Tumor Mutational Landscape and Correlation With Histopathology in Breast Cancer

Study citation and publication metadata

The PubMed entry reports the article "Tumor Mutational Landscape and Its Correlation With Histopathological Characteristics in Breast Cancer," published in Cancer Genomics Proteomics (2026 Sep–Oct;23(5):1061–1081). The record includes the DOI 10.21873/cgp.20616 and PubMed identifier PMID 42674815. The journal citation, DOI, and PMID are provided in the source metadata and are the primary bibliographic keys for locating the full text.

Authors and institutional affiliations

The PubMed record lists a large multi-author group. The first author is Eirini Papadopoulou (Genekor Medical S.A., Athens, Greece). Coauthors are affiliated with multiple institutions across Greece, including an EUSOMA-certified multidisciplinary breast center at Metropolitan Hospital (Piraeus), Henry Dunant Hospital (Athens), Papageorgiou General Hospital (Thessaloniki), Agios Savvas Anticancer Hospital (Athens), several general and specialty hospitals in Thessaloniki, Larissa, Patras, Alexandroupolis, Heraklion, and other centers. The PubMed page displays full author names and numbered affiliations corresponding to the institutions reported. Contact information for the first author is noted in the PubMed metadata as eirinipapad@genekor.com.

Stated study focus and scope from title

The article title indicates the investigators examined the tumor mutational landscape and its correlation with histopathological characteristics in breast cancer. From the title alone, the study appears to explore relationships between genomic alterations in tumor tissue and standard pathological features (for example, histologic subtype, grade, receptor status, or other microscopic parameters). The exact hypotheses, endpoints, or specific histopathological variables assessed are not contained in the PubMed snippet provided.

Available bibliographic identifiers and retrieval guidance

This PubMed entry supplies the essential identifiers needed to retrieve the full article: PMID 42674815 and DOI 10.21873/cgp.20616. The full citation (Cancer Genomics Proteomics. 2026 Sep–Oct;23(5):1061–1081) can be used to search library holdings, publisher platforms, or document delivery services. For access to detailed methods, results, tables, figures, and the authors’ interpretations, consult the journal’s full-text article via institutional subscriptions, interlibrary loan, or the publisher’s website.

Missing data and limitations of the PubMed entry

The PubMed page content provided here does not include the article abstract or the study’s data. Specifically absent from the supplied source are:

• Study design and objectives beyond the title
• Sample size and patient selection criteria
• Tumor specimen sources and processing details
• Sequencing methodology or genomic panel description (platform, gene list, coverage)
• Variant calling, filtering, and classification procedures
• Histopathological variables assessed and their definitions
• Statistical methods used to test genotype–phenotype correlations
• Quantitative results (mutation frequencies, effect sizes, p values)
• Tables, figures, or example cases
• Authors’ conclusions, clinical implications, and limitations

Because these core elements are not present in the PubMed excerpt provided, no factual statements about findings, significant associations, or recommendations can be extracted or summarized from this source alone.

Recommended next steps to access full study and data

To obtain the complete dataset and the authors’ analyses, retrieve the full text using the DOI or PMID. When accessing the article, confirm the following details for clinical or research interpretation:

• Cohort size, inclusion/exclusion criteria, and clinical characteristics of patients
• Sequencing or genomic profiling methods and analytic pipelines
• Which genes, mutation types (single nucleotide variants, indels, copy-number changes), and variant-interpretation criteria were reported
• Exact histopathological features evaluated and how they were measured or classified
• Statistical approach used to correlate mutational findings with histopathology, including adjustments for confounders
• Reported associations, effect estimates, and statistical significance
• Limitations acknowledged by the authors and any recommended future directions

Practical note for clinicians and researchers

The PubMed record reliably provides bibliographic metadata and author affiliations but does not contain outcome data in the provided excerpt. For any clinical decision-making, guideline development, or integration of genomic–pathology correlations into practice, consult the full peer-reviewed article to review methods, results, and authors’ discussion. Use the PMID 42674815 or DOI 10.21873/cgp.20616 to locate the full publication in Cancer Genomics Proteomics.

Conclusion

The PubMed entry documents a 2026 multicenter study addressing the tumor mutational landscape and its association with histopathological characteristics in breast cancer but does not include the abstract, methods, or results in the provided source text. Full-text review is required to extract and apply the study’s findings.