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.

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Eribulin Mesylate as Third or Subsequent Line Chemotherapy for Elderly Patients with Locally Recurrent or Metastatic Breast Cancer: A Multicentric Observational Study of GIOGer (Italian Group of Geriatric Oncology)-ERIBE.

BACKGROUND: Metastatic breast cancer (MBC) is highly prevalent in middle-aged or elderly patients. Eribulin is a nontaxane microtubule inhibitor, approved for the treatment of pretreated MBC. This multicentric study (sponsored by GIOGer, Italian Group for Geriatric Oncology) was designed to assess the efficacy and tolerability of eribulin, according to parameters usually used in geriatric oncology. SUBJECTS, MATERIALS, AND METHODS: An observational study was conducted on 50 consecutive elderly patients with MBC. The primary endpoint was to evaluate the change in items score of comprehensive geriatric assessment (CGA) and health-related quality of life (HRQL). Italian versions of the CGA and HRQL questionnaires were administered at baseline, before the third and fifth cycles, and then every three cycles until treatment discontinuation. Secondary endpoints were efficacy and safety. RESULTS: Overall, both EQ-5D scores and EQ-5D-3 L visual analogic scale did not significantly change from baseline; the percentage of subjects without problems doing usual activities tended to decrease during treatment (p for linear trend .018), and the percentage of patients with minor problems performing usual activities tended to increase (p for linear trend.012). Among CGA items, Instrumental Activities of Daily Living tended to decrease during treatment and Geriatric Depression Scale tended to increase. After 12 months follow-up, 24 patients (out of 47) showed clinical benefits; median progression-free survival was 4.49 months (2.10-10.33) and median OS was 7.31 months (3.70-14.03). The treatment was associated with mild toxicity. CONCLUSION: Eribulin treatment preserved quality of life and geriatric parameters included in the CGA, except for instrumental functioning and geriatric depression, in elderly patients with MBC. IMPLICATIONS FOR PRACTICE: A collaboration between oncologist and geriatric specialists is essential in the management of patients with metastatic breast cancer, who are frequently elderly or frail. The assessment of geriatric parameters in the decision-making process can contribute to direct toward the most appropriate therapeutic plan and preserve the quality of life of patients. Eribulin does not seem to affect quality of life or worsen the overall geriatric status; therefore, it can be considered a suitable option for elderly patients with metastatic breast cancer.

Efficacy and safety of lenvatinib in an elderly patient with metastatic papillary thyroid carcinoma and cardiological comorbidity: a case report.

Lenvatinib significantly prolonged progression-free survival versus placebo in patients with radio-iodine refractory differentiated thyroid carcinoma. However, the primary adverse effects of any grade that occurred in >40% of patients in the lenvatinib group of the Phase III SELECT trial was hypertension (67.8%). Therefore, this drug should be used with caution in patients with cardiological morbidity. Here, we describe the case of a 73-year-old man with hypertension, obesity and chronic atrial fibrillation, who received lenvatinib for 6 months in the absence of cardiological symptoms.

Dose intensity and efficacy of the combination of everolimus and exemestane (EVE/EXE) in a real-world population of hormone receptor-positive (ER+/PgR+), HER2-negative advanced breast cancer (ABC) patients: a multicenter Italian experience.

AIM: This retrospective analysis focused on the effect of treatment with EVE/EXE in a real-world population outside of clinical trials. We examined the efficacy of this combination in terms of PFS and RR related to dose intensity (5 mg daily versus 10 mg daily) and tolerability. METHODS: 163 HER2-negative ER+/PgR+ ABC patients, treated with EVE/EXE from May 2011 to March 2016, were included in the analysis. The primary endpoints were the correlation between the daily dose and RR and PFS, as well as an evaluation of the tolerability of the combination. Secondary endpoints were RR, PFS, and OS according to the line of treatment. Patients were classified into three different groups, each with a different dose intensity of everolimus (A, B, C). RESULTS: RR was 29.8% (A), 27.8% (B) (p = 0.953), and not evaluable (C). PFS was 9 months (95% CI 7-11) (A), 10 months (95% CI 9-11) (B), and 5 months (95% CI 2-8) (C), p = 0.956. OS was 38 months (95% CI 24-38) (A), median not reached (B), and 13 months (95% CI 10-25) (C), p = 0.002. Adverse events were stomatitis 57.7% (11.0% grade 3-4), asthenia 46.0% (6.1% grade 3-4), hypercholesterolemia 46.0% (0.6% grade 3-4), and hyperglycemia 35.6% (5.5% grade 3-4). The main reason for discontinuation/interruption was grade 2-3 stomatitis. CONCLUSIONS: No correlation was found between dose intensity (5 vs. 10 mg labeled dose) and efficacy in terms of RR and PFS. The tolerability of the higher dose was poor in our experience, although this had no impact on efficacy.

Efficacy and safety of eribulin in taxane-refractory patients in the 'real world'.

AIM: Recent clinical, randomized and observational studies showed that eribulin, an analogous of Halichondrin B, was beneficial and well-tolerated in heavily pretreated metastatic breast cancer patients. Here, we aim to evaluate the effectiveness and safety of eribulin in taxane-refractory metastatic breast cancer patients. PATIENTS & METHODS: In this subanalysis of the ESEMPIO study database, we selected 91 subjects with well-defined taxane refractoriness and complete data available. RESULTS: 41 patients (45.2%) showed clinical benefit; one complete response (2.2%) and 16 partial responses (17.6%) were observed. Median progression-free survival and median overall survival were 3.1 and 11.6 months, respectively. The most experienced adverse event was asthenia/fatigue (58%), followed by neutropenia (30%). The treatment-related toxicity led to eribulin-dose reduction in 19 patients and suspension in nine. CONCLUSION: This study shows that eribulin is effective and well tolerated also in taxane-refractory patients in clinical practice.

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.

DNA Damage Response in Early Breast Cancer: A Phase III Cohort in the Phobos Study.

We assessed the impact of DNA damage response and repair (DDR) biomarker expressions in 222 node-positive early breast cancer (BC) patients from a previous Phase III GOIM 9902 trial of adjuvant taxanes. At a median follow-up of 64 months, the original study showed no disease-free survival (DFS) or overall survival (OS) differences with the addition of docetaxel (D) to epirubicine-cyclophosphamide (EC). Immunohistochemistry was employed to assess the expression of DDR phosphoproteins (pATM, pATR, pCHK1, γH2AX, pRPA32, and pWEE1) in tumor tissue, and their association with clinical outcomes was evaluated through the Cox elastic net model. Over an extended follow-up of 234 months, we confirmed no significant differences in DFS or OS between patients treated with EC and those receiving D → EC. A DDR risk score, inversely driven by ATM and ATR expression, emerged as an independent prognostic factor for both DFS (HR = 0.41, p < 0.0001) and OS (HR = 0.61, p = 0.046). Further validation in a public adjuvant BC cohort was possible only for ATM, confirming its protective role. Overall, our findings confirm the potential role of the DDR pathway in BC prognostication and in shaping treatment strategies advocating for an integrated approach, combining molecular markers with clinical-pathological factors.

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.

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.