Publications by authors named "Florian Markowetz"

76 Publications

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning.

Nat Med 2021 Apr 15. Epub 2021 Apr 15.

Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.

Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge and existing clinical decision pathways is still an open challenge. This question is particularly important for the early detection of cancer, where high-volume workflows may benefit from (semi-)automated analysis. Here we present a deep learning framework to analyze samples of the Cytosponge-TFF3 test, a minimally invasive alternative to endoscopy, for detecting Barrett's esophagus, which is the main precursor of esophageal adenocarcinoma. We trained and independently validated the framework on data from two clinical trials, analyzing a combined total of 4,662 pathology slides from 2,331 patients. Our approach exploits decision patterns of gastrointestinal pathologists to define eight triage classes of varying priority for manual expert review. By substituting manual review with automated review in low-priority classes, we can reduce pathologist workload by 57% while matching the diagnostic performance of experienced pathologists.
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http://dx.doi.org/10.1038/s41591-021-01287-9DOI Listing
April 2021

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes.

Cell 2021 Apr 7;184(8):2239-2254.e39. Epub 2021 Apr 7.

Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK.

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.
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http://dx.doi.org/10.1016/j.cell.2021.03.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054914PMC
April 2021

FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections.

JCO Clin Cancer Inform 2021 Feb;5:176-186

Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.

Purpose: Chromosomal aberration and DNA copy number change are robust hallmarks of cancer. The gold standard for detecting copy number changes in tumor cells is fluorescence in situ hybridization (FISH) using locus-specific probes that are imaged as fluorescent spots. However, spot counting often does not perform well on solid tumor tissue sections due to partially represented or overlapping nuclei.

Materials And Methods: To overcome these challenges, we have developed a computational approach called FrenchFISH, which comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes or a homogeneous Poisson point process model for automated spot counting.

Results: We benchmarked the performance of FrenchFISH against previous approaches using a controlled simulation scenario and tested it experimentally in 12 ovarian carcinoma FFPE-tissue sections for copy number alterations at three loci (c-Myc, hTERC, and SE7). FrenchFISH outperformed standard spot counting with 74% of the automated counts having < 1 copy number difference from the manual counts and 17% having < 2 copy number differences, while taking less than one third of the time of manual counting.

Conclusion: FrenchFISH is a general approach that can be used to enhance clinical diagnosis on sections of any tissue by both speeding up and improving the accuracy of spot count estimates.
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http://dx.doi.org/10.1200/CCI.20.00075DOI Listing
February 2021

Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer.

Eur Radiol 2020 Dec 14. Epub 2020 Dec 14.

Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.

Purpose: To develop a precision tissue sampling technique that uses computed tomography (CT)-based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC).

Methods: Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy.

Results: We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7-30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).

Conclusion: We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC.

Key Points: • We developed a prevision tissue sampling technique that co-registers CT-based radiomics-based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).
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http://dx.doi.org/10.1007/s00330-020-07560-8DOI Listing
December 2020

Intestinal region-specific Wnt signalling profiles reveal interrelation between cell identity and oncogenic pathway activity in cancer development.

Cancer Cell Int 2020 Dec 3;20(1):578. Epub 2020 Dec 3.

Laboratory for Experimental Oncology and Radiobiology (LEXOR), Center for Experimental and Molecular Medicine (CEMM), Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism, Amsterdam University Medical Centers, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.

Background: Cancer results from the accumulation of mutations leading to the acquisition of cancer promoting characteristics such as increased proliferation and resistance to cell death. In colorectal cancer, an early mutation leading to such features usually occurs in the APC or CTNNB1 genes, thereby activating Wnt signalling. However, substantial phenotypic differences between cancers originating within the same organ, such as molecular subtypes, are not fully reflected by differences in mutations. Indeed, the phenotype seems to result from a complex interplay between the cell-intrinsic features and the acquired mutations, which is difficult to disentangle when established tumours are studied.

Methods: We use a 3D in vitro organoid model to study the early phase of colorectal cancer development. From three different murine intestinal locations we grow organoids. These are transformed to resemble adenomas after Wnt activation through lentiviral transduction with a stable form of β-Catenin. The gene expression before and after Wnt activation is compared within each intestinal origin and across the three locations using RNA sequencing. To validate and generalize our findings, we use gene expression data from patients.

Results: In reaction to Wnt activation we observe downregulation of location specific genes and differentiation markers. A similar effect is seen in patient data, where genes with significant differential expression between the normal left and right colon are downregulated in the cancer samples. Furthermore, the signature of Wnt target genes differs between the three intestinal locations in the organoids. The location specific Wnt signatures are dominated by genes which have been lowly expressed in the tissue of origin, and are the targets of transcription factors that are activated following enhanced Wnt signalling.

Conclusion: We observed that the region-specific cell identity has a substantial effect on the reaction to Wnt activation in a simple intestinal adenoma model. These findings provide a way forward in resolving the distinct biology between left- and right-sided human colon cancers with potential clinical relevance.
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http://dx.doi.org/10.1186/s12935-020-01661-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713000PMC
December 2020

Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform.

JCO Clin Cancer Inform 2020 08;4:736-748

Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.

Purpose: Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data.

Methods: We have developed an open-source computational framework to automatically produce patient-specific 3-dimensional-printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows.

Results: We applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes.

Conclusion: Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.
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http://dx.doi.org/10.1200/CCI.20.00026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7469624PMC
August 2020

Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer.

Insights Imaging 2020 Aug 17;11(1):94. Epub 2020 Aug 17.

Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK.

Background: Ovarian cancer survival rates have not changed in the last 20 years. The majority of cases are High-grade serous ovarian carcinomas (HGSOCs), which are typically diagnosed at an advanced stage with multiple metastatic lesions. Taking biopsies of all sites of disease is infeasible, which challenges the implementation of stratification tools based on molecular profiling.

Main Body: In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes.

Conclusion: Linking radiomics and biological correlates in a targeted manner could potentially improve the clinical management of ovarian cancer. Radiogenomic signatures could be used to monitor tumours during the course of therapy, offering additional information for clinical decision making. In summary, radiogenomics may pave the way to virtual biopsies and treatment monitoring tools for integrative tumour analysis.
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http://dx.doi.org/10.1186/s13244-020-00895-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431480PMC
August 2020

Unraveling tumor-immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy.

Nat Genet 2020 06 1;52(6):582-593. Epub 2020 Jun 1.

Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.

In metastatic cancer, the degree of heterogeneity of the tumor microenvironment (TME) and its molecular underpinnings remain largely unstudied. To characterize the tumor-immune interface at baseline and during neoadjuvant chemotherapy (NACT) in high-grade serous ovarian cancer (HGSOC), we performed immunogenomic analysis of treatment-naive and paired samples from before and after treatment with chemotherapy. In treatment-naive HGSOC, we found that immune-cell-excluded and inflammatory microenvironments coexist within the same individuals and within the same tumor sites, indicating ubiquitous variability in immune cell infiltration. Analysis of TME cell composition, DNA copy number, mutations and gene expression showed that immune cell exclusion was associated with amplification of Myc target genes and increased expression of canonical Wnt signaling in treatment-naive HGSOC. Following NACT, increased natural killer (NK) cell infiltration and oligoclonal expansion of T cells were detected. We demonstrate that the tumor-immune microenvironment of advanced HGSOC is intrinsically heterogeneous and that chemotherapy induces local immune activation, suggesting that chemotherapy can potentiate the immunogenicity of immune-excluded HGSOC tumors.
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http://dx.doi.org/10.1038/s41588-020-0630-5DOI Listing
June 2020

Allele-specific multi-sample copy number segmentation in ASCAT.

Bioinformatics 2020 May 25. Epub 2020 May 25.

Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.

Motivation: Allele-specific copy number alterations are commonly used to trace the evolution of tumours. A key step of the analysis is to segment genomic data into regions of constant copy number. For precise phylogenetic inference, breakpoints shared between samples need to be aligned to each other.

Results: Here we present asmultipcf, an algorithm for allele-specific segmentation of multiple samples that infers private and shared segment boundaries of phylogenetically related samples. The output of this algorithm can directly be used for allele-specific copy number calling using ASCAT.

Availability: asmultipcf is available as part of the ASCAT R package (version ≥ 2.5) from github.com/Crick-CancerGenomics/ascat/.
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http://dx.doi.org/10.1093/bioinformatics/btaa538DOI Listing
May 2020

Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.

Comput Biol Med 2020 05 10;120:103751. Epub 2020 Apr 10.

Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna 1090, Austria. Electronic address:

Background: Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless of macroscopic areas of hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), or intermediately dense (i.e., soft tissue) portions.

Method: With the goal of achieving the automated sub-segmentation of these three tissue types, we present here a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques. No existing approach has specifically addressed this task so far. Our tissue-specific image sub-segmentation was tested on ovarian cancer (pelvic/ovarian and omental disease) and renal cell carcinoma CT datasets using both overlap-based and distance-based metrics for evaluation.

Results: On all tested sub-segmentation tasks, our two-stage segmentation approach outperformed conventional segmentation techniques: fixed multi-thresholding, the Otsu method, and automatic cluster number selection heuristics for the K-means clustering algorithm. In addition, experiments showed that the integration of the spatial information into the FCM algorithm generally achieves more accurate segmentation results, whilst the kernelised FCM versions are not beneficial. The best spatial FCM configuration achieved average Dice similarity coefficient values starting from 81.94±4.76 and 83.43±3.81 for hyper-dense and hypo-dense components, respectively, for the investigated sub-segmentation tasks.

Conclusions: The proposed intelligent framework could be readily integrated into clinical research environments and provides robust tools for future radiomic biomarker validation.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103751DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248575PMC
May 2020

The evolutionary history of 2,658 cancers.

Nature 2020 02 6;578(7793):122-128. Epub 2020 Feb 6.

University of Toronto, Toronto, Ontario, Canada.

Cancer develops through a process of somatic evolution. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.
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http://dx.doi.org/10.1038/s41586-019-1907-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054212PMC
February 2020

Inferring structural variant cancer cell fraction.

Nat Commun 2020 02 5;11(1):730. Epub 2020 Feb 5.

Department of Computing and Information Systems, University of Melbourne, Parkville, VIC, 3010, Australia.

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.
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http://dx.doi.org/10.1038/s41467-020-14351-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002525PMC
February 2020

Data generation and network reconstruction strategies for single cell transcriptomic profiles of CRISPR-mediated gene perturbations.

Biochim Biophys Acta Gene Regul Mech 2020 06 20;1863(6):194441. Epub 2019 Nov 20.

Department of Biology, University of York, York, UK.

Recent advances in single-cell RNA-sequencing (scRNA-seq) in combination with CRISPR/Cas9 technologies have enabled the development of methods for large-scale perturbation studies with transcriptional readouts. These methods are highly scalable and have the potential to provide a wealth of information on the biological networks that underlie cellular response. Here we discuss how to overcome several key challenges to generate and analyse data for the confident reconstruction of models of the underlying cellular network. Some challenges are generic, and apply to analysing any single-cell transcriptomic data, while others are specific to combined single-cell CRISPR/Cas9 data, in particular barcode swapping, knockdown efficiency, multiplicity of infection and potential confounding factors. We also provide a curated collection of published data sets to aid the development of analysis strategies. Finally, we discuss several network reconstruction approaches, including co-expression networks and Bayesian networks, as well as their limitations, and highlight the potential of Nested Effects Models for network reconstruction from scRNA-seq data. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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http://dx.doi.org/10.1016/j.bbagrm.2019.194441DOI Listing
June 2020

Estimating the predictability of cancer evolution.

Bioinformatics 2019 07;35(14):i389-i397

Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.

Motivation: How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes.

Results: We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression.

Availability And Implementation: https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz332DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612861PMC
July 2019

Correction to: VULCAN integrates ChIP-seq with patient-derived co-expression networks to identify GRHL2 as a key co-regulator of ERa at enhancers in breast cancer.

Genome Biol 2019 06 14;20(1):122. Epub 2019 Jun 14.

CRUK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK.

Following publication of the original article [1], the authors reported that Figs. 4 and 5 had mistakenly been transposed. Please find the correct Figs. 4 and 5 below. The original article [1] has been corrected.
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http://dx.doi.org/10.1186/s13059-019-1733-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567503PMC
June 2019

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer.

Cell Rep 2019 05;27(9):2690-2708.e10

Center for Noninvasive Diagnostics, Translational Genomics Research Institute, Phoenix, AZ 85004, USA; Mayo Clinic Center for Individualized Medicine, Scottsdale, AZ, USA.

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.
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http://dx.doi.org/10.1016/j.celrep.2019.04.098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546974PMC
May 2019

VULCAN integrates ChIP-seq with patient-derived co-expression networks to identify GRHL2 as a key co-regulator of ERa at enhancers in breast cancer.

Genome Biol 2019 05 13;20(1):91. Epub 2019 May 13.

CRUK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK.

Background: VirtUaL ChIP-seq Analysis through Networks (VULCAN) infers regulatory interactions of transcription factors by overlaying networks generated from publicly available tumor expression data onto ChIP-seq data. We apply our method to dissect the regulation of estrogen receptor-alpha activation in breast cancer to identify potential co-regulators of the estrogen receptor's transcriptional response.

Results: VULCAN analysis of estrogen receptor activation in breast cancer highlights the key components of the estrogen receptor complex alongside a novel interaction with GRHL2. We demonstrate that GRHL2 is recruited to a subset of estrogen receptor binding sites and regulates transcriptional output, as evidenced by changes in estrogen receptor-associated eRNA expression and stronger estrogen receptor binding at active enhancers after GRHL2 knockdown.

Conclusions: Our findings provide new insight into the role of GRHL2 in regulating eRNA transcription as part of estrogen receptor signaling. These results demonstrate VULCAN, available from Bioconductor, as a powerful predictive tool.
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http://dx.doi.org/10.1186/s13059-019-1698-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515683PMC
May 2019

Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging.

J Neurosurg 2019 Apr 26;132(5):1465-1472. Epub 2019 Apr 26.

1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences.

Objective: The objective of this study was to characterize the abnormalities revealed by diffusion tensor imaging (DTI) using MR spectroscopy (MRS) and perfusion imaging, and to evaluate the prognostic value of a proposed quantitative measure of tumor invasiveness by combining contrast-enhancing (CE) and DTI abnormalities in patients with glioblastoma.

Methods: Eighty-four patients with glioblastoma were recruited preoperatively. DTI was decomposed into isotropic (p) and anisotropic (q) components. The relative cerebral blood volume (rCBV) was calculated from the dynamic susceptibility contrast imaging. Values of N-acetylaspartate, myoinositol, choline (Cho), lactate (Lac), and glutamate + glutamine (Glx) were measured from multivoxel MRS and normalized as ratios to creatine (Cr). Tumor regions of interest (ROIs) were manually segmented from the CE T1-weighted (CE-ROI) and DTI-q (q-ROI) maps. Perfusion and metabolic characteristics of these ROIs were measured and compared. The relative invasiveness coefficient (RIC) was calculated as a ratio of the characteristic radii of CE-ROI and q-ROI. The prognostic significance of RIC was tested using Kaplan-Meier and multivariate Cox regression analyses.

Results: The Cho/Cr, Lac/Cr, and Glx/Cr in q-ROI were significantly higher than CE-ROI (p = 0.004, p = 0.005, and p = 0.007, respectively). CE-ROI had significantly higher rCBV values than q-ROI (p < 0.001). A higher RIC was associated with worse survival in a multivariate overall survival (OS) model (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.06-1.85, p = 0.016) and progression-free survival (PFS) model (HR 1.55, 95% CI 1.16-2.07, p = 0.003). An RIC cutoff value of 0.89 significantly predicted shorter OS (median 384 vs 605 days, p = 0.002) and PFS (median 244 vs 406 days, p = 0.001).

Conclusions: DTI-q abnormalities displayed higher tumor load and hypoxic signatures compared with CE abnormalities, whereas CE regions potentially represented the tumor proliferation edge. Integrating the extents of invasion visualized by DTI-q and CE images into clinical practice may lead to improved treatment efficacy.
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http://dx.doi.org/10.3171/2018.12.JNS182926DOI Listing
April 2019

Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival.

Radiother Oncol 2019 05 31;134:17-24. Epub 2019 Jan 31.

Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neuroscience, University of Cambridge, UK.

Background And Purpose: Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management.

Materials And Methods: A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBV) with the lowest ADC quartile (ADC) and highest ADC quartile (ADC) in each tumor, two low perfusion compartments (ADC-rCBV and ADC-rCBV) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival.

Results: Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADC-rCBV compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADC-rCBV compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADC-rCBV compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005).

Conclusions: Our results suggest that the ADC-rCBV compartment may potentially indicate a clinically measurable resistant compartment.
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http://dx.doi.org/10.1016/j.radonc.2019.01.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486398PMC
May 2019

Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals.

Neoplasia 2019 05 31;21(5):442-449. Epub 2019 Mar 31.

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.

Glioblastoma is highly heterogeneous in microstructure and vasculature, creating various tumor microenvironments among patients, which may lead to different phenotypes. The purpose was to interrogate the interdependence of microstructure and vasculature using perfusion and diffusion imaging and to investigate the utility of this approach in tumor invasiveness assessment. A total of 115 primary glioblastoma patients were prospectively recruited for preoperative magnetic resonance imaging (MRI) and surgery. Apparent diffusion coefficient (ADC) was calculated from diffusion imaging, and relative cerebral blood volume (rCBV) was calculated from perfusion imaging. The empirical copula transform was applied to ADC and rCBV voxels in the contrast-enhancing tumor region to obtain their joint distribution, which was discretized to extract second-order features for an unsupervised hierarchical clustering. The lactate levels of patient subgroups, measured by MR spectroscopy, were compared. Survivals were analyzed using Kaplan-Meier and multivariate Cox regression analyses. The results showed that three patient subgroups were identified by the unsupervised clustering. These subtypes showed no significant differences in clinical characteristics but were significantly different in lactate level and patient survivals. Specifically, the subtype demonstrating high interdependence of ADC and rCBV displayed a higher lactate level than the other two subtypes (P = .016 and P = .044, respectively). Both subtypes of low and high interdependence showed worse progression-free survival than the intermediate (P = .046 and P = .009 respectively). Our results suggest that the interdependence between perfusion and diffusion imaging may be useful in stratifying patients and evaluating tumor invasiveness, providing overall measure of tumor microenvironment using multiparametric MRI.
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http://dx.doi.org/10.1016/j.neo.2019.03.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444075PMC
May 2019

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

Int J Cancer 2019 08 18;145(4):1125-1137. Epub 2019 Mar 18.

Department of Oncology, University of Oxford, Oxford, United Kingdom.

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an integrative meta-analysis of 353 patients from four different studies to derive a PDAC classification based on immunologic parameters. This consensus clustering approach indicated transcriptomic signatures based on immune infiltrate classified as adaptive, innate and immune-exclusion subtypes. This reveals the existence of microenvironmental interpatient heterogeneity within PDAC and could serve to drive novel therapeutic strategies in PDAC including immune modulation approaches to treating this disease.
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http://dx.doi.org/10.1002/ijc.32186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767191PMC
August 2019

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma.

Eur Radiol 2019 Sep 1;29(9):4718-4729. Epub 2019 Feb 1.

Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.

Objectives: Integrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables.

Methods: Eighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses.

Results: Two patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022).

Conclusions: The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers.

Key Points: • Multi-parametric magnetic resonance imaging captures multi-faceted tumor physiology. • Contrast-enhancing and non-enhancing tumor regions represent different tumor components with distinct clinical relevance. • Multi-view data analysis offers a method which can effectively select and integrate multi-parametric and multi-regional imaging features.
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http://dx.doi.org/10.1007/s00330-018-5984-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6682853PMC
September 2019

Genome-wide Estrogen Receptor-α activation is sustained, not cyclical.

Elife 2018 11 20;7. Epub 2018 Nov 20.

Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.

Estrogen Receptor-alpha (ER) drives 75% of breast cancers. Stimulation of the ER by estra-2-diol forms a transcriptionally-active chromatin-bound complex. Previous studies reported that ER binding follows a cyclical pattern. However, most studies have been limited to individual ER target genes and without replicates. Thus, the robustness and generality of ER cycling are not well understood. We present a comprehensive genome-wide analysis of the ER after activation, based on 6 replicates at 10 time-points, using our method for precise quantification of binding, Parallel-Factor ChIP-seq. In contrast to previous studies, we identified a sustained increase in affinity, alongside a class of estra-2-diol independent binding sites. Our results are corroborated by quantitative re-analysis of multiple independent studies. Our new model reconciles the conflicting studies into the ER at the TFF1 promoter and provides a detailed understanding in the context of the ER's role as both the driver and therapeutic target of breast cancer.
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http://dx.doi.org/10.7554/eLife.40854DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287946PMC
November 2018

Intratumoral Heterogeneity of Glioblastoma Infiltration Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging.

Neurosurgery 2019 10;85(4):524-534

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.

Background: Glioblastoma is a heterogeneous disease characterized by its infiltrative growth, rendering complete resection impossible. Diffusion tensor imaging (DTI) shows potential in detecting tumor infiltration by reflecting microstructure disruption.

Objective: To explore the heterogeneity of glioblastoma infiltration using joint histogram analysis of DTI, to investigate the incremental prognostic value of infiltrative patterns over clinical factors, and to identify specific subregions for targeted therapy.

Methods: A total of 115 primary glioblastoma patients were prospectively recruited for surgery and preoperative magnetic resonance imaging. The joint histograms of decomposed anisotropic and isotropic components of DTI were constructed in both contrast-enhancing and nonenhancing tumor regions. Patient survival was analyzed with joint histogram features and relevant clinical factors. The incremental prognostic values of histogram features were assessed using receiver operating characteristic curve analysis. The correlation between the proportion of diffusion patterns and tumor progression rate was tested using Pearson correlation.

Results: We found that joint histogram features were associated with patient survival and improved survival model performance. Specifically, the proportion of nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion was correlated with tumor progression rate (P = .010, r = 0.35), affected progression-free survival (hazard ratio = 1.08, P < .001), and overall survival (hazard ratio = 1.36, P < .001) in multivariate models.

Conclusion: Joint histogram features of DTI showed incremental prognostic values over clinical factors for glioblastoma patients. The nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion may indicate a more infiltrative habitat and potential treatment target.
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http://dx.doi.org/10.1093/neuros/nyy388DOI Listing
October 2019

Intratumor heterogeneity defines treatment-resistant HER2+ breast tumors.

Mol Oncol 2018 11 21;12(11):1838-1855. Epub 2018 Sep 21.

Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Norway.

Targeted therapy for patients with HER2-positive (HER2+) breast cancer has improved overall survival, but many patients still suffer relapse and death from the disease. Intratumor heterogeneity of both estrogen receptor (ER) and HER2 expression has been proposed to play a key role in treatment failure, but little work has been done to comprehensively study this heterogeneity at the single-cell level. In this study, we explored the clinical impact of intratumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations. Using combined immunofluorescence and in situ hybridization on tissue sections followed by a validated computational approach, we analyzed more than 13 000 single tumor cells across 37 HER2+ breast tumors. The samples were taken both before and after neoadjuvant chemotherapy plus HER2-targeted treatment, enabling us to study tumor evolution as well. We found that intratumor heterogeneity for HER2 copy number varied substantially between patient samples. Highly heterogeneous tumors were associated with significantly shorter disease-free survival and fewer long-term survivors. Patients for which HER2 characteristics did not change during treatment had a significantly worse outcome. This work shows the impact of intratumor heterogeneity in molecular diagnostics for treatment selection in HER2+ breast cancer patients and the power of computational scoring methods to evaluate in situ molecular markers in tissue biopsies.
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http://dx.doi.org/10.1002/1878-0261.12375DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210052PMC
November 2018

KHS101 disrupts energy metabolism in human glioblastoma cells and reduces tumor growth in mice.

Sci Transl Med 2018 08;10(454)

School of Medicine, University of Leeds, Leeds LS2 9JT, UK.

Pharmacological inhibition of uncontrolled cell growth with small-molecule inhibitors is a potential strategy for treating glioblastoma multiforme (GBM), the most malignant primary brain cancer. We showed that the synthetic small-molecule KHS101 promoted tumor cell death in diverse GBM cell models, independent of their tumor subtype, and without affecting the viability of noncancerous brain cell lines. KHS101 exerted cytotoxic effects by disrupting the mitochondrial chaperone heat shock protein family D member 1 (HSPD1). In GBM cells, KHS101 promoted aggregation of proteins regulating mitochondrial integrity and energy metabolism. Mitochondrial bioenergetic capacity and glycolytic activity were selectively impaired in KHS101-treated GBM cells. In two intracranial patient-derived xenograft tumor models in mice, systemic administration of KHS101 reduced tumor growth and increased survival without discernible side effects. These findings suggest that targeting of HSPD1-dependent metabolic pathways might be an effective strategy for treating GBM.
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http://dx.doi.org/10.1126/scitranslmed.aar2718DOI Listing
August 2018

Copy number signatures and mutational processes in ovarian carcinoma.

Nat Genet 2018 09 13;50(9):1262-1270. Epub 2018 Aug 13.

Institute of Cancer Sciences, University of Glasgow, Glasgow, UK.

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.
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http://dx.doi.org/10.1038/s41588-018-0179-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130818PMC
September 2018

A quantitative mass spectrometry-based approach to monitor the dynamics of endogenous chromatin-associated protein complexes.

Nat Commun 2018 06 13;9(1):2311. Epub 2018 Jun 13.

Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK.

Understanding the dynamics of endogenous protein-protein interactions in complex networks is pivotal in deciphering disease mechanisms. To enable the in-depth analysis of protein interactions in chromatin-associated protein complexes, we have previously developed a method termed RIME (Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins). Here, we present a quantitative multiplexed method (qPLEX-RIME), which integrates RIME with isobaric labelling and tribrid mass spectrometry for the study of protein interactome dynamics in a quantitative fashion with increased sensitivity. Using the qPLEX-RIME method, we delineate the temporal changes of the Estrogen Receptor alpha (ERα) interactome in breast cancer cells treated with 4-hydroxytamoxifen. Furthermore, we identify endogenous ERα-associated proteins in human Patient-Derived Xenograft tumours and in primary human breast cancer clinical tissue. Our results demonstrate that the combination of RIME with isobaric labelling offers a powerful tool for the in-depth and quantitative characterisation of protein interactome dynamics, which is applicable to clinical samples.
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http://dx.doi.org/10.1038/s41467-018-04619-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998130PMC
June 2018

Parallel factor ChIP provides essential internal control for quantitative differential ChIP-seq.

Nucleic Acids Res 2018 07;46(12):e75

Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire CB2 0RE, UK.

A key challenge in quantitative ChIP combined with high-throughput sequencing (ChIP-seq) is the normalization of data in the presence of genome-wide changes in occupancy. Analysis-based normalization methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor (TF) binding, these assumptions do not hold true. The challenges in normalization are confounded by experimental variability during sample preparation, processing and recovery. We present a novel normalization strategy utilizing an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalization. We compare our approach to normalization by total read depth and two alternative methods that utilize external experimental controls to study TF binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in patient-derived xenographs. This is supported by an adaptable pipeline to normalize and quantify differential TF binding genome-wide and generate metrics for differential binding at individual sites.
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http://dx.doi.org/10.1093/nar/gky252DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6093181PMC
July 2018