Evidence map›Paper›PMID 34711610›Full record

ArticleCancer research2022

Aberrant Transcript Usage Is Associated with Homologous Recombination Deficiency and Predicts Therapeutic Response.

Hyeon Gu Kang, Haeun Hwangbo, Myung Ji Kim, Sinae Kim, Eun Ji Lee, Min Ji Park, Jae-Weon Kim, Byoung-Gie Kim, Eun-Hae Cho, Suhwan Chang and 2 more

Open access · bronzeAbstract read
In one paragraph

Article in Cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 18% 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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. SIGANEO: Similarity network with GAN enhancement for immunogenic neoepitope prediction.Computational and structural biotechnology journal · 2023
    Article
  6. Article
  7. Review
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

12 authors at 6 institutions in 1 country.

Hyeon Gu KangDepartment of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.ORCID 0000-0002-5822-6825
Haeun HwangboDepartment of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
Myung Ji KimDepartment of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sinae KimDepartment of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Eun Ji LeeDepartment of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Min Ji ParkDepartment of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Jae-Weon KimDepartment of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, Republic of Korea.
Byoung-Gie KimDepartment of Obstetrics and Gynecology, Gynecologic Cancer Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID 0000-0002-0572-8450
Eun-Hae ChoGenome Research Center, GC Genome, GC Labs, Yongin-si, Gyeonggi-do, Republic of Korea. ehcho@gccorp.com suhwan.chang@amc.seoul.kr jungyunlee@yuhs.ac jungkyoon@kaist.ac.kr.ORCID 0000-0003-3056-0001
Suhwan ChangDepartment of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. ehcho@gccorp.com suhwan.chang@amc.seoul.kr jungyunlee@yuhs.ac jungkyoon@kaist.ac.kr.
Jung-Yun LeeDepartment of Obstetrics and Gynecology, Yonsei University College of Medicine, Seoul, Republic of Korea. ehcho@gccorp.com suhwan.chang@amc.seoul.kr jungyunlee@yuhs.ac jungkyoon@kaist.ac.kr.ORCID 0000-0001-7948-1350
Jung Kyoon ChoiDepartment of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. ehcho@gccorp.com suhwan.chang@amc.seoul.kr jungyunlee@yuhs.ac jungkyoon@kaist.ac.kr.
University of Ulsan · KRKorea Advanced Institute of Science and Technology · KRGenome and Company (South Korea) · KRSamsung Medical Center · KRSeoul National University Hospital · KRYonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BRCA1/2 mutations account for only a small fraction of homologous recombination (HR) deficiency (HRD) cases. Recently developed genomic HRD (gHRD) tests suffer confounding factors that cause low precision in predicting samples that will respond to PARP inhibitors and DNA damaging agents. Here we present molecular and clinical evidence of transcriptional HRD (tHRD) that is based on aberrant transcript usage (aTU) of minor isoforms. Specifically, increased TU of nonfunctional isoforms of DNA repair genes was prevalent in breast and ovarian cancer with gHRD. Functional assays validated the association of aTU with impaired HR activity. Machine learning-based tHRD detection by the transcript usage (TU) pattern of key genes was superior to directly screening for gHRD or BRCA1/2 mutations in accurately predicting responses of cell lines and patients with cancer to PARP inhibitors and genotoxic drugs. This approach demonstrated the capability of tHRD status to reflect functional HR status, including in a cohort of olaparib-treated ovarian cancer with acquired platinum resistance. Diagnostic tests based on tHRD are expected to broaden the clinical utility of PARP inhibitors. SIGNIFICANCE: A novel but widespread transcriptional mechanism by which homologous recombination deficiency arises independently of BRCA1/2 mutations can be utilized as a companion diagnostic for PARP inhibitors.

Indexed as

GenomicsHomologous RecombinationHumansWhole Genome Sequencing

Identifiers

PMID34711610
PMCPMC9397646
OpenAlexW3209436856

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.