Evidence map›Paper›PMID 38878676›Full record

ArticleEBioMedicine2024

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics.

Trishna A Desai, Åsa K Hedman, Marios Dimitriou, Mine Koprulu, Sandy Figiel, Wencheng Yin, Mattias Johansson, Eleanor L Watts, Joshua R Atkins, Aleksandr V Sokolov and 13 more

Abstract read
In one paragraph

Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

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0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors.

Trishna A DesaiCancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom. Electronic address: Trishna.Desai@ndph.ox.ac.uk.
Åsa K HedmanExternal Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Marios DimitriouExternal Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Mine KopruluMRC Epidemiology Unit, University of Cambridge, United Kingdom.
Sandy FigielUniversity of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom.
Wencheng YinUniversity of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom.
Mattias JohanssonGenomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France.
Eleanor L WattsMetabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Joshua R AtkinsCancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom.
Aleksandr V SokolovDepartment of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden.
Helgi B SchiöthDepartment of Surgical Sciences, Functional Pharmacology and Neuroscience Uppsala University, 75124, Uppsala, Sweden.
Marc J GunterGenomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom.
Konstantinos K TsilidisDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Richard M MartinPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; NIHR Bristol Biomedical Research Centre, Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, United Kingdom.
Maik PietznerMRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom.
Claudia LangenbergMRC Epidemiology Unit, University of Cambridge, United Kingdom; Computational Medicine, Berlin Institute of HealthHealth (BIH) at Charité - Univeritätsmedizin- Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, United Kingdom.
Ian G MillsUniversity of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom.
Alastair D LambUniversity of Oxford, Nuffield Department of Surgical Sciences, Oxford, United Kingdom.
Anders MälarstigExternal Science and Innovation, Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Tim J KeyCancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom.
PRACTICAL Consortium
Ruth C TravisCancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom.
Karl Smith-ByrneCancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom.

Funding

Cancer Research UK 29019World Health Organization 001
6 · The paper itself

Abstract

backgroundUnderstanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

methodsWe investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

findingsWe identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

interpretationOur findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

fundingThis work was supported by Cancer Research UK (grant no. C8221/A29017).

Indexed as

Mendelian Randomization AnalysisProstatic NeoplasmsProteomicsAge of OnsetBiomarkers, TumorGene Expression ProfilingGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleOdds RatioPolymorphism, Single NucleotideProteomeRisk FactorsTranscriptomeBiomarkers, TumorProteomeCancerGenetic epidemiologyMendelian randomisation-OmicsProteinsProteomicsSpatial transcriptomics

Identifiers

PMID38878676
PMCPMC11233900

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.