Radiotherapy enhances responses of lung cancer to CTLA-4 blockade
Bibliographic record
Abstract
Formenti et al. have recently reported the clinical outcomes and translational readouts of a trial of the anti-CTLA-4 inhibitor, ipilimumab, in combination with palliative radiotherapy in 39 patients with non-small cell lung cancer. A radiological response was seen in 18% of patients and 31% of patients experienced disease control. These clinical outcomes appear to be superior to historical studies using ipilimumab alone and suggest that radiation may have triggered systemic, so-called abscopal, immune responses in some patients. Induction of interferon-beta (IFN-β) and maximal expansion and contraction of distinct T cell receptor clones were the most significant factors predicting response. Importantly, established predictive biomarkers of response to immunotherapy alone, including the expression of PD-L1 in diagnostic biopsies and tumour mutational burden, did not predict response. The report provides important human qualification of pre-clinical mechanistic insights indicating that abscopal responses can be generated with optimised radiotherapy fractionation schedules and anti-CTLA-4 inhibition. Additionally, an intriguing mechanism by which radiation can be immunogenic is described, namely radiation-induced transcriptional upregulation of neo-antigens.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 | 0.001 |
| 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".