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Comparative genomic analysis to identify a signature of sporadic colorectal cancer development in young adults.

2024· article· en· W4391095254 on OpenAlexafffundabout
Abedalrhman Alkhateeb, Eesha Atikukke, Akram El Keilani, Tarek Elfiki, Julianna Orlando, Dora Cavallo‐Medved, Luis Rueda, Andrew Fetz, Anat Ravid-Einy, Lisa A. Porter, Govindaraja Atikukke, Sabeena Misra

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsHôtel-Dieu Grace HealthcareWestern UniversityWindsor Regional HospitalUniversity of WindsorLakehead University
FundersWindsor Cancer Centre Foundation
KeywordsMedicineColorectal cancerCancerIncidence (geometry)OncologyPopulationCancer registryDiseaseInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

208 Background: Young-onset sporadic CRC is an important yet understudied heterogeneous group of aggressive cancers with distinct clinical and histopathological features. There is a steady increase in the incidence of cancer in this group of patients and currently, there are no screening guidelines to identify these patients due to a lack of understanding of the molecular mechanisms driving cancer in this patient group. Introduction: Despite screening guidelines and recent advances in cancer treatment, colorectal cancer (CRC) remains the second most diagnosed cancer and the second and third leading cause of cancer-related mortality in Canadian men and women, respectively [Canadian Cancer Statistics 2017.]. More striking is the rise in incidence of CRC in young adults in the United States [Siegel, R.L., et al, Bailey, C.E., et al., Dozois, E.J., et al., Deen, K.I., et al.], and the expectation that by the year 2030, the incidence rate will increase by 90% in this patient population. Data from the Surveillance and Cancer registry at Cancer Care Ontario shows a similar trend with increased rates in younger adults (ages 30 to 49) by 5.2 per cent per year between 2005 and 2012 [Ontario, C.C., Cancer Fact.]. Moreover, these younger patient groups are often diagnosed with aggressive forms and advanced stages of the disease [Ontario, C.C., Cancer Fact.]. Methods: In this study, using a total of 30 colorectal cancer patient samples from Windsor Regional Hospital Cancer Program, we performed comprehensive targeted gene sequencing to identify single nucleotide variants, small insertions/deletions, copy number variants in over 500 genes that are implicated in cancer. Mean age of the patients in this study was 44 years and only patients without any personal or family history of colon cancer /other malignancies or IBD were enrolled in the study. Results: While identifying previously known colorectal cancer associated genetic variants, our preliminary data shows distinct missense mutations in several genes potentially contributing to the development and progression of cancer in these patients. In addition to showing pathogenic mutations in colorectal cancers associated genes, such as PIK3CA (H1047R, E545K, E545G, R93W, V344A), KRAS (G12V, G12D, A146T) and APC, our study showed recurring missense mutations in several genes including AXIN2, ALK, CDKN1A, MAP3K1, ROS1, EPCAM, KDM5A and AURKA in over 50% of our samples. Conclusions: The genomic profiling performed using biopsies from young colorectal cancer patients through this study provides a unique ability to identify the potential “genomic triggers” for the development and progression of cancer in young sporadic colorectal cancer patients. This information can not only be used to develop targeted treatment options for these patients but also to design new screening protocols as well as optimal surveillance strategies.

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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.092
GPT teacher head0.492
Teacher spread0.400 · 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
Published2024
Admission routes3
Has abstractyes

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