Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput ...characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.
Human cancers can be classified based on gene signatures quantifying the degree of cell proliferation and tissue remodelling (PR). However, the specific factors that drive the increased tissue ...remodelling in tumours are not fully understood. Here we address this question using colorectal cancer as a case study.
We reanalysed a reported cohort of colorectal cancer patients. The patients were stratified based on gene signatures of cell proliferation and tissue remodelling. Putative transcription factors activity was inferred using gene expression profiles and annotations of transcription factor targets as input.
We demonstrate that the PR classification performs better than the currently adopted consensus molecular subtyping (CMS). Although CMS classification differentiates patients with a mesenchymal signature, it cannot distinguish the remaining patients based on survival. We demonstrate that the missing factor is cell proliferation, which is indicative of good prognosis. We also uncover a KLF4 transcription factor activity score associated with the tissue remodelling gene signature. We further show that the KLF4 activity score is significantly higher in colorectal tumours with predicted infiltration of cells from the myeloid lineage.
The KLF4 activity score is associated with tissue remodelling, myeloid cell infiltration and poor prognosis in colorectal cancer.
Activation of cellular hypoxia pathways, orchestrated by HIF (hypoxia-inducible factor) transcription factors, is a common feature of multiple tumor types, resulting from microenvironment factors and ...oncogenic mutation. Although they help drive many of the “hallmarks” of cancer and are associated with poor outcome and resistance to therapy, the transcriptional targets of HIF vary considerably depending on the cell type. By integrating 72 genome-wide assays of HIF binding and transcriptional regulation from multiple cancer types, we define a consensus set of 48 HIF target genes that is highly conserved across cancer types and cell lineages. These genes provide an effective marker of HIF activation in bulk and single-cell transcriptomic analyses across a wide range of cancer types and in malignant and stromal cell types. This allows the tissue-orchestrated responses to the hypoxic tumor microenvironment and to oncogenic HIF activation to be deconvoluted at the tumor and single-cell level.
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•ChIP-seq and RNA-seq datasets are used to identify HIF target genes in multiple cancers•Consensus HIF target genes are used as a pan-cancer HIF metagene•The HIF metagene can be used as an endogenous marker of HIF activity/hypoxia•The HIF metagene allows analysis of tumor samples via bulk and single-cell datasets
Lombardi et al. define a consensus set of HIF target genes using 72 ChIP-seq and RNA-seq datasets and use this as an “HIF metagene” to detect HIF activation/hypoxia in clinical samples. This enables investigation of tumor- and cell-type-specific responses to HIF in bulk and single-cell RNA-seq datasets.
Summary Background Clinical prognostic groupings for localised prostate cancers are imprecise, with 30–50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to ...test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors. Methods We used DNA-based indices alone or in combination with intra-prostatic hypoxia measurements to develop four prognostic indices in 126 low-risk to intermediate-risk patients (Toronto cohort) who will receive image-guided radiotherapy. We validated these indices in two independent cohorts of 154 (Memorial Sloan Kettering Cancer Center cohort MSKCC cohort) and 117 (Cambridge cohort) radical prostatectomy specimens from low-risk to high-risk patients. We applied unsupervised and supervised machine learning techniques to the copy-number profiles of 126 pre-image-guided radiotherapy diagnostic biopsies to develop prognostic signatures. Our primary endpoint was the development of a set of prognostic measures capable of stratifying patients for risk of biochemical relapse 5 years after primary treatment. Findings Biochemical relapse was associated with indices of tumour hypoxia, genomic instability, and genomic subtypes based on multivariate analyses. We identified four genomic subtypes for prostate cancer, which had different 5-year biochemical relapse-free survival. Genomic instability is prognostic for relapse in both image-guided radiotherapy (multivariate analysis hazard ratio HR 4·5 95% CI 2·1–9·8; p=0·00013; area under the receiver operator curve AUC 0·70 95% CI 0·65–0·76) and radical prostatectomy (4·0 1·6–9·7; p=0·0024; AUC 0·57 0·52–0·61) patients with prostate cancer, and its effect is magnified by intratumoral hypoxia (3·8 1·2–12; p=0·019; AUC 0·67 0·61–0·73). A novel 100-loci DNA signature accurately classified treatment outcome in the MSKCC low-risk to intermediate-risk cohort (multivariate analysis HR 6·1 95% CI 2·0–19; p=0·0015; AUC 0·74 95% CI 0·65–0·83). In the independent MSKCC and Cambridge cohorts, this signature identified low-risk to high-risk patients who were most likely to fail treatment within 18 months (combined cohorts multivariate analysis HR 2·9 95% CI 1·4–6·0; p=0·0039; AUC 0·68 95% CI 0·63–0·73), and was better at predicting biochemical relapse than 23 previously published RNA signatures. Interpretation This is the first study of cancer outcome to integrate DNA-based and microenvironment-based failure indices to predict patient outcome. Patients exhibiting these aggressive features after biopsy should be entered into treatment intensification trials. Funding Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.
The fibroblast growth factor receptor 2 (FGFR2) locus is consistently the top hit in genome-wide association studies for oestrogen receptor-positive (ER(+)) breast cancer. Yet, its mode of action ...continues to be controversial. Here, we employ a systems biology approach to demonstrate that signalling via FGFR2 counteracts cell activation by oestrogen. In the presence of oestrogen, the oestrogen receptor (ESR1) regulon (set of ESR1 target genes) is in an active state. However, signalling by FGFR2 is able to reverse the activity of the ESR1 regulon. This effect is seen in multiple distinct FGFR2 signalling model systems, across multiple cells lines and is dependent on the presence of FGFR2. Increased oestrogen exposure has long been associated with an increased risk of breast cancer. We therefore hypothesized that risk variants should reduce FGFR2 expression and subsequent signalling. Indeed, transient transfection experiments assaying the three independent variants of the FGFR2 risk locus (rs2981578, rs35054928 and rs45631563) in their normal chromosomal context show that these single-nucleotide polymorphisms (SNPs) map to transcriptional silencer elements and that, compared with wild type, the risk alleles augment silencer activity. The presence of risk variants results in lower FGFR2 expression and increased oestrogen responsiveness. We thus propose a molecular mechanism by which FGFR2 can confer increased breast cancer risk that is consistent with oestrogen exposure as a major driver of breast cancer risk. Our findings may have implications for the clinical use of FGFR2 inhibitors.
Human cancers can be classified based on gene expression signatures quantifying the degree of cell proliferation and a feature we term tissue remodelling. Yet, specific factors regulating the gene ...expression programs of cell proliferation and tissue remodelling are not well understood. In this work, I address this question using colorectal cancer as a case study. In an initial evaluation of clinical outcome prediction in the case study cohort, our classification based on cell proliferation and tissue remodelling signatures delivers a better patient stratification than a recently reported consensus molecular subtyping (CMS). Although the CMS scheme predicts a worse prognosis for patients with a mesenchymal signature (a feature that strongly overlaps with tissue remodelling), it cannot differentiate the remaining patients based on clinical outcome. I show that cell proliferation is the missing factor, which is associated with good prognosis. In the analyses of specific factors driving the tissue remodelling programme, I identify transcription factor KLF4 and microRNA mir-22 as putative regulators. Concordant with KLF4 role in immune cell regulation, KLF4 activity scores are significantly higher in colorectal tumours with predicted myeloid cells infiltration, suggesting its association with myeloid cell infiltration and poor prognosis in colorectal cancer. However, mir-22 is not associated with immune cell infiltration, suggesting it may be expressed in the tumour stroma as well. Lastly, I propose a network that may be regulated by IRF8, SPI1, KLF4 and mir-22 in monocyte differentiation and in colorectal cancer. The correlation analysis suggests a negative feedback loop, whereby mir-22 may repress Irf8 gene to control the rate of IRF8 transcription. Taken together, I have identified specific regulators driving remodelling processes and their association with immune cell infiltration. In the future, it will be beneficial to conduct further studies to understand the mechanisms driving myeloid cell infiltration in colorectal cancer, particularly to propose treatments for patients exhibiting poor prognosis.
The discovery of cytosine hydroxymethylation (5hmC) as a mechanism that potentially controls DNA methylation changes typical of neoplasia prompted us to investigate its behaviour in colon cancer. ...5hmC is globally reduced in proliferating cells such as colon tumours and the gut crypt progenitors, from which tumours can arise.
Here, we show that colorectal tumours and cancer cells express Ten-Eleven-Translocation (TET) transcripts at levels similar to normal tissues. Genome-wide analyses show that promoters marked by 5hmC in normal tissue, and those identified as TET2 targets in colorectal cancer cells, are resistant to methylation gain in cancer. In vitro studies of TET2 in cancer cells confirm that these promoters are resistant to methylation gain independently of sustained TET2 expression. We also find that a considerable number of the methylation gain-resistant promoters marked by 5hmC in normal colon overlap with those that are marked with poised bivalent histone modifications in embryonic stem cells.
Together our results indicate that promoters that acquire 5hmC upon normal colon differentiation are innately resistant to neoplastic hypermethylation by mechanisms that do not require high levels of 5hmC in tumours. Our study highlights the potential of cytosine modifications as biomarkers of cancerous cell proliferation.
Abstract Background Localized prostate cancer is clinically heterogeneous, despite clinical risk groups that represent relative prostate cancer-specific mortality. We previously developed a 100-locus ...DNA classifier capable of substratifying patients at risk of biochemical relapse within clinical risk groups. Objective The 100-locus genomic classifier was refined to 31 functional loci and tested with standard clinical variables for the ability to predict biochemical recurrence (BCR) and metastasis. Design, setting, and participants Four retrospective cohorts of radical prostatectomy specimens from patients with localized disease were pooled, and an additional 102-patient cohort used to measure the 31-locus genomic classifier with the NanoString platform. Outcome measurements and statistical analysis The genomic classifier scores were tested for their ability to predict BCR ( n = 563) and metastasis ( n = 154), and compared with clinical risk stratification schemes. Results and limitations The 31-locus genomic classifier performs similarly to the 100-locus classifier. It identifies patients with elevated BCR rates (hazard ratio = 2.73, p < 0.001) and patients that eventually develop metastasis (hazard ratio = 7.79, p < 0.001). Combining the genomic classifier with standard clinical variables outperforms clinical models. Finally, the 31-locus genomic classifier was implemented using a NanoString assay. The study is limited to retrospective cohorts. Conclusions The 100-locus and 31-locus genomic classifiers reliably identify a cohort of men with localized disease who have an elevated risk of failure. The NanoString assay will be useful for selecting patients for treatment deescalation or escalation in prospective clinical trials based on clinico-genomic scores from pretreatment biopsies. Patient summary It is challenging to determine whether tumors confined to the prostate are aggressive, leading to significant undertreatment and overtreatment. We validated a test based on prostate tumor DNA that improves estimations of relapse risk, and that can help guide treatment planning.
Hypoxia-inducible factor (HIF) and aryl hydrocarbon receptor (AHR) are members of the bHLH-PAS family of transcription factors that underpin cellular responses to oxygen and to endogenous and ...exogenous ligands, respectively, and have central roles in the pathogenesis of renal cancer. Composed of heterodimers, they share a common HIF-1β/ARNT subunit and similar DNA-binding motifs, raising the possibility of crosstalk between the two transcriptional pathways. Here, we identify both general and locus-specific mechanisms of interaction between HIF and AHR that act both antagonistically and cooperatively. Specifically, we observe competition for the common HIF-1β/ARNT subunit, in cis synergy for chromatin binding, and overlap in their transcriptional targets. Recently, both HIF and AHR inhibitors have been developed for the treatment of solid tumours. However, inhibition of one pathway may promote the oncogenic effects of the other. Therefore, our work raises important questions as to whether combination therapy targeting both of these pro-tumourigenic pathways might show greater efficacy than targeting each system independently.
Associations between single nucleotide polymorphisms (SNPs) at 5p15 and multiple cancer types have been reported. We have previously shown evidence for a strong association between prostate cancer ...(PrCa) risk and rs2242652 at 5p15, intronic in the telomerase reverse transcriptase (TERT) gene that encodes TERT. To comprehensively evaluate the association between genetic variation across this region and PrCa, we performed a fine-mapping analysis by genotyping 134 SNPs using a custom Illumina iSelect array or Sequenom MassArray iPlex, followed by imputation of 1094 SNPs in 22 301 PrCa cases and 22 320 controls in The PRACTICAL consortium. Multiple stepwise logistic regression analysis identified four signals in the promoter or intronic regions of TERT that independently associated with PrCa risk. Gene expression analysis of normal prostate tissue showed evidence that SNPs within one of these regions also associated with TERT expression, providing a potential mechanism for predisposition to disease.
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