bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2022‒03‒06
eight papers selected by
Sergio Marchini
Humanitas Research

  1. Front Bioeng Biotechnol. 2022 ;10 836984
      Ovarian cancer has the highest mortality of all of the gynecological malignancies. There are several distinct histotypes of this malignancy characterized by specific molecular events and clinical behavior. These histotypes have differing responses to platinum-based drugs that have been the mainstay of therapy for ovarian cancer for decades. For histotypes that initially respond to a chemotherapeutic regime of carboplatin and paclitaxel such as high-grade serous ovarian cancer, the development of chemoresistance is common and underpins incurable disease. Recent discoveries have led to the clinical use of PARP (poly ADP ribose polymerase) inhibitors for ovarian cancers defective in homologous recombination repair, as well as the anti-angiogenic bevacizumab. While predictive molecular testing involving identification of a genomic scar and/or the presence of germline or somatic BRCA1 or BRCA2 mutation are in clinical use to inform the likely success of a PARP inhibitor, no similar tests are available to identify women likely to respond to bevacizumab. Functional tests to predict patient response to any drug are, in fact, essentially absent from clinical care. New drugs are needed to treat ovarian cancer. In this review, we discuss applications to address the currently unmet need of developing physiologically relevant in vitro and ex vivo models of ovarian cancer for fundamental discovery science, and personalized medicine approaches. Traditional two-dimensional (2D) in vitro cell culture of ovarian cancer lacks critical cell-to-cell interactions afforded by culture in three-dimensions. Additionally, modelling interactions with the tumor microenvironment, including the surface of organs in the peritoneal cavity that support metastatic growth of ovarian cancer, will improve the power of these models. Being able to reliably grow primary tumoroid cultures of ovarian cancer will improve the ability to recapitulate tumor heterogeneity. Three-dimensional (3D) modelling systems, from cell lines to organoid or tumoroid cultures, represent enhanced starting points from which improved translational outcomes for women with ovarian cancer will emerge.
    Keywords:  3D bio-printing; 3D cell culture; drug screening; organoids; ovarian cancer; personalized medicine; tumoroid
  2. Gynecol Oncol Rep. 2022 Apr;40 100942
      Low-grade serous ovarian cancer (LGSOC) is now considered a different entity from high-grade serous ovarian cancer. The chemoresistance inherent to this type of ovarian cancer narrows the therapeutic options, especially in the recurrent setting. It is thought that the mitogen-activated protein kinase (MAPK) pathway plays a significant role in the pathogenesis of these tumours, and about 2 to 20% of LGSOC harbour a BRAF mutation. Here we present a case report of two patients with a BRAF V600E mutation that achieved sustained clinical responses with combination treatment with dabrafenib (BRAF inhibitor) and trametinib (MEK inhibitor).
    Keywords:  BRAF mutation; Combination treatment; Dabrafenib; Low-grade serous ovarian cancer; Trametinib
  3. Epigenomes. 2022 Feb 04. pii: 6. [Epub ahead of print]6(1):
      The efficiency of conventional screening programs to identify early-stage malignancies can be limited by the low number of cancers recommended for screening as well as the high cumulative false-positive rate, and associated iatrogenic burden, resulting from repeated multimodal testing. The opportunity to use minimally invasive liquid biopsy testing to screen asymptomatic individuals at-risk for multiple cancers simultaneously could benefit from the aggregated diseases prevalence and a fixed specificity. Increasing both latter parameters is paramount to mediate high positive predictive value-a useful metric to evaluate a screening test accuracy and its potential harm-benefit. Thus, the use of a single test for multi-cancer early detection (stMCED) has emerged as an appealing strategy for increasing early cancer detection rate efficiency and benefit population health. A recent flurry of these stMCED technologies have been reported for clinical potential; however, their development is facing unique challenges to effectively improve clinical cost-benefit. One promising avenue is the analysis of circulating tumour DNA (ctDNA) for detecting DNA methylation biomarker fingerprints of malignancies-a hallmark of disease aetiology and progression holding the potential to be tissue- and cancer-type specific. Utilizing panels of epigenetic biomarkers could potentially help to detect earlier stages of malignancies as well as identify a tumour of origin from blood testing, useful information for follow-up clinical decision making and subsequent patient care improvement. Overall, this review collates the latest and most promising stMCED methodologies, summarizes their clinical performances, and discusses the specific requirements multi-cancer tests should meet to be successfully implemented into screening guidelines.
    Keywords:  DNA methylation biomarkers; cancer epigenetics; cancer screening; circulating tumour DNA; combinatorial analysis; liquid biopsy testing; multi-cancer early detection; positive predictive value; tissue-of-origin prediction
  4. Adv Exp Med Biol. 2022 ;1361 269-282
      Single-cell sequencing technologies are revolutionizing cancer research and are poised to become the standard for translational cancer studies. Rapidly decreasing costs and increasing throughput and resolution are paving the way for the adoption of single-cell technologies in clinical settings for personalized medicine applications. In this chapter, we review the state of the art of single-cell DNA and RNA sequencing technologies, the computational tools to analyze the data, and their potential application to precision oncology. We also discuss the advantages of single-cell over bulk sequencing for the dissection of intra-tumor heterogeneity and the characterization of subclonal cell populations, the implementation of targeted drug repurposing approaches, and describe advanced methodologies for multi-omics data integration and to assess cell signaling at single-cell resolution.
    Keywords:  CNV; Cell-cell signaling; Drug repurposing; Intra-tumor heterogeneity; Pathway analysis; SNV; Single-cell sequencing; scDNA-seq; scRNA-seq
  5. Adv Exp Med Biol. 2022 ;1361 235-247
      In recent years, the rapid development of next-generation sequencing (NGS) has led to a significant increase in accuracy toward molecular profiling, allowing noninvasive and real-time detection of novel biomarkers for cancer screening and dynamic monitoring of disease development. Currently, the biggest challenge liquid biopsies face is the selection of the highest signal-bearing tissues (blood/urine or others) and components for diagnosis, being either circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), or extracellular vesicles (EVs). This chapter describes the process of identifying cancer-associated molecular signals from liquid biopsies. First, we address strategies in selecting and processing samples for sequencing, and technical considerations involved in liquid biopsies under three settings: early detection, cancer diagnosis, and metastatic monitoring. Next, we discuss the methods and challenges to identify and validate prognostic signals, such as tumor burden or stage from CTC, targeted and nontargeted mutations from ctDNA, or noncoding RNAs from EVs. Finally, we review the current landscape of novel biomarkers and ongoing clinical trials for liquid biopsies to discuss the potential avenues for future precision medicine and clinical implementation.
    Keywords:  CTC; Disease variants; EVs; GWAS; NGS; WES; ctDNA
  6. J Clin Lab Anal. 2022 Mar 03. e24277
      BACKGROUND: Lung adenocarcinoma (LUAD) is a lung cancer subtype with poor prognosis. We investigated the prognostic value of methylation- and homologous recombination deficiency (HRD)-associated gene signatures in LUAD.METHODS: Data on RNA sequencing, somatic mutations, and methylation were obtained from TCGA database. HRD scores were used to stratify patients with LUAD into high and low HRD groups and identify differentially mutated and expressed genes (DMEGs). Pearson correlation analysis between DMEGs and methylation yielded methylation-associated DMEGs. Cox regression analysis was used to construct a prognostic model, and the distribution of clinical features in the high- and low-risk groups was compared.
    RESULTS: Patients with different HRD scores showed different DNA mutation patterns. There were 272 differentially mutated genes and 6294 differentially expressed genes. Fifty-seven DMEGs were obtained; the top 10 upregulated genes were COL11A1, EXO1, ASPM, COL12A1, COL2A1, COL3A1, COL5A2, DIAPH3, CAD, and SLC25A13, while the top 10 downregulated genes were C7, ERN2, DLC1, SCN7A, SMARCA2, CARD11, LAMA2, ITIH5, FRY, and EPHB6. Forty-two DMEGs were negatively correlated with 259 methylation sites. Gene ontology and pathway enrichment analysis of the DMEGs revealed enrichment of loci involved in extracellular matrix-related remodeling and signaling. Six out of the 42 methylation-associated DMEGs were significantly associated with LUAD prognosis and included in the prognostic model. The model effectively stratified high- and low-risk patients, with the high-risk group having more patients with advanced stage disease.
    CONCLUSION: We developed a novel prognostic model for LUAD based on methylation and HRD. Methylation-associated DMEGs may function as biomarkers and therapeutic targets for LUAD. Further studies are needed to elucidate their roles in LUAD carcinogenesis.
    Keywords:  homologous recombination deficiency; lung adenocarcinoma; methylation; prognosis
  7. Adv Exp Med Biol. 2022 ;1361 55-74
      Copy number variation (CNV), which is deletion and multiplication of segments of a genome, is an important genomic alteration that has been associated with many diseases including cancer. In cancer, CNVs are mostly somatic aberrations that occur during cancer evolution. Advances in sequencing technologies and arrival of next-generation sequencing data (whole-genome sequencing and whole-exome sequencing or targeted sequencing) have opened up an opportunity to detect CNVs with higher accuracy and resolution. Many computational methods have been developed for somatic CNV detection, which is a challenging task due to complexity of cancer sequencing data, high level of noise and biases in the sequencing process, and big data nature of sequencing data. Nevertheless, computational detection of CNV in sequencing data has resulted in the discovery of actionable cancer-specific CNVs to be used to guide cancer therapeutics, contributing to significant progress in precision oncology. In this chapter, we start by introducing CNVs. Then, we discuss the main approaches and methods developed for detecting somatic CNV for next-generation sequencing data, along with its challenges. Finally, we describe the overall workflow for CNV detection and introduce the most common publicly available software tools developed for somatic CNV detection and analysis.
    Keywords:  CNV detection; Copy number variation; Whole genome sequencing, Somatic aberrations; Whole-exome sequencing
  8. J Hematol Oncol. 2022 Mar 03. 15(1): 19
      The heterogeneity and the complex cellular architecture have a crucial effect on breast cancer progression and response to treatment. However, deciphering the neoplastic subtypes and their spatial organization is still challenging. Here, we combine single-nucleus RNA sequencing (snRNA-seq) with a microarray-based spatial transcriptomics (ST) to identify cell populations and their spatial distribution in breast cancer tissues. Malignant cells are clustered into distinct subpopulations. These cell clusters not only have diverse features, origins and functions, but also emerge to the crosstalk within subtypes. Furthermore, we find that these subclusters are mapped in distinct tissue regions, where discrepant enrichment of stromal cell types are observed. We also inferred the abundance of these tumorous subpopulations by deconvolution of large breast cancer RNA-seq cohorts, revealing differential association with patient survival and therapeutic response. Our study provides a novel insight for the cellular architecture of breast cancer and potential therapeutic strategies.
    Keywords:  Breast cancer; Heterogeneity; Single-nucleus RNA sequencing; Spatial transcriptomics; Tissue architecture