Ground-truth in silico spike-ins reveal limits of microbiome biomarker recovery in colorectal cancer

Microbiome-based biomarkers have been proposed for colorectal cancer (CRC), yet candidate taxa are often interpreted without knowing whether taxonomic profiling workflows can reliably detect and quantify them in human samples. Existing ground-truth studies commonly rely on simplified communities that do not preserve the biological and technical complexity of clinical stool metagenomes. We hypothesized that weak CRC-associated signals, particularly those relevant to early-stage disease, may be missed through analytical non-recovery rather than biological absence. We developed an in silico spike-in framework that embeds CRC-associated signals into clinical stool metagenomes. Ten taxa were introduced individually at six fractions (0.01-5%) or as an equally weighted community at seven total fractions (0.01-10%; effective per-taxon fractions, 0.001-1%), generating 5,770 spike-in metagenomes from 310 samples. The resulting metagenomes were profiled with Kraken2/Bracken and MetaPhlAn 4 to quantify detection, abundance accuracy, false-positive signals, biomarker recovery, and calibration against a known ground truth. Recovery depended strongly on workflow, taxon, abundance, and clinical background. At 0.01%, four taxa- F. nucleatum, P. micra, P. stomatis, and P. intermedia-showed good recovery in 85-90% of samples under Kraken2/Bracken, whereas none achieved good recovery in at least 50% under MetaPhlAn 4. Greater low-abundance recovery was accompanied by a broader artefact-prone background (54.9% versus 0.5% of non-target taxa). Artefact-prone taxa accounted for 96.3% and 100% of enriched off-target differential-abundance calls, respectively. Spike-in-derived artefact exclusion substantially reduced off-target detections where present, while abundance-response modelling provided proof-of-principle correction of systematic abundance distortions in both evaluated configurations. Overall, the evaluated profiling configurations demonstrate that known low-abundance CRC-associated signals can be missed or distorted across a complete biomarker-discovery pipeline. Analytical non-recovery may cause early-detection biomarkers to be missed rather than indicate biological absence. Importantly, artefact-aware filtering and abundance calibration show that these limitations can be partially overcome. Improvements in taxonomic profiling may help bring reliable microbiome-based CRC diagnostics closer to clinical application.