Prognostic pathological and clinical factors associated with overall survival in metastatic melanoma undergoing anti PD-1 treatment.
Bibliographic record
Abstract
e21531 Background: Anti-PD-1 immunotherapy has revolutionized metastatic melanoma treatment, as first-line monotherapy or in combination with Ipilimumab. Up to 40% of patients will progress within 3 months, with limited evidence on who derives benefit from immunotherapy. We report clinical and pathological predictive and prognostic factors from a multi-institutional cohort. Methods: Patients between 2014-2017 treated with Nivolumab and Pembrolizumab were identified from a provincial pharmacy database in Alberta, Canada. All patients had unresectable stage III or IV melanoma. Patient characteristics, investigations, treatment and clinical outcomes were obtained from electronic medical records. We utilized Cox regression and Kaplan-Meier methods to analyze progression free survival (PFS) and overall survival (OS). Results: 143 patients with either cutaneous (115) or primary unknown (28) melanoma were identified. Immunotherapy was median second line treatment and patients received a median of 7 doses. The median age was 64, and 144 (80%) were either ECOG 0 or 1 at treatment initiation. The overall response rate was 33%, with median follow up of 25 months. Ulcerated primary tumors had a lower mOS of 30 months vs. 49 months (p=0.042). Other pathologic factors (including Breslow Depth, tumor infiltrating lymphocytes, mitosis) were not associated with PFS or OS. Clinical factors associated with worsened mPFS and mOS were liver metastases, >3 sites of disease, and any visceral disease. Elevated LDH, platelets, neutrophils, and lower hemoglobin, lymphocytes, and a neutrophil/lymphocyte ratio were associated with worse mPFS and mOS. We identified 4 prognostic subgroups using LDH and number of visceral sites (Table) which was statistically significant for mPFS and mOS. Conclusions: Ulcerated primary tumors, liver metastasis, and more sites of disease had worse mPFS and mOS. We also identified 4 novel prognostic subgroups strongly associated with survival outcomes.[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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".