Cell line models for high grade serous ovarian cancer (HGSOC) are limited in number and are poorly clinically annotated. Consequently, existing models often fail to recapitulate common features of HGSOC, inhibiting mechanistic and therapeutic discovery. We generated and characterised three spontaneously immortalized continuous HGSOC cell lines named CIOV1, CIOV2, and CIOV3 and confirmed that each cell line retained the genomic and pathologic characteristics of its parental tumour. We show that subclonal cell populations present at initiation, expanded and contracted during the culturing process before converging on a stable immortalized line. These lines are new valuable models to study acquired chemoresistance of HGSOC.
Tumor-derived extracellular vesicles (EVs) have been associated with immune evasion and tumor progression. We show that the RNA-sensing receptor RIG-I within tumor cells governs biogenesis and immunomodulatory function of EVs. Cancer-intrinsic RIG-I activation releases EVs, which mediate dendritic cell maturation and T cell antitumor immunity, synergizing with immune checkpoint blockade. Intact RIG-I, autocrine interferon signaling, and the GTPase Rab27a in tumor cells are required for biogenesis of immunostimulatory EVs. Active intrinsic RIG-I signaling governs composition of the tumor EV RNA cargo including small non-coding stimulatory RNAs. High transcriptional activity of EV pathway genes and RIG-I in melanoma samples associate with prolonged patient survival and beneficial response to immunotherapy. EVs generated from human melanoma after RIG-I stimulation induce potent antigen-specific T cell responses. We thus define a molecular pathway that can be targeted in tumors to favorably alter EV immunomodulatory function. We propose "reprogramming" of tumor EVs as a personalized strategy for T cell-mediated cancer immunotherapy.
Abstract High grade serous ovarian carcinoma (HGSOC) is characterised by poor outcome and extreme chromosome instability (CIN). Therapies targeting centrosome amplification (CA), a key mediator of chromosome missegregation and CIN, may have significant clinical utility in HGSOC. However, the prevalence of CA in HGSOC, its relationship to genomic biomarkers of CIN and its potential impact on therapeutic response have not been defined. Using high-throughput multi-regional microscopy on 287 clinical HGSOC tumour tissues and 73 ovarian cancer cell lines, we show that CA through centriole overduplication is a highly recurrent and heterogeneous feature of HGSOC and is strongly associated with CIN and genome subclonality. Cell-based studies showed that high prevalence CA is phenocopied in ovarian cancer cell lines, and that high CA is associated with increased multi-treatment resistance; most notably to paclitaxel which is the most common treatment used in HGSOC. CA in HGSOC may therefore present a potential driver of tumour evolution and a powerful biomarker for response to standard-of-care treatment.
Glucocorticoids (Gcs) potently inhibit inflammation, and regulate liver energy metabolism, often acting in a hypoxic environment. We now show hypoxic conditions open a specific GR cistrome, and prevent access of GR to part of the normoxic GR cistrome. Motif analysis identified enrichment of KLF4 binding sites beneath those peaks of GR binding exclusive to normoxia, implicating KLF4 as a pioneer, or co-factor under these conditions. Hypoxia reduced KLF4 expression, however, knockdown of KLF4 did not impair GR recruitment. KLF4 is a known target of microRNAs 103 and 107, both of which are induced by hypoxia. Expression of mimics to either microRNA103, or microRNA107 inhibited GR transactivation of normoxic target genes, thereby replicating the hypoxic effect. Therefore, studies in hypoxia reveal that microRNAs 103 and 107 are potent regulators of GR function. We have now identified a new pathway linking hypoxia through microRNAs 103 and 107 to regulation of GR function.
Abstract We profiled a large heterogenous cohort of matched diagnostic-relapse tumour tissue and paired plasma-derived cell free DNA (cfDNA) from patients with relapsed and progressive solid tumours of childhood. Tissue and cfDNA sequencing results were concordant, with a wider spectrum of mutant alleles and higher degree of intra-tumour heterogeneity captured by the latter, if sufficient circulating tumour-derived DNA (ctDNA) was present. Serial tumour sequencing identified putative drivers of relapse, with alterations in epigenetic drivers being a common feature. In keeping with epigenetic alterations being a common driver of many childhood cancers, fragmentomics analysis of cfDNA identified tumour-specific epigenetic states and transcription factor binding sites accessible in chromatin. This study leverages a large and well-annotated genomic dataset of aggressive childhood malignancies, identifies genomic and epigenetic drivers of childhood cancer relapse, and highlights the power and practicality of cfDNA analysis to capture both intra-tumoural heterogeneity and the epigenetic state of cancer cells.
Abstract Low-coverage or shallow whole genome sequencing (sWGS) approaches can efficiently detect somatic copy number aberrations (SCNAs) at low cost. This is clinically important for many cancers, in particular cancers with severe chromosomal instability (CIN) that frequently lack actionable point mutations and are characterised by poor disease outcome. Absolute copy number (ACN), measured in DNA copies per cancer cell, is required for meaningful comparisons between copy number states, but is challenging to estimate and in practice often requires manual curation. Using a total of 60 cancer cell lines, 148 patient-derived xenograft (PDX) and 142 clinical tissue samples, we evaluate the performance of available tools for obtaining ACN from sWGS. We provide a validated and refined tool called Rascal ( r elative to a bsolute copy number scal ing) that provides improved fitting algorithms and enables interactive visualisation of copy number profiles. These approaches are highly applicable to both pre-clinical and translational research studies on SCNA-driven cancers and provide more robust ACN fits from sWGS data than currently available tools.
Abstract Telomere fusions (TFs) can trigger the accumulation of oncogenic alterations leading to malignant transformation and drug resistance. Despite their relevance in tumour evolution, our understanding of the patterns and consequences of TFs in human cancers remains limited. Here, we characterize the rates and spectrum of somatic TFs across >30 cancer types using whole-genome sequencing data. TFs are pervasive in human tumours with rates varying markedly across and within cancer types. In addition to end-to-end fusions, we find patterns of TFs that we mechanistically link to the activity of the alternative lengthening of telomeres (ALT) pathway. We show that TFs can be detected in the blood of cancer patients, which enables cancer detection with high specificity and sensitivity even for early-stage tumours and cancers of high unmet clinical need. Overall, we report a genomic footprint that enables characterization of the telomere maintenance mechanism of tumours and liquid biopsy analysis.
Abstract Accurate detection of somatic structural variants (SVs) and copy number aberrations (SCNAs) is critical to inform the diagnosis and treatment of human cancers. Here, we describe SAVANA, a computationally efficient algorithm designed for the joint analysis of somatic SVs, SCNAs, tumour purity and ploidy using long-read sequencing data. SAVANA relies on machine learning to distinguish true somatic SVs from artefacts and provide prediction errors for individual SVs. Using high-depth Illumina and nanopore whole-genome sequencing data for 99 human tumours and matched normal samples, we establish best practices for benchmarking SV detection algorithms across the entire genome in an unbiased and data-driven manner using simulated and sequencing replicates of tumour and matched normal samples. SAVANA shows significantly higher sensitivity, and 9- and 59-times higher specificity than the second and third-best performing algorithms, yielding orders of magnitude fewer false positives in comparison to existing long-read sequencing tools across various clonality levels, genomic regions, SV types and SV sizes. In addition, SAVANA harnesses long-range phasing information to detect somatic SVs and SCNAs at single-haplotype resolution. SVs reported by SAVANA are highly consistent with those detected using short-read sequencing, including complex events causing oncogene amplification and tumour suppressor gene inactivation. In summary, SAVANA enables the application of long-read sequencing to detect SVs and SCNAs reliably in clinical samples.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)