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Treatment patterns and outcomes in <i>ALK</i> or <i>ROS1</i> altered NSCLC: An ATOMIC Registry Study.

2022· article· en· W4282000826 on OpenAlexaff
Melina E. Marmarelis, Connor B. Grady, Geoffrey Liu, Devalben Patel, Stephen V. Liu, Gabriela Liliana Bravo Montenegro, Tejas Patil, Jorgé Nieva, Amanda Herrmann, Kristen A. Marrone, Vincent K. Lam, Fangdi Sun, Jonathan E. Dowell, Vamsidhar Velcheti, Matthew K. Nguyen, Kelsey Leigh Miller, Wade T. Iams, Wei‐Ting Hwang, D. Ross Camidge, Charu Aggarwal

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsCrizotinibROS1MedicineAlectinibInternal medicineDiscontinuationALK inhibitorOncologyCeritinibAnaplastic lymphoma kinaseCabozantinibLung cancerCancerAdenocarcinoma

Abstract

fetched live from OpenAlex

9077 Background: New tyrosine kinase inhibitors (TKIs) targeting ALK and ROS1 alterations in non-small cell lung cancer (NSCLC) have emerged over the last decade. Given the rarity of these genetic changes in NSCLC, data on long term outcomes with sequential therapies are limited. Methods: We conducted a multicenter retrospective cohort study of patients with metastatic NSCLC and ALK or ROS1 alterations across 12 Academic Thoracic Oncology Medical Investigators Consortium (ATOMIC) sites between 1/29/2007 and 3/31/2021. Data were abstracted from the electronic medical record. Median time to treatment discontinuation (TTD) of 1st TKI, overall survival (OS), and time to brain metastases were estimated using Kaplan-Meier methodology from start date of 1st TKI. Results: 566 patients with ALK (n = 464) or ROS1 (n = 102) were included. The majority (ALK: 426/464, 92%; ROS1: 88/102, 86%) received a TKI at some point during therapy (1st line TKI n = 262 ALK, 48 ROS1). Crizotinib was the most common 1st TKI (ALK: 57%; ROS1: 88%). Following crizotinib, alectinib (64%) and lorlatinib (41%) were the most common subsequent TKIs for ALK and ROS1, respectively. Alectinib (38%) and entrectinib (10.2%) were the 2nd most common initial TKIs used in ALK and ROS1, respectively. Additional treatment patterns presented in table. With a median follow up time of 31.1 (ALK, 95% CI, 27.6-35.0) and 32.6 (ROS1, 95% CI, 25.7-39.6) months, median OS from start of 1st TKI was 53.3 (ALK, 95% CI, 40.0-68.9) and 42.0 (ROS1, 95% CI, 31.8-NA) months. Out of the 321 patients with brain imaging prior to 1st line therapy, 40% (105/262, ALK) and 39% (23/59, ROS1) had CNS disease. Median time to development of brain metastases from start of 1st TKI in those without previous CNS disease (ALK: 278; ROS1: 58) was 30.0 (ALK, 95% CI, 25.3-39.1) and 27.0 (ROS1, 95% CI, 18.2-NA) months. Median TTD of 1st TKI was 11.2 (ALK) and 10.8 (ROS1) months. Conclusions: This is the largest retrospective cohort of NSCLC patients with ALK or ROS1 rearrangements treated in the real world setting. CNS metastases are common and subset analyses by agent and by year of diagnosis will be presented. Median time to CNS metastasis of > 2 years supports revision of the NCCN guidelines to include regular surveillance brain MRIs in this population. [Table: see text]

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)0.003
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.117
GPT teacher head0.522
Teacher spread0.406 · 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
Published2022
Admission routes1
Has abstractyes

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