bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2024–12–22
thirteen papers selected by
Sergio Marchini, Humanitas Research



  1. Cancer Discov. 2024 Dec 20.
      High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics to analyze human tissue samples at different stages of HGSOC development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), and invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, and dysregulated innate and adaptive immunity. FT precursors display elevated expression of MHC-class I, including HLA-E, and IFN-stimulated genes, typically linked to later-stage tumorigenesis. These molecular alterations coincide with progressive shifts in the tumor microenvironment, transitioning from immune surveillance in early STICs to immune suppression in advanced STICs and cancer. These insights identify potential biomarkers and therapeutic targets for HGSOC interception and clarify the molecular transitions from precancer to cancer.
    DOI:  https://doi.org/10.1158/2159-8290.CD-24-1366
  2. Eur J Cancer. 2024 Dec 09. pii: S0959-8049(24)01776-3. [Epub ahead of print]215 115169
    expert consensus group
       BACKGROUND: Poly (ADP ribose) polymerase inhibitors (PARPis) are a treatment option for patients with advanced high-grade serous or endometrioid ovarian carcinoma (OC). Recent guidelines have clarified how homologous recombination deficiency (HRD) may influence treatment decision-making in this setting. As a result, numerous companion diagnostic assays (CDx) have been developed to identify HRD. However, the optimal HRD testing strategy is an area of debate. Moreover, recently published clinical and translational data may impact how HRD status may be used to identify patients likely to benefit from PARPi use. We aimed to extensively compare available HRD CDx and establish a worldwide expert consensus on HRD testing in primary and recurrent OC.
    METHODS: A group of 99 global experts from 31 different countries was formed. Using a modified Delphi process, the experts aimed to establish consensus statements based on a systematic literature search and CDx information sought from investigators, companies and/or publications.
    RESULTS: Technical information, including analytical and clinical validation, were obtained from 14 of 15 available HRD CDx (7 academic; 7 commercial). Consensus was reached on 36 statements encompassing the following topics: 1) the predictive impact of HRD status on PARPi use in primary and recurrent OC; 2) analytical and clinical validation requirements of HRD CDx; 3) resource-stratified HRD testing; and 4) how future CDx may include additional approaches to help address unmet testing needs.
    CONCLUSION: This manuscript provides detailed information on currently available HRD CDx and up-to-date guidance from global experts on HRD testing in patients with primary and recurrent OC.
    Keywords:  BRCA; Companion diagnostic assays; Genomic instability; Homologous recombination deficiency; Ovarian cancer; PARP inhibitor
    DOI:  https://doi.org/10.1016/j.ejca.2024.115169
  3. J Clin Invest. 2024 Dec 16. pii: e184790. [Epub ahead of print]134(24):
      The approach and efficacy of treatments for high-grade serous carcinoma (HGSC) of the ovary have changed little in decades. Although numerous studies demonstrated immune infiltration as frequent and prognostically beneficial, clinical trials of immunotherapies have generated benefit in fewer than 15% of patients. In this issue of the JCI, Nelson and colleagues compiled 1,233 HGSC samples from patients across four continents and compared the molecular and immunologic features that associate with long-term survival (greater than 10 years). Diversity among HGSC tumors is well defined, but this study explored the combined influence of immunologic and molecular features. Long-term survivors harbored tumors with high epithelial content and overrepresentation of the C4/differentiated molecular signature, with cytotoxic T and B cells infiltrating to the tumor epithelium and stroma, respectively. These findings highlight features that might underly poor responsiveness to existing immunotherapies of most HGSC tumors and considerations for the design of future, more precise treatments for HGSC.
    DOI:  https://doi.org/10.1172/JCI184790
  4. Genome Biol. 2024 Dec 19. 25(1): 318
       BACKGROUND: Plasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the tissue origin of cfDNA through cell type deconvolution, we can detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring. Although there are many methylation-based methods for cfDNA cell type deconvolution, a comprehensive and systematic evaluation of these methods has yet to be conducted.
    RESULTS: In this study, we benchmark five methods: MethAtlas, cfNOMe toolkit, CelFiE, CelFEER, and UXM. Utilizing deep whole-genome bisulfite sequencing data from 35 human cell types, we generate in silico cfDNA samples with ground truth cell type proportions to assess the deconvolution performance of the five methods under multiple scenarios. Our findings indicate that multiple factors, including reference marker selection, sequencing depth, and reference atlas completeness, jointly influence the deconvolution performance. Notably, an incomplete reference with missing markers or cell types leads to suboptimal results. We observe performance differences among methods under varying conditions, underscoring the importance of tailoring cfDNA deconvolution analyses. To increase the clinical relevance of our findings, we further evaluate each method's performance in potential clinical applications using real-world datasets.
    CONCLUSIONS: Based on the benchmark results, we propose general guidelines to choose the suitable methods based on sequencing depth of the cfDNA data and completeness of the reference atlas to maximize the performance of methylation-based cfDNA cell type deconvolution.
    Keywords:  Benchmark; Cell-free DNA; DNA methylation; Deconvolution
    DOI:  https://doi.org/10.1186/s13059-024-03456-8
  5. Genome Med. 2024 Dec 18. 16(1): 145
       BACKGROUND: The introduction of poly(ADP-ribose) polymerase (PARP) inhibitors represented a paradigm shift in the treatment of ovarian cancer. Genomic data from patients with high-grade ovarian cancer in six phase II/III trials involving the PARP inhibitor olaparib were analyzed to better understand patterns and potential causes of genomic instability.
    PATIENTS AND METHODS: Homologous recombination deficiency (HRD) was assessed in 2147 tumor samples from SOLO1, PAOLA-1, Study 19, SOLO2, OPINION, and LIGHT using next-generation sequencing technology. Genomic instability scores (GIS) were assessed in BRCA1 and/or BRCA2 (BRCA)-mutated (BRCAm), non-BRCA homologous recombination repair-mutated (non-BRCA HRRm), and non-HRRm tumors.
    RESULTS: BRCAm was identified in 1021/2147 (47.6%) tumors. BRCAm tumors had significantly higher GIS than non-BRCAm tumors (P < 0.001) and high biallelic loss (815/838; 97.3%) regardless of germline (658/672; 97.9%) or somatic (101/108; 93.5%) BRCAm status. In non-BRCA HRRm tumors (n = 121) a similar proportion were HRD-positive (GIS ≥ 42: 55/121; 45.5%) relative to HRD-negative (GIS < 42: 52/121; 43.0%). GIS was highly variable in non-BRCA HRRm (median 42 [interquartile range (IQR) 29-58]) and non-HRRm (n = 1005; median 32 [IQR 20-55]) tumors. Gene mutations with high GIS included HRR genes BRIP1 (median 46 [IQR 41-58]), RAD51C (median 58 [IQR 48-66]), RAD51D (median 62 [IQR 54-69]), and PALB2 (median 64 [IQR 58-74]), and non-HRR genes NF1 (median 49 [IQR 25-60]) and RB1 (median 55 [IQR 30-71]). CCNE1-amplified and PIK3CA-mutated tumors had low GIS (CCNE1-amplified: median 24 [IQR 18-29]; PIK3CA-mutated: median 32 [IQR 14-52]) and were predominantly non-BRCAm.
    CONCLUSIONS: These analyses provide valuable insight into patterns of genomic instability and potential drivers of HRD, besides BRCAm, in ovarian cancer and will help guide future research into the potential clinical effectiveness of anti-cancer treatments in ovarian cancer, including PARP inhibitors as well as other precision oncology agents.
    TRIAL REGISTRATION: The SOLO1 trial was registered at ClinicalTrials.gov (NCT01844986) on April 30, 2013; the PAOLA-1 trial was registered at ClinicalTrials.gov (NCT02477644) on June 18, 2015 (retrospectively registered); Study 19 was registered at ClinicalTrials.gov (NCT00753545) on September 12, 2008 (retrospectively registered); the SOLO2 trial was registered at ClinicalTrials.gov (NCT01874353) on June 7, 2013; the OPINION trial was registered at ClinicalTrials.gov (NCT03402841) on January 3, 2018; the LIGHT trial was registered at ClinicalTrials.gov (NCT02983799) on November 4, 2016.
    Keywords:  Genomic instability; Olaparib; Ovarian cancer; Translational research
    DOI:  https://doi.org/10.1186/s13073-024-01413-5
  6. Clin Cancer Res. 2024 Dec 17.
       PURPOSE: we tested whether ctDNA changes may be used to assess early response and clinical outcome in metastatic colorectal cancer (mCRC) patients undergoing front-line systemic anti-cancer therapy (SACT).
    EXPERIMENTAL DESIGN: 862 plasma samples were collected 4-weekly from baseline (BL) until disease progression in mCRC patients receiving front line SACT. ctDNA normalization was defined as ≥99% clearance after 1 month of therapy (Mo1) in the 3 variants with the highest allele frequency in BL ctDNA.
    RESULTS: 83 paired samples from 75 patients were available for analysis. 12 pairs (14.4%) showed no variants in either BL or Mo1. In the remaining 71 comparisons (65 patients), 37 (52.1%) showed ctDNA normalization at Mo1. Patients that cleared ctDNA had significantly longer overall (45.6 months) and progression-free survival (13.9 months) compared to non-normalized patients [OS= 22.6 months (Log-rank p = 0.01) and PFS= 10.7 months (Log-rank p = 0.036) respectively]. In addition, higher response rate was observed in patients with ctDNA clearance (72.9%) compared to non-normalized cases (38.2%). Longitudinal sequencing of at least four timepoints in pts with a PFS>10 months showed emerging variants in 47.8% of cases; in all these patients the trajectory of these new "outlier" variants appeared in stark contrast with the clinical-radiological course of disease and the trend in other mutations.
    CONCLUSIONS: ctDNA clearance represents an early indicator of benefit from SACT in mCRC patients; serial tracking of multiple variants is warranted to improve specificity and to avoid misleading information due to the emergence of mutations of unknown clinical significance.
    DOI:  https://doi.org/10.1158/1078-0432.CCR-24-0924
  7. Gigascience. 2024 Jan 02. pii: giae102. [Epub ahead of print]13
       BACKGROUND: Cell-free DNA (cfDNA), a broadly applicable biomarker commonly sourced from urine or blood, is extensively used for research and diagnostic applications. In various settings, genetic and epigenetic information is derived from cfDNA. However, a unified framework for its processing is lacking, limiting the universal application of innovative analysis strategies and the joining of data sets.
    FINDINGS: Here, we describe cfDNA UniFlow, a unified, standardized, and ready-to-use workflow for processing cfDNA samples. The workflow is written in Snakemake and can be scaled from stand-alone computers to cluster environments. It includes methods for processing raw genome sequencing data as well as specialized approaches for correcting sequencing errors, filtering, and quality control. Sophisticated methods for detecting copy number alterations and estimating and correcting GC-related biases are readily incorporated. Furthermore, it includes methods for extracting, normalizing, and visualizing coverage signals around user-defined regions in case-control settings. Ultimately, all results and metrics are aggregated in a unified report, enabling easy access to a wide variety of information for further research and downstream analysis.
    CONCLUSIONS: We provide an automated pipeline for processing cell-free DNA sampled from liquid biopsies, including a wide variety of additional functionalities like bias correction and signal extraction. With our focus on scalability and extensibility, we provide a foundation for future cfDNA research and faster clinical applications. The source code and extensive documentation are available on our GitHub repository (https://github.com/kircherlab/cfDNA-UniFlow).
    Keywords:  cancer detection; cell-free DNA; liquid biopsies; sequence analysis; workflow
    DOI:  https://doi.org/10.1093/gigascience/giae102
  8. Methods Mol Biol. 2025 ;2883 281-297
      CpG islands (CGIs) are rare, interspersed DNA sequences, which possess a significant deviation from background genomic distribution by exhibiting patterns of GC-rich and CpG-rich sequence, the density of which provides a good classification feature for long noncoding RNA (lncRNA) promoters. By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CGIs, their methylation states, CpG spacing, and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be elucidated at individual gene and gene family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI), and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA-box on HK genes, is not clear. Here, we introduce such a problem-solving protocol for human genome annotation, which is based on a combination of GTEx, JBLA, and GO analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; The HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic than LCGI/TATA± based on GO enrichment analysis.
    Keywords:  CpG Island; Genome analysis; Genome annotation; LncRNA; Statistical genetics
    DOI:  https://doi.org/10.1007/978-1-0716-4290-0_12
  9. Transl Cancer Res. 2024 Nov 30. 13(11): 5883-5897
       Background: Glioblastoma (GBM) is a frequent malignant tumor in neurosurgery characterized by a high degree of heterogeneity and genetic instability. DNA double-strand breaks generated by homologous recombination deficiency (HRD) are a well-known contributor to genomic instability, which can encourage tumor development. It is unknown, however, whether the molecular characteristics linked with HRD have a predictive role in GBM. The study aims to assess the extent of genomic instability in GBM using HRD score and investigate the prognostic significance of HRD-related molecular features in GBM.
    Methods: The discovery cohort comprised 567 GBM patients from The Cancer Genome Atlas (TCGA) database. We established HRD scores using the single nucleotide polymorphism (SNP) array data and analyzed transcriptomic data from patients with different HRD scores to identify biomarkers associated with HRD. A prognostic model was built by using HRD-related differentially expressed genes (DEGs) and validated in a distinct cohort from the Chinese Glioma Genome Atlas (CGGA) database.
    Results: Based on the SNP array data, the gene expression profile data, and the clinical characteristics of GBM patients, we found that patients with a high HRD score had a better prognosis than those with a low HRD score. The DNA damage repair (DDR) signaling pathways were notably enriched in the HRD-positive subgroup. The prognostic model was developed by including HRD-related DEGs that could evaluate the clinical prognosis of patients more efficiently than the HRD score. In addition, patients with a low-risk score had a considerably augmented signature of γδT cells. Finally, through univariate and multivariate Cox regression analyses, it was demonstrated that the prognostic model was superior to other prognostic markers.
    Conclusions: In conclusion, our research has not only demonstrated that a high HRD score is a valid prognostic biomarker in GBM patients but also built a stable prognosis model [odds ratio (OR) 0.18, 95% confidence interval (CI): 0.11-0.23, P<0.001] that is more accurate than conventional prognostic markers such as O6-methylguanine-DNA methyltransferase (MGMT) methylation (OR 0.55, 95% CI: 0.33-0.91, P=0.02).
    Keywords:  Glioblastoma (GBM); homologous recombination deficient (HRD); prognostic factor; risk scoring model
    DOI:  https://doi.org/10.21037/tcr-23-2077
  10. NAR Genom Bioinform. 2024 Dec;6(4): lqae181
      Illumina-based BeadChip arrays have revolutionized genome-wide DNA methylation profiling, pushing it into diagnostics. However, comprehensive quality assessment remains challenging within a wide range of available tissue materials and sample preparation methods. This study tackles two critical issues: differentiating between biological effects and technical artefacts in suboptimal quality samples and the impact of the first sample on the Illumina-like normalization algorithm. We introduce three quality control scores based on global DNA methylation distribution (DB-Score), bin distance from copy number variation analysis (BIN-Score) and consistently methylated CpGs (CM-Score) that rely on biological features rather than internal array controls. These scores, designed to be adjustable for different analysis tools and sample cohort characteristics, were explored and benchmarked across independent cohorts. Additionally, we reveal deviations in beta values caused by different sample rankings with the Illumina-like normalization algorithm, verified these with whole-genome methylation sequencing data and showed effects on differential DNA methylation analysis. Our findings underscore the necessity of consistently utilizing a pre-defined normalization sample within the ranking process to boost reproducibility of the Illumina-like normalization algorithm. Overall, our study delivers valuable insights, practical recommendations and R functions designed to enhance reproducibility and quality assurance of DNA methylation analysis, particularly for challenging sample types.
    DOI:  https://doi.org/10.1093/nargab/lqae181
  11. bioRxiv. 2024 Dec 03. pii: 2024.11.28.625773. [Epub ahead of print]
      Spatial transcriptomics offers unprecedented insights into the complex cellular landscapes of tissues, particularly in cancer research where understanding the tumor microenvironment is crucial. However, interpreting the vast and intricate data generated by this technology remains a significant challenge. This study explores the potential of Large Language Models (LLMs) to assist in the analysis and interpretation of spatial transcriptomic data from a murine melanoma tumor model. We first evaluated the performance of multiple LLM models in describing and quantifying spatial gene expression patterns. Our benchmarking revealed that spatial transcriptomics data interpretation proved challenging for most models, with only a few demonstrating sufficient capability for this complex task. Using Claude 3.5 Sonnet, which showed the highest accuracy in spot quantification and pattern recognition, we developed a systematic workflow for analyzing the tumor immune landscape. The model first assisted in identifying and prioritizing panels of M1 and M2 macrophage-associated markers through knowledge-driven scoring. It then demonstrated remarkable ability to integrate spatial expression data with extensive immunological knowledge, providing sophisticated interpretation of local immune organization. When analyzing individual tumor regions, the model identified coordinated immunosuppressive mechanisms including metabolic barriers and disrupted pro-inflammatory signaling cascades, findings that both aligned with and extended current understanding of tumor immunology. This study highlights the potential of LLMs as powerful assistive tools in spatial transcriptomics analysis, capable of combining advanced pattern recognition with extensive knowledge integration to enhance data interpretation. While significant development work remains to make such workflows scalable, our proof of concept demonstrates that LLMs can help accelerate the translation of spatial transcriptomics data into biological insights.
    DOI:  https://doi.org/10.1101/2024.11.28.625773