bims-tumhet Biomed News
on Tumor heterogeneity
Issue of 2025–05–25
four papers selected by
Sergio Marchini, Humanitas Research



  1. Int J Gynecol Cancer. 2025 May 03. pii: S1048-891X(25)01035-7. [Epub ahead of print]35(7): 101915
      Tertiary lymphoid structures, lymphoid cell clusters formed in response to cancer or chronic disease, serve as a prognostic marker in multiple cancer types, including endometrial carcinoma. We assessed the prognostic significance of tertiary lymphoid structures, using the surrogate marker L1 cell adhesion molecule (L1CAM), in 1208 endometrial carcinoma patients in all stages, histological subtypes, and risk groups. Immunohistochemical evaluation of L1CAM in 1 tissue section from each patient revealed tertiary lymphoid structure-positivity in 287 of 1208 (23.8%) cases. In univariable analyses, patients with tertiary lymphoid structure-positive tumors had significantly longer time to recurrence (HR 0.61, p < .001) and cancer-specific survival (HR 0.53, p < .001) compared to patients with tumors without tertiary lymphoid structures. In multivariable analyses with standard clinical and pathological markers as well as modern molecular classification, the presence of tertiary lymphoid structures was an independent prognostic marker for time to recurrence (HR 0.63, p < .001) and cancer-specific survival (HR 0.54, p < .001). The presence of tertiary lymphoid structures was more frequent in POLE-mutated (59.4%) and mismatch repair deficient (32.3%) compared to p53-abnormal (15.8%) and no specific molecular profile (14.7%) tumors. In patients with p53-abnormal tumors, the presence of tertiary lymphoid structures was significantly associated with better outcomes for both time to recurrence (HR 0.51, p = .014) and cancer-specific survival (HR 0.52, p = .021) in multivariable analyses. These findings suggest that the evaluation of tertiary lymphoid structures by L1CAM may enhance prognostic precision in endometrial carcinoma.
    Keywords:  Endometrial Cancer; L1CAM; Molecular Classification; Prognosis; Tertiary Lymphoid Structure
    DOI:  https://doi.org/10.1016/j.ijgc.2025.101915
  2. Hum Genomics. 2025 May 17. 19(1): 56
       BACKGROUND: Ovarian cancer has the highest mortality rate among gynecological cancers, making early detection crucial, as the five-year survival rate drops from 92% with early-stage diagnosis compared to 31% with late-stage diagnosis. Current diagnostic methods such as histopathological examination and detection of cancer antigen 125 and human epididymis protein 4 biomarkers are either invasive or lack specificity and sensitivity. However, the Papanicolaou (Pap) test, which is widely used for cervical cancer screening, shows the potential for detecting ovarian cancer by identifying tumor DNA in cervical scrapings. Since aberrant DNA methylation patterns are linked to cancer progression, DNA methylation offers a promising avenue for early diagnosis. Therefore, this study aimed to develop a methylation-based machine-learning model to stratify patients with ovarian cancer from the cervical scraping samples collected via Pap test.
    RESULTS: Cervical scrapings were collected by gynecologists using conventional Pap smears. In total, 160 samples were collected: 95 normal, 37 benign, and 28 malignant. Methylation data were generated using the Illumina Infinium MethylationEPIC BeadChip array, which contains approximately 850,000 CpG loci. Methylation data were initially divided into training and testing sets in a 3:1 ratio comprising 120 and 40 samples, respectively. A two-step methylation-based model was trained using the training data for classification: a principal component analysis (PCA) model, consisting of 30 features, to classify samples as normal or tumor; then a gradient boosting model, containing 16 features, to further stratify tumor samples as benign or malignant. The two-step model achieved an accuracy of 0.88 and an F1-score of 0.86 on the testing data. Furthermore, an over-representation analysis was conducted to explore the functions associated with genes mapped from differentially methylated positions (DMPs) in comparisons between normal and tumor samples, as well as between benign and malignant samples. These results suggest that DMPs may be associated with olfactory transduction when comparing normal versus tumor samples, and immune regulation when comparing benign and malignant samples.
    CONCLUSIONS: Our two-step model shows promise for predicting ovarian cancer and suggests that cervical scrapings may be a viable alternative for sample collection during screening.
    Keywords:  Biomarker; Cancer screening; Epigenetics; Machine learning; Methylation; Ovarian cancer; Papanicolaou test (Pap test)
    DOI:  https://doi.org/10.1186/s40246-025-00763-4
  3. Genome Biol. 2025 May 20. 26(1): 132
      Ploidy determination across the genome has been challenging for low-pass-WGS tumor-only samples. We present BACDAC, a method that calculates tumor ploidy down to 1.2X effective tumor coverage. Allele fraction patterns displayed in the Constellation Plot verify tumor ploidy and reveal subclonal populations. BACDAC outputs a metric, 2N+LOH, that when combined with ploidy better distinguishes near-diploid from high-ploidy tumors. Validated using TCGA, BACDAC had good agreement with other methods and 88% agreement with experimental methods. Discrepancies occur mainly when BACDAC predicts diploidy with subclones rather than high-ploidy. Applied to 653 low-pass-WGS samples spanning 12 cancer subtypes, BACDAC calls 40% as high-ploidy.
    Keywords:  Loss of heterozygosity; Next generation sequencing; Ploidy; Whole genome doubling
    DOI:  https://doi.org/10.1186/s13059-025-03599-2