Immunotherapy in oncology and the kidneys: a clinical review of the evaluation and management of kidney immune-related adverse events
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICI) are now widely used in the treatment of many cancers, and currently represent the standard of care for multiple malignancies. These agents enhance the T cell immune response to target cancer tissues, and have demonstrated considerable benefits for cancer outcomes. However, despite these improved outcomes, there are important kidney immune-related adverse events (iRAEs) associated with ICI. Acute tubulo-interstitial nephritis remains the most frequent kidney iRAE, however glomerular lesions and electrolytes disturbances are increasingly being recognized and reported. In this review, we summarize clinical features and identify risk factors for kidney iRAEs, and discuss the current understanding of pathophysiologic mechanisms. We highlight the evidence basis for guideline-recommended management of ICI-related kidney injury as well as gaps in current knowledge. We advocate for judicious use of kidney biopsy to identify ICI-associated kidney injury, and early use of corticosteroid treatment where appropriate. Selected patients may also be candidates for re-challenge with ICI therapy after a kidney iRAE, in view of current data on recurrent rates of kidney injury. Risk of benefits of re-challenge must be considered on an individual considering patient preferences and prognosis. Lastly, we review current knowledge of ICI use in the setting of patients with end-stage kidney disease, including kidney transplant recipients and those receiving dialysis, which suggest that these patients should not be summarily excluded from the potential benefits of these cancer therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".