Breast Cancer: Molecular Highlights, Emerging Therapies, and Promising Strategies

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Mechanisms of Diseases".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 553

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Department of Pathology, University Vita-Salute San Raffaele, 20132 Milan, Italy
Interests: pathology; renal pathology; urological pathology; breast cancer; gastrointestinal pathology; biomarkers
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on the rapidly evolving landscape of breast cancer research, with particular emphasis on molecular insights, novel therapeutic approaches, and innovative strategies for diagnosis, treatment, and prevention. Breast cancer remains a major global health challenge, and recent advances in genomics, proteomics, and molecular pathology are transforming our understanding of its heterogeneity and progression.

We aim to showcase cutting-edge discoveries that unravel the molecular mechanisms underlying breast cancer subtypes, therapeutic resistance, and metastasis. Contributions exploring emerging targeted therapies, immunotherapeutic approaches, liquid biopsy technologies, and predictive biomarkers are especially welcome. We are also interested in studies addressing integration of artificial intelligence, digital pathology, and personalized medicine in breast cancer management. 

By bringing together original research, comprehensive reviews, and translational studies, this Special Issue seeks to provide a valuable resource for scientists, clinicians, and healthcare professionals working to improve outcomes for patients with breast cancer. We strongly encourage researchers and experts from multidisciplinary backgrounds to contribute their latest findings and perspectives. Your insights will not only enrich the scientific discourse but also help shape future directions in breast cancer management and precision oncology. Together, let us advance meaningful progress in the fight against breast cancer.

Dr. Mariia Ivanova
Guest Editor

Manuscript Submission Information

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Keywords

  • breast cancer
  • metastatic breast cancer
  • biomarkers
  • molecular profiling
  • immune landscape
  • genomics
  • personalized treatment
  • targeted therapy

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Published Papers (1 paper)

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Research

12 pages, 1004 KB  
Article
Inverse Association of p63 Expression with Hormone Receptor Status in Invasive Breast Cancer
by Panagis Lykoudis, Maria Papadoliopoulou, Alexios Kozonis, Georgios Kirkilesis, Marios-Konstantinos Tasoulis, Mahrokh Nohadani and Mihir A. Gudi
J. Pers. Med. 2026, 16(7), 359; https://doi.org/10.3390/jpm16070359 - 1 Jul 2026
Viewed by 165
Abstract
Background/Objectives: Immunohistochemistry is an integral component of the diagnostic approach in breast cancer and remains essential for tumor characterization and therapeutic decision-making. p63 gene expression may have potential diagnostic and prognostic roles in breast cancer patients. Methods: In this study, 127 [...] Read more.
Background/Objectives: Immunohistochemistry is an integral component of the diagnostic approach in breast cancer and remains essential for tumor characterization and therapeutic decision-making. p63 gene expression may have potential diagnostic and prognostic roles in breast cancer patients. Methods: In this study, 127 specimens of invasive breast carcinoma and 50 control cases were evaluated for p63 gene expression and compared to other pathology factors. Results: None of the 50 control cases was assessed as positive for p63 expression. Progesterone and estrogen receptor status were the only factors that demonstrated a statistically significant negative correlation with p63 expression (p = 0.005 and p = 0.017, respectively). Tumor size demonstrated a marginally non-significant correlation with p63 expression (p = 0.051). None of the remaining factors was significantly correlated with p63 expression. Conclusions: In conclusion, p63 expression is inversely correlated with estrogen and progesterone receptor status, and type, size and grade of the tumors are not correlated with the gene’s expression, nor is HER2 status. This conclusion might impact genotype-based stratification pertinent to diagnosis and tailored treatment. Full article
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