bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2024‒10‒20
nine papers selected by
Sergio Marchini, Humanitas Research



  1. Cell Rep Methods. 2024 Oct 09. pii: S2667-2375(24)00260-1. [Epub ahead of print] 100877
      The fragmentation patterns of cell-free DNA (cfDNA) in plasma can potentially be utilized as diagnostic biomarkers in liquid biopsy. However, our knowledge of this biological process and the information encoded in fragmentation patterns remains preliminary. Here, we investigated the cfDNA fragmentomic characteristics against nucleosome positioning patterns in hematopoietic cells. cfDNA molecules with ends located within nucleosomes were relatively shorter with altered end motif patterns, demonstrating the feasibility of enriching tumor-derived cfDNA in patients with cancer through the selection of molecules possessing such ends. We then developed three cfDNA fragmentomic metrics after end selection, which showed significant alterations in patients with cancer and enabled cancer diagnosis. By incorporating machine learning, we further built high-performance diagnostic models, which achieved an overall area under the curve of 0.95 and 85.1% sensitivity at 95% specificity. Hence, our investigations explored the end characteristics of cfDNA fragmentomics and their merits in building accurate and sensitive cancer diagnostic models.
    Keywords:  CP: Cancer biology; CP: Systems biology; N-index; cfDNA fragmentomics; liquid biopsy; machine learning; plasma
    DOI:  https://doi.org/10.1016/j.crmeth.2024.100877
  2. Clin Cancer Res. 2024 Oct 17.
      PURPOSE: ctDNA is a novel technique extensively studied in solid tumors, although not currently well defined in endometrial cancer (EC).EXPERIMENTAL DESIGN: A de-identified retrospective analysis of 1988 patients with advanced/recurrent EC was performed. In addition, an analysis of a real-world evidence (RWE) cohort was completed (n=1266). Patients underwent ctDNA testing using Guardant360 during routine clinical care. The objective was to describe and assess molecular landscape using ctDNA.
    RESULTS: Among 1988 ctDNA samples, at least one somatic alteration was detected in 91.6% (n=1821). Most frequently altered genes were TP53 (64%), PIK3CA (29%), PTEN (25%), ARID1A (20%) and KRAS (14%). Overall, 18.5% had amplifications, with the majority identified in CCNE1 (40.9%), PIK3CA (22%) and EGFR (19.3%). From the RWE cohort, those with TP53 mutations had a worse overall survival (OS) vs those without TP53 mutations (p=0.02) and those with TP53 co-mutations had an inferior OS in comparison to TP53-mutated only (p=0.016). Amongst these, patients with a PIK3CA co-mutation (p=0.012) and CCNE1 amplification (p=0.01) had inferior OS compared to those with only TP53 mutations. 57 patients with newly diagnosed EC had at least 2 serial ctDNA samples showing evolution in detected variants compared to baseline samples, with TP53 being the most frequent change.
    CONCLUSIONS: This study is one of the largest cohorts of ctDNA currently reported in EC. The presence of TP53 mutation and other co-mutations detected by ctDNA have a negative effect on outcomes. This report suggests that ctDNA analysis is feasible and could become a useful biomarker for EC.
    DOI:  https://doi.org/10.1158/1078-0432.CCR-24-2105
  3. NPJ Precis Oncol. 2024 Oct 14. 8(1): 231
      The synthetic lethal effect observed with the use of PARP inhibitors (PARPi) with tumors characterized by the loss of key players in the homologous recombination (HR) pathway, commonly referred to as "BRCAness", is maintaining high interest in oncology. While BRCAness is a well-established feature in breast, ovarian, prostate, and pancreatic carcinomas, our recent findings indicate that up to 15% of colorectal cancers (CRC) also harbor defects in the HR pathway, presenting promising opportunities for innovative therapeutic strategies in CRC patients. We developed a new tool called HRDirect, which builds upon the HRDetect algorithm and is able to predict HR deficiency (HRD) from reference-free tumor samples. We validated HRDirect using matched breast cancer and CRC patient samples. Subsequently, we assessed its efficacy in predicting response to the PARP inhibitor olaparib by comparing it with two other commercial assays: AmoyDx HRD by Amoy Diagnostics and the TruSight Oncology 500 HRD (TSO500-HRD) panel by Illumina NGS technology. While all three approaches successfully identified the most PARPi-sensitive CRC models, HRDirect demonstrated superior precision in distinguishing resistant models compared to AmoyDX and TSO500-HRD, which exhibited overlapping scores between sensitive and resistant cells. Furthermore, we propose integrating HRDirect scoring with ATM and RAD51C immunohistochemical analysis as part of our "composite biomarker approach" to enhance the identification of HRD tumors, with an immediate translational and clinical impact for CRC personalized treatment.
    DOI:  https://doi.org/10.1038/s41698-024-00706-7
  4. Mol Diagn Ther. 2024 Oct 16.
      INTRODUCTION: Epithelial ovarian cancer (EOC) represents a significant health challenge, with high-grade serous ovarian cancer (HGSOC) being the most common subtype. Early detection is hindered by nonspecific symptoms, leading to late-stage diagnoses and poor survival rates. Biomarkers are crucial for early diagnosis and personalized treatment OBJECTIVE: Our goal was to develop a robust statistical procedure to identify a set of differentially methylated probes (DMPs) that would allow differentiation between HGSOC and benign ovarian tumors.METHODOLOGY: Using the Infinium EPIC Methylation array, we analyzed the methylation profiles of 48 ovarian samples diagnosed with HGSOC, borderline ovarian tumors, or benign ovarian disease. Through a multi-step statistical procedure combining univariate and multivariate logistic regression models, we aimed to identify CpG sites of interest.
    RESULTS AND CONCLUSIONS: We discovered 21 DMPs and developed a predictive model validated in two independent cohorts. Our model, using a distance-to-centroid approach, accurately distinguished between benign and malignant disease. This model can potentially be used in other types of sample material. Moreover, the strategy of the model development and validation can also be used in other disease contexts for diagnostic purposes.
    DOI:  https://doi.org/10.1007/s40291-024-00740-y
  5. Clin Cancer Res. 2024 Oct 18.
      Ovarian cancer remains resistant to immunotherapy in most patients, highlighting the need to make these tumors more immunogenic. A recent study unveils how chemotherapy is able to induce cancer cell stress in metastatic ovarian carcinomas, inducing the formation of tertiary lymphoid structures that create a "hotter" tumor microenvironment.
    DOI:  https://doi.org/10.1158/1078-0432.CCR-24-2738
  6. Brief Bioinform. 2024 Sep 23. pii: bbae523. [Epub ahead of print]25(6):
      The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.
    Keywords:  gene filtration; multi-scale; single-cell RNA sequence; spatial transcriptomics; transformer
    DOI:  https://doi.org/10.1093/bib/bbae523
  7. Nat Rev Cancer. 2024 Oct 16.
      In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.
    DOI:  https://doi.org/10.1038/s41568-024-00757-9
  8. Brief Bioinform. 2024 Sep 23. pii: bbae516. [Epub ahead of print]25(6):
      The heterogeneity of tumor clones drives the selection and evolution of distinct tumor cell populations, resulting in an intricate and dynamic tumor evolution process. While tumor bulk DNA sequencing helps elucidate intratumor heterogeneity, challenges such as the misidentification of mutation multiplicity due to copy number variations and uncertainties in the reconstruction process hinder the accurate inference of tumor evolution. In this study, we introduce a novel approach, REconstructing Tumor Clonal Heterogeneity and Evolutionary Relationships (RETCHER), which characterizes more realistic cancer cell fractions by accurately identifying mutation multiplicity while considering uncertainty during the reconstruction process and the credibility and reasonableness of subclone clustering. This method comprehensively and accurately infers multiple forms of tumor clonal heterogeneity and phylogenetic relationships. RETCHER outperforms existing methods on simulated data and infers clearer subclone structures and evolutionary relationships in real multisample sequencing data from five tumor types. By precisely analysing the complex clonal heterogeneity within tumors, RETCHER provides a new approach to tumor evolution research and offers scientific evidence for developing precise and personalized treatment strategies. This approach is expected to play a significant role in tumor evolution research, clinical diagnosis, and treatment. RETCHER is available for free at https://github.com/zlsys3/RETCHER.
    Keywords:  bulk DNA sequencing; clonal heterogeneity; evolution; mutation multiplicity; tumor
    DOI:  https://doi.org/10.1093/bib/bbae516
  9. J Transl Med. 2024 Oct 15. 22(1): 938
      BACKGROUND: Recent studies have highlighted the importance of the cell-free DNA (cfDNA) methylation profile in detecting breast cancer (BC) and its different subtypes. We investigated whether plasma cfDNA methylation, using cell-free Methylated DNA Immunoprecipitation and High-Throughput Sequencing (cfMeDIP-seq), may be informative in characterizing breast cancer in patients with BRCA1/2 germline mutations for early cancer detection and response to therapy.METHODS: We enrolled 23 BC patients with germline mutation of BRCA1 and BRCA2 genes, 19 healthy controls without BRCA1/2 mutation, and two healthy individuals who carried BRCA1/2 mutations. Blood samples were collected for all study subjects at the diagnosis, and plasma was isolated by centrifugation. Cell-free DNA was extracted from 1 mL of plasma, and cfMeDIP-seq was performed for each sample. Shallow whole genome sequencing was performed on the immuno-precipitated samples. Then, the differentially methylated 300-bp regions (DMRs) between 25 BRCA germline mutation carriers and 19 non-carriers were identified. DMRs were compared with tumor-specific regions from public datasets to perform an unbiased analysis. Finally, two statistical classifiers were trained based on the GLMnet and random forest model to evaluate if the identified DMRs could discriminate BRCA-positive from healthy samples.
    RESULTS: We identified 7,095 hypermethylated and 212 hypomethylated regions in 25 BRCA germline mutation carriers compared to 19 controls. These regions discriminate tumors from healthy samples with high accuracy and sensitivity. We show that the circulating tumor DNA of BRCA1/2 mutant breast cancers is characterized by the hypomethylation of genes involved in DNA repair and cell cycle. We uncovered the TFs associated with these DRMs and identified that proteins of the Erythroblast Transformation Specific (ETS) family are particularly active in the hypermethylated regions. Finally, we assessed that these regions could discriminate between BRCA positives from healthy samples with an AUC of 0.95, a sensitivity of 88%, and a specificity of 94.74%.
    CONCLUSIONS: Our study emphasizes the importance of tumor cell-derived DNA methylation in BC, reporting a different methylation profile between patients carrying mutations in BRCA1, BRCA2, and wild-type controls. Our minimally invasive approach could allow early cancer diagnosis, assessment of minimal residual disease, and monitoring of response to therapy.
    Keywords:  BRCA1; BRCA2; Breast cancer; Cell-free DNA; DMRs; cfMeDIP-seq
    DOI:  https://doi.org/10.1186/s12967-024-05734-2