Review Papers Against Their Own Results

1) What the paper does well

• Comprehensive, logically organized synthesis from immunopathogenesis (smoking/COPD inflammation → oncogenesis) through immunoediting and immune dysregulation, to translational/clinical strategies; integrates preclinical mechanistic biology with clinical trial outcomes Gaissmaier & Christopoulos review strengths
• Clear conceptual framework (cancer immune set point; hot/altered/cold phenotypes) that is clinically actionable for therapy selection and trial design Immune set point concept in paper
• Balanced discussion of biomarkers (PD-L1, TMB, TLS, spatial proximity) and their limitations (standardization, predictive gaps) — a practical guide for clinicians and translational researchers Biomarker critique by Gaissmaier

2) Limitations, blind spots and sources of bias (critical)

1. Nature of evidence: narrative review — no systematic search, no quantitative meta-analysis; therefore vulnerable to publication/selection bias and overrepresentation of positive trials (authors acknowledge this classically). Evidence strength is consequently heterogeneous and not graded across claims Narrative-review limitation
2. Rapid field evolution (2020→2026): some trial results, biomarker standards, and new classes (e.g., TIGIT, bispecifics, neoantigen vaccines) have matured since publication; the review cannot incorporate late-phase outcomes and negative trials that change practice (user should cross-check with up-to-date trial reports) Time-limited nature of the review
3. Reproducibility and operationalization of recommendations: suggestions to combine TME-directed agents with PD-(L)1 inhibitors are sensible biologically but lack standardized patient-selection algorithms and safety-risk stratification; the review notes toxicity risks (e.g., combined checkpoint blockade) but does not provide operational trial designs or predictive toxicity biomarkers Toxicity and operational gaps
4. Conflicts of interest: paper declares industry relationships (author P.C.) in the back matter — this raises possibility of framing bias in therapeutic emphasis; transparency is present but readers should weigh sponsor ties when considering drug-prioritization suggestions COI disclosure in paper

3) Concrete, evidence-focused recommendations (how to move forward)

• Prospective trials: design biomarker-stratified, adaptive trials that predefine immune-contexture arms (hot / altered / cold) and test step-up strategies (PD-(L)1 → +TME modulator → +ACT/vaccine) with integrated toxicity endpoints and translational correlative sampling (TCR sequencing, spatial multiplex IHC, TLS assessment) — this operationalizes the review's conceptual framework.
• Biomarker pipeline standardization: harmonize PD-L1 assays, adopt standardized panel TMB pipelines (panel size, coverage, reporting units) and prioritize spatial assays (multiplex IF) and TLS quantitation — the review highlights these as higher-fidelity predictors vs single-analyte tests Biomarker harmonization advocated
• Mechanistic combination rationale: pair TME-reprogrammers that increase antigen presentation or reverse M2 polarization (e.g., CSF1R inhibitors, STING/TLR agonists, anti-VEGF) with ICI only in trials where baseline myeloid suppression or exclusion phenotype is demonstrated — avoid empiric combinations that increase toxicity without mechanistic enrichment.
• Data sharing and raw-data approaches: release immune-profiling raw data (single-cell, spatial) with standardized metadata to permit independent, machine-learning driven integrative biomarker discovery (the review indicates AI/radiomics promise but lacks raw-data calls; this is a critical next step).

4) Short checklist for clinicians and researchers

1. Assess immune contexture (CD8 IHC ± multiplex IF, TLS if possible) before selecting immunotherapy escalation.
2. Prioritize PD-(L)1 inhibitors in PD-L1-high NSCLC; use chemoimmunotherapy for PD-L1 low/negative disease per trial evidence summarized in the review (note: review predates some later trials—check updates) Clinical summary of PD-(L)1 role
3. Collect pre/post-treatment biospecimens (tumor tissue, blood) and standardize assays (TMB panels, PD-L1 clones, spatial IF) to enable pooled analyses and prospective biomarker validation.

5) How this review could be improved (short, practical)

Adopt a transparent systematic-search supplement (PRISMA flow) and provide a table grading evidence strength (GRADE-style) for each therapeutic recommendation; add a living-update mechanism to incorporate late-phase negative/positive trials and harmonize biomarker protocols.