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Record W2564762951 · doi:10.1158/1538-7445.am2015-763

Abstract 763: Implications of resistance patterns with NSCLC targeted agents

2015· article· en· W2564762951 on OpenAlexaff
David J. Stewart, Paul Wheatley‐Price, Rob MacRae, Jason Pantarotto

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicinePopulationTumor progressionT790MCancer researchCancerMutationDiscontinuationOncologyInternal medicineROS1BiologyAdenocarcinomaGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction: Tyrosine kinase inhibitors (TKIs) can give regression of NSCLCs with mutations of EGFR, HER2 & BRAF and fusion genes for ALK, ROS1 & RET, but resistance develops in most patients. Methods: We reviewed available clinical data for insights on potential approaches to delay resistance. Results: Target absence is a major cause of intrinsic resistance. Most NSCLCs with target undergo at least some regression although the degree varies between patients due to largely unexplored factors. Many mechanisms of acquired resistance have been defined, including outgrowth of subclones with secondary mutations or alternative pathways, and pharmacological effects & sanctuaries. Available data suggest that resistant cells are present in small numbers in the initial tumor but that their growth is suppressed by the more rapidly growing parent tumor cells until this parent population is itself suppressed by TKI initiation. If TKI is stopped due to progression, there can be tumor flare as the initial parent cell population regrows rapidly. Exponential decay nonlinear regression analysis of patient survival curves suggests that TKI discontinuation at time of progression may partially “synchronize” patient deaths. Initial tumor progression may occur in a single site. Available data indicate that focal treatment (eg, radiation) to that site combined with continuation of initial TKI may give optimal control and reduce tumor flare. Of interest, progression may occur in many sites concurrently. Since there are many potential resistance mechanisms one might expect substantial discordance of resistance mechanisms between these sites, but preliminary data with 2nd line agents (eg, anti-T790M agents in EGFR-mutant patients and 2nd generation anti-ALK agents for acquired resistance to crizotinib) indicate response in most sites if any are sensitive, suggesting that all or most sites have a common resistance mechanism. This raises the possibility that an initial resistant site is seeding other distant tumor sites with resistant cells, in keeping with animal data. The probability of a resistant cell being present in an individual tumor deposit is proportional to the total number of tumor cells in that deposit. If 1 area of resistant tumor can seed other tumor areas with resistant cells, then (particularly in oligometastatic disease) we should consider clinical trials exploring: 1) at initiation of TKI therapy treating as many tumor sites as possible (particularly large tumors) with a modality (eg radiation) that is “indifferent” to the factors giving TKI resistance; 2) treating any progressing lesions as early as possible with intensive focal therapy, if feasible, rather than waiting until symptomatic or rather than treating only with low palliative doses. Conclusion: Extrapolation from available data suggests we should explore trials using multifocal radiation at initiation of TKIs for advanced NSCLC and early, intense treatment of areas of isolated progression. Citation Format: David J. Stewart, Paul Wheatley-Price, Rob MacRae, Jason Pantarotto. Implications of resistance patterns with NSCLC targeted agents. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 763. doi:10.1158/1538-7445.AM2015-763

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.138
GPT teacher head0.482
Teacher spread0.344 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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