Sacituzumab govitecan vs. chemotherapy for metastatic breast cancer: a meta-analysis on safety outcomes.

Aim: There is limited data available regarding the comparison of Sacituzumab govitecan (SG) vs. chemotherapy in metastatic breast cancer patients. Materials & methods: We performed a systematic review and meta-analysis aimed to assess the safety profile of SG vs. chemotherapy for metastatic breast cancer (mBC) clinical trials. Results: The pooled odds ratio for outcomes such as grade 3-4 and all grade neutropenia, leukopenia, anemia and other non-hematological adverse events showed a higher risk for patients receiving SG. No statistically significant differences were reported in terms of grade 3-4 fatigue, all grade nausea, febrile neutropenia and treatment discontinuation due to adverse events. Conclusion: Our data, coupled with a statistically and clinically meaningful survival benefit, support the use of SG for mBC.

[Box: see text].

Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy.

BACKGROUND: So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information. METHOD: This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided. RESULTS: To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds. CONCLUSION: Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways.

Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approach.

BACKGROUND: About 15%-20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early-stage and operable BC. However, only 20%-40% of patients achieve pathologic complete response (pCR). According to the results of practice-changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2-positive patients based on a subset of clinical features. METHOD: First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori "Giovanni Paolo II." Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning-based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score. RESULTS: The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%-73.66%) and an accuracy of 71.67% (71.64%-73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway. CONCLUSION: Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images.

Adjuvant capecitabine in triple negative breast cancer patients with residual disease after neoadjuvant treatment: real-world evidence from CaRe, a multicentric, observational study.

Background: In triple negative breast cancer patients treated with neoadjuvant chemotherapy, residual disease at surgery is the most relevant unfavorable prognostic factor. Current guidelines consider the use of adjuvant capecitabine, based on the results of the randomized CREATE-X study, carried out in Asian patients and including a small subset of triple negative tumors. Thus far, evidence on Caucasian patients is limited, and no real-world data are available. Methods: We carried out a multicenter, observational study, involving 44 oncologic centres. Triple negative breast cancer patients with residual disease, treated with adjuvant capecitabine from January 2017 through June 2021, were recruited. We primarily focused on treatment tolerability, with toxicity being reported as potential cause of treatment discontinuation. Secondarily, we assessed effectiveness in the overall study population and in a subset having a minimum follow-up of 2 years. Results: Overall, 270 patients were retrospectively identified. The 50.4% of the patients had residual node positive disease, 7.8% and 81.9% had large or G3 residual tumor, respectively, and 80.4% a Ki-67 >20%. Toxicity-related treatment discontinuation was observed only in 10.4% of the patients. In the whole population, at a median follow-up of 15 months, 2-year disease-free survival was 62%, 2 and 3-year overall survival 84.0% and 76.2%, respectively. In 129 patients with a median follow-up of 25 months, 2-year disease-free survival was 43.4%, 2 and 3-year overall survival 78.0% and 70.8%, respectively. Six or more cycles of capecitabine were associated with more favourable outcomes compared with less than six cycles. Conclusion: The CaRe study shows an unexpectedly good tolerance of adjuvant capecitabine in a real-world setting, although effectiveness appears to be lower than that observed in the CREATE-X study. Methodological differences between the two studies impose significant limits to comparability concerning effectiveness, and strongly invite further research.

Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer.

For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori "Giovanni Paolo II". Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests.

Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.

Introduction: Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable. Methods: Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis. Results: Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames. Discussion: Thus, our framework aims at shortening the distance between AI and clinical practice.

Pathological Complete Response to Neoadjuvant Chemoimmunotherapy for Early Triple-Negative Breast Cancer: An Updated Meta-Analysis.

Immune checkpoint inhibitors (ICIs) have made a breakthrough in the systemic treatment for metastatic triple-negative breast cancer (TNBC) patients. However, results of phase II and III clinical trials assessing ICIs plus chemotherapy as neoadjuvant treatment were controversial and conflicting. We performed a meta-analysis aimed at assessing the Odds Ratio (OR) of the pathological complete response (pCR) rate in trials assessing neoadjuvant chemoimmunotherapy in TNBC. According to our results, the use of neoadjuvant chemoimmunotherapy was associated with higher pCR (OR 1.95; 95% Confidence Intervals, 1.27-2.99). In addition, we highlighted that this benefit was observed regardless of PD-L1 status since the analysis reported a statistically significant and clinically meaningful benefit in both PD-L1 positive and PD-L1 negative patients. These findings further support the exploration of the role of ICIs plus chemotherapy in early-stage TNBC, given the potentially meaningful clinical impact of these agents. Further studies aimed at more deeply investigating this emerging topic in breast cancer immunotherapy are warranted.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy.

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori "Giovanni Paolo II" in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.

An Invasive Disease Event-Free Survival Analysis to Investigate Ki67 Role with Respect to Breast Cancer Patients' Age: A Retrospective Cohort Study.

Characterization of breast cancer into intrinsic molecular profiles has allowed women to live longer, undergoing personalized treatments. With the aim of investigating the relation between different values of ki67 and the predisposition to develop a breast cancer-related IDE at different ages, we enrolled 900 patients with a first diagnosis of invasive breast cancer, and we partitioned the dataset into two sub-samples with respect to an age value equal to 50 years. For each sample, we performed a Kaplan−Meier analysis to compare the IDE-free survival curves obtained with reference to different ki67 values. The analysis on patients under 50 years old resulted in a p-value < 0.001, highlighting how the behaviors of patients characterized by a ki67 ranging from 10% to 20% and greater than 20% were statistically significantly similar. Conversely, patients over 50 years old characterized by a ki67 ranging from 10% to 20% showed an IDE-free survival probability significantly greater than patients with a ki67 greater than 20%, with a p-value of 0.01. Our work shows that the adoption of two different ki67 values, namely, 10% and 20%, might be discriminant in designing personalized treatments for patients under 50 years old and over 50 years old, respectively.

Adenosine pathway inhibitors: novel investigational agents for the treatment of metastatic breast cancer.

INTRODUCTION: The adenosine pathway has been suggested to play a key role in several carcinogenetic processes, with the metabolism of adenosine-5'-triphosphate (ATP) and its derivatives reported to be dysregulated in breast cancer. Preclinical evidence has supported the role of adenosine in the pathogenesis of this malignancy as well as the development of selective adenosine pathway inhibitors. AREAS COVERED: The paper overviews the evidence regarding the use of adenosine pathway inhibitors in breast cancer; a literature search was conducted in January 2022 of Pubmed/Medline, Cochrane library, and Scopus databases. EXPERT OPINION: The adenosine pathway regulates inflammation, apoptosis, metastasis, and cell proliferation in breast cancer cells, and adenosine pathway inhibitors have yielded encouraging results in early-phase clinical trials. Well-designed, multicenter studies focused on monotherapies and combination therapies (which include immune checkpoint inhibitors) are warranted in this setting.

A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients.

In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.

KEYNOTE-522, IMpassion031 and GeparNUEVO: changing the paradigm of neoadjuvant immune checkpoint inhibitors in early triple-negative breast cancer.

Stage I-III triple-negative breast cancer accounts for approximately 15-20% of new diagnoses of early breast cancer. Novel systemic treatment options have recently been assessed as part of the neoadjuvant approach, such as the addition of immune checkpoint inhibitors to cytotoxic chemotherapy. However, several questions remain unanswered, including the identification of predictors of response to immunotherapy in this setting, and further efforts aimed at identifying reliable predictors and clarifying the effective role of PD-L1 status, tumor mutational burden, tumor-infiltrating lymphocytes and other biomarkers are warranted. Herein we will provide an overview of recent clinical studies of neoadjuvant immune checkpoint inhibitors in patients with triple-negative breast cancer, especially focusing on the recently presented and published KEYNOTE-522, IMpassion031 and GeparNUEVO trials.

Impact of Proton Pump Inhibitors and Histamine-2-Receptor Antagonists on Non-Small Cell Lung Cancer Immunotherapy: A Systematic Review and Meta-Analysis.

(1) Background: In recent years, immunotherapy has revolutionized the treatment landscape of non-small cell lung cancer (NSCLC), representing a therapeutic breakthrough in this field. Antacid agents such as proton pump inhibitors (PPIs) and histamine-2-receptor antagonists (H2RAs) are commonly prescribed for extended periods in NSCLC patients, and these drugs have the potential to modify the efficacy of immune checkpoint inhibitors (ICIs). (2) Materials and Methods: Herein, we conducted a systematic review and meta-analysis to investigate the impact of PPIs and H2RAs on progression-free survival (PFS) and overall survival (OS) among patients receiving immunotherapy for metastatic NSCLC. Effect measures for OS were Hazard Ratios (HRs) and 95% Confidence Intervals (CIs), which were extracted from available studies. Forest plots were used to assess HRs to describe the relationship between treatment and OS in the specified cohorts of patients. (3) Results: Six studies were included in the analysis, involving 2267 patients. The pooled HRs for OS and PFS were 1.4 (95% CI, 1.25-1.58) and 1.29 (95% CI, 1.17-1.43), respectively, suggesting that PPIs and H2RAs administration was negatively associated with PFS and OS. (4) Conclusion: Concomitant antacid use could modify the activity of ICIs in NSCLC patients.

Fluoropyrimidine-based doublet chemotherapy as second-line treatment for advanced biliary tract cancer: a meta-analysis of ABC-06 and NIFTY.

BACKGROUND: The 2021 has seen the publication of two practice-changing trials on second-line fluoropyrimidine-based doublet chemotherapy for advanced biliary tract cancer (BTC) patients. Herein, we conducted a meta-analysis aimed at assessing the overall survival (OS), disease control rate (DCR), and overall response rate (ORR) in ABC-06 and NIFTY trials. METHODS: We retrieved all the relevant trials through PubMed/Medline, Cochrane library, and EMBASE; additionally, proceedings of the main international oncological meetings were also searched for relevant abstracts. Outcomes of interest included OS, DCR, and ORR. Hazard Ratios (HRs) and their 95% Confidence Intervals (CIs) for OS, and Odds Ratios (ORs) and 95% CIs for DCR and ORR, were extracted. RESULTS: According to our results, fluoropyrimidine-based doublet chemotherapy significantly decreased the risk of death (HR, 0.63; 95% CI, 0.49-0.8) compared with control treatment. In addition, higher DCR and ORR were observed in BTC patients receiving fluoropyrimidine-based combinations. CONCLUSIONS: Although ABC-06 and NIFTY have recently established fluoropyrimidine-based doublet chemotherapy as the standard of care, the role of second-line chemotherapy remains the object of debate in the BTC medical community. Further studies are required to clarify the role of second-line fluoropyrimidine-based chemotherapy in some 'neglected' populations, including BTC patients with poor ECOG-PS.

BULLYING AND MEASURES TO REDUCE IT: PERCEPTION OF HIGH SCHOOL STUDENTS IN DIFFERENT EDUCATIONAL CONTEXTS / BULLYING Y MEDIDAS PARA MINIMIZARLO: PERCEPCIÓN DE ESTUDIANTES DE ENSEÑANZA MEDIA EN DIFERENTES CONTEXTOS EDUCATIVOS / BULLYING E MEDIDAS PARA MINIMIZÁ-LO: PERCEPÇÃO DE ESTUDANTES DO ENSINO MÉDIO EM DIFERENTES CONTEXTOS EDUCACIONAIS

Bullying is a systematic aggressive act, involving threat, intimidation or coercion, practiced against someone, by an individual or a group of people. It is a problem still present in young people, especially in the school environment. The objective of this work was to analyze the reasons of bullying victimization  and measures to minimize them, in different educational contexts, perceived by students. A questionnaire was used, adapted from the "Peer Victimization Scale" model by Mynard and Joseph (2000), with two questions about the occurrence of victimization by bulliyng and measures to minimize it. Student responses were categorized for each question. The responses in category 1 were classified as: my fault, the fault of the other and naturalization. Category 2 responses were classified as: Individualized assistance, awareness, punishment, supervision and nothing to do. The categorized results were related to quantitative victimization data and considerations were made for anti-bullying actions in schools based on the results.

El Bullying es un acto agresivo sistemático que implica una amenaza, intimidación o coacción que puede ser llevado a cabo por una o más personas en contra de alguien. Es un problema que está presente en la vida de los jóvenes, principalmente en el ámbito escolar.  El objetivo de este trabajo es analizar las diferencias percibidas por los estudiantes de los motivos de la victimización por bullying y las medidas para minimizarlas en diferentes contextos educativos. Utilizamos un cuestionario adaptado del modelo Peer Victimization Scale, de Mynard y Joseph (2000), con dos preguntas sobre la aparición de victimizaciones por bulling y medidas para minimizarlas. Las respuestas de los estudiantes fueron categorizadas y relacionadas a cada pregunta. Las respuestas de la categoría 1 fueron clasificadas en: mi culpa, culpa del otro y naturalización. Las respuestas de la categoría 2 fueron clasificadas en: auxilio individual, sensibilización, punición, supervisión, nada que hacer. Los resultados categorizados fueron relacionados a los datos cuantitativos de la victimización; en base a los resultados fueron hechas consideraciones para acciones anti bullying en las escuelas.

O bullying é um ato agressivo sistemático, envolvendo ameaça, intimidação ou coação, praticado contra alguém, por um indivíduo ou um grupo de pessoas. É um problema ainda presente na vida dos jovens, principalmente no ambiente escolar. O objetivo desse trabalho foi analisar as diferenças percebidas pelos estudantes quanto aos motivos das vitimizações por bullying e medidas para minimizá-las, em diferentes contextos educacionais Utilizou-se de um questionário adaptado do modelo "Peer Victimization Scale" de Mynard e Joseph (2000) com duas perguntas sobre a ocorrência de vitimizações por bulliyng e medidas para minimizá-lo. As respostas dos estudantes foram categorizadas relativas a cada pergunta. As respostas da categoria 1 foram classificadas em: minha culpa, culpa do outro e naturalização. As respostas da categoria 2 foram classificadas em: Auxílio individualizado, sensibilização, punição, supervisão e nada a fazer. Os resultados categorizados foram relacionados aos dados quantitativos de vitimização e baseado nos resultados  foram feitas considerações para ações anti-bullying nas escolas.

Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI.

BACKGROUND: For assessing the predictability of oncology neoadjuvant therapy results, the background parenchymal enhancement (BPE) parameter in breast magnetic resonance imaging (MRI) has acquired increased interest. This work aims to qualitatively evaluate the BPE parameter as a potential predictive marker for neoadjuvant therapy. METHOD: Three radiologists examined, in triple-blind modality, the MRIs of 80 patients performed before the start of chemotherapy, after three months from the start of treatment, and after surgery. They identified the portion of fibroglandular tissue (FGT) and BPE of the contralateral breast to the tumor in the basal control pre-treatment (baseline). RESULTS: We observed a reduction of BPE classes in serial MRI checks performed during neoadjuvant therapy, as compared to baseline pre-treatment conditions, in 61.3% of patients in the intermediate step, and in 86.7% of patients in the final step. BPE reduction was significantly associated with sequential anthracyclines/taxane administration in the first cycle of neoadjuvant therapy compared to anti-HER2 containing therapies. The therapy response was also significantly related to tumor size. There were no associations with menopausal status, fibroglandular tissue (FGT) amount, age, BPE baseline, BPE in intermediate, and in the final MRI step. CONCLUSIONS: The measured variability of this parameter during therapy could predict therapy effectiveness in early stages, improving decision-making in the perspective of personalized medicine. Our preliminary results suggest that BPE may represent a predictive factor in response to neoadjuvant therapy in breast cancer, warranting future investigations in conjunction with radiomics.

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results.

The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.

Neoadjuvant chemotherapy in breast cancer: a dose-dense schedule in real life and putative role of PIK3CA mutations.

BACKGROUND: Dose-dense chemotherapy is one of the treatments of choice for neoadjuvant therapy in breast cancer (BC). Activating mutations in PIK3CA gene predict worse response to neoadjuvant chemotherapy for HER2-positive patients, while their role is less clearly defined for HER2-negative tumors. METHODS: We conducted a phase I/II study of neoadjuvant, sequential, dose-dense anthracycline/taxane chemotherapy, plus trastuzumab in HER2-positive patients and investigated the correlation of pre-treatment PIK3CA mutation status with pathologic complete response (pCR) and long-term outcome in a real-life setting. RESULTS: we established a dose-dense docetaxel recommended dose of 60 mg/m2 and 65 mg/m2, with or without trastuzumab, respectively, according to HER2-status, following dose-dense epirubicin-cyclophosphamide (90/600 mg/m2), every 2 weeks. The overall pCR rate was 21.4%; median disease-free survival (DFS) was 52 months and median overall survival (OS) was not yet reached. PIK3CA mutation status was not significantly associated with the pCR rate: 18% for both mutated and wild-type patients. The pCR rate was: 25% in the mutated and 24% in the wild-type (p 0.560) cohort of the HER2-positive subgroup; 33% both in the mutant and wild-type cohort of the triple-negative subgroup; no pCR neither in the mutant nor in the wild-type cohort of the HR-positive/HER2-negative subgroup. Among the HER2-positive population, a trend toward worse DFS was observed in case of mutation, as opposed to the triple negative population. CONCLUSIONS: This study proposes an effective and safe neoadjuvant dose-dense anthracycline/taxane schedule and suggests that PIK3CA mutation analysis can be usefully performed in real-life clinical practice.

The neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios predict efficacy of platinum-based chemotherapy in patients with metastatic triple negative breast cancer.

Platinum salts are active against metastatic triple negative breast cancer (mTNBC), and biomarkers to predict their effectiveness are urgently needed. In recent years, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) have emerged as prognostic biomarkers in many malignancies, but their predictive role in platinum-treated mTNBC patients remains unexplored. We performed a retrospective, single centre study to evaluate the association between baseline NLR or PLR and progression free survival (PFS) of mTNBC patients treated with platinum-based chemotherapy. As a control population, we analysed data from patients with hormone receptor-positive HER2-negative (HR+ HER2-) metastatic breast cancer. Among 57 mTNBC patients treated with the carboplatin-paclitaxel or carboplatin-gemcitabine combination, high NLR and PLR were associated with significantly lower PFS at both univariate and multivariable analysis. Conversely, we did not find a significant association between NLR or PLR and the PFS of 148 patients in the control population. Our findings suggest that the NLR and PLR are predictive of benefit from platinum-containing chemotherapy specifically in mTNBC patients. If validated in larger prospective studies, these easy-to-measure parameters could be combined with emerging predictive biomarkers, such as BRCA 1/2 mutations, to improve the selection of mTNBC patients more likely to benefit from platinum-based chemotherapy.