Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-positive, HER2-negative Breast Cancer Patients

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

BACKGROUND

While the efficacy of neoadjuvant chemotherapy (NACT) in treating triple-negative breast cancer (TNBC) is generally accepted, not all patients derive benefit from this preoperative treatment. Presently, there are no validated biomarkers to predict the NACT response, and previous attempts to develop predictive classifiers based on gene expression data have not demonstrated clinical utility. However, predictive models incorporating biological constraints have shown increased robustness and improved performance compared to agnostic classifiers.

METHODS

We used the preoperative transcriptomic profiles from 298 patients with TNBC to train and test a rank-based classifier, k-top scoring pairs, to predict whether the patient will have pathological complete response (pCR) or residual disease (RD) following NACT. To reduce overfitting and enhance the signature's interpretability, we constrained the training process to genes involved in the Notch signaling pathway. Subsequently, we evaluated the signature performance on two independent cohorts with 75 and 71 patients. Finally, we assessed the prognostic value of the signature by examining its association with relapse-free survival (RFS) using Kaplan‒Meier (KM) survival estimates and a multivariate Cox proportional hazards model.

RESULTS

The final signature consists of five gene pairs, whose relative ordering can be predictive of the NACT response. The signature has a robust performance at predicting pCR in TNBC patients with an area under the ROC curve (AUC) of 0.76 and 0.85 in the first and second testing cohorts, respectively, outperforming other gene signatures developed for the same purpose. Additionally, the signature was significantly associated with RFS in an independent TNBC patient cohort even after adjusting for T stage, patient age at the time of diagnosis, type of breast surgery, and menopausal status.

CONCLUSION

We introduce a robust gene signature to predict pathological complete response (pCR) in patients with TNBC. This signature applies easily interpretable, rank-based decision rules to genes regulated by the Notch signaling pathway, a known determinant in breast cancer chemoresistance. The robust predictive and prognostic performance of the signature make it a strong candidate for clinical implementation, aiding in the stratification of TNBC patients undergoing NACT.

Predictive value of machine learning for breast cancer recurrence: a systematic review and meta-analysis

PURPOSE

Recurrence of breast cancer leads to a high lifetime risk and a low 5 year survival rate. Researchers have used machine learning to predict the risk of recurrence in patients with breast cancer, but the predictive performance of machine learning remains controversial. Hence, this study aimed to explore the accuracy of machine learning in predicting breast cancer recurrence risk and aggregate predictive variables to provide guidance for the development of subsequent risk scoring systems.

METHODS

We searched Pubmed, EMBASE, Cochrane, and Web of Science. The risk of bias in the included studies was evaluated using prediction model risk of bias assessment tool (PROBAST). Meta-regression was adopted to explore whether there was a significant difference in the recurrence time by machine learning.

RESULTS

Thirty-four studies involving 67,560 subjects were included, among whom 8695 experienced breast cancer recurrence. The c-index of prediction models was 0.814 (95%CI 0.802-0.826) and 0.770 (95%CI 0.737-0.803) in the training and validation sets, respectively; the sensitivity and specificity were 0.69 (95% CI 0.64-0.74), 0.89 (95% CI 0.86-0.92) in the training, and 0.64 (95% CI 0.58-0.70), 0.88 (95% CI 0.82-0.92) in the validation, respectively. Age, histological grading, and lymph node status are the most commonly used variables in model construction. Attention should be paid to unhealthy lifestyles such as drinking, smoking and BMI as modeling variables. Risk prediction models based on machine learning have long-term monitoring value for breast cancer population, and subsequent studies should consider using large-sample and multi-center data to establish risk equations for verification.

CONCLUSION

Machine learning may be used as a predictive tool for breast cancer recurrence. Currently, there is a lack of effective and universally applicable machine learning models in clinical practice. We expect to incorporate multi-center studies in the future and attempt to develop tools for predicting breast cancer recurrence risk, so as to effectively identify populations at high risk of recurrence and develop personalized follow-up strategies and prognostic interventions to reduce the risk of recurrence.

A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

Medical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer.

METHODS

Data on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced breast MRI were included. Response to NAC was assessed using the Miller-Payne system on the excised tumor. Tumor segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator regression. A support vector machine learning model was used to classify response to NAC.

RESULTS

74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity < 90%, n = 44) and an excellent response (> 90% reduction in cellularity, n = 30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor status improved the accuracy of the model with an AUC of 0.811.

CONCLUSION

This study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review

Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

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

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

Tumor-Infiltrating Lymphocytes Improve Magee Equation-Based Prediction of Pathologic Complete Response in HR-Positive/HER2-Negative Breast Cancer

OBJECTIVES

Magee equation 3 (ME3) is predictive of the pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer but with insufficient predictive performance. This study was designed to improve predictive ability by combining ME3 with additional clinicopathologic markers.

METHODS

We retrospectively enrolled 460 patients with HR-positive/HER2-negative breast cancer from 2 centers. We obtained baseline characteristics, the ME3 score, and the number of stromal tumor-infiltrating lymphocytes (sTILs). After performing a logistic regression analysis, a predictive nomogram was built and validated externally.

RESULTS

ME3 score (adjusted odds ratio [OR], 1.14 [95% confidence interval (CI), 1.10-1.17]; P < .001) and TILs (adjusted OR, 5.21 [95% CI, 3.33-8.14]; P < .001) were independently correlated with pCR. The nomogram (named ME3+) was established using ME3 and sTILs, and it demonstrated an area under the curve of 0.816 and 0.862 in internal and external validation, respectively, outperforming the ME3 score alone. sTILs and ME3 scores were also found to be positively correlated across the entire cohort (P < .001).

CONCLUSIONS

The combination of sTILs and ME3 score potentially shows better performance for predicting pCR than ME3 alone. Larger validations are required for widespread application of ME3+ nomogram in NAC settings for HR-positive/HER2-negative breast cancer.

Gene-expression signatures to inform neoadjuvant treatment decision in HR+/HER2- breast cancer: Available evidence and clinical implications

Over the last few years, the indication for chemotherapy use in HR+/HER2- early BC has been significantly modified by the introduction of gene-expression profiling. In the adjuvant setting, several gene-expression signatures have been validated to discriminate early stage HR+/HER2- BC with different prognosis and to identify patients for which adjuvant chemotherapy can be spared. Considering their ability to optimize the choice of adjuvant treatment and the increasing use of neoadjuvant approach in early BC, the potential use of gene-expression signatures to discriminate patients to be candidate to neoadjuvant chemotherapy or endocrine treatment appears particularly appealing. Indeed, the San Gallen Consensus Conference panel recently endorsed the use of genomic assays on core biopsies as a potential strategy for choosing the type of neoadjuvant treatment (chemotherapy or endocrine therapy) in selected patients. In this context, we here review evidence supporting the use of most common commercially available gene-expression signatures (Oncotype DX, MammaPrint, PAM50, EndoPredict and Breast Cancer Index) in patients receiving neoadjuvant therapy for HR+/HER2- BC. Data on the association of gene expression signatures and response to neoadjuvant chemotherapy or neoadjuvant endocrine therapy will be reviewed and the clinical implications of this data to guide the clinical decision-making process in early HR+/HER2- BC will be discussed.

Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review

Simple Summary

Breast cancer is the most common cancer in women worldwide. Although many studies have aimed to understand the genetic basis of breast cancer, leading to increasingly accurate diagnoses, only a few molecular biomarkers are used in clinical practice to predict response to therapy. Current studies aim to develop more personalized therapies to decrease the adverse effects of chemotherapy. Personalized medicine not only requires clinical, but also molecular characterization of tumors, which allows the use of more effective drugs for each patient. The aim of this study was to identify potential molecular biomarkers that can predict the response to therapy after neoadjuvant chemotherapy in patients with breast cancer. In this review, we summarize genomic, transcriptomic, and proteomic biomarkers that can help predict the response to therapy.

Abstract

Neoadjuvant chemotherapy (NAC) is often used to treat locally advanced disease for tumor downstaging, thus improving the chances of breast-conserving surgery. From the NAC response, it is possible to obtain prognostic information as patients may reach a pathological complete response (pCR). Those who do might have significant advantages in terms of survival rates. Breast cancer (BC) is a heterogeneous disease that requires personalized treatment strategies. The development of targeted therapies depends on identifying biomarkers that can be used to assess treatment efficacy as well as the discovery of new and more accurate therapeutic agents. With the development of new “OMICS” technologies, i.e., genomics, transcriptomics, and proteomics, among others, the discovery of new biomarkers is increasingly being used in the context of clinical practice, bringing us closer to personalized management of BC treatment. The aim of this review is to compile the main biomarkers that predict pCR in BC after NAC.

VGH-TAYLOR: Comprehensive precision medicine study protocol on the heterogeneity of Taiwanese breast cancer patients

Heterogeneity in breast cancer leads to diverse morphological features and different clinical outcomes. There are inherent differences in breast cancer between the populations in Asia and in western countries. The use of immune-based treatment in breast cancer is currently in the developmental stage. The VGH-TAYLOR study is designed to understand the genetic profiling of different subtypes of breast cancer in Taiwan and define the molecular risk factors for breast cancer recurrence. The T-cell receptor repertoire and the potential effects of immunotherapy in breast cancer subjects is evaluated. The favorable biomarkers for early detection of tumor recurrence, diagnosis and prognosis may provide clues for the selection of individualized treatment regimens and improvement in breast cancer therapy.

Novel and Highly Potent ATR Inhibitor M4344 Kills Cancer Cells With Replication Stress, and Enhances the Chemotherapeutic Activity of Widely Used DNA Damaging Agents

Although several ATR inhibitors are in development, there are unresolved questions regarding their differential potency, molecular signatures of patients with cancer for predicting activity, and most effective therapeutic combinations. Here, we elucidate how to improve ATR-based chemotherapy with the newly developed ATR inhibitor, M4344 using in vitro and in vivo models. The potency of M4344 was compared with the clinically developed ATR inhibitors BAY1895344, berzosertib, and ceralasertib. The anticancer activity of M4344 was investigated as monotherapy and combination with clinical DNA damaging agents in multiple cancer cell lines, patient-derived tumor organoids, and mouse xenograft models. We also elucidated the anticancer mechanisms and potential biomarkers for M4344. We demonstrate that M4344 is highly potent among the clinically developed ATR inhibitors. Replication stress (RepStress) and neuroendocrine (NE) gene expression signatures are significantly associated with a response to M4344 treatment. M4344 kills cancer cells by inducing cellular catastrophe and DNA damage. M4344 is highly synergistic with a broad range of DNA-targeting anticancer agents. It significantly synergizes with topotecan and irinotecan in patient-derived tumor organoids and xenograft models. Taken together, M4344 is a promising and highly potent ATR inhibitor. It enhances the activity of clinical DNA damaging agents commonly used in cancer treatment including topoisomerase inhibitors, gemcitabine, cisplatin, and talazoparib. RepStress and NE gene expression signatures can be exploited as predictive markers for M4344.

OncotypeDX© Recurrence Score in BRCA mutation carriers: a systematic review and meta-analysis

INTRODUCTION

There are limited data comparing the OncotypeDX© Recurrence Score (RS) among BRCA mutation carriers and patients with sporadic breast cancer.

AIM

To compare RS results among BRCA mutation carriers and patients with sporadic breast cancer in oestrogen receptor positive (ER+), human epidermal growth factor receptor-2 negative (HER2-) breast cancer.

METHODS

A systematic review was performed in accordance with PRISMA and MOOSE guidelines. Retrospective cohort studies comparing RS in BRCA mutation carriers and cases of sporadic cancer were included. Dichotomous variables were pooled as odds ratios (ORs) and associated 95% confidence intervals (CIs) using the Mantel-Haenszel method.

RESULTS

Five studies involving 4286 patients were included with a mean age of 60 years (range 22-85). Overall, 7.8% were BRCA mutation carriers (333/4286). The mean RS was 18.0 (range 0-71), and the mean RS in BRCA carriers was 25 (range 10-71) versus 18.4 in cases of sporadic disease (range 0-62). Patients with sporadic cancers were more likely to have RS < 18 (OR 0.27, 95% CI 0.14-0.51, P = 0.010). BRCA mutation carriers were more likely to have RS 18-30 (OR 1.74, 95% CI 1.28-2.37, P < 0.001) and RS > 30 (OR 3.71, 95% CI 2.55-5.40, P < 0.001).

CONCLUSION

There is an increased likelihood of high-risk RS among patients with known germline BRCA mutations when compared to patients developing sporadic ER+/HER2-early breast cancer. This study offers insight into genomic testing results within BRCA mutation carriers which may be useful in counselling patients with BRCA mutations in future practice.

A nine-lncRNA signature predicts distant relapse-free survival of HER2-negative breast cancer patients receiving taxane and anthracycline-based neoadjuvant chemotherapy

Multi-gene prognostic signatures of long non-coding RNAs (lncRNAs) provide new insights into mechanisms of HER2-negative breast cancer development and progression, and predict distant relapse-free survival (DRFS) of patients receiving taxane and anthracycline-based neoadjuvant chemotherapy. The aim of this study was to develop such a multi-lncRNAs signature. Optimal multiple candidate signature lncRNAs associated with DRFS were firstly identified by a univariate Cox proportional hazard regression survival analysis and a robust likelihood-based survival analysis of the GEO dataset GSE25055. A nine-lncRNA prognostic risk score model Risk Score = 0.0289 × EXP_LOC100507388 - 0.0814 × EXP_LINC00094 - 0.2422 × EXP_SMG7-AS1 - 0.2433 × EXP_PP14571 + 0.4690 × EXP_ASAP1-IT1 - 0.2483 × EXP_LOC103344931 - 0.2464 × EXP_FAM182A + 0.3349 × EXP_HCG26 - 0.0216 × EXP_LINC00963 was built according to the coefficients of multivariate survival analysis of the association between the candidate lncRNAs and survival. EXP_lncRNA was the standardized log2-transformed expression level of the gene. According to this model, higher scores predicted lower survival probability. The area under Receiver operating characteristic (ROC) curve (AUC) was 0.777 to 0.823 from 1- to 7- year survival rate. The model and its individual lncRNAs differentiated survival probability between the higher scores (expression) and the lower scores (expression). The nine-lncRNA signature had the robust prognostic power compared with ER, PR, tumor size (T), lymph node invasion (N), TNM stage, pathologic response, chemosensitivity prediction and PAM50 signature. These results were consistent with those based on the GEO dataset GSE25065. The predictive nomograms integrating both the nine-lncRNA signature classifier and clinical-pathological risk factors were robust in predicting 1-, 3- and 5- year survival probabilities. These results supported that the nine-lncRNA signature was a robust and effective model in predicting DRFS of patients with HER2-negative breast cancer following taxane and anthracycline-based neoadjuvant chemotherapy.