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Abstract 35: Novel genetic and clinical determinants of Constitutional Mismatch Repair Deficiency syndrome: Report from the CMMRD consortium

2014· article· en· W2080593350 on OpenAlexaff
Doua Bakry, Brittany Campbell, Carol Durno, Melyssa Aronson, Qasim Alharbi, Musa Alharbi, Shlomi Constantini, Aaron Pollett, Shay Ben‐Shachar, Jordan Lerner‐Ellis, Steven Gallinger, Ronit Elhasid, Roula Farah, Ibrahim Qaddoumi, Matthew Mistry, Lily Ramyar, Steve Keiles, Rina Dvir, Derek Stephens, David Malkin, Éric Bouffet, Cynthia Hawkins, Uri Tabori

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsMount Sinai HospitalHospital for Sick Children
Fundersnot available
KeywordsPMS2MSH6Lynch syndromeMicrosatellite instabilityMedicineMLH1Germline mutationGermlineCancerGenetic testingAsymptomaticDNA mismatch repairMSH2Internal medicineOncologyMutationAlleleColorectal cancerGeneticsGeneBiologyMicrosatellite

Abstract

fetched live from OpenAlex

Abstract Purpose: Constitutional mismatch repair deficiency (CMMRD) is a devastating cancer predisposition syndrome affecting children born with two mutated alleles in one of four mismatch repair genes. Data regarding clinical manifestations, molecular screening tools and management are limited. Patients and methods: We established an international CMMRD consortium and collected comprehensive clinical and genetic data. Molecular diagnosis of tumor and germline biospecimens were performed. A surveillance protocol was developed and implemented. Results: Overall, 27/30 (90%) of children with CMMR-D developed 48 different tumors. While childhood CMMR-D related tumors were observed in all families, Lynch related tumors in adults were observed in only 2/17 families (p<0.0001). All children with CMMR-D had cafe-au-lait spots and 14/17 families were consanguineous. We detected 17 different germline MMR mutations. These included mutations in PMS2(8), MSH6(7) and MLH1(2). Importantly 7/17 mutations were previously unreported. Brain tumors were the most common cancers reported (44%) followed by gastrointestinal (33%) and hematological malignancies (17%). Importantly, 14 (29%) of these were low grade and respectable cancers. Tumor immunohistochemistry was 100% sensitive and specific in diagnosing MMR deficiency of the corresponding gene while microsatellite instability was neither sensitive nor specific as a diagnostic tool (p<0.0001). Furthermore, screening of normal tissue by immunohistochemistry correlated with genetic confirmation of CMMR-D. The surveillance protocol detected 43 lesions which included asymptomatic malignant gliomas and gastrointestinal carcinomas. All tumors were amenable to complete resection and all patients undergoing surveillance are alive. Conclusion: CMMR-D is a highly penetrant syndrome where family history of cancer may not be contributory. Screening tumors and normal tissues using immunohistochemistry for abnormal expression of MMR gene products helps in diagnosis and early implementation of surveillance for these children. Citation Format: Doua Bakry, Brittany Campbell, Carol Durno, Melyssa Aronson, Qasim Alharbi, Musa Alharbi, Shlomi Constantini, Aaron Pollett, Shay Ben-Shachar, Jordan Lerner-Ellis, Steven Gallinger, Ronit Elhasid, Roula Farah, Ibrahim Qaddoumi, Matthew Mistry, Ramyar Lily, Steve Keiles, Rina Dvir, Derek Stephens, David Malkin, Eric Bouffet, Cynthia Hawkins, Uri Tabori. Novel genetic and clinical determinants of Constitutional Mismatch Repair Deficiency syndrome: Report from the CMMRD consortium. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Susceptibility and Cancer Susceptibility Syndromes; Jan 29-Feb 1, 2014; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(23 Suppl):Abstract nr 35. doi:10.1158/1538-7445.CANSUSC14-35

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)0.003
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.124
GPT teacher head0.442
Teacher spread0.318 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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