What question did this study set out to answer?

This research aims to quantify evolutionary drivers of treatment resistance in solid tumors.

December 8, 2025

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

Key Points

This research aims to quantify evolutionary drivers of treatment resistance in solid tumors.
Meta-analysis of cohort studies and trials with single-cell sequencing and phylogenetic data in solid tumors.
Data sources included PubMed, Scopus, and Web of Science from 2015 to 2025.
Analyzed studies using random-effects models to compute hazard ratios and other metrics.
28 studies involving 2,456 patients were included, showing higher subclonal diversity predicts treatment resistance.
Pooled hazard ratio for progression-free survival was 2.10 across studies, with stronger effects in lung tumors.
Meta-regression revealed branching evolution correlates with worse patient outcomes.

Abstract

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; P0. 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 (HR2) 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 (23Suppl): Abstract nr A005.

اسأل الذكاء الاصطناعي

Bookmark