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Record W4388562165 · doi:10.1101/2023.11.09.23298321

Concurrent RB1 loss and <i>BRCA</i> -deficiency predicts enhanced immunological response and long-term survival in tubo-ovarian high-grade serous carcinoma

2023· preprint· en· W4388562165 on OpenAlexafffund
Flurina A.M. Saner, Kazuaki Takahashi, Timothy Budden, Ahwan Pandey, Dinuka Ariyaratne, Tibor A. Zwimpfer, Nicola S. Meagher, Sián Fereday, Laura Twomey, Kathleen I. Pishas, Therese Hoang, Adelyn Bolithon, Nadia Traficante, Kathryn Alsop, Elizabeth L. Christie, Eunyoung Kang, Gregg Nelson, Prafull Ghatage, Cheng‐Han Lee, Marjorie J. Riggan, Jennifer Alsop, Matthias W. Beckmann, Jessica Boros, Alison H. Brand, Angela Brooks‐Wilson, Michael E. Carney, Penny Coulson, Madeleine Courtney‐Brooks, Kara L. Cushing‐Haugen, Cezary Cybulski, Mona El‐Bahrawy, Esther Elishaev, Ramona Erber, Simon A. Gayther, Aleksandra Gentry‐Maharaj, C. Blake Gilks, Paul R. Harnett, Holly R. Harris, Arndt Hartmann, Alexander Hein, Joy Hendley, Brenda Y. Hernandez, Anna Jakubowska, Mercedes Jimenez‐Liñan, Michael E. Jones, Scott H. Kaufmann, Catherine J. Kennedy, Tomasz Kluz, Jennifer M. Koziak, Björg Kristjánsdóttir, Nhu D. Le, Marcin Lener, Jenny Lester, Jan Lubiński, Constantina Mateoiu, Sandra Oršulić, Matthias Ruebner, Minouk J. Schoemaker, Mitul Shah, Raghwa Sharma, Mark E. Sherman, Yurii B. Shvetsov, Naveena Singh, T. Rinda Soong, Helen Steed, Paniti Sukumvanich, Aline Talhouk, Sarah E. Taylor, Robert A. Vierkant, Chen Wang, Martin Widschwendter, Lynne R. Wilkens, Stacey J. Winham, Michael S. Anglesio, Andrew Berchuck, James D. Brenton, Ian Campbell, Linda Cook, Jennifer A. Doherty, Peter A. Fasching, Renée T. Fortner, Marc T. Goodman, Jacek Gronwald, David G. Huntsman, Beth Y. Karlan, Linda E. Kelemen, Usha Menon, Francesmary Modugno, Paul D.P. Pharoah, Joellen M. Schildkraut, Karin Sundfeldt, Anthony J. Swerdlow, Ellen L. Goode, Anna DeFazio, Martin Köbel, Susan J. Ramus, David D.L. Bowtell, Dale W. Garsed

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsVancouver General HospitalAlberta Health ServicesUniversity of British ColumbiaCanada's Michael Smith Genome Sciences CentreBC Cancer AgencyFoothills Medical CentreUniversity of AlbertaUniversity of Calgary
FundersMedical Research and Materiel CommandNational Center for Advancing Translational SciencesBC Cancer FoundationCancer Institute NSWNational Cancer InstituteUniversity College LondonNational Health and Medical Research CouncilUniversity of CambridgeCancer Research UKMichael Smith Health Research BCNational Institute for Health and Care ResearchNational Institutes of HealthNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchOak FoundationMedical Research CouncilSwedish Cancer FoundationEuropean CommissionCedars-Sinai Medical CenterMarshfield Clinic Research Foundation
KeywordsOvarian cancerOvarian carcinomaSerous fluidGermlineSerous carcinomaBiologyCancer researchOncologyCancerInternal medicineMedicineGeneGenetics

Abstract

fetched live from OpenAlex

ABSTRACT Background Somatic loss of the tumour suppressor RB1 is a common event in tubo-ovarian high-grade serous carcinoma (HGSC), which frequently co-occurs with alterations in homologous recombination DNA repair genes including BRCA1 and BRCA2 ( BRCA ). We examined whether tumour expression of RB1 was associated with survival across ovarian cancer histotypes (HGSC, endometrioid (ENOC), clear cell (CCOC), mucinous (MOC), low-grade serous carcinoma (LGSC)), and how co-occurrence of germline BRCA pathogenic variants and RB1 loss influences long-term survival in a large series of HGSC. Patients and methods RB1 protein expression patterns were classified by immunohistochemistry in epithelial ovarian carcinomas of 7436 patients from 20 studies participating in the Ovarian Tumor Tissue Analysis consortium and assessed for associations with overall survival (OS), accounting for patient age at diagnosis and FIGO stage. We examined RB1 expression and germline BRCA status in a subset of 1134 HGSC, and related genotype to survival, tumour infiltrating CD8+ lymphocyte counts and transcriptomic subtypes. Using CRISPR-Cas9, we deleted RB1 in HGSC cell lines with and without BRCA1 mutations to model co-loss with treatment response. We also performed genomic analyses on 126 primary HGSC to explore the molecular characteristics of concurrent homologous recombination deficiency and RB1 loss. Results RB1 protein loss was most frequent in HGSC (16.4%) and was highly correlated with RB1 mRNA expression. RB1 loss was associated with longer OS in HGSC (hazard ratio [HR] 0.74, 95% confidence interval [CI] 0.66-0.83, P = 6.8 × 10 -7 ), but with poorer prognosis in ENOC (HR 2.17, 95% CI 1.17-4.03, P = 0.0140). Germline BRCA mutations and RB1 loss co-occurred in HGSC ( P < 0.0001). Patients with both RB1 loss and germline BRCA mutations had a superior OS (HR 0.38, 95% CI 0.25-0.58, P = 5.2 x10 -6 ) compared to patients with either alteration alone, and their median OS was three times longer than non-carriers whose tumours retained RB1 expression (9.3 years vs. 3.1 years). Enhanced sensitivity to cisplatin ( P < 0.01) and paclitaxel ( P < 0.05) was seen in BRCA1 mutated cell lines with RB1 knockout. Among 126 patients with whole-genome and transcriptome sequence data, combined RB1 loss and genomic evidence of homologous recombination deficiency was correlated with transcriptional markers of enhanced interferon response, cell cycle deregulation, and reduced epithelial-mesenchymal transition in primary HGSC. CD8+ lymphocytes were most prevalent in BRCA -deficient HGSC with co-loss of RB1 . Conclusions Co-occurrence of RB1 loss and BRCA mutation was associated with exceptionally long survival in patients with HGSC, potentially due to better treatment response and immune stimulation.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.040
GPT teacher head0.296
Teacher spread0.257 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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