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Record W2506051311 · doi:10.1158/1538-7445.am2016-3405

Abstract 3405: The analysis of TRPV6 expression in lymphoid tumours

2016· article· en· W2506051311 on OpenAlexaff
Ashley DiPasquale, Tarek Rahmeh, Lauren Andrew, Jane Agar, Kim Miller, Matthew Finniss, Alli Murugesan, Tony Reiman

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of New BrunswickSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsLymphomaInternal medicineMedicineFollicular lymphomaOncologyChronic lymphocytic leukemiaSurvival analysisProportional hazards modelDiffuse large B-cell lymphomaPathologyLeukemia

Abstract

fetched live from OpenAlex

Abstract Introduction: TRPV6 is a calcium channel that is found overexpressed in various malignancies and is correlated with the prognosis of patients with prostate cancer. TRPV6 targeted anticancer therapies are in development. We are interested in the potential of TRPV6 as a potential biomarker and therapeutic target in lymphoma. This retrospective study examines the expression levels of TRPV6 in various lymphoid tumour types, and correlates expression levels with grade, prognostic scores, and survival rates. Methods: A clinical-pathological database was constructed using the health records and lymphoid tumor samples of patients diagnosed with diffuse large B-cell lymphoma (DLBCL), Follicular lymphoma (FL), small lymphocytic lymphoma/chronic lymphocytic leukemia (SLL/CLL), and Hodgkin's lymphoma (HL). Immunohistochemical studies were performed on lymphoid tumor samples to analyze the level of TRPV6 expression. Tumor samples were graded on a 3-point scale. A score of +3 indicated dense staining with high expression levels, and a score of +1 indicated minimal staining with low expression levels. TRPV6 expression levels were correlated with prognostic scores (IPI, FLIPI) and survival rates using Chi-squared, Mantel-Cox, and Kaplan-Meier survival curves. Descriptive statistics were used to describe patient demographics. Results: We found high TRPV6 expression levels in DLBCL and HL tumor samples with > 40% of samples scoring +3. We found low TRPV6 expression levels in FL and SLL/CLL with > 40% of samples scoring +1. We found no significant correlation between the level of TRPV6 expression and prognostic scores or survival rates in any of the lymphoma subtypes studied. In follicular lymphoma tumor samples, it was noted on observation that the large tumor cells generally stained +3 while the small tumor cells generally stained +1. Conclusion: This retrospective study showed DLBCL and HL tumors have high levels of TRPV6 expression. It also showed that TRPV6 is found highly expressed in the large transformed cells (centroblasts) in follicular lymphoma. These results demonstrate a need to further assess the role of TRPV6 in the aggressiveness of follicular lymphoma, as well as the prognosis and survival in DLBCL and HL with a larger sample size. Further studies of the potential of TRPV6 as a therapeutic target in lymphoma will focus on DLBCL, high grade/transformed FL, and HL. Citation Format: Ashley M. DiPasquale, Tarek Rahmeh, Lauren Andrew, Jane Agar, Kim Miller, Matthew Finniss, Alli Murugesan, Anthony Reiman. The analysis of TRPV6 expression in lymphoid tumours. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3405.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.085
GPT teacher head0.432
Teacher spread0.346 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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