Evidence map›Paper›PMID 32980694›Full record

ArticleEBioMedicine2020

Genetic characterization of pancreatic cancer patients and prediction of carrier status of germline pathogenic variants in cancer-predisposing genes.

Keijiro Mizukami, Yusuke Iwasaki, Eiryo Kawakami, Makoto Hirata, Yoichiro Kamatani, Koichi Matsuda, Mikiko Endo, Kokichi Sugano, Teruhiko Yoshida, Yoshinori Murakami and 3 more

Open access · goldAbstract read
In one paragraph

Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
3.3field-weighted citation impact, top 6% of its field
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

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.

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  3. Comprehensive evidence for the pathogenicity of theJournal of medical genetics · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 2 countries.

Keijiro MizukamiLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
Yusuke IwasakiLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
Eiryo KawakamiMedical Sciences Innovation Hub Program, RIKEN, Yokohama, Kanagawa, Japan; Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
Makoto HirataInstitute of Medical Science, The University of Tokyo, Tokyo, Japan; Department of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo Japan.
Yoichiro KamataniGraduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Koichi MatsudaGraduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Mikiko EndoLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
Kokichi SuganoDepartment of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo Japan; Division of Cancer Prevention and Genetic Counseling, Genome Center, Tochigi Cancer Center, Japan.
Teruhiko YoshidaDepartment of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo Japan.
Yoshinori MurakamiInstitute of Medical Science, The University of Tokyo, Tokyo, Japan.
Hidewaki NakagawaLaboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Amanda B SpurdleDivision of Genetics and Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Yukihide MomozawaLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. Electronic address: momozawa@riken.jp.
RIKEN Center for Integrative Medical Sciences · JPThe University of Tokyo · JPNational Cancer Center Hospital East · JPChiba University · JPQIMR Berghofer Medical Research Institute · AUTochigi Cancer Center · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNational Comprehensive Cancer Network (NCCN) recently recommended germline genetic testing for all pancreatic cancer patients. However, the genes targeted by genetic testing and the feasibility of selecting patients likely to carry pathogenic variants have not been sufficiently verified. The purpose of this study was to genetically characterize Japanese patients and examine whether the current guideline is applicable in this population.

methodsUsing targeted sequencing, we analyzed the coding regions of 27 cancer-predisposing genes in 1,005 pancreatic cancer patients and 23,705 controls in Japan. We compared the pathogenic variant frequency between cases and controls and documented the demographic and clinical characteristics of carrier patients. We then examined if it was possible to use machine learning to predict carrier status based on those characteristics.

findingsWe identified 205 pathogenic variants across the 27 genes. Pathogenic variants in BRCA2, ATM, and BRCA1 were significantly associated with pancreatic cancer. Characteristics associated with carrier status were inconsistent with previous investigations. Machine learning classifiers had a low performance in determining the carrier status of pancreatic cancer patients, while the same classifiers, when applied to breast cancer data as a positive control, had a higher performance that was comparable to that of the NCCN guideline.

interpretationOur findings support the clinical significance of multigene panel testing for pancreatic cancer and indicate that at least 3.4% of Japanese patients may respond to poly (ADP ribose) polymerase inhibitor treatments. The difficulty in predicting carrier status suggests that offering germline genetic testing for all pancreatic cancer patients is reasonable.

fundingAMED under Grant Number JP19kk0305010 and Australian National Health and Medical Research funding (ID177524).

Indexed as

AllelesGenetic Predisposition to DiseaseGerm-Line MutationAdultAgedAged, 80 and overBRCA1 ProteinBRCA2 ProteinComputational BiologyFemaleGenetic Association StudiesGenetic TestingHumansMachine LearningMaleMiddle AgedBRCA1 ProteinBRCA1 protein, humanBRCA2 ProteinBRCA2 protein, humanATMBRCAMachine learningPancreatic cancerPathogenic variantsUniversal screening for patients

Identifiers

PMID32980694
PMCPMC7519363
OpenAlexW3087975731

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.