Evidence map›Paper›PMID 36397716›Full record

ArticleCancer medicine2023

Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate-risk clear cell kidney cancer.

Neal Patel, Alexander Hakansson, Shinji Ohtake, Peter Muraki, James A Proudfout, Yang Liu, Lisa Webber, Arkaitz Ibarra, Vinnie Y T Liu, Elai Davicioni and 3 more

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Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Neal PatelDepartment of Urology, Institute of Urologic Oncology, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-2654-1628
Alexander HakanssonVeracyte, Inc, South San Francisco, California, USA.
Shinji OhtakeDepartment of Urology, Institute of Urologic Oncology, University of California, Los Angeles, Los Angeles, California, USA.
Peter MurakiDepartment of Urology, Institute of Urologic Oncology, University of California, Los Angeles, Los Angeles, California, USA.
James A ProudfoutVeracyte, Inc, South San Francisco, California, USA.
Yang LiuVeracyte, Inc, South San Francisco, California, USA.
Lisa WebberVeracyte, Inc, South San Francisco, California, USA.
Arkaitz IbarraVeracyte, Inc, South San Francisco, California, USA.
Vinnie Y T LiuVeracyte, Inc, South San Francisco, California, USA.
Elai DavicioniVeracyte, Inc, South San Francisco, California, USA.
Karim ChamieDepartment of Urology, Institute of Urologic Oncology, University of California, Los Angeles, Los Angeles, California, USA.
Allan PantuckDepartment of Urology, Institute of Urologic Oncology, University of California, Los Angeles, Los Angeles, California, USA.
Brian ShuchDepartment of Urology, Institute of Urologic Oncology, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0003-2065-3570

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRisk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear.

methodsData from patients with clear cell renal cell carcinoma (ccRCC) was downloaded from The Cancer Genome Atlas. Clinicopathologic variables were used to calculate SSIGN (stage, size, grade, and necrosis) scores. The 16 gene recurrence score (RS) signature was generated using RNA-seq data. Transcriptomic risk groups were calculated using the original thresholds. SSIGN groups were divided into low, intermediate, and high risk. Disease-free status was the primary endpoint assessed.

resultsSSIGN and RS were calculated for 428 patients with non-metastatic ccRCC. SSIGN low-, intermediate-, and high-risk groups demonstrated 2.7%, 15.2%, and 27.5%, 3-year recurrence risk, respectively. On multivariable analysis, the RS was associated with disease-free status (sub-distribution hazard ratio (sHR) 1.43 per 25 RS [95% CI (1.00-1.43)], p = 0.05). By risk groups, RS further risk stratified the SSIGN intermediate-risk group (sHR 2.22 [95% CI 1.10-4.50], p = 0.03). SSIGN intermediate-risk patients with low and high RS had a 3-year recurrence rate of 8.0% and 25.2%, respectively. Within this risk group, the area under the curve (AUC) at 3 years was 0.69 for SSIGN, 0.74 for RS, and 0.78 for their combination.

conclusionsTranscriptomic recurrence scores improve risk prediction even when controlling for clinicopathologic factors. Utility may be best suited for intermediate-risk patients who have heterogeneous outcomes and further refinement for clinical utility is warranted.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsHumansNeoplasm Recurrence, LocalNeoplasm StagingNephrectomyPrognosisRisk FactorsTranscriptomeadjuvant therapykidney cancertranscriptomics

Identifiers

PMID36397716
PMCPMC10028022

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