared with that of Orucevic et al.’s nomogram. Invasive disease‐free survival (IDFS) was analyzed in the IHC3 predictive low‐recurrence risk (pLR) group and the predictive high‐recurrence risk (pHR) group. The Pearson chi‐square test, Fisher exact test, and log‐rank test were used for analysis.

Results

The pLR and pHR group could be easily stratified using the decision tree model without network dependence. The accuracies of the IHC3 model were 86.1% in the development set and 87.7% in the validation set. The predictive accuracy of the IHC3 model and Orucevic et al.’s nomogram for the whole cohort was 86.5% and 86.9%, respectively. After a 52‐month of median follow‐up, a significant difference was found in IDFS between of the IHC3 pLR and the pHR groups (P = 0.001) but not in the IDFS between the low‐ and high‐recurrence risk groups according to the Surexam® 21‐gene RS and the TAILORx clinical trial criteria (P = 0.556) or 21‐gene binary RS group (P = 0.511).

Conclusions

The proposed IHC3 model could reliably predict low and high recurrence risks in most HR‐positive EBC patients. This easy‐to‐use predictive model may be a reliable replacement for the 21‐gene genomic assay in patients with EBC who have no access to or cannot afford the 21‐gene genomic assay.

The healthcare value of the Magee Decision Algorithm™: use of Magee Equations™ and mitosis score to safely forgo molecular testing in breast cancer

Magee Equations™ are multivariable models that can estimate oncotype DX® Recurrence Score, and Magee Equation 3 has been shown to have chemopredictive value in the neoadjuvant setting as a standalone test. The current study tests the accuracy of Magee Decision Algorithm™ using a large in-house database. According to the algorithm, if all Magee Equation scores are <18, or 18-25 with a mitosis score of 1, then oncotype testing is not required as the actual oncotype recurrence score is expected to be ≤25 (labeled "do not send"). If all Magee Equation scores are 31 or higher, then also oncotype testing is not required as the actual score is expected to be >25 (also "do not send"). All other cases could be considered for testing (labeled "send"). Of the 2196 ER+, HER2-negative cases sent for oncotype testing, 1538 (70%) were classified as "do not send" and 658 (30%) as "send". The classification accuracy in the "do not send" group was 95.1%. Of the 75 (4.9%) discordant cases (expected score ≤25 by decision algorithm but the actual oncotype score >25), 26 received endocrine therapy alone. None of these 26 patients experienced distant recurrence (average follow-up of 73 months). The Magee Decision Algorithm accurately identifies cases that will not benefit from oncotype testing. Such cases constitute ~70% of the routine clinical oncotype requests, an estimated saving of $300,000 per 100 test requests. The occasional discordant cases (expected ≤25, but actual oncotype score >25) appears to have an excellent outcome on endocrine therapy alone.

Practice Changing Potential of TAILORx: A Retrospective Review of the National Cancer Data Base from 2010 to 2015

BACKGROUND

Uncertainty regarding chemotherapy benefit among breast cancer patients with intermediate Oncotype Dx^® recurrence scores (RS; 11-25) led to the TAILORx study. We evaluated chemotherapy use in patients with intermediate RS to determine practice change potential based on the TAILORx results.

METHODS

National Cancer Data Base patients with hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, N0 breast cancer were identified and were divided into three groups: Group A, ≤ 50 years of age (RS 11-15); Group B, ≤ 50 years of age (RS 16-25); and Group C, > 50 years of age (RS 11-25). Demographic and clinical factors were compared using Chi square tests and Poisson regression models to determine predictors of chemotherapy receipt.

RESULTS

Overall, 37,087 patients met the inclusion criteria, with 6.3% in Group A and 11.7% in Group C having received chemotherapy that may have been avoided based on TAILORx. The majority of Group B (64.7%) did not receive chemotherapy, whereas TAILORx showed potential benefit from treatment. Chemotherapy use decreased over time for all intermediate RS patients. T2 tumors, high grade, and treatment before 2012 increased the likelihood of chemotherapy receipt among both groups. Younger patients with the lower intermediate RS (Group A) were more likely to receive chemotherapy if they had treatment at community or comprehensive centers, whereas moderate grade was also a significant factor to receive chemotherapy in Group B. Significant factors in older patients (Group C) were Black race, estrogen receptor-positive/progesterone receptor-negative, and moderate/high grade.

CONCLUSIONS

The most potential impact of TAILORx findings on practice change is for patients ≤ 50 years of age with RS of 16-25 who did not receive chemotherapy but may benefit. These findings may serve as a baseline for future analysis of practice patterns related to TAILORx.