The PAM50 risk-of-recurrence score predicts risk for late distant recurrence after endocrine therapy in postmenopausal women with endocrine-responsive early breast cancer

Gene Expression Assays to Tailor Adjuvant Endocrine Therapy for HR+/HER2- Breast Cancer

Adjuvant endocrine therapy (ET) represents the standard of care for almost all hormone receptor (HR)+/HER2- breast cancers, and different agents and durations are currently available. In this context, the tailoring and optimization of adjuvant endocrine treatment by reducing unnecessary toxic treatment while taking into account the biological heterogeneity of HR+/HER2- breast cancer represents a clinical priority. There is therefore a significant need for the integration of biological biomarkers in the choice of adjuvant ET beyond currently used clinicopathological characteristics. Several gene expression assays have been developed to identify patients with HR+/HER2- breast cancer who will not derive benefit from the addition of adjuvant chemotherapy. By enhancing risk stratification and predicting therapeutic response, genomic assays have also shown to be a promising tool for optimizing endocrine treatment decisions. In this study, we review evidence supporting the use of most common commercially available gene expression assays [Oncotype DX, MammaPrint, Breast Cancer Index (BCI), Prosigna, and EndoPredict] in tailoring adjuvant ET. Available data on the use of genomic tests to inform extended adjuvant treatment choice based on the risk of late relapse and on the estimated benefit of a prolonged ET are discussed. Moreover, preliminary evidence regarding the use of genomic assays to inform de-escalation of endocrine treatment, such as shorter durations or omission, for low-risk patients is reviewed. Overall, gene expression assays are emerging as potential tools to further personalize adjuvant treatment for patients with HR+/HER2- breast cancers.

High Caveolin-1 mRNA expression in triple-negative breast cancer is associated with an aggressive tumor microenvironment, chemoresistance, and poor clinical outcome

BACKGROUND

Currently, there are few treatment-predictive and prognostic biomarkers in triple-negative breast cancer (TNBC). Caveolin-1 (CAV1) is linked to chemoresistance and several important processes involved in tumor progression and metastasis, such as epithelial-mesenchymal transition (EMT). Herein, we report that high CAV1 gene expression is an independent factor of poor prognosis in TNBC.

METHODS

CAV1 gene expression was compared across different molecular features (e.g., PAM50 subtypes). CAV1 expression was assessed in relation to clinical outcomes using Cox regression adjusted for clinicopathological predictors. Differential gene expression and gene set enrichment analyses were applied to compare high- and low-expressing CAV1 tumors. Tumor microenvironment composition of high- and low-expressing CAV1 tumors was estimated using ECOTYPER. Tumor tissue microarrays were used to evaluate CAV1 protein levels in stromal and malignant cells.

RESULTS

In the SCAN-B (n = 525) and GSE31519 (n = 327) cohorts, patients with CAV1-high tumors had an increased incidence of early recurrence adjusted HR 1.78 (95% CI 1.12-2.81) and 2.20 (95% CI 1.39-3.47), respectively. In further analysis, high CAV1 gene expression was associated with a molecular profile indicating altered metabolism, neovascularization, chemoresistance, EMT, suppressed immune response, and active tumor microenvironment. Protein levels of CAV1 in malignant and stromal cells were not correlated with CAV1 gene expression.

CONCLUSION

CAV1 gene expression in TNBC is a biomarker that merits further investigation in clinical trials and as a therapeutic target.

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

PURPOSE

To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients categorized as intermediate risk based on classic clinicopathological variables and eligible for chemotherapy.

METHODS

In a case series comprising 234 invasive ER-positive/HER2-negative tumors, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved. The digitized HE slides were analysed by Stratipath Breast.

RESULTS

Our findings showed that the Stratipath Breast analysis identified 49.6% of the clinically intermediate tumors as low risk and 50.4% as high risk. The Prosigna assay classified 32.5%, 47.0% and 20.5% tumors as low, intermediate and high risk, respectively. Among Prosigna intermediate-risk tumors, 47.3% were stratified as Stratipath low risk and 52.7% as high risk. In addition, 89.7% of Stratipath low-risk cases were classified as Prosigna low/intermediate risk. The overall agreement between the two tests for low-risk and high-risk groups (N = 124) was 71.0%, with a Cohen's kappa of 0.42. For both risk profiling tests, grade and Ki67 differed significantly between risk groups.

CONCLUSION

The results from this clinical evaluation of image-based risk stratification shows a considerable agreement to an established gene expression assay in routine breast pathology.

Clinical genetics in breast cancer

As genetic testing becomes increasingly more accessible and more applicable with a broader range of clinical implications, it may also become more challenging for breast cancer providers to remain up-to-date. This review outlines some of the current clinical guidelines and recent literature surrounding germline genetic testing, as well as genomic testing, in the screening, prevention, diagnosis, and treatment of breast cancer, while identifying potential areas of further research.

Clinical and molecular predictors of very late recurrence in oestrogen receptor-positive breast cancer patients

BACKGROUND

Risk of recurrence from primary ER+ breast cancer continues for at least 20 years. We aimed to identify clinical and molecular features associated with risk of recurrence after 10 years.

METHODS

ER+ breast cancers from patients with and without recurrence were analysed with the BC360 NanoString Panel and an 87 gene targeted-exome panel. Frequency of clinical, pathologic and molecular characteristics was compared between cases (recurred between 10 and 20 years) and controls (no recurrence by 20 years) in the Very Late Recurrence (VLR) cohort. Analogous data from METABRIC were examined to confirm or refute findings.

RESULTS

VLR cases had larger tumours and higher node positivity. Both VLR and METABRIC cases had higher clinical treatment score at 5 years (CTS5). There was a trend for fewer GATA3 mutations in cases in both VLR and METABRIC but no statistically significant differences in mutation frequency. Cell cycle and proliferation genes were strongly expressed in VLR cases. Immune-related genes and cell cycle inhibitors were highly expressed in controls. Neither of these changes were significant after correction for multiple testing.

CONCLUSIONS

Clinicopathologic features are prognostic beyond 10 years. Conversely, molecular features, such as copy number alterations, TP53 mutations and intrinsic subtype which have early prognostic significance, have little prognostic value after 10 years.

Enhancing clinical decision support with genomic tools in breast cancer: A Scottish perspective

INTRODUCTION

The Oncotype DX Breast RS test has been adopted in Scotland and has been the subject of a large population-based study by a Scottish Consensus Group to assess the uptake of the recurrence score (RS), evaluate co-variates associated with the RS and to analyse the effect it may have had on clinical practice.

MATERIALS & METHODS

Pan-Scotland study between August 2018-August 2021 evaluating 833 patients who had a RS test performed as part of their diagnostic pathway. Data was extracted retrospectively from electronic records and analysis conducted to describe change in chemotherapy administration (by direct comparison with conventional risk assessment tools), and univariate/multivariate analysis to assess relationship between covariates and the RS.

RESULTS

Chemotherapy treatment was strongly influenced by the RS (p < 0.001). Only 30 % of patients received chemotherapy treatment in the intermediate and high risk PREDICT groups, where chemotherapy is considered. Additionally, 55.5 % of patients with a high risk PREDICT had a low RS and did not receive chemotherapy. There were 17 % of patients with a low risk PREDICT but high RS who received chemotherapy. Multivariate regression analysis showed the progesterone receptor Allred score (PR score) to be a strong independent predictor of the RS, with a negative PR score being associated with high RS (OR 4.49, p < 0.001). Increasing grade was also associated with high RS (OR 3.81, p < 0.001). Classic lobular pathology was associated with a low RS in comparison to other tumour pathology (p < 0.01). Nodal disease was associated with a lower RS (p = 0.012) on univariate analysis, with menopausal status (p = 0.43) not influencing the RS on univariate or multivariate analysis.

CONCLUSIONS

Genomic assays offer the potential for risk-stratified decision making regarding the use of chemotherapy. They can help reduce unnecessary chemotherapy treatment and identify a subgroup of patients with more adverse genomic tumour biology. A recent publication by Health Improvement Scotland (HIS) has updated guidance on use of the RS test for NHS Scotland. It suggests to limit its use to the intermediate risk PREDICT group. Our study shows the impact of the RS test in the low and high risk PREDICT groups. The implementation across Scotland has resulted in a notable shift in practice, leading to a significant reduction in chemotherapy administration in the setting of high risk PREDICT scores returning low risk RS. There has also been utility for the test in the low risk PREDICT group to detect a small subgroup with a high RS. We have found the PR score to have a strong independent association with high risk RS. This finding was not evaluated by the key RS test papers, and the potential prognostic information provided by the PR score as a surrogate biomarker is an outstanding question that requires more research to validate.

Breast Cancer Index and Prediction of Extended Aromatase Inhibitor Therapy Benefit in Hormone Receptor-Positive Breast Cancer from the NRG Oncology/NSABP B-42 Trial

PURPOSE

BCI (H/I) has been shown to predict extended endocrine therapy (EET) benefit. We examined BCI (H/I) for EET benefit prediction in NSABP B-42, which evaluated extended letrozole therapy (ELT) in patients with hormone receptor-positive breast cancer after 5 years of ET.

EXPERIMENTAL DESIGN

A stratified Cox model was used to analyze RFI as the primary endpoint, with DR, BCFI, and DFS as secondary endpoints. Because of a nonproportional effect of ELT on DR, time-dependent analyses were performed.

RESULTS

The translational cohort included 2,178 patients (45% BCI (H/I)-High, 55% BCI (H/I)-Low). ELT showed an absolute 10-year RFI benefit of 1.6% (P = 0.10), resulting in an underpowered primary analysis (50% power). ELT benefit and BCI (H/I) did not show a significant interaction for RFI (BCI (H/I)-Low: 10 years absolute benefit 1.1% [HR, 0.70; 95% confidence interval (CI), 0.43-1.12; P = 0.13]; BCI (H/I)-High: 2.4% [HR, 0.83; 95% CI, 0.55-1.26; P = 0.38]; Pinteraction = 0.56). Time-dependent DR analysis showed that after 4 years, BCI (H/I)-High patients had significant ELT benefit (HR = 0.29; 95% CI, 0.12-0.69; P < 0.01), whereas BCI (H/I)-Low patients were less likely to benefit (HR, 0.68; 95% CI, 0.33-1.39; P = 0.29; Pinteraction = 0.14). Prediction of ELT benefit by BCI (H/I) was more apparent in the HER2- subset after 4 years (ELT-by-BCI (H/I) Pinteraction = 0.04).

CONCLUSIONS

BCI (H/I)-High versus BCI (H/I)-Low did not show a statistically significant difference in ELT benefit for the primary endpoint (RFI). However, in time-dependent DR analysis, BCI (H/I)-High patients experienced statistically significant benefit from ELT after 4 years, whereas (H/I)-Low patients did not. Because BCI (H/I) has been validated as a predictive marker of EET benefit in other trials, additional follow-up may enable further characterization of BCI's predictive ability.

Caveolin-1 gene expression provides additional prognostic information combined with PAM50 risk of recurrence (ROR) score in breast cancer

Combining information from the tumor microenvironment (TME) with PAM50 Risk of Recurrence (ROR) score could improve breast cancer prognostication. Caveolin-1 (CAV1) is a marker of an active TME. CAV1 is a membrane protein involved in cell signaling, extracellular matrix organization, and tumor-stroma interactions. We sought to investigate CAV1 gene expression in relation to PAM50 subtypes, ROR score, and their joint prognostic impact. CAV1 expression was compared between PAM50 subtypes and ROR categories in two cohorts (SCAN-B, n = 5326 and METABRIC, n = 1980). CAV1 expression was assessed in relation to clinical outcomes using Cox regression and adjusted for clinicopathological predictors. Effect modifications between CAV1 expression and ROR categories on clinical outcome were investigated using multiplicative and additive two-way interaction analyses. Differential gene expression and gene set enrichment analyses were applied to compare high and low expressing CAV1 tumors. All samples expressed CAV1 with the highest expression in the Normal-like subtype. Gene modules consistent with epithelial-mesenchymal transition (EMT), hypoxia, and stromal activation were associated with high CAV1 expression. CAV1 expression was inversely associated with ROR category. Interactions between CAV1 expression and ROR categories were observed in both cohorts. High expressing CAV1 tumors conferred worse prognosis only within the group classified as ROR high. ROR gave markedly different prognostic information depending on the underlying CAV1 expression. CAV1, a potential mediator between the malignant cells and TME, could be a useful biomarker that enhances and further refines PAM50 ROR risk stratification in patients with ROR high tumors and a potential therapeutic target.

Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis

PURPOSE

Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy.

METHODS

We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers.

RESULTS

Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal.

CONCLUSIONS

Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.

Long-term prognostic value of the GenesWell BCT score in Asian women with hormone receptor-positive/HER2-negative early breast cancer

BACKGROUND

Accurate prediction of the risk of recurrence is crucial for optimal treatment decisions in hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative early breast cancer. The GenesWell BCT is a molecular assay to predict the 10-year risk of distant metastasis. In this study, we evaluated the long-term prognostic value of the GenesWell BCT assay.

METHODS

The BCT score was assessed in patients with HR-positive/HER2-negative early breast cancer who did not receive chemotherapy. We compared the 15-year distant metastasis-free survival (DMFS) between risk groups classified based on the BCT score. The risk of early (0-5 years) and late (5-15 years) recurrence was evaluated based on the BCT score classification.

RESULTS

According to the BCT score, 366 patients from Japan and Korea were categorized as BCT low risk (83.6%) and high risk (16.4%) for distant metastasis. Median follow-up time was 17.4 years. The 15-year DMFS rate was significantly lower in the BCT high-risk group (63.3%) than in the BCT low-risk group (93.6%) (P < 0.001). The BCT risk group was an independent prognostic factor for 15-year DMFS (hazard ratio, 4.59; 95% confidence interval 2.13-9.88; P < 0.001). Furthermore, the BCT score was a significant predictor of late recurrence (5-15 years) in patients aged ≤ 50 years and those aged > 50 years, and added prognostic information to traditional clinical prognostic factors.

CONCLUSION

The BCT score can identify patients at low risk for recurrence who may not require adjuvant chemotherapy or extended endocrine therapy, regardless of age.

Ten-year update: NRG Oncology/National Surgical Adjuvant Breast and Bowel Project B-42 randomized trial: extended letrozole therapy in early-stage breast cancer

BACKGROUND

The National Surgical Adjuvant Breast and Bowel Project B-42 trial evaluated extended letrozole therapy (ELT) in postmenopausal breast cancer patients who were disease free after 5 years of aromatase inhibitor (AI)-based therapy. Seven-year results demonstrated a nonstatistically significant trend in disease-free survival (DFS) in favor of ELT. We present 10-year outcome results.

METHODS

In this double-blind, phase III trial, patients with stage I-IIIA hormone receptor-positive breast cancer, disease free after 5 years of an AI or tamoxifen followed by an AI, were randomly assigned to 5 years of letrozole or placebo. Primary endpoint was DFS, defined as time from random assignment to breast cancer recurrence, second primary malignancy, or death. All statistical tests are 2-sided.

RESULTS

Between September 2006 and January 2010, 3966 patients were randomly assigned (letrozole: 1983; placebo: 1983). Median follow-up time for 3923 patients included in efficacy analyses was 10.3 years. There was statistically significant improvement in DFS in favor of letrozole compared with placebo (hazard ratio [HR] = 0.85, 95% confidence interval [CI] = 0.74 to 0.96; P  = .01; 10-year DFS: placebo = 72.6%, letrozole = 75.9%, absolute difference = 3.3%). There was no difference in the effect of letrozole on overall survival (HR = 0.97, 95% CI = 0.82 to 1.15; P  = .74). Letrozole statistically significantly reduced breast cancer-free interval events (HR = 0.75, 95% CI = 0.62 to 0.91; P = .003; absolute difference in cumulative incidence = 2.7%) and distant recurrences (HR = 0.72, 95% CI = 0.55 to 0.92; P = .01; absolute difference = 1.8%). The rates of osteoporotic fractures and arterial thrombotic events did not differ between treatment groups.

CONCLUSIONS

The beneficial effect of ELT on DFS persisted at 10 years. Letrozole also improved breast cancer-free interval and distant recurrences without improving overall survival. Careful assessment of potential risks and benefits is necessary for selecting appropriate candidates for ELT.

Precision medicine in extended adjuvant endocrine therapy for breast cancer

PURPOSE OF REVIEW

In this review, the evolving role of currently available genomic assays for hormone receptor-positive, early-stage breast cancer in the selection of patients for extended adjuvant endocrine therapy will be discussed.

RECENT FINDINGS

Several studies have investigated the prognostic performance of the Oncotype DX, Breast Cancer Index (BCI), Prosigna, and EndoPredict genomic assays in the late recurrence setting (>5 years after diagnosis), beyond standardly used clinicopathologic parameters, with mixed results. Recently, BCI has also been validated to predict the likelihood of benefit from extended endocrine therapy, though certain data limitations may need to be addressed to justify routine use in clinical practice.

SUMMARY

Even after 5 years of adjuvant endocrine therapy, patients with hormone receptor-positive breast cancer have a significant risk for late recurrence, including distant metastases, that might be prevented with longer durations of endocrine therapy. However, the added toxicity and variable benefit derived from extended endocrine therapy make optimal patient selection crucial. Genomic assays are in development to risk-stratify patients for late recurrence and determine efficacy of extended endocrine therapy, with the aim to help guide extended endocrine therapy decisions for clinicians and individualize treatment strategies for patients.

Incorporating Tumor Biology to Select Patients for the Omission of Radiation Therapy

Although adjuvant breast radiotherapy has long been a universal component of breast conservation therapy (BCT), it is now clear that "breast cancer" is a broad class of many disparate diseases with varying natural histories and risk profiles. In turn, some breast conservation patients enjoy exceedingly favorable outcomes following surgery alone. Ongoing trials seek to identify such low-risk patient populations, hypothesizing that some may safely forego radiotherapy. Whereas prior-generation trials focused on clinicopathologic features for risk stratification, contemporary studies are employing molecular biomarkers to identify those patients who are unlikely to benefit significantly from radiotherapy.

Neoadjuvant endocrine therapy in ER-positive breast cancer: evolution, indication, and tailored treatment strategy

In recent years, endocrine therapy (ET), an effective systemic treatment for the management of estrogen receptor (ER)-positive breast cancers, has regained interest as a neoadjuvant therapy based on evidence that ET can fulfill the aim of neoadjuvant systemic treatment for tumor shrinkage as well as elucidate important clinical information on endocrine sensitivity that enables the prognostication of patients. Moreover, neoadjuvant endocrine therapy (NET) potentially provides an opportunity for early assessment of the clinical efficacy of novel agents. Furthermore, recently reported trials have generated evidence for a more tailored approach for perioperative management of ER-positive breast cancer using clinical and molecular biomarkers, and this has provided a rationale that enables the broadening of clinical indications for NET. This review discusses the current evidence for NET, the evolution of NET trials, clinical indications, and NET-based treatment strategies.

Managing a Long and Winding Road: Estrogen Receptor-Positive Breast Cancer

We review key topics in the management of estrogen receptor (ER)-positive human epidermal growth factor receptor 2-negative breast cancer. The single biggest challenge in management of this disease is late relapse, and we review new methods for identifying which patients are at risk of late relapse and potential therapeutic approaches in clinical trials. CDK4/6 inhibitors have become a standard treatment option for high-risk patients in both the adjuvant setting and the first-line metastatic setting, and we review data on optimal treatment after progression on CDK4/6 inhibitors. Targeting the estrogen receptor remains the single most effective way of targeting the cancer, and we review the developments in new oral selective ER degraders that are becoming a standard of care in cancers with ESR1 mutations and potential future directions.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival

PURPOSE

PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples.

METHODS

We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival.

RESULTS

Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival.

CONCLUSION

Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Lessons from other fields of medicine, Part 1: Breast cancer

Through the understanding of multiple etiologies, pathologies, and disease progression trajectories, breast cancer shifted historically from a singular malignancy of the breast to a complex of molecular/biological entities, translating into individualized disease-modifying treatments. As a result, this led to various de-escalations of treatment compared with the gold standard in the era preceding systems biology: radical mastectomy. Targeted therapies have minimized morbidity from the treatments and mortality from the disease. Biomarkers further individualized tumor genetics and molecular biology to optimize treatments targeting specific cancer cells. Landmark discoveries in breast cancer management have evolved through histology, hormone receptors, human epidermal growth factor, single-gene prognostic markers, and multigene prognostic markers. Relevant to the reliance on histopathology in neurodegenerative disorders, histopathology evaluation in breast cancer can serve as a marker of overall prognosis rather than predict response to therapies. This chapter reviews the successes and failures of breast cancer research through history, with focus on the transition from a universal approach for all patients to divergent biomarker development and individualized targeted therapies, discussing future areas of growth in the field that may apply to neurodegenerative disorders.

Evaluation and comparison of different breast cancer prognosis scores based on gene expression data

BACKGROUND

Breast cancer is one of the three most common cancers worldwide and is the most common malignancy in women. Treatment approaches for breast cancer are diverse and varied. Clinicians must balance risks and benefits when deciding treatments, and models have been developed to support this decision-making. Genomic risk scores (GRSs) may offer greater clinical value than standard clinicopathological models, but there is limited evidence as to whether these models perform better than the current clinical standard of care.

METHODS

PREDICT and GRSs were adapted using data from the original papers. Univariable Cox proportional hazards models were produced with breast cancer-specific survival (BCSS) as the outcome. Independent predictors of BCSS were used to build multivariable models with PREDICT. Signatures which provided independent prognostic information in multivariable models were incorporated into the PREDICT algorithm and assessed for calibration, discrimination and reclassification.

RESULTS

EndoPredict, MammaPrint and Prosigna demonstrated prognostic power independent of PREDICT in multivariable models for ER-positive patients; no score predicted BCSS in ER-negative patients. Incorporating these models into PREDICT had only a modest impact upon calibration (with absolute improvements of 0.2-0.8%), discrimination (with no statistically significant c-index improvements) and reclassification (with 4-10% of patients being reclassified).

CONCLUSION

Addition of GRSs to PREDICT had limited impact on model fit or treatment received. This analysis does not support widespread adoption of current GRSs based on our implementations of commercial products.

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients

BACKGROUND

Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery.

METHODS

In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC).

RESULTS

A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model.

CONCLUSIONS

A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.

Neoadjuvant endocrine therapy for luminal breast tumors: State of the art, challenges and future perspectives

Neoadjuvant endocrine treatment (NET) associates to satisfactory rates of breast conservative surgery and conversions from inoperable to operable hormone receptor-positive (HR+)/HER2-negative breast cancer (BC), with less toxicities than neoadjuvant chemotherapy (NACT) and similar outcomes. Hence, it has been proposed as a logical alternative to NACT in patients with HR+/HER2- BC candidate to a neoadjuvant approach. Nevertheless, potential barriers to the widespread use of NET include the heterogeneous nature of patient response coupled with the long duration needed to achieve a clinical response. However, interest in NET has significantly increased in the last decade, owing to more in-depth investigation of several biomarkers for a more adequate patient selection and on-treatment benefit monitoring, such as PEPI score, Ki67 and genomic assays. This review is intended to describe the state-of-the-art regarding NET, its future perspectives and potential integration with molecular biomarkers for the optimal selection of patients, regimen and duration of (neo)adjuvant treatments.

Transcriptomic alterations underlying metaplasia into specific metaplastic components in metaplastic breast carcinoma

BACKGROUND

Metaplastic breast carcinoma (MpBC) typically consists of carcinoma of no special type (NST) with various metaplastic components. Although previous transcriptomic and proteomic studies have reported subtype-related heterogeneity, the intracase transcriptomic alterations between metaplastic components and paired NST components, which are critical for understanding the pathogenesis underlying the metaplastic processes, remain unclear.

METHODS

Fifty-nine NST components and paired metaplastic components (spindle carcinomatous [SPS], matrix-producing, rhabdoid [RHA], and squamous carcinomatous [SQC] components) were microdissected from specimens obtained from 27 patients with MpBC for gene expression profiling using the NanoString Breast Cancer 360 Panel on a NanoString nCounter FLEX platform. BC360-defined signatures were scored using nSolver software.

RESULTS

Hierarchical clustering and principal component analysis revealed a heterogeneous gene expression profile (GEP) corresponding to the NST components, but the GEP of metaplastic components exhibited subtype dependence. Compared with the paired NST components, the SPS components demonstrated the upregulation of genes related to stem cells and epithelial-mesenchymal transition and displayed enrichment in claudin-low and macrophage signatures. Despite certain overlaps in the enriched functions and signatures between the RHA and SPS components, the specific differentially expressed genes differed. We observed the RHA-specific upregulation of genes associated with vascular endothelial growth factor signaling. The chondroid matrix-producing components demonstrated the upregulation of hypoxia-related genes and the downregulation of the immune-related MHC2 signature and the TIGIT gene. In the SQC components, TGF-β and genes associated with cell adhesion were upregulated. The differentially expressed genes among metaplastic components in the 22 MpBC cases with one or predominantly one metaplastic component clustered paired NST samples into clusters with correlation with their associated metaplastic types. These genes could be used to separate the 31 metaplastic components according to respective metaplastic types with an accuracy of 74.2%, suggesting that intrinsic signatures of NST may determine paired metaplastic type. Finally, the EMT activity and stem cell traits in the NST components were correlated with specimens displaying lymph node metastasis.

CONCLUSIONS

We presented the distinct transcriptomic alterations underlying metaplasia into specific metaplastic components in MpBCs, which contributes to the understanding of the pathogenesis underlying morphologically distinct metaplasia in MpBCs.

The biomarkers related to immune infiltration to predict distant metastasis in breast cancer patients

Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC (p = 0.01, HR = 0.63), and it has been validated in internal (p = 0.023, HR = 0.58) and external (p = 0.0065, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.

Combined 70- and 80-gene signatures identify tumors with genomically luminal biology responsive to neoadjuvant endocrine therapy and are prognostic of 5-year outcome in early-stage breast cancer

BACKGROUND

As more patients with early-stage breast cancer receive neoadjuvant endocrine therapy (NET), there is a need for reliable biomarkers that can identify patients with HR+ HER2- tumors who are likely to benefit from NET. NBRST (NCT01479101) compared the prognostic value of the 70-gene risk classification and 80-gene molecular subtyping signatures with conventional pathological classification methods in response to neoadjuvant therapy. We evaluated the association of these signatures with clinical response and 5-year outcome of patients treated with NET.

METHODS

1091 patients with early-stage breast cancer scheduled to receive neoadjuvant therapy were prospectively enrolled into NBRST, and a sub-analysis of 67 patients treated with NET was performed. Patients received standard of care genomic testing using the 70-gene and 80-gene signatures and were treated with NET, per physician's discretion. The primary endpoint was pathologic partial response (pPR) and secondary endpoints were distant metastasis-free survival (DMFS) and overall survival (OS). Clinical benefit was defined as having a pPR or stable disease (SD) with NET.

RESULTS

Overall, 94.4% of patients with genomically (g) Luminal A-Type (50.0% pPR and 44.4% SD) and 95.0% with Luminal B-Type tumors (55.0% pPR and 40.0% SD) exhibited clinical benefit. At 5 years, patients with gLuminal B tumors had significantly worse DMFS (75.6%, 95% CI 50.8-89.1) than patients with gLuminal A (91.1%; 95% CI 74.8-97.1; p = 0.047), with a similar trend for OS, albeit not significant (81.0%, 95% CI 56.9-92.4 and 91.1%, 95% CI 74.8-97.1, respectively; p = 0.13).

CONCLUSIONS

Genomic assays offer a broader understanding of the underlying tumor biology, which adds precision to pathology as a preoperative risk classifier. Patients with 70-gene signature Low Risk, gLuminal A tumors treated with endocrine therapy alone have excellent 5-year outcomes. Most patients with genomically-defined Luminal A- and B-Type tumors respond well to NET, suggesting these patients may be safely treated with NET, while those with gLuminal B tumors will also require post-operative chemotherapy or CDK4/6 inhibitors to improve long-term outcomes. Overall, these findings demonstrate that genomic classification, defined by the combined 70- and 80-gene signatures, is associated with tumor response and prognostic of long-term outcomes.

How Reliable Are Gene Expression-Based and Immunohistochemical Biomarkers Assessed on a Core-Needle Biopsy? A Study of Paired Core-Needle Biopsies and Surgical Specimens in Early Breast Cancer

In early breast cancer, a preoperative core-needle biopsy (CNB) is vital to confirm the malignancy of suspected lesions and for assessing the expression of treatment predictive and prognostic biomarkers in the tumor to choose the optimal treatments, emphasizing the importance of obtaining reliable results when biomarker status is assessed on a CNB specimen. This study aims to determine the concordance between biomarker status assessed as part of clinical workup on a CNB compared to a medically untreated surgical specimen. Paired CNB and surgical specimens from 259 patients that were part of the SCAN-B cohort were studied. The concordance between immunohistochemical (IHC) and gene expression (GEX) based biomarker status was investigated. Biomarkers of interest included estrogen receptor (ER; specifically, the alpha variant), progesterone receptor (PgR), Ki67, HER2, and tumor molecular subtype. In general, moderate to very good correlation in biomarker status between the paired CNB and surgical specimens was observed for both IHC assessment (83–99% agreement, kappa range 0.474–0.917) and GEX assessment (70–97% agreement, kappa range 0.552–0.800), respectively. However, using IHC, 52% of cases with low Ki67 status in the CNB shifted to high Ki67 status in the surgical specimen (McNemar’s p = 0.011). Similarly, when using GEX, a significant shift from negative to positive ER (47%) and from low to high Ki67 (16%) was observed between the CNB and surgical specimen (McNemar’s p = 0.027 and p = 0.002 respectively). When comparing biomarker status between different techniques (IHC vs. GEX) performed on either CNBs or surgical specimens, the agreement in ER, PgR, and HER2 status was generally over 80% in both CNBs and surgical specimens (kappa range 0.395–0.708), but Ki67 and tumor molecular subtype showed lower concordance levels between IHC and GEX (48–62% agreement, kappa range 0.152–0.398). These results suggest that both the techniques used for collecting tissue samples and analyzing biomarker status have the potential to affect the results of biomarker assessment, potentially also impacting treatment decisions and patient survival outcomes.

Expression of SOX10 in Triple-Negative Breast Carcinoma in Pakistan

Background

The term triple-negative breast cancer (TNBC) refers to a particular class of aggressive, poorly differentiated neoplasms that show the absence of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER2) antibodies. SOX10 (SRY-related HMG-box 10) is a nuclear transcription factor that is commonly used to identify cancers of neural origin, but it has recently been linked to TNBC. The purpose of this study is to determine SOX10 expression in TNBC, its association with tumor grade for molecular categorization, and to determine the diagnostic significance of SOX10 in TNBC at the metastatic site in the case of an unknown primary.

Methodology

SOX10 was used to stain a tissue microarray of 100 patients. According to the tumor grade, SOX10 staining was classified as negative (<1%), patchy (1-10%), focal (10-70%), and diffuse (70-100%). SPSS version 22 (IBM Corp., Armonk, NY, USA) software was used to conduct the statistical analysis.

Results

The expression of SOX10 regarding positivity and intensity was higher in high-grade tumors than in intermediate-grade tumors (p = 0.001 and p = 0.007, respectively).

Conclusions

SOX10 is a reliable novel marker for the diagnosis of TNBC and has diagnostic utility in the unknown primary at the metastatic site.

Tailoring neoadjuvant treatment of HR-positive/HER2-negative breast cancers: Which role for gene expression assays?

Neoadjuvant chemotherapy (NACT) for breast cancer (BC) increases surgical and conservative surgery chances. However, a significant proportion of patients will not be eligible for conservative surgery following NACT because of large tumor size and/or low chemosensitivity, especially for hormone receptor (HR)-positive/ human epidermal growth factor receptor 2 (HER2)-negative tumors, for which pathological complete response rates are lower than for other BC subtypes. On the other hand, for luminal BC neoadjuvant endocrine therapy could represent a valid alternative. Several gene expression assays have been introduced into clinical practice in last decades, in order to define prognosis more accurately than clinico-pathological features alone and to predict the benefit of adjuvant treatments. A series of studies have demonstrated the feasibility of using core needle biopsy for gene expression risk testing, finding a high concordance rate in the risk result between biopsy sample and surgical samples. Based on these premises, recent efforts have focused on the utility of gene expression signatures to guide therapeutic decisions even in the neoadjuvant setting. Several prospective and retrospective studies have investigated the correlation between gene expression risk score from core needle biopsy before neoadjuvant therapy and the likelihood of 1) clinical and pathological response to neoadjuvant chemotherapy and endocrine therapy, 2) conservative surgery after neoadjuvant chemotherapy and endocrine therapy, and 3) survival following neoadjuvant chemotherapy and endocrine therapy. The purpose of this review is to provide an overview of the potential clinical utility of the main commercially available gene expression panels (Oncotype DX, MammaPrint, EndoPredict, Prosigna/PAM50 and Breast Cancer Index) in the neoadjuvant setting, in order to better inform decision making for luminal BC beyond the exclusive contribution of clinico-pathological features.

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.

Molecular Subtyping of Invasive Breast Cancer Using a PAM50-Based Multigene Expression Test-Comparison with Molecular-Like Subtyping by Tumor Grade/Immunohistochemistry and Influence on Oncologist's Decision on Systemic Therapy in a Real-World Setting

In intermediate risk hormone receptor (HR) positive, HER2 negative breast cancer (BC), the decision regarding adjuvant chemotherapy might be facilitated by multigene expression tests. In all, 142 intermediate risk BCs were investigated using the PAM50-based multigene expression test Prosigna® in a prospective multicentric study. In 119/142 cases, Prosigna® molecular subtyping was compared with local and two central (C1 and C6) molecular-like subtypes relying on both immunohistochemistry (IHC; HRs, HER2, Ki-67) and IHC + tumor grade (IHC+G) subtyping. According to local IHC, 35.4% were Luminal A-like and 64.6% Luminal B-like subtypes (local IHC+G subtype: 31.9% Luminal A-like; 68.1% Luminal B-like). In contrast to local and C1 subtyping, C6 classified >2/3 of cases as Luminal A-like. Pairwise agreement between Prosigna® subtyping and molecular-like subtypes was fair to moderate depending on molecular-like subtyping method and center. The best agreement was observed between Prosigna® (53.8% Luminal A; 44.5% Luminal B) and C1 surrogate subtyping (Cohen’s kappa = 0.455). Adjuvant chemotherapy was suggested to 44.2% and 88.6% of Prosigna® Luminal A and Luminal B cases, respectively. Out of all Luminal A-like cases (locally IHC/IHC+G subtyping), adjuvant chemotherapy was recommended if Prosigna® testing classified as Prosigna® Luminal A at high / intermediate risk or upgraded to Prosigna® Luminal B.

Update on prognostic and predictive biomarkers of breast cancer

Breast cancer represents a heterogeneous group of human cancer at both histological and molecular levels. Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are the most commonly used biomarkers in clinical practice for making treatment plans for breast cancer patients by oncologists. Recently, PD-L1 testing plays an important role for immunotherapy for triple-negative breast cancer. With the increased understanding of the molecular characterization of breast cancer and the emergence of novel targeted therapies, more potential biomarkers are needed for the development of more personalized treatments. In this review, we summarized several main prognostic and predictive biomarkers in breast cancer at genomic, transcriptomic and proteomic levels, including hormone receptors, HER2, Ki67, multiple gene expression assays, PD-L1 testing, mismatch repair deficiency/microsatellite instability, tumor mutational burden, PIK3CA, ESR1 andNTRK and briefly introduced the roles of digital imaging analysis in breast biomarker evaluation.

Clinical Utility of Multigene Profiling Assays in Early-Stage Invasive Breast Cancer: An Ontario Health (Cancer Care Ontario) Clinical Practice Guideline

OBJECTIVE

The purpose of this guideline is to determine the clinical utility of multigene profiling assays in individuals with early-stage invasive breast cancer.

METHODS

This guideline was developed by Ontario Health (Cancer Care Ontario)'s Program in Evidence-Based Care (PEBC) through a systematic review of relevant literature, patient- and caregiver-specific consultation and internal and external reviews. Recommendation 1: In patients with early-stage estrogen receptor (ER)-positive/human epidermal growth factor 2 (HER2)-negative breast cancer, clinicians should consider using multigene profiling assays (i.e., Oncotype DX, MammaPrint, Prosigna, EndoPredict, and the Breast Cancer Index) to help guide the use of systemic therapy. Recommendation 2: In patients with early-stage node-negative ER-positive/HER2-negative disease, clinicians may use a low-risk result from Oncotype DX, MammaPrint, Prosigna, EndoPredict/EPclin, or Breast Cancer Index assays to support a decision not to use adjuvant chemotherapy. Recommendation 3: In patients with node-negative ER-positive/HER2-negative disease, clinicians may use a high-risk result from Oncotype DX to support a decision to offer chemotherapy. A high Oncotype DX recurrence score is capable of predicting adjuvant chemotherapy benefit. Recommendation 4: In postmenopausal patients with ER-positive/HER2-negative tumours and one to three nodes involved (N1a disease), clinicians may withhold chemotherapy based on a low-risk Oncotype DX or MammaPrint score if the decision is supported by other clinical, pathological, or patient-related factors. Recommendation 5: The evidence to support the use of molecular profiling to select the duration of endocrine therapy is evolving. In patients with ER-positive disease, clinicians may consider using a Breast Cancer Index (H/I) high assay result to support a decision to extend adjuvant endocrine therapy if the decision is supported by other clinical, pathological, or patient-related factors.

How we treat HR-positive, HER2-negative early breast cancer

The present goal of therapy for early hormone receptor-positive (HR+)/human EGF receptor 2-negative (HER2-) BC is to optimize disease-free survival (DFS) and overall survival (OS) rates with the currently available therapies while avoiding any relevant long-term sequalae. Local therapies have evolved toward less aggressive techniques (i.e. breast-preserving surgery, sentinel lymph node biopsy and intraoperative radiotherapy), which significantly reduce the long-term sequalae observed with more radical treatments. Endocrine therapy (ET) is still the cornerstone of adjuvant treatment because it significantly reduces BC relapse and mortality. Adjuvant chemotherapy is today recommended only for a particular subset of patients with a high risk of recurrence with ET alone, identified through genomic assays, age and/or disease stage. Bisphosphonates reduce the risk of bone metastasis and produce a slight although statistically significant improvement in survival in postmenopausal women. The CDK 4/6 inhibitor abemaciclib has been recently approved by the US FDA for patients at high risk of relapse.

The Clinical Research and Latest Application of Genomic Assays in Early-Stage Breast Cancer

Breast cancer is a kind of malignant tumor that seriously endangers women's life and health. Once diagnosed, most patients will receive a combination of treatments to achieve a cure. However, breast cancer is a heterogeneous disease. Even with the same clinical stage and pathological features, its response to treatment and postoperative recurrence risk may still be completely different. With the advent of genomic assay, some patients with early-stage breast cancer who originally needed treatment can still achieve long-term disease-free survival without adjuvant chemotherapy, so as to achieve personalized and accurate treatment mode to a certain extent. In this paper, we reviewed the 5 most widely used and studied genomic panel technologies in breast cancer, namely Oncotype DX, MammaPrint, RecurIndex, PAM50, and EndoPredict, according to accessibility and availability. Based on the results of the completed or ongoing clinical studies, we summarized the origin, applicable population, and clinical efficacy of each detection method, and discussed the potential development prospect of detection technology in the future.

Association of mammography and ultrasound features with MammaPrint in patients with estrogen receptor-positive, HER2-negative, node-positive invasive breast cancer

BACKGROUND

MammaPrint is a 70-gene signature microarray assay that predicts the likelihood of recurrence of breast cancer and chemotherapeutic benefits.

PURPOSE

To investigate the association between mammography and ultrasound (US) features and MammaPrint results in patients with estrogen receptor (ER)-positive, HER2-negative, node-positive invasive breast cancer, and to identify the predictive factors for high risk of recurrence.

MATERIAL AND METHODS

This retrospective study included 251 patients with ER-positive, HER2-negative, 1-3 node-positive invasive breast cancer. Mammography and US findings were reviewed according to the BI-RADS criteria. The association between MammaPrint results and the clinicopathological and imaging features was evaluated. Logistic regression analysis was performed to identify independent predictors for high risk of recurrence.

RESULTS

Of the patients, 143 (57.0%) and 108 (43.0%) had low and high risks for recurrence on MammaPrint, respectively. Young age (odds ratio [OR] 1.08; 95% confidence interval (CI) 1.04-1.12; P<0.001), posterior enhancement on US (OR 2.45; 95% CI 1.16-5.20; P = 0.019), absence of posterior shadowing on US (OR 3.19; 95% CI 1.17-8.62; P = 0.023), high histologic grade (OR 113.36; 95% CI 6.79-1893.53; P = 0.001), and high Ki-67 level (OR 4.90; 95% CI 2.62-9.17; P<0.001) were independently associated with high risk of recurrence on multivariate logistic regression analysis.

CONCLUSION

Posterior features in US may predict a high risk of recurrence in patients with ER-positive, HER2-negative, node-positive invasive breast cancer, which may be useful in enhancing the diagnostic value of MammaPrint and aid in the decision-making process regarding treatment.

Surrogate endpoints for early-stage breast cancer: a review of the state of the art, controversies, and future prospects

Drug approval for early-stage breast cancer (EBC) has been historically granted in the context of registration trials based on adequate outcomes such as disease-free survival and overall survival. Improvements in long-term outcomes have made it more difficult to demonstrate the clinical benefit of a new cancer drug in large, randomized, comparative clinical trials. Therefore, the use of surrogate endpoints rather than traditional measures allows for cancer drug trials to proceed with smaller sample sizes and shorter follow-up periods, which reduces drug development time. Among surrogate endpoints for breast cancer, the increase in pathological complete response (pCR) rates was considered appropriate for accelerated drug approval. The association between pCR and long-term outcomes was strongest in patients with aggressive tumor subtypes, such as triple-negative and human epidermal growth factor receptor 2 (HER2)-positive/hormone receptor-negative breast cancers. Whereas in hormone receptor-positive/HER2-negative EBC, the most accepted surrogate markers for endocrine therapy-based trials include changes in Ki67 and the preoperative endocrine prognostic index. Beyond the classic endpoints, further prognostic tools are required to provide EBC patients with individualized and effective therapies, and the neoadjuvant setting provides an excellent platform for drug development and biomarker discovery. Nowadays, the availability of multigene signatures is offering a standardized quantitative and reproducible tool to potentiate the efficacy of standard treatment for high-risk patients and develop de-escalated treatments for patients at lower risk of relapse. In this article, we first evaluate the surrogacies used for long-term outcomes and the underlying evidence supporting the use of each surrogate endpoint for the accelerated or regular drug approval process in EBC. Next, we provide an overview of the most recent studies and innovative strategies in a (neo)adjuvant setting as a platform to accelerate new drug approval. Finally, we highlight some clinical trials aimed at tailoring systemic treatment of EBC using prognosis-related factors or early biomarkers of drug sensitivity or resistance.

Molecular Testing in Breast Cancer: Current Status and Future Directions

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

The OncoMasTR test predicts distant recurrence in estrogen receptor-positive, HER2-negative early-stage breast cancer: A validation study in ABCSG Trial 8

PURPOSE

To validate the clinical performance of the OncoMasTR Risk Score in the biomarker cohort of ABCSG Trial 8.

EXPERIMENTAL DESIGN

We evaluated the OncoMasTR test in 1200 formalin-fixed, paraffin-embedded surgical specimens from postmenopausal women with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative primary breast cancer with 0-3 involved lymph nodes in the prospective, randomized ABCSG Trial 8. Time to distant recurrence (DR) was analyzed by Cox models.

RESULTS

The OncoMasTR Risk Score categorized 850 of 1087 (78.2%) evaluable patients as "low risk". At 10 years, the DR rate for patients in the low-risk group was 5.8% versus 21.1% for patients in the high-risk group (P<0.0001, absolute risk reduction 15.3%). The OncoMasTR Risk Score was highly prognostic for prediction of DR in years 0-10 in all patients (hazard ratio (HR) 1.91, 95% confidence interval (CI) 1.62 to 2.26, P<0.0001; C-index 0.73), in node-negative patients (HR 1.79, 95% CI 1.43 to 2.24, P<0.0001; C-index 0.72), and in patients with 1-3 involved lymph nodes (HR 1.93, 95% CI 1.44 to 2.58, P<0.0001; C-index 0.71). The OncoMasTR Risk Score provided significant additional prognostic information beyond clinical parameters, Ki67, Nottingham Prognostic Index, and Clinical Treatment Score.

CONCLUSIONS

OncoMasTR Risk Score is highly prognostic for DR in postmenopausal women with ER-positive, HER2-negative primary breast cancer with 0-3 involved lymph nodes. In combination with prior validation studies, this fully independent validation in ABCSG Trial 8 provides level 1B evidence for the prognostic capability of the OncoMasTR Risk Score.

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.

The Signal Transducer IL6ST (gp130) as a Predictive and Prognostic Biomarker in Breast Cancer

Novel biomarkers are needed to continue to improve breast cancer clinical management and outcome. IL6-like cytokines, whose pleiotropic functions include roles in many hallmarks of malignancy, rely on the signal transducer IL6ST (gp130) for all their signalling. To date, 10 separate independent studies based on the analysis of clinical breast cancer samples have identified IL6ST as a predictor. Consistent findings suggest that IL6ST is a positive prognostic factor and is associated with ER status. Interestingly, these studies include 4 multigene signatures (EndoPredict, EER4, IRSN-23 and 42GC) that incorporate IL6ST to predict risk of recurrence or outcome from endocrine or chemotherapy. Here we review the existing evidence on the promising predictive and prognostic value of IL6ST. We also discuss how this potential could be further translated into clinical practice beyond the EndoPredict tool, which is already available in the clinic. The most promising route to further exploit IL6ST's promising predicting power will likely be through additional hybrid multifactor signatures that allow for more robust stratification of ER+ breast tumours into discrete groups with distinct outcomes, thus enabling greater refinement of the treatment-selection process.

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor

Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.

Real-world analysis of clinical and economic impact of 21-gene recurrence score (RS) testing in early-stage breast cancer (ESBC) in Ireland

PURPOSE

Results from TAILOR-X suggest that up to 70% of hormone receptor-positive (HR+) node-negative (N0) ESBC patients (pts) may avoid chemotherapy (CT) with RS ≤ 25. We assess clinical and economic impacts of RS testing on treatment using real-world data.

METHODS

From October 2011 to February 2019, a retrospective, cross-sectional observational study was conducted of HR+ N0 ESBC pts who had RS testing in Ireland. Pts were classified low risk (RS ≤ 25) and high risk (RS > 25). Clinical risk was calculated. Data were collected via electronic patient records. Cost data were supplied by the National Healthcare Pricing Regulatory Authority.

RESULTS

963 pts. Mean age is 56 years. Mean tumour size is 1.7 cm. 114 (11.8%), 635 (66%), 211 (22%), 3 (0.2%) pts had G1, G2, G3 and unknown G, respectively. 796 pts (82.8%) low RS, 159 (16.5%) high RS and 8 pts (0.7%) unknown RS. 263 pts (26%) were aged ≤ 50 at diagnosis; 117 (45%) had RS 0-15, 63 (24.5%) 16-20, 39 (15.3%) 21-25 and 40 (15.2%) RS 26-100. 4 pts (1.5%) had unknown RS. Post-RS testing, 602 pts (62.5%) had a change in CT decision; 593 changed to hormone therapy (HT) alone. In total, 262 pts received CT. Of pts receiving CT; 138 (53%) had RS > 25, 124 (47%) had RS ≤ 25. Of pts aged ≤ 50, 153 (58%) had high clinical risk, of whom 28 had RS 16-20. Assay use achieved a 62.5% change in treatment with 73% of pts avoiding CT. This resulted in savings of €4 million in treatment costs. Deducting assay costs, savings of €1.9 million were achieved.

CONCLUSION

Over the 8 years of the study, a 62.5% reduction in CT use was achieved with savings of over €1,900,000.

Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®

Multi-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (Risk Of Recurrence, ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor-positive (ER+), HER2-negative breast cancer. Here, we describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data. RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2- breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology. Gene expression was measured using NanoString nCounter. For RS and EP, conversion factors to adjust for cross-platform variation were estimated using linear regression. For ROR, the steps to perform subgroup-specific normalisation of the gene expression data and calibration factors to calculate the 46-gene ROR score were assessed and verified. Training with bootstrapping (n = 59) was followed by validation (n = 48) using adjusted, research use only (RUO) NanoString-based algorithms. In the validation set, there was excellent concordance between the RUO scores and their commercial counterparts (rc(RS) = 0.96, 95% CI 0.93-0.97 with level of agreement (LoA) of -7.69 to 8.12; rc(EP) = 0.97, 95% CI 0.96-0.98 with LoA of -0.64 to 1.26 and rc(ROR) = 0.97 (95% CI 0.94-0.98) with LoA of -8.65 to 10.54). There was also a strong agreement in risk stratification: (RS: κ = 0.86, p < 0.0001; EP: κ = 0.87, p < 0.0001; ROR: κ = 0.92, p < 0.001). In conclusion, the calibrated algorithms recapitulate the commercial RS and EP scores on individual biopsies and ROR scores on samples based on subgroup-centreing method using NanoString expression data.

A clinical validation study of MammaPrint in hormone receptor-positive breast cancer from the Austrian Breast and Colorectal Cancer Study Group 8 (ABCSG-8) biomarker cohort

Background

MammaPrint is a prognostic assay based on gene expression in tumors from patients with early breast cancer. MammaPrint has been extensively validated and Food and Drug Administration cleared in fresh and formalin-fixed and paraffin-embedded (FFPE) tissue. We aimed to assess its prognostic performance in the biomarker cohort of the Austrian Breast and Colorectal Cancer Study Group 8 (ABCSG-8) patient population, and to obtain a higher level of evidence with regard to its clinical validity after RNA extraction from FFPE biobank tissue.

Patients and methods

A prespecified retrospective analysis to test the prognostic performance of the MammaPrint test to predict distant recurrence-free survival at 5 and 10 years as primary end point was carried out. MammaPrint risk, clinicopathological factors (after central pathological review), and clinical risk (using a modified version of Adjuvant! Online) were evaluated by Cox regression analyses.

Results

From 1347 available samples, 607 (45%) failed quality control after RNA extraction. In total, 658 (49%) patients were included in survival analyses: MammaPrint low risk versus high risk is a significant prognostic factor for distant recurrence-free survival at 5 years (94.0% versus 91.6%) with a significant risk reduction of 6.5% at 10 years (log-rank P value = 0.017, low risk 91.3% versus high risk 84.8%). The multivariable models suggest that hazard ratio (HR) is primarily driven by tumor stage (5-year HR 3.89; confidence interval 1.97-7.71) and nodal status (5-year HR 1.73; confidence interval 0.91-3.21). After adjustment for clinical risk groups, MammaPrint HRs remain stable with values just below 2.0 after the first 3 years.

Conclusions

The MammaPrint test showed significant prognostic performance at 5 and 10 years of follow-up. In the particular cohort of ABCSG-8, the statistical independence from clinically assessed covariates remains unclear, and no conclusions concerning the clinical validity of the test can be drawn.

•

MammaPrint is a prognostic assay based on gene expression in tumors from patients with early breast cancer.

•

MammaPrint has been extensively and successfully validated- but mostly in fresh tissue.

•

We aimed to assess its prognostic performance in FFPE tissue from the ABCSG 8 biomarker cohort.

•

The MammaPrint test showed significant prognostic performance at 5 and 10 years of follow-up.

•

The statistical independence from clinically assessed covariates is unclear- the clinical validity in ABCSG 8 uncertain.

Development of a Prognostic Tool to Guide the Decision to Extend Adjuvant Aromatase Inhibitors for up to Ten Years in Postmenopausal Early Breast Cancer Patients

Background: The selection of women with hormone receptor-positive (HR+) early breast cancer (EBC) at high risk of relapse after five years (yrs.) of adjuvant aromatase inhibitors (AIs) is crucial, as the benefit of extending AIs is counterbalanced by toxicity. We developed a clinicopathological tool to estimate the residual risk of relapse after five years of adjuvant AIs. Methods: The Institut de Cancérologie de l'Ouest (ICO) database was used to determine a prognostic score of post-five-year AI relapse. Cox regression models estimated our score's prognostic performance. Results: In total, 1105 women were included. Median follow-up was 44 months (IQR = 21-70) post-AI treatment. From the Cox models, we designed a dichotomous prognostic score including the number of macrometastases, age (>70 yrs. vs. ≤70 yrs.), tumor size (≥T2 vs. not), and mitotic activity (≥2 vs. not). Overall, 77.5% of patients were classified as being at low risk and 22.5% at high risk of late recurrence. Low-risk patients had a five- to ten-year local or distant recurrence risk of 7.6% (95% CI, 5.4% to 10.6%) as compared with 26.9% (95% CI, 19.9% to 35.7%) for the high-risk roup. Conclusion: In this study, we developed a simple tool to identify women at high risk of relapse despite completing five years of AIs.

Clinical validity of clinical treatment score 5 (CTS5) for estimating risk of late recurrence in unselected, non-trial patients with early oestrogen receptor-positive breast cancer

Abstract

Purpose

Clinical Treatment Score at 5 years (CTS5) is a prognostic tool to estimate distant recurrence (DR) risk after 5 years of endocrine therapy for postmenopausal women with oestrogen receptor-positive (ER-positive) breast cancer.

Methods

The validity of CTS5 was tested in a retrospective cohort of patients diagnosed with early ER-positive breast cancer. The primary endpoint was DR in years 5–10. The primary analysis cohort consisted of postmenopausal women, with premenopausal women as a secondary analysis cohort. Cox regression models were used to determine the prognostic value of CTS5 and Kaplan–Meier curves were used with associated 10-year DR risks (%).

Results

2428 women were included with a median follow-up of 13.4 years. The CTS5 was significantly prognostic in both postmenopausal (N = 1662, HR = 2.18 95% CI (1.78–2.67)) and premenopausal women (N = 766, HR = 1.84 95% CI (1.32–2.56)). The 10-year DR risks were 2.9% (1.9–4.5), 7.2% (5.3–9.9), and 12.9% (10.0–16.7) for low, intermediate and high risk in postmenopausal women and 3.8% (2.2–6.7), 6.9% (4.4–10.8), and 11.1% (7.4–16.5) in premenopausal women, respectively. The number of observed DRs was significantly greater than expected in those predicted to be at high risk by CTS5 but this discordance was lost when those receiving more than 60 months of endocrine therapy were excluded.

Conclusions

The CTS5 demonstrated clinical validity for predicting late DR within a large cohort of unselected postmenopausal patients but less so in premenopausal patients. Calibration of the CTS5 was good in patients who did not receive extended endocrine therapy. The CTS5 low-risk cohort has risk of DR so low as to not warrant extended endocrine therapy.

Electronic supplementary material

The online version of this article (10.1007/s10549-020-06013-6) contains supplementary material, which is available to authorized users.

Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis

BACKGROUND

Breast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse.

OBJECTIVES

To conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA), MammaPrint^® (Agendia, Inc., Amsterdam, the Netherlands), Prosigna^® (NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict^® (Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services.

DESIGN

A systematic review and health economic analysis were conducted.

REVIEW METHODS

The systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model.

RESULTS

A total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotype DX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotype DX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1-3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1-3 subgroup; (4) EndoPredict Clinical, for the LN1-3 subgroup only; and (5) MammaPrint, for no subgroups.

LIMITATIONS

There was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotype DX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotype DX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations.

CONCLUSIONS

The review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotype DX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42017059561.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Genomic Signatures in Luminal Breast Cancer

Background

Breast cancer is a very heterogeneous disease and luminal breast carcinomas represent the hormone receptor-positive tumors among all breast cancer subtypes. In this context, multigene signatures were developed to gain further prognostic and predictive information beyond clinical parameters and traditional immunohistochemical markers.

Summary

For early breast cancer patients these molecular tools can guide clinicians to decide on the extension of endocrine therapy to avoid over- and undertreatment by adjuvant chemotherapy. Beside the predictive and prognostic value, a few genomic tests are also able to provide intrinsic subtype classification. In this review, we compare the most frequently used and commercially available molecular tests (OncotypeDX®, MammaPrint®, Prosigna®, EndoPredict®, and Breast Cancer Index^SM). Moreover, we discuss the clinical utility of molecular profiling for advanced breast cancer of the luminal subtype.

Key Messages

Multigene assays can help to de-escalate systemic therapy in early-stage breast cancer. Only the Oncotype DX® and MammaPrint®test are validated by entirely prospective and randomized phase 3 trials. More clinical evidence is needed to support the use of genomic tests in node-positive disease. Recent developments in high-throughput sequencing technology will provide further insights to understand the heterogeneity of luminal breast cancers in early-stage and metastatic disease.

Beyond Chemotherapies: Recent Strategies in Breast Cancer Treatment

In 2018, about 2.1 million women have been diagnosed with breast cancer worldwide. Treatments include-among others-surgery, chemotherapy, radiotherapy, or endocrine therapy. The current policy of care tends rather at therapeutic de-escalation, and systemic treatment such as chemotherapies alone are not systematically considered as the best option anymore. With recent advances in the understanding of cancer biology, and as a complement to anatomic staging, some biological factors (assessed notably via gene-expression signatures) are taken into account to evaluate the benefit of a chemotherapy regimen. The first aim of this review will be to summarize when chemotherapies can be avoided or used only combined with other treatments. The second aim will focus on molecules that can be used instead of chemotherapeutic drugs or used in combination with chemotherapeutic drugs to improve treatment outcomes. These therapeutic molecules have emerged from the collaboration between fundamental and clinical research, and include molecules, such as tyrosine kinase inhibitors, CDK4/6 inhibitors, and monoclonal antibodies (such as anti-PD-L1). In the fight against cancer, new tools aiding decision making are of the utmost importance: gene-expression signatures have proven to be valuable in the clinic, notably, to know when chemotherapies can be avoided. When substitution treatments are also available, a big step can be made toward personalized medicine for the patient's benefit.

Current advances in the diagnosis and personalized treatment of breast cancer: lessons from tumor biology

Breast cancer treatment has advanced enormously in the last decade. Most of this is due to advances reached in the knowledge regarding tumor biology, mainly in the field of diagnosis and treatment. This review brings information about how the genomics-based information contributed to advances in breast cancer diagnosis and prognosis perspective, as well as presents how tumor biology discoveries fostered the main therapeutic approaches available to treat such patients, based on a personalized point of view.