Chromosomal instability (CIN)—which is a high rate of loss or gain of whole or parts of chromosomes—is a characteristic of most human cancers and a cause of tumour aneuploidy and intra‐tumour ...heterogeneity. CIN is associated with poor patient outcome and drug resistance, which could be mediated by evolutionary adaptation fostered by intra‐tumour heterogeneity. In this review, we discuss the clinical consequences of CIN and the challenges inherent to its measurement in tumour specimens. The relationship between CIN and prognosis supports assessment of CIN status in the clinical setting and suggests that stratifying tumours according to levels of CIN could facilitate clinical risk assessment.
This review provides a much‐needed translational perspective into the issue of aneuploidy and chromosomal instability, discussing the prognostic value of CIN assessment in human tumours, methods to analyze it and how it could be therapeutically targeted.
Neuroblastoma accounts for 15% of childhood cancer mortality. Amplification of the oncogene N-Myc is a well-established poor prognostic marker for neuroblastoma. Whilst N-Myc amplification status ...strongly correlates with higher tumour aggression and resistance to treatment, the role of N-Myc in the aggressiveness of the disease is poorly understood. Exosomes are released by many cell types including cancer cells and are implicated as key mediators in cell-cell communication via the transfer of molecular cargo. Hence, characterising the exosomal protein components from N-Myc amplified and non-amplified neuroblastoma cells will improve our understanding on their role in the progression of neuroblastoma. In this study, a comparative proteomic analysis of exosomes isolated from cells with varying N-Myc amplification status was performed. Label-free quantitative proteomic profiling revealed 968 proteins that are differentially abundant in exosomes released by the neuroblastoma cells. Gene ontology-based analysis highlighted the enrichment of proteins involved in cell communication and signal transduction in N-Myc amplified exosomes. Treatment of SH-SY5Y cells with N-Myc amplified SK-N-BE2 cell-derived exosomes increased the migratory potential, colony forming abilities and conferred resistance to doxorubicin induced apoptosis. Incubation of exosomes from N-Myc knocked down SK-N-BE2 cells abolished the transfer of resistance to doxorubicin induced apoptosis. These findings suggest that exosomes could play a pivotal role in N-Myc-driven aggressive neuroblastoma and transfer of chemoresistance between cells.
Abbreviations: RNA = ribonucleic acid; DNA = deoxyribonucleic acid; FCS = foetal calf serum; NTA = nanoparticle tracking analysis; LC-MS = liquid chromatography-mass spectrometry; KD = knockdown; LTQ = linear trap quadropole; TEM = transmission electron microscopy
Abstract Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to ...capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics – the high-throughput extraction of large amounts of image features from radiographic images – addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.
Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It ...has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.
Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).
We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22–3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03–3.3; p = 3.99 ×10−2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).
We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.
Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.
•Deep learning predicts spatial mRNA expression from HE histopathology images.•The proposed method enables scalable quantification of intra-tumour heterogeneity.•The intra-tumour heterogeneity measurement has independent prognostic value.•Prognostic value was successfully validated in one external cohort.•The proposed method may contribute to improved patient risk stratification.
•An individual-based model describing interactions between tumour and CD8+ T cells.•The model takes into account tumour antigen expression and presentation.•The degree of tumour heterogeneity has an ...impact on immune response efficacy.•Wider ranges of antigens promote immune escape of tumours.•Lower levels of antigen presentation diminish the immune action.
Intra-tumour heterogeneity (ITH) has a strong impact on the efficacy of the immune response against solid tumours. The number of sub-populations of cancer cells expressing different antigens and the percentage of immunogenic cells (i.e. tumour cells that are effectively targeted by immune cells) in a tumour are both expressions of ITH. Here, we present a spatially explicit stochastic individual-based model of the interaction dynamics between tumour cells and CD8+ T cells, which makes it possible to dissect out the specific impact of these two expressions of ITH on anti-tumour immune response. The set-up of numerical simulations of the model is defined so as to mimic scenarios considered in previous experimental studies. Moreover, the ability of the model to qualitatively reproduce experimental observations of successful and unsuccessful immune surveillance is demonstrated. First, the results of numerical simulations of this model indicate that the presence of a larger number of sub-populations of tumour cells that express different antigens is associated with a reduced ability of CD8+ T cells to mount an effective anti-tumour immune response. Secondly, the presence of a larger percentage of tumour cells that are not effectively targeted by CD8+ T cells may reduce the effectiveness of anti-tumour immunity. Ultimately, the mathematical model presented in this paper may provide a framework to help biologists and clinicians to better understand the mechanisms that are responsible for the emergence of different outcomes of immunotherapy.
•Lung epithelial cell-derived single-cell clones display phenotypic heterogeneity.•Morphologically mesenchymal single cell-derived clones display altered EMP marker expression.•Lung epithelial ...cell-derived clones do not display genetic alterations.•Single cell-derived clones show diversity in their metabolic reflux, with increased glycolytic index in many of the clones.
Epithelial-mesenchymal plasticity (EMP) is a hallmark of cancer. By enabling cells to shift between different morphological and functional states, EMP promotes invasion, metastasis and therapy resistance. We report that near-diploid non-cancerous human epithelial lung cells spontaneously shift along the EMP spectrum without genetic changes. Strikingly, more than half of single cell-derived clones adopt a mesenchymal morphology. We independently characterise epithelial-like and mesenchymal-like clones. Mesenchymal clones lose epithelial markers, display larger cell aspect ratios and lower motility, with mostly unaltered proliferation rates. Stemness marker expression and metabolic rewiring diverge independently of phenotypes. In 3D culture, more epithelial clones become mesenchymal-like. Thus, non-cancerous epithelial cells may acquire cancer metastasis-associated features prior to genetic alterations and cancerous transformation.
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Breast cancer is a remarkably complex and diverse disease. Subtyping based on morphology, genomics, biomarkers and/or clinical parameters seeks to stratify optimal approaches for management, but it ...is clear that every breast cancer is fundamentally unique. Intra-tumour heterogeneity adds further complexity and impacts a patient's response to neoadjuvant or adjuvant therapy. Here, we review some established and more recent evidence related to the complex nature of breast cancer evolution. We describe morphologic and genomic diversity as it arises spontaneously during the early stages of tumour evolution, and also in the context of treatment where the changing subclonal architecture of a tumour is driven by the inherent adaptability of tumour cells to evolve and resist the selective pressures of therapy.
Ovarian cancer (OC) is the deadliest gynaecologic cancer characterised by a high heterogeneity not only at the clinical point of view but also at the molecular level. This review focuses on the new ...insights about the OC molecular classification.
We performed a bibliographic search for different indexed articles focused on the new molecular classification of OC. All of them have been published in PubMed and included information about the most frequent molecular alterations in OC confirmed by omics approaches. In addition, we have extracted information about the role of liquid biopsy in the OC diagnosis and prognosis.
New molecular insights into OC have allowed novel clinical entities to be defined. Among OC, high-grade serous ovarian carcinoma (HGSOC) which is the most common OC is characterised by omics approaches, mutations in TP53 and in other genes involved in the homologous recombination repair, especially BRCA1/2. Recent studies in HGSOC have allowed a new molecular classification in subgroups according to their mutational, transcriptional, methylation and copy number variation signatures with a real impact in the characterisation of new therapeutic targets for OC to be defined. Furthermore, despite the intrinsic intra-tumour heterogeneity, the advances in next generation sequencing (NGS) analyses of ascetic liquid from OC have opened new ways for its characterisation and treatment.
The advances in genomic approaches have been used for the identification of new molecular profiling techniques which define OC subgroups and has supposed advances in the diagnosis and in the personalised treatment of OC.
•Classification of ovarian cancer regarding to widespread genetic and genomic data.•Highlighted role of p53 and BRCA1/2 in ovarian cancer for diagnosis and treatment.•Intra-tumour genetic heterogeneity in ovarian cancer.•Useful of liquid biopsy study in ovarian cancer diagnosis.
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