Sunitinib-related macrocytosis in patients with advanced renal cell cancer (RCC)
Bibliographic record
Abstract
15580 Background: Sunitinib is an oral multi-targeted tyrosine kinase inhibitor with anti-tumor and anti-angiogenic activity approved and widely used for the treatment of advanced RCC. Based on a clinical observation that several RCC patients receiving Sunitinib developed macrocytosis, this study was performed to describe and examine macrocytosis in this patient population. Methods: A retrospective review of all patients treated with Sunitinib for RCC from 08/05–11/06 was performed. Data regarding hematologic parameters (e.g. CBC, mean corpuscular volume- MCV), concomitant medications, liver function, and time on therapy (cycle length varied from 4–7 weeks) was recorded. Vitamin B12 and folate levels, iron studies, and thyroid function were recorded if available. A standardized, laboratory-specific MCV was used to define macrocytosis. Results: 43 patients formed the study cohort, receiving a median of 4 cycles (24 weeks) of Sunitinib. Overall 19 (44%) patients developed macrocytosis, at a median of 3 cycles (16 weeks). Of the 31 patients who received > 2 cycles of Sunitinib, 48% developed macrocytosis compared to 33% of the 12 patients receiving =2 cycles. Among all patients with macrocytosis, only 8 (42%) had anemia at the time. Of the 24 patients who did not become macrocytic, 19 (79%) had a continual rise in their MCV during therapy. Other causes of macrocytosis including vitamin B12 and folate deficiency, hypothyroidism, and other medications, did not contribute to the macrocytosis identified in this cohort. On preliminary analysis, it did not appear that macrocytosis was associated with response or toxicity. Conclusions: This is the first study to document the common occurrence of Sunitinib-associated macrocytosis in RCC patients. The risk of macrocytosis increased with duration of treatment. Macrocytosis did not appear to be associated with significant clinical sequelae; however this, as well as the mechanism of macrocytosis, requires further study. No significant financial relationships to disclose.
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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 | 0.001 | 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".