Frontline DLBCL NMA: Updated Network Meta-Analysis of 7 Phase III RCTs (N=5,463)

[htlin222]

🔬 Frontline DLBCL Network Meta-Analysis

Updated Bayesian NMA of 7 Phase III RCTs (N=5,463) comparing frontline chemoimmunotherapy regimens for diffuse large B-cell lymphoma.

Project path: projects/dlbcl-frontline-nma/

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Background

R-CHOP has been the standard frontline DLBCL treatment for 20+ years, but ~35% of patients relapse. Six novel regimens have been tested against R-CHOP in Phase III trials, yet no head-to-head comparisons exist between them. This NMA establishes a comparative efficacy hierarchy using both Bayesian (gemtc) and frequentist (netmeta) methodology.

Included Trials

Trial	Regimen	N	PFS HR (95% CI)
POLARIX 5y	Pola-R-CHP	879	0.77 (0.62-0.96)
PHOENIX	Ibrutinib+R-CHOP	838	0.92 (0.71-1.18)
ROBUST	Lenalidomide+R-CHOP	570	0.85 (0.63-1.14)
ECOG-E1412	Lenalidomide+R-CHOP	349	0.66 (0.43-1.01)
REMoDL-B 5y	Bortezomib+R-CHOP	918	0.81 (approx)
GOYA	G-CHOP	1,418	0.94 (0.78-1.13)
CALGB 50303	DA-EPOCH-R	491	0.93 (0.68-1.27)

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Figure 1: Network Geometry

Star-shaped network with R-CHOP as common comparator. 7 Phase III RCTs, 5,463 patients. Lena+R-CHOP has 2 contributing studies (ROBUST + ECOG-E1412).

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Figure 2: Forest Plot — PFS (Primary Outcome)

Pola-R-CHP is the only treatment with a statistically significant PFS improvement (HR 0.77, p=0.022). I²=0%, tau²<0.0001.

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Figure 3: Treatment Rankings (SUCRA)

Rank	Treatment	SUCRA	HR vs R-CHOP
1	Lena+R-CHOP	70.5%	0.78 (0.61-1.00)
2	Pola-R-CHP	69.1%	0.77 (0.62-0.96) ✓
3	Bort+R-CHOP	62.1%	0.81 (0.64-1.03)
4	Ibrut+R-CHOP	43.1%	0.92 (0.71-1.18)
5	DA-EPOCH-R	41.9%	0.93 (0.68-1.27)
6	G-CHOP	39.2%	0.94 (0.78-1.13)
7	R-CHOP	24.1%	ref

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Figure 4: Rankograms

Posterior rank probability distributions from Bayesian NMA. Wide distributions reflect overlapping CIs in the star-shaped network.

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Figure 5: Overall Survival (Secondary Outcome)

No treatment achieved statistically significant OS improvement vs R-CHOP (4 trials with OS data).

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Key Findings

1. Pola-R-CHP is the only statistically significant winner — supports NCCN Category 1 recommendation for IPI ≥2
2. ABC/non-GCB subtype shows consistent benefit across 3 mechanistically distinct agents (Pola HR 0.34, Bort HR 0.65, Lena HR 0.68) — argues for COO-guided therapy
3. GCB + low-IPI patients have a ceiling effect with R-CHOP (~77% 5y PFS) — adding drugs doesn't help
4. TP53 mutation remains the critical gap — no frontline regimen overcomes it
5. PHOENIX age paradox: benefit in <60 (HR 0.56), harm in ≥60 (HR 1.20) — toxicity-driven treatment attrition negates efficacy

Clinical Recommendation Framework

ABC/non-GCB + IPI 3-5  → Pola-R-CHP (Category 1)
ABC/non-GCB + DEL      → Pola-R-CHP (HR 0.64)
ABC/non-GCB + Age ≥70  → Pola-R-CHP (HR 0.63)
GCB + IPI 0-2          → R-CHOP sufficient
GCB + IPI 3-5          → No clear winner
TP53 mutant            → Unmet need (consider CAR-T, venetoclax)

Methodology

• Primary: Bayesian NMA (gemtc, JAGS) — random-effects consistency model
• Sensitivity: Frequentist NMA (netmeta, REML)
• Convergence: All Rhat = 1.0, Bayesian-frequentist concordance confirmed
• Heterogeneity: I² = 0%
• Sensitivity analyses: Leave-one-out, ROBUST exclusion, OS NMA, fixed/random comparison

Pipeline

Full end-to-end meta-analysis pipeline:

• 01_protocol → 02_search (PubMed 194 records) → 03_screening (7 RCTs) → 05_extraction → 06_analysis (10 R scripts) → 07_manuscript (~4,800 words + 1,627 word discussion) → 08-09 QA

Comparison with Prior NMA

Updates Chen et al., Ann Hematol 2023:

• ✅ POLARIX 5-year data (they had 2-year)
• ✅ Bayesian methodology (2026 NICE/Cochrane consensus)
• ✅ Subgroup analysis across COO, IPI, DEL, age
• ✅ TP53 gap analysis

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Generated with meta-pipe — AI-assisted meta-analysis pipeline