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Record W4389839591 · doi:10.1016/j.jtocrr.2023.100623

Brief Report: Comprehensive Clinicogenomic Profiling of Small Cell Transformation From EGFR-Mutant NSCLC Informs Potential Therapeutic Targets

2023· article· en· W4389839591 on OpenAlexfundno aff
Bingnan Zhang, Whitney E. Lewis, C. Allison Stewart, Benjamin B. Morris, Luisa M. Solis, Alejandra G. Serrano, Yuanxin Xi, Qi Wang, Elyse R. Lopez, Kyle Concannon, Simon Heeke, Ximing Tang, Gabriela Raso, Robert J. Cardnell, Natalie I. Vokes, George Blumenschein, Yasir Y. Elamin, Frank Fosella, Anne S. Tsao, Ferdinandos Skoulidis, Celyne Bueno Hume, Koji Sasak, Jeff Lewis, Waree Rinsurongkawong, Vadeerat Rinsurongkawong, Hai T. Tran, Jianjun Zhang, Don L. Gibbons, Ara A. Vaporciyan, Jing Wang, Keunchil Park, John V. Heymach, Lauren A. Byers, Carl M. Gay, Xiuning Le

Bibliographic record

VenueJTO Clinical and Research Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsnot available
FundersNational Cancer InstituteGenentechNational Institutes of HealthMirati TherapeuticsRegeneron PharmaceuticalsDaiichi Sankyo EuropeSanofiGlaxoSmithKlineEli Lilly and CompanyAstraZenecaDamon Runyon Cancer Research FoundationLUNGevity FoundationComputer Modelling GroupAmgenCancer Prevention and Research Institute of TexasUniversity of Texas MD Anderson Cancer CenterV Foundation for Cancer Research
KeywordsProfiling (computer programming)MutantTransformation (genetics)Cancer researchOncologyComputational biologyMedicineBiologyComputer scienceGeneGenetics

Abstract

fetched live from OpenAlex

Introduction NSCLC transformation to SCLC has been best characterized with EGFR -mutant NSCLC, with emerging case reports seen in ALK , RET , and KRAS -altered NSCLC. Previous reports revealed transformed SCLC from EGFR -mutant NSCLC portends very poor prognosis and lack effective treatment. Genomic analyses revealed TP53 and RB1 loss of function increase the risk of SCLC transformation. Little has been reported on the detailed clinicogenomic characteristics and potential therapeutic targets for this patient population. Methods In this study, we conducted a single-center retrospective analysis of clinical and genomic characteristics of patients with EGFR -mutant NSCLC transformed to SCLC. Demographic data, treatment course, and clinical molecular testing reports were extracted from electronic medical records. Kaplan-Meier analyses were used to estimate survival outcomes. Next generation sequencing-based assays was used to identify EGFR and co-occurring genetic alterations in tissue or plasma before and after SCLC transformation. Single-cell RNA sequencing (scRNA-seq) was performed on a patient-derived-xenograft model generated from a patient with EGFR-NSCLC transformed SCLC tumor. Results A total of 34 patients were identified in our study. Median age at initial diagnosis was 58, and median time to SCLC transformation was 24.2 months. 68% were female and 82% were never smokers. 79% of patients were diagnosed as stage IV disease, and over half had brain metastases at baseline. Median overall survival of the entire cohort was 38.3 months from initial diagnoses and 12.4 months from time of SCLC transformation. Most patients harbored EGFR exon19 deletions as opposed to exon21 L858R alteration. Continuing EGFR tyrosine kinase inhibitor post-transformation did not improve overall survival compared with those patients where tyrosine kinase inhibitor was stopped in our cohort. In the 20 paired pretransformed and post-transformed patient samples, statistically significant enrichment was seen with PIK3CA alterations (p = 0.04) post-transformation. Profiling of longitudinal liquid biopsy samples suggest emergence of SCLC genetic alterations before biopsy-proven SCLC, as shown by increasing variant allele frequency of TP53, RB1, PIK3CA alterations. ScRNA-seq revealed potential therapeutic targets including DLL3, CD276 (B7-H3) and PTK7 were widely expressed in transformed SCLC. Conclusions SCLC transformation is a potential treatment resistance mechanism in driver-mutant NSCLC. In our cohort of 34 EGFR -mutant NSCLC, poor prognosis was observed after SCLC transformation. Clinicogenomic analyses of paired and longitudinal samples identified genomic alterations emerging post-transformation and scRNA-seq reveal potential therapeutic targets in this population. Further studies are needed to rigorously validate biomarkers and therapeutic targets for this patient population.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.342
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.140
GPT teacher head0.448
Teacher spread0.308 · 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.

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

Citations10
Published2023
Admission routes1
Has abstractyes

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