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Abstract P1-08-09: High mid-treatment RNA disruption in patients with HER2-negative breast cancer predicts survival benefit after neoadjuvant chemotherapy

2022· article· en· W4220772552 on OpenAlexaff
Laura B. Pritzker, Mads H. Haugen, Hedda von der Lippe Gythfeldt, Twinkle Masilamani, Gabriel Thériault, Renée St-Onge, Lavina D’costa, Ole Christian Lingjærde, Amadeo M. Parissenti, Olav Engebraaten

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsLaurentian UniversityUniversity of Sudbury
Fundersnot available
KeywordsMedicineBreast cancerInternal medicineChemotherapyOncologyRNACancerNeoadjuvant therapyBevacizumabGastroenterologyBiology

Abstract

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Abstract BACKGROUND: Ribosomal RNA fragmentation (RNA disruption) measured mid-therapy in breast cancer tumors has been shown to correlate with pCR and improved disease-free survival. Data from the NeoAva clinical trial was used to evaluate RNA disruption index values (RDI) for measurement of therapy efficacy. METHODS: The randomized phase II clinical trial NeoAva investigated the effects of neoadjuvant chemotherapy (FEC) in HER2-negative breast cancer patients with or without bevacizumab. Serial biopsies were taken prior to treatment and after 12 and 25 weeks of treatment. The majority of patients in this study were diagnosed with ER positive tumors (88% of the total 12-week samples). RNA isolation from frozen tumor tissue was carried out using the AllPrep DNA/RNA Mini kit with QIACube (Qiagen), and the quality of the RNA assessed (Agilent 2100 Bioanalyzer). Treatment response was measured using both pathological complete response (pCR) and residual cancer burden (RCB) at the time of surgery, and median follow-up time was 6.8 years. Using the RNA electropherogram data (Agilent Bioanalyzer) generated in this study, RDI values were assessed in pre-therapy samples (n=109), 12-week samples (n=98) and 25-week samples (n=106). RESULTS: RDI values were measured at three timepoints, however only the RDI values assessed from 12 week samples correlated with clinical outcomes. RDI values were higher in patients that achieved a RCB class of either 0 or 1 (n=28) (median RDI=2.6) compared with those patients that had an RCB class of 3 (n=17) (median RDI=1.3) (Mann-Whitney p=0.006). Out of the samples that were analyzed by the RNA disruption assay (RDA), 18 patients achieved a pCR and had a median RDI value of 2.6 compared with a median RDI of 1.8 for those who did not achieve a pCR (n=80); this finding did not reach statistical significance. RDI values measured in samples taken prior to therapy or at 25-weeks did not correlate with either RCB class or subsequent pCR. With 93% and 94% of patients having mid-therapy RDI values greater than 1.1 for RCB class of 0/1 or pCR, respectively, this was chosen as a cut point to generate Kaplan-Meier curves of recurrence free survival. These curves illustrated statistically significant improved survival (HR =2.6; 95% CI 0.8-8.1) for patients with a RDI > 1.1 (p=0.05). Similarly, Kaplan-Meier curves of breast cancer specific survival also demonstrated improved survival (HR = 3.1; 95% CI 0.8-11.7) for patients with a RDI > 1.1 (p=0.03). Kaplan-Meier curves based on pCR/no pCR were generated from the set of patients that had 12-week RDI values assessed and did not demonstrate improved survival in this small, predominantly ER+ group. No survival benefit was found based on RDI values measured prior to therapy or at 25 weeks. Patients that received bevacizumab (n=45) had significantly higher RDI values at 12 weeks (median RDI = 2.5) than patients that did not receive the drug (median RDI = 1.4) (n=53) (Mann-Whitney test p=0.0005). This overall increase in RDI values with bevacizumab resulted in improved survival for patients at a higher RDI cutoff values of 1.6 (p=0.03)(HR = 4.4; 95% CI 0.8-25.1). CONCLUSION: Taken together, our findings suggest that increased RNA disruption measured in tumor biopsies during neoadjuvant chemotherapy correlates with a survival benefit for patients and may be a more useful indicator of therapy efficacy than pCR, particularly for patients with ER positive tumors. Citation Format: Laura B Pritzker, Mads Haugland Haugen, Hedda von der Lippe Gythfeldt, Twinkle Masilamani, Gabriel Theriault, Renee St-Onge, Lavina D'costa, Ole Christian Lingjaerde, Amadeo Parissenti, Olav Engebraaten. High mid-treatment RNA disruption in patients with HER2-negative breast cancer predicts survival benefit after neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-09.

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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 score0.003
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.319
Teacher spread0.300 · 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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Citations3
Published2022
Admission routes1
Has abstractyes

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