Two Cases of Durable and Deep Responses to Immune Checkpoint Inhibition-Refractory Metastatic Melanoma after Addition of Camu Camu Prebiotic
Bibliographic record
Abstract
Camu camu (CC) is a prebiotic that selectively stimulates growth and activity of beneficial gut microbiota. Work in murine models demonstrated that castalagin, the active compound in CC, preferentially binds to beneficial gut microbiome bacteria, promoting a stronger CD8+T cell anti-cancer response. We present two patients with metastatic melanoma whose cancer progressed on immune checkpoint inhibitors (ICIs) and developed clinically significant immune-related adverse events (irAEs). They were rechallenged with ICIs in combination with CC. The first patient is a 71-year-old woman with metastatic melanoma, whose ICI treatment was complicated by immune-related pneumonitis and colitis. Upon progression on maintenance nivolumab, CC was added to nivolumab, leading to a near complete response (CR). The second patient is a 90-year-old man with recurrent unresectable melanoma, treated with nivolumab, complicated by immune-related rash and diabetes. He developed new subcutaneous calf lesions and a metastatic popliteal lymph node. CC was added to nivolumab. One month later, the patient experienced a CR. Both patients have been on nivolumab and CC with durable responses for more than a year, with minimal irAEs. These two cases suggest that CC may modulate the microbiome, synergizing with ICIs to produce deep, durable responses with minimal irAEs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".