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The survival outcomes of the metastatic renal cell carcinoma with rhabdoid differentiation in immunotherapy era: Princess Margaret Cancer Center experience.

2022· article· en· W4213359718 on OpenAlexaffabout
Esmail Mutahar Al-Ezzi, Abhenil Mittal, Brooke E. Wilson, Marco Iafolla, Pavlina Spiliopoulou, Srikala S. Sridhar, Nazanin Fallah‐Rad, Peter Chung, Nathan Perlis, Aaron R. Hansen

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsWilliam Osler Health SystemPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineRenal cell carcinomaHazard ratioTargeted therapyInternal medicineOncologyCancerProportional hazards modelImmunotherapySurvival analysisConfidence interval

Abstract

fetched live from OpenAlex

333 Background: Patients (pts) diagnosed with metastatic renal cell carcinoma (mRCC) with rhabdoid differentiation (RD) have a poor prognosis due to aggressive tumor behavior and inherent treatment resistance to targeted therapies. However, recent data has demonstrated the survival benefit of immunotherapy (IO) in mRCC. Here, we report survival outcomes of pts with mRCC with RD treated with targeted therapy and or IO. Methods: This retrospective survival analysis was performed in pts with mRCC and RD treated with targeted treatment and IO at Princess Margaret Cancer Centre (PM), Toronto. Demographics, disease characteristics and survival outcomes were collected. Overall survival (OS) was calculated using the Kaplan-Meier method (log-rank). OS hazard ratio (HR) were calculated using cox proportional hazards model. IBM SPSS Statistics v26 was used to conduct statistical analyses. Results: We identified 474 pts diagnosed with mRCC at PM between 2002 and 2019. A total of 57 (12%) pts diagnosed with mRCC had RD and were treated with targeted and or IO agents. Of these, 42 (73.7%) pts had pure RD and 15 (26.3%) pts had mixed RD and sarcomatoid features. Median age was 62 yrs (35-86yrs) and 42 (73.7%) were male. Overall, as per the IMDC score, 5(8.8%), 27(47.4%) and 25(43.8%) pts were categorized as good, intermediate, and poor risk, respectively. In total, 34 (59.6%) pts were treated with targeted therapy only during their first and second line treatment course and 23 (40.4%) pts received IO alone or in combination with targeted treatment in the first or second line. With a median follow up of 53.4 months (range, 38.3-68.4 months), the median OS for the whole mRCC with RD cohort was 23.1 months (95% CI: 14.6-31.5). The median OS in all pts treated with targeted therapy only vs IO receipt was 13.1 months (95%CI: 5.4-20.8 months) vs not reached; p = 0.026, respectively. HR for OS was 0.44 (95%CI: 0.22-0.93; p = 0.03) favoring IO receipt. Conclusions: While the number of pts included in our retrospective review was small, our analysis has suggested that pts with mRCC and RD have poor survival outcomes that may be improved with IO treatment. RD is a histopathological feature that could identify pts who may benefit from IO therapy. Further analysis is needed to explore the impact of RD on IO treatment response.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.016
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.384
Teacher spread0.340 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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