Recommendations from the European Commission Initiative on Breast Cancer for multigene testing to guide the use of adjuvant chemotherapy in patients with early breast cancer, hormone receptor positive, HER-2 negative

21-gene recurrence score in predicting the outcome of postoperative radiotherapy in T1-2N1 luminal breast cancer after breast-conserving surgery

BACKGROUND

In those with one to three positive lymph nodes (N1) breast cancer (BC), the 21-gene recurrence score (RS) classification can be referred for decision-making on adjuvant chemotherapy. This study aimed to investigate the effect of RS in predicting the survival benefit of postoperative radiotherapy (PORT) in T1-2N1 BC with estrogen receptor-positive and human epidermal growth factor receptor 2-negative disease after breast-conserving surgery (BCS).

METHODS

We included patients with BC and available RS data from the Surveillance, Epidemiology, and End Results Oncotype DX database. The chi-square test, Kaplan-Meier method, propensity score matching (PSM) as well as multivariable Cox proportional hazard analyses were used for statistical analyses.

RESULTS

We included 6509 patients in the analysis. Of these patients, 5302 (85.5%) were treated with BCS + PORT, and 207 (15.5%) had BCS alone. There were 1419 (21.8%), 4319 (66.4%), and 771 (11.8%) patients being low-, intermediate-, and high-risk RS, respectively. After PSM, PORT was significantly associated with a 5-year overall survival (OS) advantage (95.1% vs. 90.5%, P < 0.001) compared to those without PORT, which similar breast cancer-specific survival (BCSS) was found between the treatment arms (P = 0.126). The sensitivity analyses showed that PORT was not associated with a better BCSS (P = 0.472) and OS (P = 0.650) than those without PORT in the low-risk RS cohort. However, PORT was associated with a better BCSS (P = 0.031) and OS (P < 0.001) compared to those without PORT in the intermediate/high-risk RS cohorts.

CONCLUSIONS

Our study highlights the possible role of the RS in predicting the outcome of PORT in T1-2N1 luminal BC patients undergoing BCS.

Prediction of the MammaPrint Risk Group Using MRI Features in Women With Estrogen Receptor-Positive, HER2-Negative, and 1 to 3 Node-Positive Invasive Breast Cancer

BACKGROUND

MammaPrint assigns chemotherapeutic benefits to patients with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and 1 to 3 node-positive invasive breast cancer. However, its cost and time-consuming nature limit its use in certain clinical settings. We aimed to develop and validate the prediction models for the low MammaPrint risk group using clinicopathologic and MRI features.

PATIENTS AND METHODS

Overall, 352 women with ER-positive, HER2-negative, and 1 to 3 node-positive invasive breast cancer were retrospectively reviewed and assigned to development (n = 235) and validation sets (n = 117). Univariate and multivariate analyses identified features associated with the low MammaPrint risk group. The area under the receiver operating characteristic curves (AUROCs) of models based on clinicopathologic, MRI, and combined features were evaluated.

RESULTS

Development set multivariate analysis showed that clinicopathologic features including low histologic grade (odds ratio [OR], 5.29; P = .02), progesterone receptor-positivity (OR, 3.23; P = .01), and low Ki-67 (OR, 6.05; P < .001) and MRI features, including peritumoral edema absence (OR, 2.24; P = .04) and a high proportion of persistent components (OR, 1.15; P = .004) were significantly associated with the low MammaPrint risk group. The AUROCs of models based on clinicopathologic, MRI, and combined features were 0.77, 0.64, and 0.80 in the development and 0.66, 0.60, and 0.70 in the validation sets, respectively.

CONCLUSION

The combined model incorporating clinicopathologic and MRI features showed potential in predicting the low MammaPrint risk group, and may support decision-making in clinical settings with limited access to MammaPrint.

Utilization and outcomes of the 21-gene recurrence score in de novo metastatic breast cancer

BACKGROUND

Limited data exist regarding the utility and validity of the 21-gene recurrence score (RS) in patients with de novo metastatic breast cancer (dnMBC). This study aimed to investigate the practice patterns as well as associated survival outcomes based on 21-gene RS in dnMBC.

RESEARCH DESIGN AND METHODS

The Surveillance, Epidemiology, and End Results Oncotype database was queried for women with hormone receptor-positive and Her2-negative dnMBC.

RESULTS

A total of 153 patients were identified, including 62.7% and 37.3% of patients who had RS < 26 and ≥ 26, respectively. Patients with RS ≥ 26 were more likely to receive chemotherapy compared to those with RS < 26 (61.4% vs. 28.1%, p < 0.001). Patients with RS ≥ 26 had an inferior breast cancer-specific survival (BCSS) (2-year BCSS: 84.3% vs. 89.5, p = 0.067) and overall survival (OS) compared to those with RS < 26 (2-year OS: 76.9% vs. 87.4%, p = 0.018). The multivariate Cox proportional hazard models showed that those with RS ≥ 26 had a significantly inferior BCSS (hazard ratio [HR] 2.251, 95% confidence interval [CI] 1.056-4.799, p = 0.036) and OS (HR 2.151, 95%CI 1.123-4.120, p = 0.021) compared to those with RS < 26.

CONCLUSIONS

The 21-gene RS assay is an important prognostic factor in patients with dnMBC.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers

BACKGROUND

Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers.

METHODS

We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers.

RESULTS

We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort.

CONCLUSIONS

This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

Genomic signature to guide adjuvant chemotherapy treatment decisions for early breast cancer patients in France: a cost-effectiveness analysis

Introduction

Chemotherapy (CT) is commonly used as an adjuvant treatment for women with early breast cancer (BC). However, not all patients benefit from CT, while all are exposed to its short- and long-term toxicity. The Oncotype DX^® test assesses cancer-related gene expression to estimate the risk of BC recurrence and predict the benefit of chemotherapy. The aim of this study was to estimate, from the French National Health Insurance (NHI) perspective, the cost-effectiveness of the Oncotype DX^® test compared to standard of care (SoC; involving clinicopathological risk assessment only) among women with early, hormone receptor-positive, human epidermal growth factor receptor 2-negative BC considered at high clinicopathological risk of recurrence.

Methods

Clinical outcomes and costs were estimated over a lifetime horizon based on a two-component model that comprised a short-term decision tree representing the adjuvant treatment choice guided by the therapeutic decision support strategy (Oncotype DX^® test or SoC) and a Markov model to capture long-term outcomes.

Results

In the base case, the Oncotype DX^® test reduced CT use by 55.2% and resulted in 0.337 incremental quality-adjusted life-years gained and cost savings of €3,412 per patient, compared with SoC. Being more effective and less costly than SoC, Oncotype DX^® testing was the dominant strategy.

Discussion

Widespread implementation of Oncotype DX^® testing would improve patient care, provide equitable access to more personalized medicine, and bring cost savings to the health system.

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.

Ten-year distant-recurrence risk prediction in breast cancer by CanAssist Breast (CAB) in Dutch sub-cohort of the randomized TEAM trial

BACKGROUND

Hormone receptor (HR)-positive, HER2/neu-negative breast cancers have a sustained risk of recurrence up to 20 years from diagnosis. TEAM (Tamoxifen, Exemestane Adjuvant Multinational) is a large, multi-country, phase III trial that randomized 9776 women for the use of hormonal therapy. Of these 2754 were Dutch patients. The current study aims for the first time to correlate the ten-year clinical outcomes with predictions by CanAssist Breast (CAB)-a prognostic test developed in South East Asia, on a Dutch sub-cohort that participated in the TEAM. The total Dutch TEAM cohort and the current Dutch sub-cohort were almost similar with respect to patient age and tumor anatomical features.

METHODS

Of the 2754 patients from the Netherlands, which are part of the original TEAM trial, 592 patients' samples were available with Leiden University Medical Center (LUMC). The risk stratification of CAB was correlated with outcomes of patients using logistic regression approaches entailing Kaplan-Meier survival curves, univariate and multivariate cox-regression hazards model. We used hazard ratios (HRs), the cumulative incidence of distant metastasis/death due to breast cancer (DM), and distant recurrence-free interval (DRFi) for assessment.

RESULTS

Out of 433 patients finally included, the majority, 68.4% had lymph node-positive disease, while only a minority received chemotherapy (20.8%) in addition to endocrine therapy. CAB stratified 67.5% of the total cohort as low-risk [DM = 11.5% (95% CI, 7.6-15.2)] and 32.5% as high-risk [DM = 30.2% (95% CI, 21.9-37.6)] with an HR of 2.90 (95% CI, 1.75-4.80; P < 0.001) at ten years. CAB risk score was an independent prognostic factor in the consideration of clinical parameters in multivariate analysis. At ten years, CAB high-risk had the worst DRFi of 69.8%, CAB low-risk in the exemestane monotherapy arm had the best DRFi of 92.7% [vs CAB high-risk, HR, 0.21 (95% CI, 0.11-0.43), P < 0.001], and CAB low-risk in the sequential arm had a DRFi of 84.2% [vs CAB high-risk, HR, 0.48 (95% CI, 0.28-0.82), P = 0.009].

CONCLUSIONS

Cost-effective CAB is a statistically robust prognostic and predictive tool for ten-year DM for postmenopausal women with HR+/HER2-, early breast cancer. CAB low-risk patients who received exemestane monotherapy had an excellent ten-year DRFi.

Targeting Breast Cancer: An Overlook on Current Strategies

Breast cancer (BC) is one of the most widely diagnosed cancers and a leading cause of cancer death among women worldwide. Globally, BC is the second most frequent cancer and first most frequent gynecological one, affecting women with a relatively low case-mortality rate. Surgery, radiotherapy, and chemotherapy are the main treatments for BC, even though the latter are often not aways successful because of the common side effects and the damage caused to healthy tissues and organs. Aggressive and metastatic BCs are difficult to treat, thus new studies are needed in order to find new therapies and strategies for managing these diseases. In this review, we intend to give an overview of studies in this field, presenting the data from the literature concerning the classification of BCs and the drugs used in therapy for the treatment of BCs, along with drugs in clinical studies.

Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer

Objective

By identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients.

Method

The Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts.

Results

Cox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P<0.05). With this premise, the nomogram model was constructed and evaluated. The C-index value of the nomogram model was 0.830 in the training group and 0.914 in the validation group. Moreover, both the calibration and ROC curves for the proposed model exhibited reliable performance, and the clinical decision curve analysis showed that the proposed model can bring clinical benefits.

Conclusions

The constructed nomogram can accurately predict individual survival probabilities and may serve as a clinical decision support tool for clinicians to optimize treatment in individuals.

m6A readers, writers, erasers, and the m6A epitranscriptome in breast cancer

Epitranscriptomic modification of RNA regulates human development, health, and disease. The true diversity of the transcriptome in breast cancer including chemical modification of transcribed RNA (epitranscriptomics) is not well understood due to limitations of technology and bioinformatic analysis. N-6-methyladenosine (m6A) is the most abundant epitranscriptomic modification of mRNA and regulates splicing, stability, translation, and intracellular localization of transcripts depending on m6A association with reader RNA-binding proteins. m6A methylation is catalyzed by the METTL3 complex and removed by specific m6A demethylase ALKBH5, with the role of FTO as an 'eraser' uncertain. In this review, we provide an overview of epitranscriptomics related to mRNA and focus on m6A in mRNA and its detection. We summarize current knowledge on altered levels of writers, readers, and erasers of m6A and their roles in breast cancer and their association with prognosis. We summarize studies identifying m6A peaks and sites in genes in breast cancer cells.

[Associating prognostic factors with clinical results in locally advanced breast cancer]

Background

Breast cancer is the most frequent malignant tumor in women.

Objective

To identify clinico-pathological and molecular markers as predictors of survival in patients with locally advanced breast cancer (LABC).

Methods

Retrospective and observational study. The clinical factors of clinico-pathological and molecular predictors in relation with overall survival (OS) were assessed by the survival function, baseline hazard with smoothing and Cox regression.

Results

126 patients were assessed. OS at five years was significantly superior in patients with clinical stage IIIA (87%; p < 0.001), grade 2 tumor (81%; p < 0.001), pathological node stage (ypN0: 90%; p < .001), low-risk Nottingham prognostic index (86%; p < 0.001) and luminal A subtype (88%; p = 0.022). Baseline hazard with smoothing exhibited an increase in the mortality rate at 50 months for the luminal B/ HER2+ subtype compared with other subtypes. The multivariate analysis ascertained that the stage ypN2-3 (hazard ratio [HR] = 7.3; 95% confidence interval [95% CI]: 2.2 to 23.9) and the HER2+ nonluminal (HR = 7.8; 95% CI: 2 to 29.6) and triple negative (HR = 5.4; 95% CI: 1.7 to 17.2) subtypes were associated with a poor OS.

Conclusions

The comprehensive evaluation of the molecular marker and clinico-pathological factors provides more accurate predictive and prognostic information. The nodal stage and molecular subtype are suitable clinical parameters on survival for LABC patients.

Research Status of Systemic Adjuvant Therapy for Early Breast Cancer

Breast cancer has surpassed lung cancer as the most common cause of cancer deaths, worldwide. Early breast cancers are treatment sensitive and patients under standardized treatment have prolonged. Breast cancer treatment has significantly evolved from the conventional surgical approach and radiotherapy to local and systemic adjuvant therapies. Though localized breast cancers are clinically manageable, distant recurrence is a cause of morbid concern. Adjuvant systemic therapy is effective in both distant and local recurrences and hence gained significant attention. Early breast cancer prognosis has greatly improved in the past 3 decades with reduced mortality rates due to the widespread use of adjuvant therapy. It can markedly increase the cure rate of breast cancers, and postoperative adjuvant therapy became a part of comprehensive breast cancer treatment. Further research to understand the early breast cancer characteristics could expand the treatment modalities that can improve the outcomes and survival benefits of breast cancer patients.

Validation study of a nomogram for predicting probability of low risk of MammaPrint results in women with clinically high-risk breast cancer

BACKGROUND

MammaPrint (MMP) helps clinicians identify the ideal time for adjuvant treatment for patients with early HR+/HER2- breast cancer. We aimed to validate a nomogram designed to predict probability of low risk of MMP results and to evaluate the difference in survival outcome between two groups divided by nomogram score.

METHODS

In this retrospective cohort study, we evaluated 172 patients from Asan Medical Center, Seoul, Korea, who underwent breast cancer surgery and MMP during 2020-2021. First, we validated the nomogram by calculating the area under the curve (AUC) and using calibration. Additionally, with the data of 1,835 T1-3N0-1M0 HR+/HER2- patients from Asan Medical Center during 2010-2013, we compared the disease-free survival (DFS), overall survival (OS), and breast cancer-specific survival (BCSS) rates by Kaplan-Meier analysis between the two groups divided by nomogram total point (TP) of 183.

RESULTS

The AUC calculated by validation of 172 patients was 0.73 (95% confidence interval [CI], 0.66-0.81). The discrimination and calibration of the prediction model were satisfactory following additional validation of 1835 patients. The high-risk and low-risk groups had different 5-year OS (97.9% vs. 98.1%, p = 0.056), DFS (98.6% vs. 99.4%, p = 0.008), and BCSS rates (98.6% vs. 99.4%, p = 0.002).

CONCLUSION

For treatment decision-making among clinically high-risk patients with HR+/HER2- and node-positive disease, the nomogram showed satisfactory performance in predicting patients with low genomic risk. Survival outcome significantly differed between two groups divided by nomogram TP. More studies are needed to validate this model in international cohorts and large prospective cohorts from other institutions.

Favorable locoregional control in clinically node-negative hormone-receptor positive breast cancer with low 21-gene recurrence scores: a single-institution study with 10-year follow-up

BACKGROUND

Recent studies have shown a lower likelihood of locoregional recurrences in patients with a low 21-gene recurrence score (RS). In this single-institution study, we investigated whether there are any associations between different cutoff values of 21-gene RS, histopathological factors, and outcome in patients with long-term follow-up.

METHODS

The study included 61 patients who had early-stage (I-II) clinically node-negative hormone receptor-positive and HER2-negative breast cancer and were tested with the 21-gene RS assay between February 2010 and February 2013. Demographic, clinicopathological, treatment, and outcome characteristics were analyzed.

RESULTS

The median age was 48 years (range, 29-72 years). Patients with high histologic grade (HG), Ki-67 ≥ 25%, or Ki-67 ≥ 30% were more likely to have intermediate/high RS (≥ 18). Based on the 21-gene RS assay, only 19 patients (31%) received adjuvant chemotherapy. At a median follow-up of 112 months, 3 patients developed locoregional recurrences (4.9%), which were treated with endocrine therapy alone. Among patients treated with endocrine treatment alone (n = 42), the following clinicopathological characteristics were not found to be significantly associated with 10-year locoregional recurrence free survival (LRRFS): age < 40 years, age < 50 years, high histological or nuclear grade, high Ki-67-scores (≥ 15%, ≥ 20%, ≥ 25%, ≥ 30%), presence of lymphovascular invasion, luminal-A type, multifocality, lymph node positivity, tumor size more than 2 cm, RS ≥ 18, and RS > 11. However, patients with RS ≥ 16 had significantly poorer 10-year LRRFS compared to those with RS < 16 (75% vs. 100%, respectively; p = 0.039).

CONCLUSIONS

The results suggest that patients with clinically node-negative disease and RS ≥ 16 are more likely to benefit from adjuvant chemotherapies. However, those with RS < 16 have an excellent outcome and local control in long-term follow-up with endocrine treatment alone.

Prediction of BRAF V600E variant from cancer gene expression data

Background

BRAF inhibitors have been approved for the treatment of melanoma, non-small cell lung cancer, and colon cancer. Real-time polymerase chain reaction or next-generation sequencing were clinically used for BRAF variant detection to select who responds to BRAF inhibitors. The prediction of BRAF variants using gene expression data might be an alternative test when the direct variant sequencing test is not feasible. In this study, we built a prediction model to detect BRAF V600 variants with mRNA gene expression data in various cancer types.

Methods

We adopted a penalized logistic regression for the BRAF V600E variants prediction model. Ten times bootstrap resampling was done with a combined target variable and cancer type stratification. Data preprocessing included knnimputation for missing value imputation, YeoJohnson transformation for skewness correction, center, and scale for standardization, synthetic minority over-sampling technique for class imbalance. Hyperparameter optimization with a grid search was undertaken for model selection in terms of area under the precision-recall.

Results

The area under the curve of the receiver operating characteristic curve on the test set was 0.98 in thyroid carcinoma, 0.90 in colon adenocarcinoma, and 0.85 in cutaneous melanoma. The area under the precision-recall of the test set was 0.98 in thyroid carcinoma, 0.71 in colon adenocarcinoma, and 0.65 in cutaneous melanoma.

Conclusions

Our penalized logistic regression model can predict BRAF V600E variants with good performance in thyroid carcinoma, cutaneous melanoma, and colon adenocarcinoma.

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

Simple Summary

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

Abstract

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

Breast Cancer Patients: Who Would Benefit from Neoadjuvant Chemotherapies?

Neoadjuvant chemotherapy (NACT) was developed with the aims of shrinking tumors or stopping cancer cells from spreading before surgery. Unfortunately, not all breast cancer patients will benefit from NACT, and thus, patients must weigh the risks and benefits of treatment prior to the initiation of therapy. Currently, the data for predicting the efficacy of NACT is limited. Molecular testing, such as Oncotype DX, MammaPrint, and Curebest 95GC, have been developed to assist which breast cancer patients will benefit from the treatment. Patients with an increased level of Human Leukocyte Antigen-DR isotype, tumor-infiltrating lymphocytes, Fizzy-related protein homolog, and a decreased level of tumor-associated macrophages appear to benefit most from NACT.

Risk-adapted modulation through de-intensification of cancer treatments: an ESMO classification

BACKGROUND

The landscape of clinical trials testing risk-adapted modulations of cancer treatments is complex. Multiple trial designs, endpoints, and thresholds for non-inferiority have been used; however, no consensus or convention has ever been agreed to categorise biomarkers useful to inform the treatment intensity modulation of cancer treatments.

METHODS

An expert subgroup under the European Society for Medical Oncology (ESMO) Precision Medicine Working Group shaped an international collaborative project to develop a classification system for biomarkers used in the cancer treatment de-intensification, based on a tiered approach. A group of disease-oriented clinical, translational, methodology and public health experts, and patients' representatives provided an analysis of the status quo, and scanned the horizon of ongoing clinical trials. The classification was developed through multiple rounds of expert revisions and inputs.

RESULTS

The working group agreed on a univocal definition of treatment de-intensification. Evidence of reduction in the dose-density, intensity, or cumulative dose, including intermittent schedules or shorter treatment duration or deletion of segment(s) of the standard regimens, compound(s), or treatment modality must be demonstrated, to define a treatment de-intensification. De-intensified regimens must also portend a positive impact on toxicity, quality of life, health system burden, or financial toxicity. ESMO classification categorises the biomarkers for treatment modulation in three tiers, based on the level of evidence. Tier A includes biomarkers validated in prospective, randomised, non-inferiority clinical trials. The working group agreed that in non-inferiority clinical trials, boundaries are highly dependent upon the disease scenario and endpoint being studied and that the absolute differences in the outcomes are the most relevant measures, rather than relative differences. Biomarkers tested in single-arm studies with a threshold of non-inferiority are classified as Tier B. Tier C is when the validation occurs in prospective-retrospective quality cohort investigations.

CONCLUSIONS

ESMO classification for the risk-guided intensity modulation of cancer treatments provides a set of evidence-based criteria to categorise biomarkers deemed to inform de-intensification of cancer treatments, in risk-defined patients. The classification aims at harmonising definitions on this matter, therefore offering a common language for all the relevant stakeholders, including clinicians, patients, decision-makers, and for clinical trials.

Intrinsic subtypes in Ethiopian breast cancer patient

PURPOSE

The recent development of multi-gene assays for gene expression profiling has contributed significantly to the understanding of the clinically and biologically heterogeneous breast cancer (BC) disease. PAM50 is one of these assays used to stratify BC patients and individualize treatment. The present study was conducted to characterize PAM50-based intrinsic subtypes among Ethiopian BC patients.

PATIENTS AND METHODS

Formalin-fixed paraffin-embedded tissues were collected from 334 BC patients who attended five different Ethiopian health facilities. All samples were assessed using the PAM50 algorithm for intrinsic subtyping.

RESULTS

The tumor samples were classified into PAM50 intrinsic subtypes as follows: 104 samples (31.1%) were luminal A, 91 samples (27.2%) were luminal B, 62 samples (18.6%) were HER2-enriched and 77 samples (23.1%) were basal-like. The intrinsic subtypes were found to be associated with clinical and histopathological parameters such as steroid hormone receptor status, HER2 status, Ki-67 proliferation index and tumor differentiation, but not with age, tumor size or histological type. An immunohistochemistry-based classification of tumors (IHC groups) was found to correlate with intrinsic subtypes.

CONCLUSION

The distribution of the intrinsic subtypes confirms previous immunohistochemistry-based studies from Ethiopia showing potentially endocrine-sensitive tumors in more than half of the patients. Health workers in primary or secondary level health care facilities can be trained to offer endocrine therapy to improve breast cancer care. Additionally, the findings indicate that PAM50-based classification offers a robust method for the molecular classification of tumors in the Ethiopian context.

A nomogram for predicting probability of low risk of MammaPrint results in women with clinically high-risk breast cancer

We aimed to develop a prediction MammaPrint (MMP) genomic risk assessment nomogram model for hormone-receptor positive (HR+) and human epidermal growth factor receptor-2 negative (HER2–) breast cancer and minimal axillary burden (N0-1) tumors using clinicopathological factors of patients who underwent an MMP test for decision making regarding adjuvant chemotherapy. A total of 409 T1-3 N0-1 M0 HR + and HER2– breast cancer patients whose MMP genomic risk results and clinicopathological factors were available from 2017 to 2020 were analyzed. With randomly selected 306 patients, we developed a nomogram for predicting a low-risk subgroup of MMP results and externally validated with remaining patients (n = 103). Multivariate analysis revealed that the age at diagnosis, progesterone receptor (PR) score, nuclear grade, and Ki-67 were significantly associated with MMP risk results. We developed an MMP low-risk predictive nomogram. With a cut off value at 5% and 95% probability of low-risk MMP, the nomogram accurately predicted the results with 100% positive predictive value (PPV) and negative predictive value respectively. When applied to cut-off value at 35%, the specificity and PPV was 95% and 86% respectively. The area under the receiver operating characteristic curve was 0.82 (95% confidence interval [CI] 0.77 to 0.87). When applied to the validation group, the nomogram was accurate with an area under the curve of 0.77 (95% CI 0.68 to 0.86). Our nomogram, which incorporates four traditional prognostic factors, i.e., age, PR, nuclear grade, and Ki-67, could predict the probability of obtaining a low MMP risk in a cohort of high clinical risk patients. This nomogram can aid the prompt selection of patients who does not need additional MMP testing.