An open-label, multidrug, biomarker-directed, multicentre phase II umbrella study in patients with non-small cell lung cancer, who progressed on an anti-PD-1/PD-L1 containing therapy (HUDSON).
Bibliographic record
Abstract
TPS3120 Background: Immune checkpoint inhibitor (ICI) containing regimens have significantly improved survival outcomes in first- and second-line non-small cell lung cancer (NSCLC). However, the majority of patients do not respond or have non-durable responses leading to a new ICI-resistant population. HUDSON addresses the urgent need to identify treatments and understand ICI-resistance for this emerging population. Methods: HUDSON is a multi-centre, international multi-arm umbrella study that will 1) evaluate therapies to reverse ICI-resistance and 2) define mechanisms of ICI-resistance in NSCLC patients who have progressed following standard-of-care platinum and ICI based therapies. HUDSON is a platform study that consists of two groups; a biomarker matched and a biomarker non-matched group. Within the biomarker matched group, different cohorts will test 1) homologous recombination repair (HRR) defects and 2) LKB1 aberration for response to durvalumab and olaparib (PARP inhibitor), 3) ATM deficiency for response to durvalumab and AZD6738 (ATR inhibitor) and 4) RICTOR amplification for response to durvalumab and vistusertib (mTORC1/2 inhibitor). In the biomarker non-matched group, cohorts will test durvalumab in combination with either i) olaparib, ii) AZD9150 (STAT3 inhibitor) or iii) AZD6738. New cohorts will be added as new translational hypotheses are established. Translational research will be performed on serial peripheral blood samples (including ctDNA) and tumour biopsies. HUDSON enrolls ICI-resistant/refractory patients in a signal searching manner. Biomarker matched and non-matched groups will be opened simultaneously, and all eligible patients can be allocated a treatment option irrespective of their tumour profile. Enrolment is ongoing, Clinical trial information: NCT03334617.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".