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Abstract A45: HPV sequencing facilitates ultrasensitive detection of HPV circulating tumor DNA

2020· article· en· W3036495611 on OpenAlexaff
Eric Leung, Kathy Han, Jinfeng Zou, Zhen Zhao, Yangqiao Zheng, Ting Ting Wang, Lillian L. Siu, Trevor J. Pugh, Scott V. Bratman

Bibliographic record

VenueClinical Cancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreOccupational Cancer Research Centre
Fundersnot available
KeywordsMedicineOncologyInternal medicineCervixMinimal residual diseaseChemoradiotherapyCancerPrecision medicineAdjuvantPersonalized medicineCervical cancerDiseaseStage (stratigraphy)PathologyBioinformaticsBiology

Abstract

fetched live from OpenAlex

Abstract Background: Human papillomavirus (HPV)-associated cancers often present with locoregionally confined disease and are treated with curative intent. An emerging treatment paradigm includes radical therapy (i.e., chemoradiotherapy or surgery) followed by adjuvant treatment. Accurate detection of minimal residual disease (MRD) following radical therapy could enable personalized use of adjuvant treatment. The HPV genome offers a convenient circulating tumor (ct)DNA marker for HPV-associated cancers, but current methods such as digital (d)PCR provide insufficient accuracy for accurate MRD detection and for other clinical applications in patients with low disease burden. We asked whether a next-generation sequencing approach (HPV-seq) could provide quantitative and qualitative assessment of HPV ctDNA in low-disease-burden settings. Methods: We conducted preclinical technical validation studies on HPV-seq using cervix cancer cell lines. We then applied HPV-seq retrospectively to a prospective multicenter cohort study of locally advanced cervix cancer patients accrued from 2015 to 2016 (NCT02388698). Patients were treated with radical chemoradiotherapy with blood obtained at baseline, end of treatment, and 3 months post-treatment. Median follow-up was 27.5 months. In 38 plasma samples from 17 patients, HPV-seq results were compared with dPCR. The primary outcome was progression-free survival according to end-of-treatment HPV ctDNA detectability. Results: HPV-seq achieved reproducible detection of HPV DNA at levels <0.01%. HPV-seq and dPCR results were highly correlated (R=0.98, p=1.8E-22) with HPV-seq detecting ctDNA at levels down to 0.001% in dPCR-negative post-treatment samples. Detectable HPV ctDNA at the end-of-treatment timepoint was associated with inferior progression-free survival (log-rank p=0.05) with 100% sensitivity and 67% specificity for recurrence. Accurate HPV genotyping was successful from 100% of pretreatment plasma samples. HPV ctDNA fragment sizes were consistently shorter than non-cancer-derived cell-free DNA fragments (median fragment size difference: 22 bp) regardless of HPV genotype or clinical setting. Conclusions: HPV-seq is a quantitative method for ctDNA detection that outperforms dPCR. HPV-seq also reveals qualitative information about ctDNA fragments such as HPV genotype and ctDNA fragment length distribution. Our findings will have implications for MRD detection in HPV-related cancers. Future prospective studies are needed to confirm that patients with undetectable HPV ctDNA following chemoradiotherapy have exceptionally high cure rates. Citation Format: Eric Leung, Kathy Han, Jinfeng Zou, Zhen Zhao, Yangqiao Zheng, Ting Ting Wang, Lillian L. Siu, Trevor J. Pugh, Scott V. Bratman. HPV sequencing facilitates ultrasensitive detection of HPV circulating tumor DNA [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A45.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.170
GPT teacher head0.438
Teacher spread0.268 · 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 designBench or experimental
Domainnot available
GenreOther

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

Citations3
Published2020
Admission routes1
Has abstractyes

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