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Besides certain exceptions, healing of most tissues in the human body occurs via formation of scar tissue, rather than restoration of lost structures. After extensive acute injuries, this phenomenon substantially limits the possibility of lost function recovery and, in case of chronic injury, it leads to pathological remodeling of organs affected. Managing outcomes of damaged tissue repair is one of the main objectives of regenerative medicine. The first priority for reaching it is comparative investigation of mechanisms responsible for complete restoration of damaged tissues and mechanisms of scarring. However, human body tissues that undergo complete scar-free healing are scarce. The endometrium is a unique mucous membrane in the human body that heals without scarring after various injuries, as well as during each menstrual cycle (i.e., up to 400 times during a woman’s life). We hypothesized that absence of scarring during endometrial healing may be associated with tissue-specific features of its stromal cells (SCs) or their microenvironment, since SCs transform into myofibroblasts—the main effector link of scarring. We found that during healing of the endometrium, soluble factors are formed that inhibit the transition of SCs into myofibroblasts. Without influence of these factors, the SCs of the endometrium undergo transformation into myofibroblasts after transforming growth factor β1 (TGF-β1) treatment as well as the SCs from tissues that heal by scarring—skin or fat. However, unlike the latter, endometrial SCs organize extracellular matrix (ECM) in a specific way and are not prone to formation of bulky connective tissue structures. Thus, we may suggest that tissue-specific features of endometrial SCs along with effects of soluble factors secreted in utero during menstruation ensure scar-free healing of human endometrium.

Background and aims: The aim of this study was to investigate the influence of a single injection of Evolocumab on the dynamics of Lp(a), fractions of apoB100-containing lipoproteins, PCSK9, and their complexes in healthy individuals with elevated Lp(a) levels. Methods: This open-label, 4-week clinical study involved 10 statin-naive volunteers with Lp(a) >30 mg/dL, LDL-C < 4.9 mmol/L, and a moderate risk of cardiovascular events. The concentrations of Lp(a), lipids, PCSK9, circulating immune complexes (CIC), and plasma complexes of PCSK9 with apoB100-containing lipoproteins (Lp(a)–PCSK9 and LDL–PCSK9) were measured before and each week after Evolocumab (MABs) administration. Results: After a single dose injection of 140 mg of MABs, the median concentration of PCSK9 in serum increased from 496 to 3944 ng/mL; however, the entire pool of circulating PCSK9 remained bound with MABs for 2–3 weeks. LDL-C level decreased significantly from 3.36 mmol/L to 2.27 mmol/L during the first two weeks after the injection. Lp(a) concentrations demonstrated multidirectional changes in different patients with the maximal decrease on the second week. There were no positive correlations between the changes in levels of Lp(a), LDL-C, and TC. The change in the amount of circulating complex of PCSK9–Lp(a) was significantly less than of PCSK9–apoB100 (−5% and −47% after 1 week, respectively). Conclusions: A single administration of monoclonal antibodies against PCSK9 (Evolocumab) in healthy individuals with hyperlipoproteinemia(a) resulted in a decrease of Lp(a) of 14%, a 5% decrease in PCSK9–Lp(a), a 36% reduction of LDL-C, a 47% decrease in PCSK9–apoB100 and a tenfold increase in total serum PCSK9 concentration.

The tumor suppressor TP53 is frequently mutated in hormone receptor-negative, HER2-positive breast cancer (BC), contributing to tumor aggressiveness. Traditional ancillary methods like immunohistochemistry (IHC) to assess TP53 functionality face pre- and post-analytical challenges. This proof-of-concept study employed a deep learning (DL) algorithm to predict TP53 mutational status from H&E-stained whole slide images (WSIs) of BC tissue. Using a pre-trained convolutional neural network, the model identified tumor areas and predicted TP53 mutations with a Dice coefficient score of 0.82. Predictions were validated through IHC and next-generation sequencing (NGS), confirming TP53 aberrant expression in 92 % of the tumor area, closely matching IHC findings (90 %). The DL model exhibited high accuracy in tissue quantification and TP53 status prediction, outperforming traditional methods in terms of precision and efficiency. DL-based approaches offer significant promise for enhancing biomarker testing and precision oncology by reducing intra- and inter-observer variability, but further validation is required to optimize their integration into real-world clinical workflows. This study underscores the potential of DL algorithms to predict key genetic alterations, such as TP53 mutations, in BC. DL-based histopathological analysis represents a valuable tool for improving patient management and tailoring treatment approaches based on molecular biomarker status., Competing Interests: A. M. has received support from Menarini Group and served on the Speakers' Bureau for Roche and AstraZeneca. C. C. has participated in advisory or consultancy roles and speakers' bureau engagements for Eli Lilly, Pfizer, Novartis, Roche, AstraZeneca, MSD, Daiichi Sankyo, Gilead, and Seagen. G. Curi. has received honoraria for speaker engagements from Roche, Seattle Genetics, Novartis, Lilly, Pfizer, Foundation Medicine, NanoString, Samsung, Celltrion, BMS, and MSD; honoraria for consultancy from Roche, Seattle Genetics, and NanoString; honoraria for participation in advisory boards from Roche, Lilly, Pfizer, Foundation Medicine, Samsung, Celltrion, and Mylan; honoraria for writing engagements from Novartis and BMS; and honoraria for participation in the Ellipsis Scientific Affairs Group. He has also received institutional research funding for conducting phase I and II clinical trials from Pfizer, Roche, Novartis, Sanofi, Celgene, Servier, Orion, AstraZeneca, Seattle Genetics, AbbVie, Tesaro, BMS, Merck Serono, Merck Sharp & Dohme, Janssen-Cilag, Philogen, Bayer, Medivation, and Medimmune. E. G-R. has received advisory fees, honoraria, travel accommodations/expenses, grants, and/or non-financial support from AstraZeneca, Exact Sciences, GSK, Illumina, MSD, Novartis, Roche, and Thermo Fisher Scientific. N.F. has received honoraria for consulting, advisory roles, speakers' bureau participation, travel, and/or research grants from Merck Sharp & Dohme (MSD), Novartis, AstraZeneca, Roche, Menarini, Daiichi Sankyo, GlaxoSmithKline (GSK), Gilead, Diaceutics, Adicet Bio, Sermonix, Reply, and Leica Biosystems. The remaining authors declare no conflicts of interest., (© 2024 The Authors.)

Mutations in the estrogen receptor alpha gene (ESR1) can lead to resistance to endocrine therapy (ET) in hormone receptor-positive (HR+)/ HER2- metastatic breast cancer (MBC). ESR1 mutations can be detected in up to 40 % of patients pretreated with ET in circulating tumor DNA (ctDNA). Data from prospective randomized trials highlight those patients with HR+/HER2- MBC with detectable ESR1 mutations experience better outcomes when receiving novel selective estrogen receptor degraders (SERDs). There is a high need for optimizing ESR1 testing strategies on liquid biopsy samples in HR+/HER2- MBC, including a hugh quality workflow implementation and molecular pathology reporting standardization. Our manuscript aims to elucidate the clinical and biological rationale for ESR1 testing in MBC, while critically examining the currently available guidelines and recommendations for this specific type of molecular testing on ctDNA. The objective will extend to the critical aspects of harmonization and standardization, specifically focusing on the pathology laboratory workflow. Finally, we propose a clear and comprehensive model for reporting ESR1 testing results on ctDNA in HR+/HER2- MBC., Competing Interests: Declaration of Competing Interest E.G-R. has relevant relationship (advisory fees, honoraria, travel accommodation and expenses, grants, and non-financial support) with AstraZeneca, Exact Sciences, GlaxoSmithKline (GSK), Novartis, Roche, Thermo Fisher Scientific unrelated to the current work. C.C. reported grants from Gilead, Seagen, personal fees from Pfizer, Lilly, Novartis, MSD, Daiichi Sankyo, and AstraZeneca outside the submitted work. G.C. reports funding from Astra Zeneca, Daichii Sankyo, Merck; consulting fees from BMS, Roche, Pfizer, Novartis, Lilly, Astra Zeneca, Daichii Sankyo, Merck, Seagen, Ellipsis; honoraria from Pfizer, Lilly; support for attending meetings from Roche, Pfizer. C.R. has received speaker honoraria from AstraZeneca, Roche, and MSD; advisory board honoraria from Inivata, Archer, Boston Pharmaceuticals, MD Serono, and Novartis; and institutional research funding for his work as Coordinating Principal Investigator from Pfizer and EMD Serono. U.M. has received personal fees (as consultant and/or speaker bureau) from Boehringer Ingelheim, Roche, MSD, Amgen, Thermo Fisher Scientific, Eli Lilly, Diaceutics, GSK, Merck and AstraZeneca, Janssen, Diatech, Novartis and Hedera. N.F. has received honoraria for consulting, advisory role, speaker bureau, travel, and/or research grants from Merck Sharp & Dohme (MSD), Merck, Novartis, AstraZeneca, Roche, Menarini, Daiichi Sankyo, GlaxoSmithKline (GSK), Gilead, Adicet Bio, Sysmex, Reply, Veracyte Inc., Sakura, Leica Biosystems, Lilly. These companies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and/or in the decision to publish the results. All other authors declare no potential conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score ® , MammaPrint, Prosigna ® , EndoPredict ® , and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict ® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes., Competing Interests: M.I. received honoraria from Agilent Technologies Denmark ApS and Diaceutics PLC. C.S. received honoraria for consulting, advisory roles, speaker bureaus, and/or research grants from Bristol Myers Squibb, Astra Zeneca, Daiichi-Sankyo, Gilead, Roche SPA, Novartis, Menarini, Veracyte Inc. G.d.A. received honoraria for consulting, advisory roles, and speaker bureaus from Merck Sharp & Dohme (MSD), Novartis, AstraZeneca, Roche, and Daiichi Sankyo. G.C. received honoraria from Roche and others from Novartis, Lilly, Pfizer, Astra Zeneca, Daichii Sankyo, Ellipsis, Veracyte, Exact Science, Celcuity, Merck, BMS, Gilead, Sanofi, Menarini. N.F. received honoraria for consulting, advisory roles, speaker bureaus, travel, and/or research grants from Merck Sharp & Dohme (MSD), Merck, Novartis, AstraZeneca, Roche, Menarini, Daiichi Sankyo, GlaxoSmithKline (GSK), Gilead, Adicet Bio, Sermonix, Reply, Veracyte Inc., Leica Biosystems, and Lilly. These companies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. All other authors have no relevant financial or non-financial interests to disclose.

Introduction: PIK3CA gene mutations occur in approximately 40% of hormone receptor-positive/HER2-negative (HR + /HER2 - ) metastatic breast cancers (MBCs), electing them to targeted therapy. Testing PIK3CA status is complex due to selection of biological specimen and testing method. Materials & methods: This work investigates real-life experience on PIK3CA testing in HR + /HER2 - MBC. Clinical, technical and molecular data on PIK3CA testing were collected from two referral laboratories. Additionally, the results of a nationwide PIK3CA survey involving 116 institutions were assessed. Results: Overall, n = 35 MBCs were PIK3CA -mutated, with mutations mostly occurring in exons 9 (n = 19; 51.4%) and 20 (n = 15; 40.5%). The nationwide survey revealed significant variability across laboratories in terms of sampling methodology, technical assessment and clinical report signing healthcare figures for PIK3CA molecular testing in diagnostic routine practice. Conclusion: This study provides insights into the real-world routine of PIK3CA testing in HR + /HER2 - MBC and highlights the need for standardization and networking in predictive pathology.

Activating mutations of the estrogen receptor alpha gene (ESR1) are common mechanisms of endocrine therapy (ET) resistance in hormone receptor-positive (HR + )/Human Epidermal Growth Factor Receptor 2 (HER2)-negative metastatic breast cancer (MBC). Recent clinical findings emphasize that both old and new generations of selective ER degraders (SERDs) demonstrate enhanced clinical effectiveness in patients with MBC who have detectable ESR1 mutations via liquid biopsy. This stands in contrast to individuals with MBC carrying these mutations and undergoing conventional endocrine monotherapies like aromatase inhibitors (AIs). Liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), has emerged as a promising, minimally invasive alternative to conventional tissue-based testing for identifying ESR1 mutations. Within the context of the PADA-1 and EMERALD trials, distinct molecular methodologies and assays, specifically digital droplet PCR (ddPCR) and next-generation sequencing (NGS), have been employed to evaluate the mutational status of ESR1 within ctDNA. This manuscript critically examines the advantages and indications of various ctDNA testing methods on liquid biopsy for HR+/HER2-negative MBC. Specifically, we delve into the capabilities of ddPCR and NGS in identifying ESR1 mutations. Each methodology boasts unique strengths and limitations: ddPCR excels in its analytical sensitivity for pinpointing hotspot mutations, while NGS offers comprehensive coverage of the spectrum of ESR1 mutations. The significance of meticulous sample handling and timely analysis is emphasized, acknowledging the transient nature of cfDNA. Furthermore, we underscore the importance of detecting sub-clonal ESR1 mutations, as these variants can exert a pivotal influence on predicting both endocrine therapy resistance and responsiveness to SERDs. In essence, this work discusses the role of ctDNA analysis for detecting ESR1 mutations and their implications in tailoring effective therapeutic strategies for HR+/HER2- MBC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research., Methods: In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis., Results: These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies., Conclusions: The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.