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Triple negative breast cancers (TNBC) present a poor prognosis primarily due to their resistance to chemotherapy. This resistance is known to be associated with elevated expression of certain anti-apoptotic members within the proteins of the BCL-2 family (namely BCL-xL, MCL-1 and BCL-2). These regulate cell death by inhibiting pro-apoptotic protein activation through binding and sequestration and they can be selectively antagonized by BH3 mimetics. Yet the individual influences of BCL-xL, MCL-1, and BCL-2 on the sensitivity of TNBC cells to chemotherapy, and their regulation by cancer-associated fibroblasts (CAFs), major components of the tumor stroma and key contributors to therapy resistance remain to be delineated. Using gene editing or BH3 mimetics to inhibit anti-apoptotic BCL-2 family proteins in TNBC line MDA-MB-231, we show that BCL-xL and MCL-1 promote cancer cell survival through compensatory mechanisms. This cell line shows limited sensitivity to chemotherapy, in line with the clinical resistance observed in TNBC patients. We elucidate that BCL-xL plays a pivotal role in therapy response, as its depletion or pharmacological inhibition heightened chemotherapy effectiveness. Moreover, BCL-xL expression is associated with chemotherapy resistance in patient-derived tumoroids where its pharmacological inhibition enhances ex vivo response to chemotherapy. In a co-culture model of cancer cells and CAFs, we observe that even in a context where BCL-xL reduced expression renders cancer cells more susceptible to chemotherapy, those in contact with CAFs display reduced sensitivity to chemotherapy. Thus CAFs exert a profound pro-survival effect in breast cancer cells, even in a setting highly favoring cell death through combined chemotherapy and absence of the main actor of chemoresistance, BCL-xL. [ABSTRACT FROM AUTHOR]

Background: Longer times between diagnosis and treatments of cancer patients have been estimated as effects of the COVID-19 pandemic. However, relatively few studies attempted to estimate actual delay to treatment at the patient level. Objective: To assess changes in delays to first treatment and surgery among newly diagnosed patients with localized breast cancer (BC) during the COVID-19 pandemic. Methods: We used data from the PAPESCO-19 multicenter cohort study, which included patients from 4 French comprehensive cancer centers. We measured the delay to first treatment as the number of days between diagnosis and the first treatment regardless of whether this was neoadjuvant chemotherapy or surgery. COVID-19 pandemic exposure was estimated with a composite index that considered both the severity of the pandemic and the level of lockdown restrictions. We ran generalized linear models with a log link function and a gamma distribution to model the association between delay and the pandemic. Results: Of the 187 patients included in the analysis, the median delay to first treatment was 42 (IQR:32–54) days for patients diagnosed before and after the start of the 1st lockdown (N = 99 and 88, respectively). After adjusting for age and centers of inclusion, a higher composite pandemic index (> = 50 V.S. <50) had only a small, non-significant effect on times to treatment. Longer delays were associated with factors other than the COVID-19 pandemic. Conclusion: We found evidence of no direct impact of the pandemic on the actual delay to treatment among patients with localized BC. [ABSTRACT FROM AUTHOR]

Background: Long-term treatment-related toxicities, such as neurological and metabolic toxicities, are major issues in breast cancer. We investigated the interest of metabolomic profiling to predict toxicities., Methods: Untargeted high-resolution metabolomic profiles of 992 patients with ER+/HER2- breast cancer from the prospective CANTO cohort were acquired (n=1935 metabolites). A residual-based modeling strategy with a discovery and validation cohort was used to benchmark machine learning algorithms, taking into account confounding variables., Results: Adaptive LASSO has a good predictive performance, has limited optimism bias, and allows the selection of metabolites of interest for future translational research. The addition of low-frequency metabolites and non-annotated metabolites increases the predictive power. Metabolomics adds extra performance to clinical variables to predict various neurological and metabolic toxicity profiles., Conclusions: Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.

Vepdegestrant (ARV-471) is an oral PROTAC ER degrader that binds an E3 ubiquitin ligase and ER to directly trigger ubiquitination of ER and its subsequent proteasomal degradation. In a first-in-human Phase I/II study, vepdegestrant monotherapy was well tolerated with clinical activity in pretreated patients with ER+/HER2- advanced breast cancer. The global, randomized Phase III VERITAC-2 study compares efficacy and safety of vepdegestrant versus fulvestrant in adults with ER+/HER2- advanced breast cancer after treatment with a CDK4/6 inhibitor plus endocrine therapy. Progression-free survival by blinded independent central review (primary end point) will be assessed in the intention-to-treat population and ESR1 mutation-positive subpopulation. Secondary end points include overall survival, tumor response, safety, pharmacokinetics, patient-reported outcomes, and circulating tumor DNA biomarkers. Clinical trial registration: NCT05654623 (ClinicalTrials.gov).

Purpose: To identify potential predictors of response and resistance mechanisms in patients with hormone receptor-positive (HR+), HER2-negative (HER2-) advanced breast cancer (ABC) treated with the cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitor abemaciclib ± endocrine therapy (ET), baseline and acquired genomic alterations in circulating tumor DNA (ctDNA) were analyzed and associated with clinical outcomes., Experimental Design: MONARCH 3: postmenopausal women with HR+, HER2- ABC and no prior systemic therapy in the advanced setting were randomly assigned to abemaciclib or placebo plus nonsteroidal aromatase inhibitor (NSAI). nextMONARCH: women with HR+, HER2- metastatic breast cancer that progressed on/after prior ET and chemotherapy were randomly assigned to abemaciclib alone (two doses) or plus tamoxifen. Baseline and end-of-treatment plasma samples from patients in MONARCH 3 and nextMONARCH (monotherapy arms) were analyzed to identify somatic genomic alterations. Association between genomic alterations and median progression-free survival (mPFS) was assessed., Results: Most patients had ≥1 genomic alteration detected in baseline ctDNA. In MONARCH 3, abemaciclib+NSAI was associated with improved mPFS versus placebo+NSAI, regardless of baseline alterations. ESR1 alterations were less frequently acquired in the abemaciclib+NSAI arm than placebo+NSAI. Acquired alterations potentially associated with resistance to abemaciclib ± NSAI included RB1 and MYC., Conclusions: In MONARCH 3, certain baseline ctDNA genomic alterations were prognostic for ET but not predictive of abemaciclib response. Further studies are warranted to assess whether ctDNA alterations acquired during abemaciclib treatment differ from other CDK4/6 inhibitors. Findings are hypothesis generating; further exploration is warranted into mechanisms of resistance to abemaciclib and ET. See related commentary by Wander and Bardia, p. 2008., (©2023 The Authors; Published by the American Association for Cancer Research.)

Background: Value-based healthcare (VBHC) is a conceptual framework to improve the value of healthcare by health, care-process and economic outcomes. Benchmarking should provide useful information to identify best practices and therefore a good instrument to improve quality across healthcare organizations. This paper aims to provide a proof-of-concept of the feasibility of an international VBHC benchmarking in breast cancer, with the ultimate aim of being used to share best practices with a data-driven approach among healthcare organizations from different health systems., Methods: In the VOICE community-a European healthcare centre cluster intending to address VBHC from theory to practice-information on patient-reported, clinical-related, care-process-related and economic-related outcomes were collected. Patient archetypes were identified using clustering techniques and an indicator set following a modified Delphi was defined. Benchmarking was performed using regression models controlling for patient archetypes and socio-demographic characteristics., Results: Six hundred and ninety patients from six healthcare centres were included. A set of 50 health, care-process and economic indicators was distilled for benchmarking. Statistically significant differences across sites have been found in most health outcomes, half of the care-process indicators, and all economic indicators, allowing for identifying the best and worst performers., Conclusions: To the best of our knowledge, this is the first international experience providing evidence to be used with VBHC benchmarking intention. Differences in indicators across healthcare centres should be used to identify best practices and improve healthcare quality following further research. Applied methods might help to move forward with VBHC benchmarking in other medical conditions., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Public Health Association.)