AIHA and Pancytopenia as Complications of Pembrolizumab Therapy for Metastatic Melanoma: A Case Report
Bibliographic record
Abstract
Immunotherapy has been an emerging treatment for metastatic melanoma and several other malignancies since 2015. Hematological immune-mediated adverse effects from immunotherapy are rarely reported but they can cause serious harm to patients. Antibodies such as ipilimumab, nivolumab and pembrolizumab target different immune checkpoints to promote T cell anti-tumour response. In particular, pembrolizumab is an antibody that inhibits programmed cell death receptor 1 (PD-1) to upregulate tumour suppression. In this report, we present a case of pembrolizumab-induced autoimmune hemolytic anemia and pancytopenia in a patient who was receiving pembrolizumab treatment for metastatic melanoma. This patient has a history of chronic lymphocytic leukemia and was diagnosed with metastatic melanoma in 2017. He developed symptomatic AIHA and pancytopenia after receiving 8 cycles of pembrolizumab in 2018. Pembrolizumab treatment was discontinued and he was treated with blood transfusion and prednisone. After 5 months of tapering prednisone treatment, his anemia and pancytopenia have improved toward successful recovery. Cancer patients already face an increased risk of immunosuppression with conventional chemotherapy. This case report also summarized all reported cases of PD-1 inhibitor hematological adverse effects in the treatment of oncological diseases. These incidents reflect the risk of immune-mediated hematologic adverse effects, which should be considered in all patients using immunotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".