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Abstract AS01: Molecular genetic approaches to understanding ovarian cancer

2015· article· en· W2565248748 on OpenAlexaff
Leora Witkowski, Jian Carrot‐Zhang, Jacek Majewski, William D. Foulkes

Bibliographic record

VenueClinical Cancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsMcGill University Health CentreMcGill UniversityMcGill University and Génome Québec Innovation CentreJewish General Hospital
Fundersnot available
KeywordsSanger sequencingCOLD-PCROvarian cancerBiologyCancer researchExome sequencingMutationCarboplatinMetastasisCancerChemotherapyGeneticsGenePoint mutation

Abstract

fetched live from OpenAlex

Abstract Ovarian cancer remains a challenging condition to prevent, diagnose and treat. We have conducted several different studies that have attempted to advance knowledge in one or more of these areas. These projects are briefly summarized below. 1) Sequencing of samples from a BRCA1 mutation carrier with high grade serous carcinoma We performed whole exome sequencing (WES) on the blood, primary tumor, omental metastasis and recurrence following therapy with carboplatin and paclitaxel, from a patient carrying a BRCA1 p.S1841R mutation. We observed loss of heterozygosity in the BRCA1 mutation in the primary and subsequent tumors, and somatic mutations in TP53 and NF1 were identified, suggesting their role along with BRCA1 driving the tumor development. We were able to detect large chromosomal rearrangements using WES. A large deletion was present in the three tumors in the regions containing BRCA1, TP53, and NF1 mutations, and an amplification in the regions containing MYC. We did not observe the emergence of new mutations among tumors from diagnosis to relapse after chemotherapy. Mutations present in the primary tumor were the main drivers of metastases and chemotherapy resistance. 2) Identification of DICER1 mutations in non -epithelial ovarian cancer We used Sanger sequencing to sequence the RNase IIIa and IIIb domains of DICER1 in 154 gonadal tumours from 135 females and 19 males, and 43 extra-gonadal germ cell tumors (GCTs) from 26 females and 17 males. We identified heterozygous non-synonymous mutations in the RNase IIIb domain of DICER1 in 14/197 non-epithelial tumours (7.1%). Mutations were found in 9/28 SCSTs (32%), 5/118 gonadal GCTs (4.2%), 0/43 extra-gonadal GCTs and 0/8 miscellaneous tumours. The 14 mutations affected only five residues: E1705, D1709, E1788, D1810 and E1813. In all five patients where matched and constitutional DNA was available, the mutations were somatic. There were no mutations found in the RNase IIIa domain. More than half (8/15) of Sertoli-Leydig cell tumours (SLCTs) harbour DICER1 mutations in the RNase IIIb domain, while mutations are rarely found in GCTs. 3) Characterization of small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) We sequenced the exomes of six individuals from three families with SCCOHT. After discovering segregating deleterious germline mutations in SMARCA4 in all three families, we tested DNA from a fourth affected family, which also carried a segregating SMARCA4 germline mutation. All the familial tumors sequenced harbored either a somatic mutation or loss of the wild-type allele. Immunohistochemical analysis of these cases and additional familial and non-familial cases showed loss of SMARCA4 (BRG1) protein in 38 of 40 tumors overall. Sequencing of cases with available DNA identified at least one germline or somatic deleterious SMARCA4 mutation in 30 of 32 cases. Additionally, the SCCOHT cell line BIN-67 had biallelic deleterious mutations in SMARCA4. Our findings identify alterations in SMARCA4 as the major cause of SCCOHT, which could lead to improvements in genetic counseling and new treatment approaches. Citation Format: Leora Witkowski, Jian Carrot-Zhang, Jacek Majewski, William D. Foulkes. Molecular genetic approaches to understanding ovarian cancer [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr AS01.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.761
GPT teacher head0.530
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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