Tucatinib Combination Treatment After Trastuzumab-Deruxtecan in Patients With ERBB2-Positive Metastatic Breast Cancer.

Importance: Little is known regarding the outcomes associated with tucatinib combined with trastuzumab and capecitabine (TTC) after trastuzumab-deruxtecan exposure among patients with ERBB2 (previously HER2)-positive metastatic breast cancer (MBC). Objective: To investigate outcomes following TTC treatment in patients with ERBB2-positive MBC who had previously received trastuzumab-deruxtecan. Design, Setting, and Participants: This cohort study included all patients with MBC who were treated in 12 French comprehensive cancer centers between August 1, 2020, and December 31, 2022. Exposure: Tucatinib combined with trastuzumab and capecitabine administered at the recommended dose. Main Outcomes and Measures: Clinical end points included progression-free survival (PFS), time to next treatment (TTNT), overall survival (OS), and overall response rate (ORR). Results: A total of 101 patients with MBC were included (median age, 56 [range, 31-85] years). The median number of prior treatment lines for metastatic disease at TTC treatment initiation was 4 (range, 2-15), including 82 patients (81.2%) with previous trastuzumab and/or pertuzumab and 94 (93.1%) with previous ado-trastuzumab-emtansine) exposure. The median duration of trastuzumab-deruxtecan treatment was 8.9 (range, 1.4-25.8) months, and 82 patients (81.2%) had disease progression during trastuzumab-deruxtecan treatment, whereas 18 (17.8%) had stopped trastuzumab-deruxtecan for toxic effects and 1 (1.0%) for other reasons. Tucatinib combined with trastuzumab and capecitabine was provided as a third- or fourth-line treatment in 37 patients (36.6%) and was the immediate treatment after trastuzumab-deruxtecan in 86 (85.1%). With a median follow-up of 11.6 (95% CI, 10.5-13.4) months, 76 of 101 patients (75.2%) stopped TTC treatment due to disease progression. The median PFS was 4.7 (95% CI, 3.9-5.6) months; median TTNT, 5.2 (95% CI, 4.5-7.0) months; and median OS, 13.4 (95% CI, 11.1 to not reached [NR]) months. Patients who received TTC immediately after trastuzumab-deruxtecan had a median PFS of 5.0 (95% CI, 4.2-6.0) months; median TTNT of 5.5 (95% CI, 4.8-7.2) months, and median OS of 13.4 (95% CI, 11.9-NR) months. Those who received TTC due to trastuzumab-deruxtecan toxicity-related discontinuation had a median PFS of 7.3 (95% CI, 3.0-NR) months. Best ORR was 29 of 89 patients (32.6%). Sixteen patients with active brain metastasis had a median PFS of 4.7 (95% CI, 3.0-7.3) months, median TTNT of 5.6 (95% CI, 4.4 to NR), and median OS of 12.4 (95% CI, 8.3-NR) months. Conclusions and Relevance: In this study, TTC therapy was associated with clinically meaningful outcomes in patients with ERBB2-positive MBC after previous trastuzumab-deruxtecan treatment, including those with brain metastases. Prospective data on optimal drug sequencing in this rapidly changing therapeutic landscape are needed.

Artificial Intelligence and Anticancer Drug Development-Keep a Cool Head.

Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.

First-line real-world treatment patterns and survival outcomes in women younger or older than 40 years with metastatic breast cancer in the real-life multicenter French ESME cohort.

AIM: To describe first-line treatment patterns, overall survival (OS) and real-world progression-free survival (rwPFS) in young women (<40) with metastatic breast cancer (mBC), as compared to women aged 40-69. MATERIALS AND METHODS: Data on adult women diagnosed with mBC (2008-2017) were extracted from the ESME mBC database (NCT03275311) which includes consecutive patients starting first-line metastatic treatment in one of the 18 French Comprehensive cancer centers. We reported first-line therapeutic strategy and prognostic factors of OS and rwPFS for women aged < 40 and 40-69. RESULTS: In total, 14,897 mBC women were included (1512 aged <40). HR+ /HER2- mBC was the most frequent subtype. First-line treatment differed between young patients and older ones for HR+ /HER2- and Triple Negative (TN) mBC. Median OS for women aged < 40 and 40-69, respectively, was 46.9 and 46.2 months for HR+ /HER2- mBC; 13.5 and 15.2 for TN mBC; and, 60.7 and 55.1 for HER2 + mBC. Median rwPFS under first line treatment was 11.6 and 11.9 months for HR+ /HER2- in women aged < 40 and 40-69, respectively; 5.5 and 5.9 for TN, and, 13.3 and 12.9 for HER2 + . Factors associated with shorter OS and rwPFS were similar for both women aged < 40 and 40-69 and included ≥ 3 metastatic sites, visceral metastases, and longer MFI, with time-varying effects observed for several prognostic factors. CONCLUSION: Young women presented more frequently with TN and HER2 + subtypes and aggressive mBC than women aged 40-69 did. Prognostic factors of OS and rwPFS were quite similar between age groups and mBC subtypes.

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data.

Purpose: Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental design: Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. Results: PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. Conclusion: Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.

Prognostic impact of post-treatment FDG PET/CT in anal canal cancer: A prospective study.

BACKGROUND AND PURPOSE: The aim of our prospective study was to assess the prognostic value of 18F-FDG PET/CT performed two months post treatment for anal canal neoplasm. POPULATION AND METHODS: Consecutive patients with histologically proved anal cancer, with 18F-FDG PET/CT pre and two months post treatment were included. Patients were not previously treated for this neoplasm and then received radiotherapy ± chemotherapy. Clinical and pathologic data were collected and for 18F-FDG PET/CT visual and quantitative analysis (standardized uptake value, metabolic volume) were performed; response was classified according to EORTC and PERCIST criteria. The results were assessed for disease free survival and local recurrence free survival using the log-Rank test RESULTS: From December 2014 to September 2019, 94 consecutive patients were screened and 78 were included in this study. Median follow-up was 51 months. Two months post treatment, 37 patients (47.4%) had a complete radiological response according to both EORTC and PERCIST criteria, 66 patients (84.6%) had a clinical complete response. For disease free survival, the prognostic value of complete response was statistically significant (p=0.02) with 18F-FDG PET/CT and with clinical examination (p<0.001). For local recurrence free survival, the prognostic value with 18F-FDG PET/CT was lower (p=0.04) than clinical examination (p < 0.007). CONCLUSION: While clinical examination remains the gold standard for post treatment evaluation in anal cancer, 18F-FDG PET/CT has a statistically significant prognostic value. These two assessments could be combined to improve early evaluation.

Validation of RUBY for Breast Cancer Knowledge Extraction From a Large French Electronic Medical Record System.

PURPOSE: RUBY is a tool for extracting clinical data on breast cancer from French medical records on the basis of named entity recognition models combined with keyword extraction and postprocessing rules. Although initial results showed a high precision of the system in extracting clinical information from surgery, pathology, and biopsy reports (≥92.7%) and good precision in extracting data from consultation reports (81.8%), its validation is needed before its use in routine practice. METHODS: In this work, we analyzed RUBY's performance compared with the manual entry and we evaluated the generalizability of the approach on different sets of reports collected on a span of 40 years. RESULTS: RUBY performed similarly or better than the manual entry for 15 of 27 variables. It showed similar performances when structuring newer reports but failed to extract entities for which changes in terminology appeared. Finally, our tool could automatically structure 15,990 reports in 77 minutes. CONCLUSION: RUBY can automate the data entry process of a set of variables and reduce its burden, but a continuous evaluation of the format and structure of the reports and a subsequent update of the system is necessary to ensure its robustness.

Metabolomic Signatures of Scarff-Bloom-Richardson (SBR) Grade in Non-Metastatic Breast Cancer.

PURPOSE: Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer. METHODS: This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I-II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry. RESULTS: A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples. CONCLUSIONS: This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.

CHEOPS trial: a GINECO group randomized phase II assessing addition of a non-steroidal aromatase inhibitor to oral vinorelbine in pre-treated metastatic breast cancer patients.

BACKGROUND: The objective of the CHEOPS trial was to assess the benefit of adding aromatase inhibitor (AI) to metronomic chemotherapy, oral vinorelbine, 50 mg, three times a week for pre-treated, HR + /HER2- metastatic breast cancer patients. METHODS: In this multicentric phase II study, patients had to have progressed on AI and one or two lines of chemotherapy. They were randomized between oral vinorelbine (Arm A) and oral vinorelbine with non-steroidal AI (Arm B). RESULTS: 121 patients were included, 61 patients in Arm A and 60 patients in Arm B. The median age was 68 years. 109 patients had visceral metastases. They all had previously received an AI. The study had been prematurely stopped following the third death due to febrile neutropenia. Median PFS trend was found to be different with 2.3 months and 3.7 months in Arm A and Arm B, respectively (HR 0.73, 95%CI 0.50-1.06, p value = 0.0929). No statistical difference was shown in OS and better tumor response. 56 serious adverse events corresponding to 25 patients (21%) were reported (respectively, 12 (20%) versus 13 (22%) for arms A and B) (NS). CONCLUSION: The addition of AI to oral vinorelbine over oral vinorelbine alone in aromatase inhibitor-resistant metastatic breast cancer was associated with a non-significant improvement of PFS. Several unexpected serious adverse events were reported. Metronomic oral vinorelbine schedule, at 50 mg three times a week, requires close biological monitoring. The question of hormonal treatment and chemotherapy combination remains open.

[Therapeutic strategies for the treatment of endocrine resistant hormone receptor positive advanced breast cancer]. / Stratégies pour la prise en charge des cancers du sein métastatiques récepteurs hormonaux positifs hormonorésistants.

HR+ breast cancers are defined by the prominence of signaling pathways dependent on the estrogen receptor. Endocrine therapy is the standard treatment for these advanced diseases. Resistance to these treatments, called hormone resistance, appears invariably with biological mechanisms that have led to the development of therapeutic opportunities. An exhaustive literature review was carried out concerning the biology of the hormone resistance pathways, the therapeutic options before the era of CDK4/6 inhibitors, the rise of CDK4/6 inhibitors and the therapeutic prospects in a situation of hormone resistance. Various biological abnormalities have been identified in the mechanisms of hormone resistance such as changes in the estrogen receptor, mutations in the ESR1 gene, aberrant activation of the PI3K pathway or cell cycle deregulations. Historical strategies for circumventing this hormone resistance have been based on hormonal manipulation, on the development of new endocrine therapy such as fulvestrant (selective estrogen receptor inhibitor, SERD), on combinations of treatments such as everolimus, a mTOR inhibitor. This strategy combining endocrine therapy and targeted therapy has led to the development of combinations with CDK4/6 inhibitors which have now become a standard treatment in the hormone resistance phase. The future of this therapeutic era remains to be written with new combinations of hormone therapy and targeted therapy such as PI3K inhibitors or even with the positioning of new SERDs in clinical development.

RUBY: Natural Language Processing of French Electronic Medical Records for Breast Cancer Research.

PURPOSE: Electronic medical records are a valuable source of information about patients' clinical status but are often free-text documents that require laborious manual review to be exploited. Techniques from computer science have been investigated, but the literature has marginally focused on non-English language texts. We developed RUBY, a tool designed in collaboration with IBM-France to automatically structure clinical information from French medical records of patients with breast cancer. MATERIALS AND METHODS: RUBY, which exploits state-of-the-art Named Entity Recognition models combined with keyword extraction and postprocessing rules, was applied on clinical texts. We investigated the precision of RUBY in extracting the target information. RESULTS: RUBY has an average precision of 92.8% for the Surgery report, 92.7% for the Pathology report, 98.1% for the Biopsy report, and 81.8% for the Consultation report. CONCLUSION: These results show that the automatic approach has the potential to effectively extract clinical knowledge from an extensive set of electronic medical records, reducing the manual effort required and saving a significant amount of time. A deeper semantic analysis and further understanding of the context in the text, as well as training on a larger and more recent set of reports, including those containing highly variable entities and the use of ontologies, could further improve the results.

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection-an application in metabolomics studies.

BACKGROUND: Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH. RESULTS: PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate. CONCLUSION: PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate.

Aspects cliniques : Cancers HER2 et atteinte du système nerveux central, que faire en 2021 ?: Central nervous system metastases from HER2 positive breast cancers: what to do in 2021?

Metastatic breast cancer is the second most common cause of brain metastasis (BM), and this problem is particularly marked for the amplified HER2 subtype (HER2+), with a cumulative incidence reaching up to 49 % in the ER-/HER2+ subgroup. Literature review shows that therapeutic progress has been major since the marketing of systemic anti-HER2+ treatments, with life expectancies now relatively unaffected by brain development. The recommended treatments are, on the one hand, specific treatment for brain development and, on the other hand, appropriate systemic treatment. Regarding local treatments, we will always favor surgery when possible, especially for large metastases, and stereotaxic radiotherapy, possibly iterative. One should be wary of whole brain irradiation which has never been shown to improve overall survival, but which is clearly associated with more cognitive toxicities. All the systemic anti-HER2 treatments currently on the market have shown efficacy on BM from HER2+ breast cancer and must therefore be chosen above all on the basis of their potential activity on the systemic disease at the time of cerebral evolution. If BM evolution happen without concomitant systemic progression, and local treatment can control it, it is not recommended to change the current medical treatment. Finally, randomized clinical studies opened to patients with active brain disease are starting to be published. The first of them showed the benefit of the triple combination tucatinib-trastuzumab-capecitabine in this context.

Treatment strategies for breast cancer brain metastases.

Brain metastases from breast cancer (BCBM) constitute the second most common cause of brain metastasis (BM), and the incidence of these frequently lethal lesions is currently increasing, following better systemic treatment. Patients with ER-negative and HER2-positive metastatic breast cancer (BC) are the most likely to develop BM, but if this diagnosis remains associated with a worse prognosis, long survival is now common for patients with HER2-positive BC. BCBM represents a therapeutic challenge that needs a coordinated treatment strategy along international guidelines. Surgery has always to be considered when feasible. It is now well established that stereotaxic radiosurgery allows for equivalent control and less-cognitive toxicities than whole-brain radiation therapy, which should be delayed as much as possible. Medical treatment for BCBM is currently a rapidly evolving field. It has been shown that the blood-brain barrier (BBB) is often impaired in macroscopic BM, and several chemotherapy regimens, antibody-drug conjugates and tyrosine-kinase inhibitors have been shown to be active on BCBM and can be part of the global treatment strategy. This paper provides an overview of the therapeutic option for BCBM that is currently available and outlines potential new approaches for tackling these deadly secondary tumours.

Prevention of brain metastases in human epidermal growth factor receptor 2-positive breast cancer.

PURPOSE OF REVIEW: For patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer, treatments that could prevent or delay occurrence of brain metastases would improve outcome. RECENT FINDINGS: Few studies were specifically designed to assess brain metastasis prevention. Most evidence derives from subgroup analyses of randomized trials. In the first-line metastatic setting, lapatinib, was not superior to trastuzumab to prevent CNS metastases as first site of relapse. Pertuzumab when added to trastuzumab and taxane significantly delay occurrence of brain metastases. In the second line setting, trastuzumab-emtansine has shown to improve overall survival of patients with brain metastases when compared with capecitabine-lapatinib, but there was no significant delay in brain metastases progression. Neratinib, has shown that it was able to delay brain metastases progression. Finally, tucatinib, has demonstrated benefit in progression-free survival and overall survival in combination with trastuzumab and capecitabine over trastuzumab and capecitabine for patients with or without brain metastases. SUMMARY: There has been an impressive improvement of the outcome of patients with HER2-positive metastatic breast cancer, with improved control of systemic disease and delayed occurrence of CNS progression. Specific studies are needed to assess TKI for brain metastases prevention, particularly in the adjuvant setting.

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer.

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

Baseline metabolic tumor volume as a strong predictive and prognostic biomarker in patients with non-small cell lung cancer treated with PD1 inhibitors: a prospective study.

BACKGROUND: Reliable predictive and prognostic markers are still lacking for patients treated with programmed death receptor 1 (PD1) inhibitors for non-small cell lung cancer (NSCLC). The purpose of this study was to investigate the prognostic and predictive values of different baseline metabolic parameters, including metabolic tumor volume (MTV), from 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) scans in patients with NSCLC treated with PD1 inhibitors. METHODS: Maximum and peak standardized uptake values, MTV and total lesion glycolysis (TLG), as well as clinical and biological parameters, were recorded in 75 prospectively included patients with NSCLC treated with PD1 inhibitors. Associations between these parameters and overall survival (OS) were evaluated as well as their accuracy to predict early treatment discontinuation (ETD). RESULTS: A high MTV and a high TLG were significantly associated with a lower OS (p<0.001). The median OS in patients with MTV above the median (36.5 cm3) was 10.5 months (95% CI: 6.2 to upper limit: unreached), while the median OS in patients with MTV below the median was not reached. Patients with no prior chemotherapy had a poorer OS than patients who had received prior systemic treatment (p=0.04). MTV and TLG could reliably predict ETD (area under the receiver operating characteristic curve=0.76, 95% CI: 0.65 to 0.87 and 0.72, 95% CI: 0.62 to 0.84, respectively). CONCLUSION: MTV is a strong prognostic and predictive factor in patients with NSCLC treated with PD1 inhibitors and can be easily determined from routine 18F-FDG PET/CT scans. MTV, could help to personalize immunotherapy and be used to stratify patients in future clinical studies.

Matrix Stiffening and EGFR Cooperate to Promote the Collective Invasion of Cancer Cells.

In squamous cell carcinoma (SCC), tissue invasion by collectively invading cells requires physical forces applied by tumor cells on their surrounding extracellular matrix (ECM). Cancer-related ECM is composed of thick collagen bundles organized by carcinoma-associated fibroblasts (CAF) within the tumor stroma. Here, we show that SCC cell collective invasion is driven by the matrix-dependent mechano-sensitization of EGF signaling in cancer cells. Calcium (Ca2+) was a potent intracellular second messenger that drove actomyosin contractility. Tumor-derived matrix stiffness and EGFR signaling triggered increased intracellular Ca2+ through CaV1.1 expression in SCC cells. Blocking L-type calcium channel expression or activity using Ca2+ channel blockers verapamil and diltiazem reduced SCC cell collective invasion both in vitro and in vivo These results identify verapamil and diltiazem, two drugs long used in medical care, as novel therapeutic strategies to block the tumor-promoting activity of the tumor niche.Significance: This work demonstrates that calcium channels blockers verapamil and diltiazem inhibit mechano-sensitization of EGF-dependent cancer cell collective invasion, introducing potential clinical strategies against stromal-dependent collective invasion.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/18/5229/F1.large.jpg Cancer Res; 78(18); 5229-42. ©2018 AACR.

Hormone receptors status: a strong determinant of the kinetics of brain metastases occurrence compared with HER2 status in breast cancer.

Breast cancer (BC) metastatic behavior varies according to the hormone receptors (HR) and HER2 statuses. Indeed, patients with triple-negative (TN) and HER2+ tumors are at higher risk of brain metastases (BM). The objective of this multinational cohort was to evaluate BM kinetics depending on the BC subtype. We retrospectively analyzed a series of BC patients with BM diagnosed in four European institutions (1996-2016). The delay between BC and BM diagnoses (BM-free survival) according to tumor biology was estimated with the Kaplan-Meier method. A multivariate analysis was performed using the Cox proportional hazards regression model. 649 women were included: 32.0% HER2-/HR+, 24.8% TN, 22.2% HER2+/HR- and 21.0% HER2+/HR+ tumors. Median age at BM diagnosis was 56 (25-85). In univariate analysis, BM-free survival differed depending on tumor biology: HER2-/HR+ 5.3 years (95% CI 4.6-5.9), HER2+/HR+ 4.4 years (95% CI 3.4-5.2), HER2+/HR- 2.6 years (95% CI 2.2-3.1) and TN 2.2 years (95% CI 1.9-2.7) (p < 0.001). It was significantly different between HR+ and HR- tumors (5.0 vs. 2.5 years, p < 0.001), and between HER2+ and HER2- tumors (3.2 vs. 3.8 years, p = 0.039). In multivariate analysis, estrogen-receptors (ER) and progesterone-receptors (PR) negativity, but not HER2 status, were independently associated with BM-free survival (hazard ratio = 1.36 for ER, p = 0.013, 1.31 for PR, p = 0.021, and 1.01 for HER2+ vs. HER2- tumors, p = 0.880). HR- and HER2+ tumors are overrepresented in BC patients with BM, supporting a higher risk of BM in these biological subtypes. HR status, but not HER2 status, impacts the kinetics of BM occurrence.