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Record W2140087277 · doi:10.18632/oncotarget.5142

Prognostic role and implications of mutation status of tumor suppressor gene ARID1A in cancer: a systematic review and meta-analysis

2015· review· en· W2140087277 on OpenAlex
Claudio Luchini, Nicola Veronese, Marco Solmi, Hanbyoul Cho, Jae‐Hoon Kim, Angela Chou, Anthony J. Gill, Sheila F. Faraj, Alcides Chaux, George J. Netto, Kentaro Nakayama, Satoru Kyo, Soo Young Lee, Duck-Woo Kim, George M. Yousef, Andreas Scorilas, Gregg Nelson, Martin Köbel, Steve E. Kalloger, David F. Schaeffer, Hai-Bo Yan, Feng Liu, Yoshihito Yokoyama, Xianyu Zhang, Da Pang, Zsuzsanna Lichner, Giuseppe Sergi, Enzo Manzato, Paola Capelli, Laura D. Wood, Aldo Scarpa, Christoph U. Correll

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOncotarget · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsUniversity of British ColumbiaUniversity of CalgarySt. Michael's Hospital
FundersNational Institute of Mental HealthH. Lundbeck A/SGenentechAssociazione Italiana per la Ricerca sul CancroNational Alliance for Research on Schizophrenia and DepressionSunovionPfizerTeva Pharmaceutical IndustriesEli Lilly and CompanyBristol-Myers Squibb
KeywordsARID1AMedicineConfoundingHazard ratioOncologyCancerInternal medicineConfidence intervalMeta-analysisGeneMutationGeneticsBiology

Abstract

fetched live from OpenAlex

// Claudio Luchini 1, 2 , Nicola Veronese 3 , Marco Solmi 4 , Hanbyoul Cho 5 , Jae-Hoon Kim 5 , Angela Chou 6, 7 , Anthony J. Gill 6 , Sheila F. Faraj 2 , Alcides Chaux 2, 8 , George J. Netto 2 , Kentaro Nakayama 9 , Satoru Kyo 9 , Soo Young Lee 10 , Duck-Woo Kim 11 , George M. Yousef 12 , Andreas Scorilas 13 , Gregg S. Nelson 14 , Martin Köbel 15 , Steve E. Kalloger 16 , David F. Schaeffer 16 , Hai-Bo Yan 17 , Feng Liu 17 , Yoshihito Yokoyama 18 , Xianyu Zhang 19 , Da Pang 19 , Zsuzsanna Lichner 20 , Giuseppe Sergi 3 , Enzo Manzato 3 , Paola Capelli 1 , Laura D. Wood 2 , Aldo Scarpa 1 , Christoph U. Correll 21, 22, 23, 24 1 Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy 2 Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA 3 Department of Medicine, Geriatrics Division, University of Padova, Padova, Italy 4 Department of Neurosciences, University of Padova, Padova, Italy 5 Department of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea 6 Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, St. Leonards, Australia, Sydney Vital Translational Research Centre St. Leonards Australia and University of Sydney, Sydney, NSW, Australia 7 Department of Anatomical Pathology, SYDPATH St. Vincent’s Hospital, Sydney, NSW, Australia 8 Centro para el Desarrollo de la Investigación Científica (CEDIC), Asunción, Paraguay 9 Department of Obstetrics and Gynecology, Shimane University School of Medicine, Shimane, Japan 10 Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon, South Korea 11 Department of Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea 12 Department of Laboratory Medicine and Keenan Research Centre, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada 13 Department of Biochemistry and Molecular Biology, Faculty of Biology, University of Athens, Athens, Greece 14 Department of Gynecologic Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada 15 Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada 16 Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada 17 Department of Systems Biology for Medicine of School of Basic Medical Sciences, and Institutes of Biomedical Sciences, Fudan University, Shanghai, China 18 Department of Obstetrics and Gynecology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan 19 Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China 20 Department of Laboratory Medicine and Keenan Research Centre, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada 21 The Zucker Hillside Hospital, Psychiatry Research, North Shore - Long Island Jewish Health System, Glen Oaks, New York, USA 22 Hofstra North Shore LIJ School of Medicine, Hempstead, New York, USA 23 The Feinstein Institute for Medical Research, Manhasset, New York, USA 24 Albert Einstein College of Medicine, Bronx, New York, USA Correspondence to: Claudio Luchini, e-mail: claudio.luchini@katamail.com , claudio.luchini@univr.it Keywords: ARID1A, SWI/SNF, chromatin remodeling, targeted therapy, tumor suppressor gene Received: July 07, 2015      Accepted: August 27, 2015      Published: September 08, 2015 ABSTRACT Loss of the tumor suppressor gene AT-rich interactive domain-containing protein 1A (ARID1A) has been demonstrated in several cancers, but its prognostic role is unknown. We aimed to investigate the risk associated with loss of ARID1A (ARID1A-) for all-cause mortality, cancer-specific mortality and recurrence of disease in subjects with cancer. PubMed and SCOPUS search from database inception until 01/31/2015 without language restriction was conducted, contacting authors for unpublished data. Eligible were prospective studies reporting data on prognostic parameters in subjects with cancer, comparing participants with presence of ARID1A (ARID1A+) vs. ARID1A-, assessed either via immunohistochemistry (loss of expression) or with genetic testing (presence of mutation). Data were summarized using risk ratios (RR) for number of deaths/recurrences and hazard ratios (HR) for time-dependent risk related to ARID1A- adjusted for potential confounders. Of 136 hits, 25 studies with 5,651 participants (28 cohorts; ARID1A-: n = 1,701; ARID1A+: n = 3,950), with a mean follow-up period of 4.7 ± 1.8 years, were meta-analyzed. Compared to ARID1A+, ARID1A- significantly increased cancer-specific mortality (studies = 3; RR = 1.55, 95% confidence interval (CI) = 1.19–2.00, I 2 = 31%). Using HRs adjusted for potential confounders, ARID1A- was associated with a greater risk of cancer-specific mortality (studies = 2; HR = 2.55, 95%CI = 1.19–5.45, I 2 = 19%) and cancer recurrence (studies = 10; HR = 1.93, 95%CI = 1.22–3.05, I 2 = 76%). On the basis of these results, we have demonstrated that loss of ARID1A shortened time to cancer-specific mortality, and to recurrence of cancer when adjusting for potential confounders. For its role, this gene should be considered as an important potential target for personalized medicine in cancer treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.367
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it