Aplastic anemia secondary to nivolumab and ipilimumab in a patient with metastatic melanoma: a case report
Bibliographic record
Abstract
Immune checkpoint blockade (ICB) is becoming an increasingly prevalent strategy in the clinical realm of cancer therapeutics. With more patients being administered ICB for a host of tumor types, the scope of adverse events associated with these drugs will likely grow. Here we report a case of aplastic anemia (AA) in a patient with metastatic melanoma secondary to dual ICB therapy. To our knowledge, this is only the second case of AA secondary to dual ICB in the literature, and the first to have a positive patient outcome. A 51-year old male with metastatic melanoma was started on dual immune checkpoint blockade, in the form ipilimumab (3 mg/kg) and nivolumab (1 mg/kg). Two weeks following the second cycle, he presented to the emergency department with profound polypipsia, polyuria and fatigue. The patient was diagnosed with diabetic ketoacidosis secondary to immune therapy induced type-1 diabetes and was admitted to the ICU. While in hospital the patient developed a symptomatic anemia and neutropenia. A bone marrow biopsy revealed a markedly hypocellular marrow with trinlineage hypoplasia with no evidence of myelodysplasia, neoplasm or excess blasts. Flow cytometry revealed an inverted CD4 + :CD8 + ratio and an absence of hematogones. Taken together the presumed etiology was AA secondary to immunotherapy. The patient was subsequently started in IV methylprednisone 70 mg/day for 8 days, followed by a prednisone taper. This intervention rectified the bicytopenia and to date the patient has shown stable blood counts. With the use of ICBs becoming increasingly prevalent in the clinical arena, the number of patients presenting with immune-related adverse events will likely increase. The current case illustrates the need to be vigilant when managing cancer patients receiving ICB. The resolution of this patient’s AA with corticosteroids highlights the value of early detection and appropriate treatment of these rare immune-mediated adverse events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| 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".