Abstract A005: Meta-analysis of evolutionary drivers of treatment resistance in solid tumors: Insights from phylogenetic and single-cell sequencing studies

Abstract Background: Treatment resistance in solid tumors is driven by clonal evolution, with therapies selecting for resistant subpopulations. Single-cell sequencing (SCS) and phylogenetic analyses reveal branching patterns and subclonal dynamics, but no meta-analysis has synthesized their prognostic impact. This study quantifies evolutionary drivers of resistance across solid tumors, informing adaptive therapy design. Methods: PubMed, Scopus, and Web of Science (2015–2025) were searched for cohort studies or trials reporting SCS or phylogenetic data in solid tumors (e.g., breast, lung, colorectal) with resistance endpoints (e.g., progression-free survival [PFS], clonal shifts). Inclusion: human studies, ≥10 patients, evolutionary metrics (e.g., clonal diversity, variant allele frequencies), resistance defined (HR >1.5 for PFS). Exclusion: non-solid tumors, reviews. Data extracted: study design, tumor type, sample size, diversity metrics (e.g., Shannon index), and outcomes (HR for PFS/OS). Risk of bias used QUADAS-2. Random-effects models in RevMan 5.4 computed standardized mean differences (SMD) for subclonal fraction and hazard ratios (HR) with 95% CIs. Heterogeneity assessed via I2; publication bias via Egger’s test. Results: From 1,247 records, 28 studies (n=2,456 patients; 58% female, mean age 62) were included: breast (n=9), lung (n=10), colorectal (n=6), other (n=3). Most used SCS (e.g., 10x Genomics) or phylogenetic tools (e.g., PyClone). Pooled subclonal fraction was higher in resistant tumors (SMD 1.82, 95% CI 1.40–2.24; I2=70%; 22 studies; P<0.001). Pooled HR for PFS was 2.10 (95% CI 1.66–2.65; I2=64%; 18 studies), with lung tumors showing stronger effects (HR 2.42, 95% CI 1.87–3.14) than breast (HR 1.75, 95% CI 1.30–2.36). Meta-regression linked branching evolution to worse outcomes (β=0.30, P=0.03). No publication bias (Egger’s P=0.22). Sensitivity analyses excluding high-bias studies (n=3) altered HR by <5%. Conclusions: Higher subclonal diversity predicts resistance (HR>2) across solid tumors, supporting evolutionary metrics in trial designs. This first meta-analysis of phylogenetic/SCS data resolves prior inconsistencies, aligning with AACR’s focus on tumor progression dynamics. Citation Format: Noureddine SAMAI. Meta-analysis of evolutionary drivers of treatment resistance in solid tumors: Insights from phylogenetic and single-cell sequencing studies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr A005.