bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2025–02–09
seven papers selected by
Sergio Marchini, Humanitas Research



  1. BMC Med. 2025 Feb 04. 23(1): 59
       BACKGROUND: Tertiary lymphoid structures (TLS) correlate with tumour prognosis and immunotherapy responses in gastric cancer (GC) studies. However, understanding the complex and diverse immune microenvironment within TLS requires comprehensive analysis.
    METHODS: We examined the prognostic impact of TLS within the tumour core (TC) of 59 GC patients undergoing immunotherapy. Multispectral fluorescence imaging was employed to evaluate variations in immune cell infiltration across different TLS sites among 110 GC patients, by quantifying immune cell density and spatial characteristics. We also generated a single-cell transcriptomic atlas of TLS-positive (n = 4) and TLS-negative (n = 8) microenvironments and performed spatial transcriptomics (ST) analysis on two samples.
    RESULTS: TLS presence in the TC significantly correlated with improved immune-related overall survival (P = 0.049). CD8+LAG-3-PD-1+TIM-3-, CD4+PD-L1+, and CD4+FoxP3- T cell densities were significantly higher in the TLS within TC compared to tumour and stromal regions. Immune cells within TLS exhibited closer intercellular proximity than those outside TLS. Five key density and spatial characteristics of immune cells within TLS in the TC were selected to develop the Density and Spatial Score risk model. Single-cell RNA sequencing revealed strong intercellular interactions in the presence of TLS within the microenvironment. However, TLS-absent environment facilitated tumour cell interactions with immune cells through MIF- and galectin-dependent pathways, recruiting immunosuppressive cells. ST analysis confirmed that T and B cells co-localise within TLS, enhancing immune response activation compared to cancer nests and exerting a strong anti-tumour effect.
    CONCLUSIONS: TLS presence facilitates frequent cell-to-cell communication, forming an active immune microenvironment, highlighting the prognostic value of TLS.
    Keywords:  Gastric cancer; Immunotherapy; Prognostic model; Single-cell RNA sequencing; Spatial transcriptomics; Tertiary lymphoid structures; Tumour microenvironment
    DOI:  https://doi.org/10.1186/s12916-025-03889-3
  2. Lancet Oncol. 2025 Feb;pii: S1470-2045(24)00674-0. [Epub ahead of print]26(2): 249-264
       BACKGROUND: In the ARIEL4 trial of rucaparib versus standard-of-care chemotherapy in patients with relapsed BRCA-mutated ovarian carcinoma, the primary endpoint was met, showing improved investigator-assessed progression-free survival with rucaparib. Here, we present the final overall survival analysis of the trial and other post-progression outcomes.
    METHODS: This open-label, randomised, controlled phase 3 trial was done at 64 hospitals and cancer centres in 12 countries, including Brazil, Canada, Czech Republic, Hungary, Israel, Italy, Poland, Russia, Spain, Ukraine, the UK, and the USA. Eligible patients were women aged 18 or older with BRCA1 or BRCA2-mutated ovarian carcinoma and had received at least two previous chemotherapy regimens. Patients had to have evaluable disease as per Response Evaluation Criteria in Solid Tumors (RECIST; version 1.1) criteria and an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients were randomly assigned (2:1) using an interactive response technology and block randomisation (block size of six) and stratified by progression-free interval after the most recent platinum-containing therapy to receive oral rucaparib (600 mg twice daily administered in 28-day cycles) or chemotherapy on the basis of platinum-sensitivity status. In the chemotherapy group, patients with platinum-resistant disease (progression-free interval ≥1 to <6 months) or partially platinum-sensitive disease (progression-free interval ≥6 to <12 months) received weekly paclitaxel (starting dose 60-80 mg/m2 on days 1, 8, and 15). Patients with fully platinum-sensitive disease (progression-free interval ≥12 months) received the investigator's choice of platinum-based chemotherapy (single-agent cisplatin or carboplatin, or platinum-doublet chemotherapy), in 21-day or 28-day cycles. The primary endpoint (previously reported) was investigator-assessed progression-free survival, assessed in the efficacy population (all randomly assigned patients with deleterious BRCA1 or BRCA2 mutations without reversion mutations) and in the intention-to-treat population (all randomly assigned patients). Overall survival was a prespecified secondary endpoint and was analysed in the intention-to-treat population. Safety was assessed in all patients who received at least one dose of assigned study treatment. The cutoff date was April 10, 2022. This study is registered with ClinicalTrials.gov, NCT02855944; enrolment is complete and the study is closed.
    FINDINGS: Between March 1, 2017, and Sept 24, 2020, 349 eligible patients were randomly assigned to receive rucaparib (n=233) or chemotherapy (n=116). 332 (95%) of 349 patients were white and 17 (5%) patients were other or of unknown race. In the chemotherapy group, 80 (69%) of 116 patients crossed over to receive rucaparib. Median follow-up was 41·2 months (IQR 37·8-44·6). At data cutoff for this final analysis (April 10, 2022), 244 (70%) of 349 patients had died: 167 (72%) of 233 in the rucaparib group and 77 (66%) of 116 in the rucaparib group. Median overall survival was 19·4 months (95% CI 15·2-23·6) in the rucaparib group versus 25·4 months (21·4-27·6) in the chemotherapy group (hazard ratio 1·3 [95% CI 1·0-1·7], p=0·047). No new safety signals were observed, including during crossover to rucaparib. The most common grade 3-4 adverse events across treatment groups included anaemia or decreased haemoglobin (reported in 59 [25%] of 232 patients in the rucaparib group and seven [6%] of 113 in the chemotherapy group), and neutropenia or decreased neutrophil count (in 26 [11%] of 232 in the rucaparib group and 16 [14%] of 113 patients in the chemotherapy group). Serious adverse events were reported in 66 (28%) of 232 patients in the rucaparib group and 14 (12%) of 113 patients in the chemotherapy group. Ten treatment-related deaths were reported in the rucaparib group, two of which were linked to judged to be related to rucaparib (cardiac disorder and myelodysplastic syndrome), and one death related to treatment was reported in the chemotherapy group, with no specific cause linked to the treatment.
    INTERPRETATION: These data highlight the need for a better understanding of the most appropriate treatment for patients who have progressed on a poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitor, and the optimal sequencing of chemotherapy and PARP inhibitors in advanced ovarian cancer.
    FUNDING: Clovis Oncology.
    DOI:  https://doi.org/10.1016/S1470-2045(24)00674-0
  3. Cancer Immunol Immunother. 2025 Feb 01. 74(3): 84
      With the incorporation of immune checkpoint inhibitors into the treatment of endometrial cancer (EC), a deeper understanding of the tumor immune microenvironment is critical. Tertiary lymphoid structures (TLSs) are considered favorable prognostic factors for EC, but the significance of their spatial distribution remains unclear. B cell receptor repertoire analysis performed using six TLS samples located at various distances from the tumor showed that TLSs in distal areas had more shared B cell clones with tumor-infiltrating lymphocytes. To comprehensively investigate the distribution of TLSs, we developed an artificial intelligence model to detect TLSs and determine their spatial locations in whole-slide images. Our model effectively quantified TLSs, and TLSs were detected in 69% of the patients with EC. We identified them as proximal or distal to the tumor margin and demonstrated that patients with distal TLSs (dTLSs) had significantly prolonged overall survival and progression-free survival (PFS) across multiple cohorts [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.36-0.88; p = 0.01 for overall survival; HR, 0.58; 95% CI, 0.40-0.84; p = 0.004 for PFS]. When analyzed by molecular subtype, patients with dTLSs in the copy-number-high EC subtype had significantly longer PFS (HR, 0.51; 95% CI, 0.29-0.91; p = 0.02). Moreover, patients with dTLSs had a higher response rate to immune checkpoint inhibitors (87.5 vs. 41.7%) and a trend toward improved PFS. Our findings indicate that the functions and prognostic implications of TLSs may vary with their locations, and dTLSs may serve as prognostic factors and predictors of treatment efficacy. This may facilitate personalized therapy for patients with EC.
    Keywords:  Artificial intelligence; B cell receptor repertoire; Endometrial cancer; Immune checkpoint inhibitors; Tertiary lymphoid structure
    DOI:  https://doi.org/10.1007/s00262-024-03929-6
  4. Front Immunol. 2025 ;16 1555677
      
    Keywords:  biomarkers; immune checkpoint inhibitors (ICI); immunotherapy; prognosis; solid tumors (ST); tertiary lymphoid structures (TLSs); tumor microenvironment (TME)
    DOI:  https://doi.org/10.3389/fimmu.2025.1555677
  5. Mol Biol Rep. 2025 Feb 04. 52(1): 197
      Tertiary lymphoid structures (TLSs) are aberrant lymphoid tissues found in persistent inflammatory settings, including malignancies, autoimmune disorders, and transplanted organs. The organization and architecture of TLS closely resemble that of secondary lymphoid organs (SLOs). The formation of TLS is an ongoing process, with varying structural features observed at different stages of maturation. The tumor microenvironment (TME) is a multifaceted milieu comprising cells, molecules, and extracellular matrix components in close proximity to the neoplasm. TLS within the TME have the capacity to actively elicit anti-tumor immune responses. TLSs exhibit tumor-specific and individual-specific characteristics, leading to varying immune responses towards tumor immunity based on their distinct cellular components, maturity levels, and spatial distribution. Cell interaction is the foundational elements of tumor immunity. Despite differences in the cellular composition of TLS, B cells and T cells are the main components of tumor-associated TLS。Recent research has highlighted the significance of diverse subtypes of B cells and T cells within TLSs in influencing the therapeutic outcomes and prognostic indicators of individual tumors. This review elucidates the diversity of TLS in terms of cellular composition, developmental stage, anatomical location, and the influence of cytokines on their initiation and progression. Furthermore, the article examines the involvement of B and T cells within TLS and the significance of TLS in relation to tumor prognosis.
    Keywords:  Cytokines; Heterogeneity; Prognosis; Tertiary lymphoid structure; Tumor
    DOI:  https://doi.org/10.1007/s11033-025-10319-3
  6. Clin Transl Med. 2025 Feb;15(2): e70225
       BACKGROUND: Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.
    METHODS: To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.
    RESULTS: Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.
    CONCLUSIONS: With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.
    KEYPOINTS: Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.
    Keywords:  cell‐free DNA; early cancer screening; epigenomics; liquid biopsy; lung cancer
    DOI:  https://doi.org/10.1002/ctm2.70225
  7. Discov Oncol. 2025 Feb 04. 16(1): 115
      Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome Atlas (TCGA), we identified significant prognostic genes through differential expression and survival analysis, integrating these with clinical features and immune landscape assessments including immune cell infiltration and checkpoint expression. The risk score effectively predicted patient survival, distinguishing between high and low-risk groups with significant outcome differences. High-risk patients demonstrated poor prognosis, greater immune checkpoint expression, and higher tumor mutational burdens (TMB), suggesting potential responsiveness to immunotherapy. The model's predictive capacity was validated across multiple cohorts, showing consistent performance in survival prediction and treatment response. Calibration curves and decision curve analysis confirmed the model's clinical utility. This study highlights the potential of an integrated approach to enhance personalized treatment strategies in ovarian cancer, aiming to improve patient management and outcomes.
    Keywords:  Immune infiltration; Ovarian cancer; Personalized medicine; Prognostic model; Tumor mutational burden
    DOI:  https://doi.org/10.1007/s12672-025-01819-6