Plasma exosome microRNAs in patients with advanced renal cell carcinoma treated with nivolumab and ipilimumab: Potential biomarkers of response to therapy.
Bibliographic record
Abstract
338 Background: There is a critical unmet need for predictive biomarkers in the management of metastatic renal cell carcinoma (mRCC). We sought to quantify plasma exosome microRNAs (miRNAs) and correlate with response to first line nivolumab and ipilimumab (N/I) to potentially serve as such a biomarker. Methods: We evaluated the expression of 11 miRNAs in 19 patients with mRCC (prior to initiation of N/I) and in 32 healthy volunteers. Exosomes were extracted from 500 uL of plasma (qEV original, Izon Science) and once confirmed, were used for miRNAs extraction. MiRNAs expression was evaluated by real time polymerase chain reaction using a TaqMan miRNA assay (Applied Biosystems). The relative quantity of each miRNA in patients was compared to healthy volunteers. The expression of each miRNA was correlated to the best response to N/I, categorizing patients as either responders or non-responders. Results: Clinical characteristics are summarized in the table below. Median age at the start of systemic therapy was 64.3 years. MiR200b demonstrated a significantly higher expression in mRCC patients than in healthy volunteers (unpaired t-test; p=0.04). We observed a variable pattern of miRNA expression based on response to N/I. Although not statistically significant, 4 miRNA (miR138, 155, 200b, 221) were upregulated in non-responders, while two (miR200a and 497) were upregulated in responders. Of note, the only patient to achieve a complete response had the lowest expression of miR138 and the highest expression of miR497. Conclusions: Although preliminary and limited by a small number of patients, these initial observational results are promising and suggest a potential role for miRNAs as predictive biomarkers in mRCC. MiR138 and 497 are known to regulate CTLA-4 and PD-L1, respectively. We speculate that these miRNA are potentially involved in response to immune checkpoint therapy. Ongoing work in evaluating expression of these and other miRNAs in blood and in tissue along with clinical correlation continues. [Table: see text]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".