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Record W4223601704 · doi:10.1007/s12079-022-00679-x

Analysis of multiple basal cell carcinomas (BCCs) arising in one individual highlights genetic tumor heterogeneity and identifies novel driver mutations

2022· letter· en· W4223601704 on OpenAlexafffund
Pingxing Xie, Philippe Lefrançois, Denis Sasseville, Laurent Parmentier, Ivan V. Litvinov

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2022
Typeletter
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchCancer Research Society
KeywordsExome sequencingBasal cell carcinomaSkin cancerGenetic heterogeneityExomeMedicineCancerMutationPersonalized medicineBasal cellDermatologyGeneCancer researchBioinformaticsBiologyPathologyGeneticsInternal medicinePhenotype

Abstract

fetched live from OpenAlex

Basal cell carcinoma (BCC) is the most common human cancer, especially in individuals with light skin phototypes (i.e., Fitzpatrick I-II skin type). Many affected develop multiple BCCs during their lifetime. It is not uncommon to observe elderly patients with >5 BCCs. In this study, we explored whether for patients diagnosed with multiple BCCs, analyzing the genomic mutations in one tumor could be sufficient to derive meaningful molecular/genetic conclusions regarding the other BCC tumors. Following the Genome Analysis Toolkit (GATK) best practices we have completed the study of 6 BCCs that occurred in an 83-year-old Caucasian male due to sun exposure. We have analyzed exome sequencing data of each BCC tumor and matched normal skin samples. We identified that BCCs from the same patient shared some of the key driver mutations, but they also displayed significant intertumoral heterogeneity. This finding may in part explain the different clinical progression/evolution of BCCs observed in the same patient. This work also highlights the value of characterizing multiple BCCs in one individual to identify patient-specific genetic events with a potential link to other malignancies and implications for personalized medicine.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.272
Teacher spread0.228 · 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 teacher head, 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

Citations4
Published2022
Admission routes2
Has abstractyes

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