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Record W3179417079 · doi:10.1158/1538-7445.am2021-1412

Abstract 1412: Whole exome sequencing reveals PI3K-ATK pathway alterations are frequent in relapsed small cell-lung cancer after chemoradiation

2021· article· en· W3179417079 on OpenAlexaff
Ying Jin, Yamei Chen, Xiao Hu, Huarong Tang, Qian Li, Pansong Li, Xinze Lv, Xuefeng Xia, Jianjun Zhang, Ming Chen

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsExome sequencingLung cancerCopy-number variationCancerOncologyPrimary tumorMedicineChemotherapyExomeInternal medicineCancer researchBiologyGeneMutationGeneticsGenomeMetastasis

Abstract

fetched live from OpenAlex

Abstract Background: Although small-cell lung cancer (SCLC) is sensitive to chemotherapy and radiotherapy initially, nearly all patients recur often with treatment-resistant disease. Comparing genomic profiles of paired treatment-naïve and recurrent tumors to understand the clonal architecture and molecular evolution of SCLC under treatment may provide novel insights into mechanisms underlying recurrence and susceptibility to further treatment. Methods: Paired tumor samples procured at diagnosis and relapse were collected from 11 patients with limited-stage SCLC treated with concurrent chemoradiation (CCRT). All tissues underwent whole exome sequencing (WES). Genomic landscape including somatic mutations, somatic copy number alterations (SCNAs), and clonal architecture were compared between treatment-naïve and paired recurrent tumor samples. Baseline and paired recurrent plasma samples from another 9 patients with SCLC treated with CCRT were performed deep sequencing using a targeted panel containing 1021 cancer related genes. Results: In both pre- and post-treatment tumors, TP53 (73% vs 73%), FAM135B (55% vs 64%), RB1 (45% vs 55%), and CTNND2 (45% vs 55%) were the top four most frequently mutated genes. Tobacco exposure related mutational signature was predominant in all samples. Compared with primary tumors, relapsed tumors showed significantly increased number of mutations (220 in recurrent SCLC vs 210 in primary tumor, Wilcoxon paired test, p-value = 0.016). Six of the 11 patients had increased chromosomal instability (CIS) in recurrent tumors. Relapsed tumors had more genes with copy number amplification compared to pre-treatment primary tumors although the difference did not reach statistical significance (18 vs 10, p-value = 0.066). In all of the patients, an average of 94% mutations at baseline were present in relapsed tumors. The number of clones in recurrent samples was higher than that in pre-treatment samples (13 vs 12, p-value = 0.004), indicating that new clones emerged under the treatment and tumor heterogeneity increased. A total of 648 acquired mutations in 600 genes and 140 acquired SCNAs in 123 genes were identified, in which 126 mutations were clonal (CCF>0.6) in relapsed tumors. These genes were enriched in PI3K-ATK signaling pathway and covered 91% (10/11) patients, implying the potential mechanism of chemoradiation resistance. Based on the specific mutations and mutations with increased CCF in relapsed plasma samples from the other 9 SCLCs, acquired alterations were also enriched in PI3K-ATK signaling pathway. Conclusions: Acquired mutations and chromosomal copy number gains may play a role in relapse of limited stage SCLC post CCRT. PI3K-ATK pathway alterations are frequent in recurrent SCLCs, which may be a candidate resistant mechanism after chemoradiation. Citation Format: Ying Jin, Yamei Chen, Xiao Hu, Huarong Tang, Qian Li, Pansong Li, Xinze Lv, Xuefeng Xia, Jianjun Zhang, Ming Chen. Whole exome sequencing reveals PI3K-ATK pathway alterations are frequent in relapsed small cell-lung cancer after chemoradiation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1412.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.405
Teacher spread0.318 · 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.

Study designBench or experimental
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

Citations0
Published2021
Admission routes1
Has abstractyes

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