Patterns of response of organ specific metastasis in patients with metastatic renal cell cancer (mRCC) treated with an immune checkpoint inhibitor (CPI) post tyrosine kinase inhibitor (TKI).
Bibliographic record
Abstract
605 Background: Both TKIs and the novel immune CPIs targeting PD/PDL1 are now standard treatments for patients with mRCC. Mixed responses in metastatic lesions have been observed. We examined organ specific radiologic responses in a cohort of patients with mRCC who were initially treated with TKI followed by CPI. Methods: We identified patients who were treated with first line TKI followed by CPI from a prospectively maintained database of all mRCC patients treated at our institution from 2010 to 2018. We examined best response to TKI and CPI, as well as organ specific responses to TKI and CPI. Statistical analysis was performed using odds ratios and Fishers exact test. Results: We identified 31 patients with evaluable disease who were treated with a TKI followed by CPI. Best responses to TKI were partial response (PR) (n = 7), stable disease (SD) (n = 17) and progressive disease (PD) (n = 6). Of TKI responders, 4 had lesions in ≥2 organs respond and 3 had a lesion in a single organ respond. Of the 3 patients who responded to CPI, all had lesions in ≥2 organs respond. Patients who had PD to TKI did not respond to CPI. Organ specific metastases at time of initiation of TKI and CPI are described in the table. In lung lesions, the odds of a response to CPI with or without an initial response to TKI were 0.16 and 0.57 respectively (OR 0.29, p = 0.595). In lymph node (LN) metastases, the odds of a response to CPI with or without an initial response to TKI were 0.16 and 0.62 respectively (OR 0.27, p = 0.354). Two patients had a response to TKI in the liver but none had a response to subsequent CPI. Of 3 patients who had a response to TKI in the renal primary, 1 had a response to CPI. Conclusions: In this small series, these data suggest that first line TKI response may dictate subsequent CPI response in an organ specific manner, with higher numbers of CPI responses in lung and LN metastases in patients who did not have TKI response in these organs. This may be due to immune modulation by prior TKI or to tumor heterogeneity. Studies are needed to further elucidate this phenomenon. [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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".