bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2023‒10‒29
seven papers selected by
Sergio Marchini, Humanitas Research
  1. There is no place for ovarian cancer screening in hereditary breast-ovarian cancer syndromes.
  2. Liquid biopsy epigenomic profiling for cancer subtyping.
  3. Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics.
  4. Feasibility of Sentinel Lymph Node Biopsy in Early-Stage Epithelial Ovarian Cancer: A Systematic Review and Meta-Analysis.
  5. Genome sequencing detects a wide range of clinically relevant copy number variants and other genomic alterations.
  6. Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.
  7. The impact of genomic context on outcomes of solid cancer patients treated with genotype-matched targeted therapies: a comprehensive review.
  1. Ann Oncol. 2023 Oct 20. pii: S0923-7534(23)04324-7. [Epub ahead of print]
    There is no place for ovarian cancer screening in hereditary breast-ovarian cancer syndromes.
    Wiebren A A Tjalma.
      
    Keywords:  HBOC; breast; cancer; hereditary; ovarian; screenings; ultrasound
    DOI:  https://doi.org/10.1016/j.annonc.2023.10.118
  2. Nat Med. 2023 Oct 21.
    Liquid biopsy epigenomic profiling for cancer subtyping.
    Sylvan C Baca, Ji-Heui Seo, Matthew P Davidsohn, Brad Fortunato, Karl Semaan, Shahabbedin Sotudian, Gitanjali Lakshminarayanan, Miklos Diossy, Xintao Qiu, Talal El Zarif, Hunter Savignano, John Canniff, Ikenna Madueke, Renee Maria Saliby, Ziwei Zhang, Rong Li, Yijia Jiang, Len Taing, Mark Awad, Cindy H Chau, James A DeCaprio, William D Figg, Tim F Greten, Aaron N Hata, F Stephen Hodi, Melissa E Hughes, Keith L Ligon, Nancy Lin, Kimmie Ng, Matthew G Oser, Catherine Meador, Heather A Parsons, Mark M Pomerantz, Arun Rajan, Jerome Ritz, Manisha Thakuria, Sara M Tolaney, Patrick Y Wen, Henry Long, Jacob E Berchuck, Zoltan Szallasi, Toni K Choueiri, Matthew L Freedman.
      Although circulating tumor DNA (ctDNA) assays are increasingly used to inform clinical decisions in cancer care, they have limited ability to identify the transcriptional programs that govern cancer phenotypes and their dynamic changes during the course of disease. To address these limitations, we developed a method for comprehensive epigenomic profiling of cancer from 1 ml of patient plasma. Using an immunoprecipitation-based approach targeting histone modifications and DNA methylation, we measured 1,268 epigenomic profiles in plasma from 433 individuals with one of 15 cancers. Our assay provided a robust proxy for transcriptional activity, allowing us to infer the expression levels of diagnostic markers and drug targets, measure the activity of therapeutically targetable transcription factors and detect epigenetic mechanisms of resistance. This proof-of-concept study in advanced cancers shows how plasma epigenomic profiling has the potential to unlock clinically actionable information that is currently accessible only via direct tissue sampling.
    DOI:  https://doi.org/10.1038/s41591-023-02605-z
  3. Front Oncol. 2023 ;13 1258245
    Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics.
    Keir Pickard, Emily Stephenson, Alex Mitchell, Laura Jardine, Chris M Bacon.
      Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.
    Keywords:  lymphoma; personalized medicine; single cell RNA sequencing; spatial transcriptomics; tumor microenvironment
    DOI:  https://doi.org/10.3389/fonc.2023.1258245
  4. Diagnostics (Basel). 2023 Oct 14. pii: 3209. [Epub ahead of print]13(20):
    Feasibility of Sentinel Lymph Node Biopsy in Early-Stage Epithelial Ovarian Cancer: A Systematic Review and Meta-Analysis.
    Georgia Zachou, Gabriella Yongue, Dhivya Chandrasekaran.
      Sentinel lymph node biopsy (SLNB) has been widely adopted in the management of early-stage gynaecological cancers such as endometrial, vulvar and cervical cancer. Comprehensive surgical staging is crucial for patients with early-stage ovarian cancer and currently, that includes bilateral pelvic and para-aortic lymph node assessment. SLNB allows the identification, excision and pathological assessment of the first draining lymph nodes, thus negating the need for a full lymphadenectomy. We systematically searched the MEDLINE, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) databases (from inception to 3 November 2022) in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Our search identified 153 articles from which 11 were eligible for inclusion. Patients with clinical stage I-II ovarian cancer undergoing sentinel lymph node biopsy were included. Statistical analysis was performed in RStudio using the meta package, where meta-analysis was performed for the detection. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies C (QUADAS-C) tool. Overall, 11 observational studies met the predetermined criteria and these included 194 women. The meta-analysis showed that the detection rate of sentinel lymph nodes in early-stage ovarian cancer was 94% (95% CI of 86% to 1.00%). Significant heterogeneity was noted among the studies with Q = 47.6, p < 0.0001, I2 = 79% and τ2 = 0.02. Sentinel lymph nodes in early-stage ovarian cancer have a high detection rate and can potentially have applicability in clinical practice. However, considering the small number of participants in the studies, the heterogeneity among them and the low quality of evidence, the results should be interpreted with caution. Larger trials are needed before a change in clinical practice is recommended.
    Keywords:  early-stage ovarian cancer; meta-analysis; sentinel lymph node; systematic review
    DOI:  https://doi.org/10.3390/diagnostics13203209
  5. Genet Med. 2023 Oct 20. pii: S1098-3600(23)01022-5. [Epub ahead of print] 101006
    Genome sequencing detects a wide range of clinically relevant copy number variants and other genomic alterations.
    K N James, S Chowdhury, Y Ding, S Batalov, K Watkins, Y H Kwon, L Van Der Kraan, K Ellsworth, S F Kingsmore, L Guidugli.
      PURPOSE: Copy number variants (CNVs) and other non-SNV/indel variant types contribute an important proportion of diagnoses in individuals with suspected genetic disease. This study describes the range of such variants detected by genome sequencing (GS).METHODS: For a pediatric cohort of 1032 participants undergoing clinical GS, we characterize the CNVs and other non-SNV/indel variant types that were reported, including aneuploidies, mobile element insertions, and uniparental disomies, and we describe the bioinformatic pipeline used to detect these variants.
    RESULTS: Together, these genetic alterations accounted for 15.8% of reported variants. Notably, 67.9% of these were deletions, 32.9% of which overlapped a single gene, and many deletions were reported together with a second variant in the same gene in cases of recessive disease. A retrospective medical record review in a subset of this cohort revealed that up to six additional genetic tests were ordered in 68% (26/38) of cases, some of which failed to report the CNVs/rare variants reported on GS.
    CONCLUSION: GS detected a broad range of reported variant types, including CNVs ranging in size from 1 Kb to 46 Mb.
    Keywords:  bioinformatic pipeline; clinical genome sequencing; copy number variant
    DOI:  https://doi.org/10.1016/j.gim.2023.101006
  6. Nat Commun. 2023 10 24. 14(1): 6756
    Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.
    Mireia Crispin-Ortuzar, Ramona Woitek, Marika A V Reinius, Elizabeth Moore, Lucian Beer, Vlad Bura, Leonardo Rundo, Cathal McCague, Stephan Ursprung, Lorena Escudero Sanchez, Paula Martin-Gonzalez, Florent Mouliere, Dineika Chandrananda, James Morris, Teodora Goranova, Anna M Piskorz, Naveena Singh, Anju Sahdev, Roxana Pintican, Marta Zerunian, Nitzan Rosenfeld, Helen Addley, Mercedes Jimenez-Linan, Florian Markowetz, Evis Sala, James D Brenton.
      High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.
    DOI:  https://doi.org/10.1038/s41467-023-41820-7
  7. Ann Oncol. 2023 Oct 22. pii: S0923-7534(23)04330-2. [Epub ahead of print]
    The impact of genomic context on outcomes of solid cancer patients treated with genotype-matched targeted therapies: a comprehensive review.
    T Fulton-Ward, G Middleton.
      BACKGROUND: A critical need in the field of genotype-matched targeted therapy in cancer is to identify patients unlikely to respond to precision medicines. This will manage expectations of individualised therapies and avoid clinical progression to a point where institution of alternative treatments might not be possible. We examined the evidence base of the impact of genomic context on which targeted alterations are inscribed to identify baseline biomarkers distinguishing those obtaining the expected response from those with less benefit from targeted therapies.METHODS: A comprehensive narrative review was conducted: scoping searches were undertaken in PubMed, Cochrane Database of Systematic Reviews, and PROSPERO. Outcomes included in meta-analysis were progression-free and overall survival. Data was extracted from Kaplan-Meier and used to calculate hazard ratios. Studies presenting data on two molecular sub-cohorts (e.g., co-mutation vs no co-mutation) were included in fixed meta-analysis. Other studies were used for descriptive purposes.
    RESULTS: The presence of concomitant driver mutations, higher tumour mutational burden(TMB), greater copy number burden and APOBEC signatures significantly reduces benefits of targeted therapy in lung cancers in never smokers(LCINS) and breast cancer; cancers with low TMB. LCINS have significantly poorer outcomes if their cancers harbour p53 co-mutations, an effect also seen in HER2+ breast cancer patients (trastuzumab) and head and neck cancer (PI3K inhibition). PI3K co-alterations have less impact when targeting EGFR-mutations and ALK-fusions, but significantly reduce the impact of targeting HER2- and MET-amplifications. SMARCA4 co-mutations predict for poor outcome in patients treated with osimertinib and sotorasib. In BRAF-mutant melanoma, whilst there are no genomic features distinguishing exceptional responders from primary progressors, there are clear transcriptomic features dichotomising these outcomes.
    CONCLUSION: To our knowledge this is the most comprehensive review to date of the impact of genomic context on outcomes with targeted therapy. It represents a valuable resource informing progress towards contextualised precision medicine.
    Keywords:  exceptional responders; genomic context; precision medicine; primary progressors; targeted therapy
    DOI:  https://doi.org/10.1016/j.annonc.2023.10.124
This page is being maintained by Thomas Krichel. It was last updated on 2023‒10‒30 at 9:13.