ReviewThe American journal of pathology2013
Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine.
Review in The American journal of pathology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 184 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
184 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Pertuzumab, Trastuzumab, and an Aromatase Inhibitor for HER2-Positive and Hormone Receptor-Positive Metastatic or Locally Advanced Breast Cancer: PERTAIN Final Analysis.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Trial
- Evaluation of primary breast cancers using dedicated breast PET and whole-body PET.Scientific reports · 2020Trial
- Artificial Intelligence (AI)-Assisted Human Epidermal Growth Factor Receptor 2 (HER2) Scoring in Breast Cancer: Diagnostic Agreement and Understanding Discordance.Laboratory investigation; a journal of technical methods and pathology · 2026Article
- Adipocyte-Derived Palmitic Acid Promotes Breast Cancer Malignancy via ZDHHC15-Mediated S-Palmitoylation of PPARγ.Cancer science · 2026Article
- Retrospective Cohort Study: Extracting Coexisting Background Breast-Lesion Features from Stage I-III Invasive Breast Cancer.Cancers · 2026Article
- scHILL: deciphering individual-level immune cell heterogeneity with single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Treatment Patterns and Clinical Outcomes in Metastatic HR+/HER2- and Triple-Negative Breast Cancer in Canada: The HER2- TRENDS Study.Advances in therapy · 2026Article
- Spatial Chromatin Accessibility Analysis of Intratumor Heterogeneity in Breast Cancer.Genomics, proteomics & bioinformatics · 2026Article
- A Predictive Immunological Signature Associated with Pathological Response in Breast Cancer Treated with Neoadjuvant Chemotherapy.Biomedicines · 2026Article
- Arsenic induced Hippo pathway dysregulation linked to hTERT hypo-methylation in breast cancer patients.Frontiers in epigenetics and epigenomics · 2026Article
- Elevated serum omentin levels correlate with tumor aggressiveness and disease progression in breast cancer patients.Frontiers in oncology · 2026Article
- Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.Annals of medicine · 2025Article
- Integrated spatial proteomic analysis of breast cancer heterogeneity unravels cancer cell phenotypic plasticity.Nature communications · 2025Article
- Single-nucleotide variants in microRNAs associated with breast cancer in women from western Mexico.World journal of clinical oncology · 2025Article
- Targeting GD2 with naxitamab overcomes GD3 synthase-driven immune suppression in triple-negative breast cancer.NPJ breast cancer · 2025Article
- Advancing Breast Cancer Management Through an Enhanced Understanding of Disease Heterogeneity from Initiation to Metastasis.The American journal of pathology · 2025Article
- Multiscale Cell-Cell Interactive Spatial Transcriptomics Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models.iScience · 2025Review
- The role of RANBP1 in regulating MiRNA expression and apoptosis in breast cancer cells.Genes & genomics · 2025Article
124 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Breast cancer is noted for disparate clinical behaviors and patient outcomes, despite common histopathological features at diagnosis. Molecular pathogenesis studies suggest that breast cancer is a collection of diseases with variable molecular underpinnings that modulate therapeutic responses, disease-free intervals, and long-term survival. Traditional therapeutic strategies for individual patients are guided by the expression status of the estrogen and progesterone receptors (ER and PR) and human epidermal growth factor receptor 2 (HER2). Although such methods for clinical classification have utility in selection of targeted therapies, short-term patient responses and long-term survival remain difficult to predict. Molecular signatures of breast cancer based on complex gene expression patterns have utility in prediction of long-term patient outcomes, but are not yet used for guiding therapy. Examination of the correspondence between these methods for breast cancer classification reveals a lack of agreement affecting a significant percentage of cases. To realize true personalized breast cancer therapy, a more complete analysis and evaluation of the molecular characteristics of the disease in the individual patient is required, together with an understanding of the contributions of specific genetic and epigenetic alterations (and their combinations) to management of the patient. Here, we discuss the molecular and cellular heterogeneity of breast cancer, the impact of this heterogeneity on practical breast cancer classification, and the challenges for personalized breast cancer treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.