Multi-platform characterization of cutaneous melanoma from patients treated with immune checkpoint inhibitors.
Bibliographic record
Abstract
e15071 Background: Therapeutics targeting inhibitory immune checkpoints have revolutionized the treatment of advanced and metastatic melanoma. Clinical trials of dual treatment with anti-CTLA4 and anti-PD-1 monoclonal antibodies observed up to 57.6% response rates with 11.5% of patients achieving complete response and 13.1% having stable disease for over 12 months of follow-up (Larkin et al. 2015). However, side effects continue to be a major problem with over 40% of patients reporting a severe adverse event. A number of groups have identified that tumour mutation burden, gene expression signatures and tumor aneuploidy are predictors of immunotherapy response (reviewed in Sharma et al. 2017). A significant knowledge gap remains regarding which combination of factors best predict a priori response to immune checkpoint inhibitors. Methods: We performed a multi-platform integrative analysis of 42 regional metastatic melanoma samples from patients treated with immune checkpoint inhibitors. Platforms included in this study were focused sequencing of >400 cancer-associated genes using the CANCERPLEX platform from Kew Group Inc., DNA copy number, 7-colour immunofluorescence of immune infiltration markers, and analysis of peripheral blood. Results: Our focused sequencing gene panel was shown to predict tumour mutation burden and to accurately identify known melanoma driver mutations. Tumour lymphocyte infiltration predicted immune checkpoint response, and highly infiltrated tumours had improved survival (p<0.05). Furthermore, elevated pre-treatment eosinophil and basophil counts were associated with improved treatment response, while high lactate dehydrogenase levels were associated with poor survival and decreased response rates. Conclusions: This study adds to a growing body of knowledge to understand the molecular determinants of response to immune checkpoint inhibitors to further personalize melanoma care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".