Breast Cancer Research最新文献

{"title":"PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.","authors":"Witali Aswolinskiy, Enrico Munari, Hugo M Horlings, Lennart Mulder, Giuseppe Bogina, Joyce Sanders, Yat-Hee Liu, Alexandra W van den Belt-Dusebout, Leslie Tessier, Maschenka Balkenhol, Michelle Stegeman, Jeffrey Hoven, Jelle Wesseling, Jeroen van der Laak, Esther H Lips, Francesco Ciompi","doi":"10.1186/s13058-023-01726-0","DOIUrl":"10.1186/s13058-023-01726-0","url":null,"abstract":"<p><strong>Background: </strong>Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy.</p><p><strong>Methods: </strong>In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs).</p><p><strong>Results: </strong>We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts.</p><p><strong>Conclusion: </strong>The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92157168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AMEERA-4: a randomized, preoperative window-of-opportunity study of amcenestrant versus letrozole in early breast cancer.","authors":"Mario Campone, François-Clément Bidard, Patrick Neven, Lei Wang, Bin Ling, Yvonne Dong, Gautier Paux, Christina Herold, Ugo De Giorgi","doi":"10.1186/s13058-023-01740-2","DOIUrl":"10.1186/s13058-023-01740-2","url":null,"abstract":"<p><strong>Background: </strong>Window-of-opportunity (WOO) studies provide insights into the clinical activity of new drugs in breast cancer.</p><p><strong>Methods: </strong>AMEERA-4 (NCT04191382) was a WOO study undertaken to compare the pharmacodynamic effects of amcenestrant, a selective estrogen receptor degrader, with those of letrozole in postmenopausal women with newly diagnosed, operable estrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancer. Women were randomized (1:1:1) to receive amcenestrant 400 mg, amcenestrant 200 mg, or letrozole 2.5 mg once daily for 14 days before breast surgery. The primary endpoint was change in Ki67 between baseline and Day 15 (i.e., day of surgery).</p><p><strong>Results: </strong>Enrollment was stopped early because of slow recruitment, in the context of the COVID-19 pandemic. The modified intent-to-treat population consisted of 95 study participants with baseline and post-treatment Ki67 values, whereas the safety population included 104 participants who had received at least one dose of study medication. Relative change from baseline in Ki67 was - 75.9% (95% confidence interval [CI] - 81.9 to - 67.9) for amcenestrant 400 mg, - 68.2% (- 75.7 to - 58.4) for amcenestrant 200 mg, and - 77.7% (- 83.4 to - 70.0) for letrozole (geometric least-squares mean [LSM] estimates). Absolute change in ER H-score from baseline (LSM estimate) was - 176.7 in the amcenestrant 400 mg arm, - 202.9 in the amcenestrant 200 mg arm, and - 32.5 in the letrozole arm. There were no Grade ≥ 3 treatment-related adverse events.</p><p><strong>Conclusions: </strong>Both amcenestrant and letrozole demonstrated antiproliferative activity in postmenopausal women with previously untreated, operable ER+/HER2- breast cancer and had good overall tolerability.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov, NCT04191382 https://clinicaltrials.gov/ct2/show/NCT04191382 . Registered 9 December 2019.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blocking channels to metastasis: targeting sodium transport in breast cancer.","authors":"William J Brackenbury, Carlo Palmieri","doi":"10.1186/s13058-023-01741-1","DOIUrl":"10.1186/s13058-023-01741-1","url":null,"abstract":"<p><p>The development of therapies that can suppress invasion and prevent metastasis, 'anti-metastatic drugs', is an important area of unmet therapeutic need. The new results of a recent open-label, multicentre randomised trial published in J Clin Oncol showed a significant disease-free survival (DFS) benefit for breast cancer patients receiving presurgical, peritumoral injection of lidocaine, an amide local anaesthetic, which blocks voltage-gated sodium channels (VGSCs). VGSCs are expressed on electrically excitable cells, including neurons and cardiomyocytes, where they sustain rapid membrane depolarisation during action potential firing. As a result of this key biophysical function, VGSCs are important drug targets for excitability-related disorders, including epilepsy, neuropathic pain, affective disorders and cardiac arrhythmia. A growing body of preclinical evidence highlights VGSCs as key protagonists in regulating altered sodium influx in breast cancer cells, thus driving invasion and metastasis. Furthermore, prescription of certain VGSC-inhibiting medications has been associated with reduced cancer incidence and improved survival in several observational studies. Thus, VGSC-inhibiting drugs already in clinical use may be ideal candidates for repurposing as possible anti-metastatic therapies. While these results are promising, further work is required to establish whether other VGSC inhibitors may afford superior metastasis suppression. Finally, increasing preclinical evidence suggests that several other ion channels are also key drivers of cancer hallmarks; thus, there are undoubtedly further opportunities to harness ion transport inhibition that should also be explored.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High inter-laboratory variability in the assessment of HER2-low breast cancer: a national registry study on 50,714 Danish patients.","authors":"Kåre Nielsen, Michael Sode, Maj-Britt Jensen, Tobias Berg, Ann Knoop, Bent Ejlertsen, Anne-Vibeke Lænkholm","doi":"10.1186/s13058-023-01739-9","DOIUrl":"10.1186/s13058-023-01739-9","url":null,"abstract":"<p><strong>Background: </strong>Considering the recent advancements in the treatment of breast cancer with low expression of human epidermal growth factor receptor 2 (HER2), we aimed to examine inter-laboratory variability in the assessment of HER2-low breast cancer across all Danish pathology departments.</p><p><strong>Methods: </strong>From the Danish Breast Cancer Group, we obtained data on all women diagnosed with primary invasive breast cancer in 2007-2019 who were subsequently assigned for curatively intended treatment.</p><p><strong>Results: </strong>Of 50,714 patients, HER2 score and status were recorded for 48,382, among whom 59.2% belonged to the HER2-low group (score 1+ or 2+ without gene amplification), 26.8% had a HER2 score of 0, and 14.0% were HER2 positive. The proportion of HER2-low cases ranged from 46.3 to 71.8% among pathology departments (P < 0.0001) and from 49.3 to 65.6% over the years (P < 0.0001). In comparison, HER2 positivity rates ranged from 11.8 to 17.2% among departments (P < 0.0001) and from 12.6 to 15.7% over the years (P = 0.005). In the eight departments with the highest number of patients, variability in HER2-low cases increased from 2011 to 2019, although the same immunohistochemical assay was used. By multivariable logistic regression, the examining department was significantly related to both HER2 score 0 and HER2 positivity (P < 0.0001) but showed greater dispersion in odds ratios in the former case (range 0.25-1.41 vs. 0.84-1.27).</p><p><strong>Conclusions: </strong>Our data showed high inter-laboratory variability in the assessment of HER2-low breast cancer. The findings cast doubt on whether the current test method for HER2 is robust and reliable enough to select HER2-low patients for HER2-targeted treatment in daily clinical practice.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72015918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer.","authors":"Anum S Kazerouni, Lanell M Peterson, Isaac Jenkins, Alena Novakova-Jiresova, Hannah M Linden, Julie R Gralow, David M Hockenbery, David A Mankoff, Peggy L Porter, Savannah C Partridge, Jennifer M Specht","doi":"10.1186/s13058-023-01722-4","DOIUrl":"10.1186/s13058-023-01722-4","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate combined MRI and ¹⁸F-FDG PET for assessing breast tumor metabolism/perfusion mismatch and predicting pathological response and recurrence-free survival (RFS) in women treated for breast cancer.</p><p><strong>Methods: </strong>Patients undergoing neoadjuvant chemotherapy (NAC) for locally-advanced breast cancer were imaged at three timepoints (pre, mid, and post-NAC), prior to surgery. Imaging included diffusion-weighted and dynamic contrast-enhanced (DCE-) MRI and quantitative ¹⁸F-FDG PET. Tumor imaging measures included apparent diffusion coefficient, peak percent enhancement (PE), peak signal enhancement ratio (SER), functional tumor volume, and washout volume on MRI and standardized uptake value (SUVmax), glucose delivery (K₁) and FDG metabolic rate (MRFDG) on PET, with percentage changes from baseline calculated at mid- and post-NAC. Associations of imaging measures with pathological response (residual cancer burden [RCB] 0/I vs. II/III) and RFS were evaluated.</p><p><strong>Results: </strong>Thirty-five patients with stage II/III invasive breast cancer were enrolled in the prospective study (median age: 43, range: 31-66 years, RCB 0/I: N = 11/35, 31%). Baseline imaging metrics were not significantly associated with pathologic response or RFS (p > 0.05). Greater mid-treatment decreases in peak PE, along with greater post-treatment decreases in several DCE-MRI and ¹⁸F-FDG PET measures were associated with RCB 0/I after NAC (p < 0.05). Additionally, greater mid- and post-treatment decreases in DCE-MRI (peak SER, washout volume) and ¹⁸F-FDG PET (K₁) were predictive of prolonged RFS. Mid-treatment decreases in metabolism/perfusion ratios (MRFDG/peak PE, MRFDG/peak SER) were associated with improved RFS.</p><p><strong>Conclusion: </strong>Mid-treatment changes in both PET and MRI measures were predictive of RCB status and RFS following NAC. Specifically, our results indicate a complementary relationship between DCE-MRI and ¹⁸F-FDG PET metrics and potential value of metabolism/perfusion mismatch as a marker of patient outcome.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72015919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A catchment and location-allocation analysis of mammography access in Delaware, US: implications for disparities in geographic access to breast cancer screening.","authors":"Jessica L Webster, Neal D Goldstein, Jennifer P Rowland, Catherine M Tuite, Scott D Siegel","doi":"10.1186/s13058-023-01738-w","DOIUrl":"10.1186/s13058-023-01738-w","url":null,"abstract":"<p><strong>Background: </strong>Despite a 40% reduction in breast cancer mortality over the last 30 years, not all groups have benefited equally from these gains. A consistent link between later stage of diagnosis and disparities in breast cancer mortality has been observed by race, socioeconomic status, and rurality. Therefore, ensuring equitable geographic access to screening mammography represents an important priority for reducing breast cancer disparities. Access to breast cancer screening was evaluated in Delaware, a state that experiences an elevated burden from breast cancer but is otherwise representative of the US in terms of race and urban-rural characteristics. We first conducted a catchment analysis of mammography facilities. Finding evidence of disparities by race and rurality, we next conducted a location-allocation analysis to identify candidate locations for the establishment of new mammography facilities to optimize equitable access.</p><p><strong>Methods: </strong>A catchment analysis using the ArcGIS Pro Service Area analytic tool characterized the geographic distribution of mammography sites and Breast Imaging Centers of Excellence (BICOEs). Poisson regression analyses identified census tract-level correlates of access. Next, the ArcGIS Pro Location-Allocation analytic tool identified candidate locations for the placement of additional mammography sites in Delaware according to several sets of breast cancer screening guidelines.</p><p><strong>Results: </strong>The catchment analysis showed that for each standard deviation increase in the number of Black women in a census tract, there were 68% (95% CI 38-85%) fewer mammography units and 89% (95% CI 60-98%) fewer BICOEs. The more rural counties in the state accounted for 41% of the population but only 22% of the BICOEs. The results of the location-allocation analysis depended on which set of screening guidelines were adopted, which included increasing mammography sites in communities with a greater proportion of younger Black women and in rural areas.</p><p><strong>Conclusions: </strong>The results of this study illustrate how catchment and location-allocation analytic tools can be leveraged to guide the equitable selection of new mammography facility locations as part of a larger strategy to close breast cancer disparities.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction Note: Progesterone receptor assembly of a transcriptional complex along with activator protein 1, signal transducer and activator of transcription 3 and ErbB-2 governs breast cancer growth and predicts response to endocrine therapy.","authors":"María C Díaz Flaqué,&nbsp;Natalia M Galigniana,&nbsp;Wendy Béguelin,&nbsp;Rocío Vicario,&nbsp;Cecilia J Proietti,&nbsp;Rosalía Cordo Russo,&nbsp;Martín A Rivas,&nbsp;Mercedes Tkach,&nbsp;Pablo Guzmán,&nbsp;Juan C Roa,&nbsp;Esteban Maronna,&nbsp;Viviana Pineda,&nbsp;Sergio Muñoz,&nbsp;María Florencia Mercogliano,&nbsp;Eduardo H Charreau,&nbsp;Patricio Yankilevich,&nbsp;Roxana Schillaci,&nbsp;Patricia V Elizalde","doi":"10.1186/s13058-023-01735-z","DOIUrl":"10.1186/s13058-023-01735-z","url":null,"abstract":"","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71428318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.","authors":"Yunfang Yu,&nbsp;Wei Ren,&nbsp;Zifan He,&nbsp;Yongjian Chen,&nbsp;Yujie Tan,&nbsp;Luhui Mao,&nbsp;Wenhao Ouyang,&nbsp;Nian Lu,&nbsp;Jie Ouyang,&nbsp;Kai Chen,&nbsp;Chenchen Li,&nbsp;Rong Zhang,&nbsp;Zhuo Wu,&nbsp;Fengxi Su,&nbsp;Zehua Wang,&nbsp;Qiugen Hu,&nbsp;Chuanmiao Xie,&nbsp;Herui Yao","doi":"10.1186/s13058-023-01688-3","DOIUrl":"10.1186/s13058-023-01688-3","url":null,"abstract":"<p><strong>Background: </strong>Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms.</p><p><strong>Methods: </strong>In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics.</p><p><strong>Results: </strong>The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort.</p><p><strong>Conclusions: </strong>This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71428317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mTOR inhibition abrogates human mammary stem cells and early breast cancer progression markers.","authors":"Hakim Bouamar, Larry Esteban Broome, Kate Ida Lathrop, Ismail Jatoi, Andrew Jacob Brenner, Alia Nazarullah, Karla Moncada Gorena, Michael Garcia, Yidong Chen, Virginia Kaklamani, Lu-Zhe Sun","doi":"10.1186/s13058-023-01727-z","DOIUrl":"10.1186/s13058-023-01727-z","url":null,"abstract":"<p><strong>Background: </strong>Mammary physiology is distinguished in containing adult stem/progenitor cells that are actively amending the breast tissue throughout the reproductive lifespan of women. Despite their importance in both mammary gland development, physiological maintenance, and reproduction, the exact role of mammary stem/progenitor cells in mammary tumorigenesis has not been fully elucidated in humans or animal models. The implications of modulating adult stem/progenitor cells in women could lead to a better understanding of not only their function, but also toward possible breast cancer prevention led us to evaluate the efficacy of rapamycin in reducing mammary stem/progenitor cell activity and malignant progression markers.</p><p><strong>Methods: </strong>We analyzed a large number of human breast tissues for their basal and luminal cell composition with flow cytometry and their stem and progenitor cell function with sphere formation assay with respect to age and menopausal status in connection with a clinical study (NCT02642094) involving a low-dose (2 mg/day) and short-term (5-7 days) treatment of the mTOR inhibitor sirolimus. The expression of biomarkers in biopsies and surgical breast samples were measured with quantitative analysis of immunohistochemistry.</p><p><strong>Results: </strong>Sirolimus treatment significantly abrogated mammary stem cell activity, particularly in postmenopausal patients. It did not affect the frequency of luminal progenitors but decreased their self-renewal capacity. While sirolimus had no effect on basal cell population, it decreased luminal cell population, particularly in postmenopausal patients. It also significantly diminished prognostic biomarkers associated with breast cancer progression from ductal carcinoma in situ to invasive breast cancer including p16INK4A, COX-2, and Ki67, as well as markers of the senescence-associated secretary phenotype, thereby possibly functioning in preventing early breast cancer progression.</p><p><strong>Conclusion: </strong>Overall, these findings indicate a link from mTOR signaling to mammary stem and progenitor cell activity and cancer progression. Trial registration This study involves a clinical trial registered under the ClinicalTrials.gov identifier NCT02642094 registered December 30, 2015.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71414930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectories of body mass index in adulthood and risk of subtypes of postmenopausal breast cancer.","authors":"Marit Busund,&nbsp;Giske Ursin,&nbsp;Eiliv Lund,&nbsp;Tom Wilsgaard,&nbsp;Charlotta Rylander","doi":"10.1186/s13058-023-01729-x","DOIUrl":"10.1186/s13058-023-01729-x","url":null,"abstract":"<p><strong>Background: </strong>Body fatness is a dynamic exposure throughout life. To provide more insight into the association between body mass index (BMI) and postmenopausal breast cancer, we aimed to examine the age at onset, duration, intensity, and trajectories of body fatness in adulthood in relation to risk of breast cancer subtypes.</p><p><strong>Methods: </strong>Based on self-reported anthropometry in the prospective Norwegian Women and Cancer Study, we calculated the age at onset, duration, and intensity of overweight and obesity using linear mixed-effects models. BMI trajectories in adulthood were modeled using group-based trajectory modeling. We used Cox proportional hazards models to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between BMI exposures and breast cancer subtypes in 148,866 postmenopausal women.</p><p><strong>Results: </strong>A total of 7223 incident invasive postmenopausal breast cancer cases occurred during follow-up. Increased overweight duration and age at the onset of overweight or obesity were associated with luminal A-like breast cancer. Significant heterogeneity was observed in the association between age at overweight and overweight duration and the intrinsic-like subtypes (p_{heterogeneity} 0.03). Compared with women who remained at normal weight throughout adulthood, women with a descending BMI trajectory had a reduced risk of luminal A-like breast cancer (HR 0.54, 95% CI 0.33-0.90), whereas women with ascending BMI trajectories were at increased risk (HR 1.09; 95% CI 1.01-1.17 for \"Normal-overweight\"; HR 1.20; 95% CI 1.07-1.33 for \"Normal-obesity\"). Overweight duration and weighted cumulative years of overweight and obesity were inversely associated with luminal B-like breast cancer.</p><p><strong>Conclusions: </strong>In this exploratory analysis, decreasing body fatness from obesity in adulthood was inversely associated with overall, hormone receptor-positive and luminal A-like breast cancer in postmenopausal women. This study highlights the potential health benefits of reducing weight in adulthood and the health risks associated with increasing weight throughout adult life. Moreover, our data provide evidence of intrinsic-like tumor heterogeneity with regard to age at onset and duration of overweight.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66784500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}