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Predictors of objective response to first-line immuno-oncology combination therapies in metastatic renal cell carcinoma: Results from the international metastatic renal cell database consortium (IMDC).

2022· article· en· W4212989002 on OpenAlexafffund
Vishal Navani, Matthew Scott Ernst, Connor Wells, Takeshi Yuasa, Kosuke Takemura, Frede Donskov, Naveen S. Basappa, Andrew Schmidt, Sumanta K. Pal, Luís Meza, Lori Wood, D. Scott Ernst, Bernadett Szabados, Rana R. McKay, Andrew Weickhardt, Cristina Suárez, Anil Kapoor, Jae‐Lyun Lee, Toni K. Choueiri, Daniel Yick Chin Heng

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsJuravinski Cancer CentreMcMaster UniversityLondon Health Sciences CentreWestern UniversityDalhousie UniversityUniversity of AlbertaQueen Elizabeth II Health Sciences CentreBC Cancer AgencyUniversity of Calgary
FundersAlberta Cancer Foundation
KeywordsMedicineNivolumabAxitinibInternal medicineIpilimumabRenal cell carcinomaPembrolizumabOncologyCabozantinibResponse Evaluation Criteria in Solid TumorsProgressive diseaseExpanded accessPazopanibSunitinibCancerDiseaseImmunotherapy

Abstract

fetched live from OpenAlex

310 Background: Predictors of objective response to first-line (1L) immuno-oncology (IO) combination therapies remain elusive. We sought to characterise clinical variables and their association with investigator assessed best overall response. Methods: Using the IMDC, we retrospectively identified patients treated with 1L ipilimumab nivolumab (IPI-NIVO) or approved IO/vascular endothelial growth factor (VEGF) inhibitor combinations (IOVE). Patients were classified, per RECIST v1.1, as responders (complete or partial response (CR or PR)) or non-responders (stable or progressive disease (SD or PD)). Logistic regression was used to adjust for IMDC criteria. Results: Out of 1084 patients, 794 (73%) received IPI-NIVO and 290 (27%) received IOVE (axitinib+pembrolizumab, cabozantinib+nivolumab, axitinib+avelumab, lenvatinib+pembrolizumab). Favourable, intermediate and poor IMDC risk comprised 147 (16%), 517 (55%) and 272 (29%) respectively. Of the 898 patients with evaluable responses, 37 (4%) achieved a best response of CR, 343 (38%) PR, 315 (35%) SD and 203 (23%) PD. Corresponding median overall survival from time of 1L initiation was: not reached, 55.9, 48.1, and 13 months respectively (logrank p < 0.0001). In a multivariable model, lung metastases and cytoreductive nephrectomy (CN) (performed after diagnosis of metastatic disease and before 1L therapy) retained independent association with response, after adjustment for IMDC criteria. Factors not associated with response included (with univariable p values): gender (p = 0.58), age (p = 0.06), sarcomatoid histology (p = 0.99), smoking status (p = 0.39), liver (p = 0.63) and brain (p = 0.12) metastases. As in the VEGF monotherapy era, improved IMDC prognostic risk was associated with response. Results were similar when restricted to the IPI-NIVO cohort. Conclusions: Presence of lung metastases, CN and better IMDC risk group are associated with a higher probability of response to 1L immunotherapy combination regimens. Further work to identify reliable predictors of response to guide treatment selection and patient counselling is warranted.[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)0.002
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.090
GPT teacher head0.389
Teacher spread0.299 · 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".

Quick stats

Citations2
Published2022
Admission routes2
Has abstractyes

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