Abstract 1642: Alectinib in treatment-naïve anaplastic lymphoma kinase-positive (<i>ALK</i>+) metastatic non-small-cell lung cancer (mNSCLC): Systematic literature review (SLR) and network meta-analysis (NMA)
Bibliographic record
Abstract
Abstract Background: The standard of care for treatment-naïve ALK+ mNSCLC patients (pts) is an ALK tyrosine kinase inhibitor (TKI). Pts treated with crizotinib generally experience disease progression within 1 year, often within the central nervous system (CNS). As such, there is a high unmet need for CNS-active ALK TKIs. Phase III data from 2 ALK TKIs, alectinib and ceritinib, showed improved efficacy vs crizotinib and chemotherapy, respectively; however, no studies have directly compared these TKIs. We report a NMA to compare the efficacy/safety of these therapies. Methods: A SLR was conducted to identify ALK+ mNSCLC randomized controlled trials (RCTs) in treatment-naïve pts. Efficacy/safety endpoints were extracted and the feasibility of using the data in a NMA was assessed. Data were analyzed using a fixed effect analysis in WinBUGs following NICE guidelines. Results: 1194 citations were identified, of which 4 RCTs were in treatment-naïve pts (ALEX [NCT02075840], ASCEND-4 [NCT01828099], PROFILE 1014 [NCT01154140] and PROFILE 1029 [NCT01639001]). All were open-label, phase III RCTs and pts were similar in terms of age, ECOG PS, disease stage, sex and smoking status. More pts had baseline CNS metastasis (mets) in ALEX (40%) vs the other trials (26-32%). PROFILE 1029 was conducted in East Asian pts and only included in a sensitivity analysis. Chemotherapy arms differed in that pemetrexed maintenance was included in ASCEND-4 but not in the PROFILE studies. Key efficacy/safety results are shown in the Table. Data from the sensitivity analyses were similar. Conclusion: Alectinib significantly improved progression-free survival compared with other treatments in the ITT population including in pts with baseline CNS mets. Alectinib showed significantly fewer grade 3-4 AEs than ceritinib. The difference in chemotherapy arms of the 2 trials may have impacted the NMA efficacy results. Table. NMA results in treatment-naïve ALK+ mNSCLC patientsComparisonEfficacy/Safety Endpoints PFS by IRC* HR (95% CrI)Subgroup CNS Mets at Baseline* PFS by IRC HR (95% CrI)OS* HR (95% CrI)Grade 3 or 4 AEs§OR (95% CrI)Alectinib vs chemotherapy0.23 (0.15-0.34)0.21 (0.10-0.44)0.63 (0.34-1.15)0.81 (0.44-1.52)Alectinib vs crizotinib0.50 (0.36-0.70)0.37 (0.22-0.63)0.76 (0.49-1.20)0.65 (0.41-1.04)Alectinib vs ceritinib0.41 (0.25-0.67)0.31 (0.13-0.71)0.85 (0.42-1.73)0.36 (0.17-0.79)Crizotinib vs chemotherapy0.45 (0.35-0.58)0.57 (0.35-0.93)0.82 (0.54-1.24)1.24 (0.81-1.91)Ceritinib vs chemotherapy0.55 (0.42-0.72)0.70 (0.44-1.11)0.73 (0.50-1.06)2.25 (1.42-3.61)Ceritinib vs crizotinib1.22 (0.84-1.79)1.22 (0.62-2.43)0.90 (0.52-1.57)1.82 (0.96-3.44)Statistically significant differences indicated in bold based on 95% CrI; *Hazard ratio <1 indicates lower hazard (higher likelihood of PFS or OS) compared with control; §Odds ratio <1 indicates lower odds of an event compared with control. AEs, adverse events; CNS, central nervous system; CrI, credible interval; HR, hazard ratio; IRC, independent review committee; Mets, metastases; OR, odds ratio; OS, overall survival; PFS, progression-free survival Citation Format: Anna Steenrod, Michelle Orme, Katherine S. MacGilchrist, Rachel Rosenthal, Juliane Schaefer, Vlatka Smoljanovic, Emmanuel Mitry, Parneet K. Cheema. Alectinib in treatment-naïve anaplastic lymphoma kinase-positive (ALK+) metastatic non-small-cell lung cancer (mNSCLC): Systematic literature review (SLR) and network meta-analysis (NMA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1642.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".