Molecular profiling-directed individualized adjuvant therapy in colorectal cancer: Bridging consensus guidelines to clinical disparities

Table 1 Key molecular biomarkers in colorectal cancer adjuvant therapy: Consensus recommendations, practical discrepancies, root causes, and evidence type

Biomarker	Evidence type	Consensus recommendation (guideline-based)	Discrepancies and dilemmas in clinical practice	Root causes of discrepancies
dMMR/MSI-H	Predictive	Standard testing for all newly diagnosed CRC. Strong predictive biomarker for immunotherapy in metastatic setting	Uncertainty in adjuvant setting: Lack of mature phase III data (e.g., ATOMIC trial) leads to variation: Standard adjuvant chemotherapy vs seeking clinical trials for adjuvant immunotherapy. Sequence optimization: Uncertainty about the optimal timing (neoadjuvant vs adjuvant) for immunotherapy	Evidence gap: High-level evidence for adjuvant immunotherapy is still maturing. Interpretation challenge: Extrapolating from metastatic setting data to adjuvant setting requires careful consideration
RAS and BRAF V600E mutation	Prognostic	Standard testing. Prognostic biomarkers associated with poorer outcomes. RAS mutation is a negative predictive marker for anti-EGFR therapy	Lack of targeted strategies: No effective adjuvant targeted therapy exists specifically for these mutations (unmet need). Chemotherapy intensity: Debate on whether BRAF V600E mutant patients should receive more intensive regimens (e.g., FOLFOXIRI)	Evidence gap: Insufficient data from adjuvant trials to support specific targeted interventions. Biological complexity: These are primarily prognostic rather than predictive biomarkers in the adjuvant setting
Emerging actionable targets	Predictive	Testing may be recommended in advanced/metastatic setting to guide therapy	Decision-making in adjuvant setting: No high-level evidence or guideline recommendations for adjuvant use of corresponding targeted agents. Decisions rely on extrapolation from metastatic data, leading to significant uncertainty and variability	Evidence gap: Almost complete lack of adjuvant clinical trial data for these rare subgroups. Technical and economic challenges): Low prevalence makes large trials difficult; cost-effectiveness of routine NGS testing in adjuvant setting is debated
CtDNA for MRD detection	Prognostic	Highly promising prognostic tool for dynamic risk stratification. Currently recommended predominantly in the context of clinical trials or research	Lack of standard-of-care: Although clinical demand is high, most clinicians are hesitant to base definitive treatment decisions solely on ctDNA outside trials. Intervention dilemma: No consensus on the optimal management strategy for ctDNA-positive patients post-surgery/residual disease	Evidence gap: While prognostic value is clear, predictive value (how to treat based on result) is under investigation in ongoing trials (e.g., DYNAMIC, CIRCULATE). Technical standardization: Lack of uniformity in assay platforms, timing of testing, and definition of “positivity”
Multigene classifiers	Prognostic	Not yet standard-of-care for clinical decision-making, but provide deep biological insight	Limited clinical utility: Challenges in practical implementation due to tumor heterogeneity, assay stability on FFPE tissue, and lack of direct therapeutic implications for most subtypes. Integration challenge: How to integrate molecular subtyping (e.g., CMS4) with clinical risk stratification (e.g., IDEA study) to refine chemotherapy duration (3 months vs 6 months) remains an enigma	Technical limitation: Analytical and validation challenges for complex assays in routine diagnostics. Evidence gap: Lack of prospective trials demonstrating that treatment changes based on these classifiers improve outcomes

dMMR: Mismatch repair deficient; MSI-H: Microsatellite instability-high; RAS: Rat sarcoma viral oncogene homolog; BRAF: B-rapidly accelerated fibrosarcoma; ctDNA: Circulating tumor DNA; MRD: Minimal residual disease; CRC: Colorectal cancer; EGFR: Epidermal growth factor receptor; FFPE: Formalin-fixed paraffin-embedded; CMS: Consensus molecular subtypes; NGS: Next-generation sequencing.

Table 2 Key technical parameters for circulating tumor DNA-based minimal residual disease detection in colorectal cancer

Parameter	Common options/considerations	Clinical implications and challenges
Assay technology	Tumor-informed (PCR-based, NGS); tumor-agnostic (methylation-based, fixed-panel NGS)	Tumor-informed: Higher sensitivity, requires tumor tissue. Tumor-agnostic: Faster turnaround, may have lower sensitivity. Choice impacts cost and logistics
Optimal timing window	Post-operative baseline: 4-6 weeks after surgery; adjuvant therapy monitoring: Every 3-6 months during treatment; surveillance: Every 3-6 months for up to 2-3 years	No universally standardized timeline. Early testing (2-4 weeks) may detect surgical shedding, while testing too late may miss early recurrence
Positivity threshold	Varies by assay (e.g., MTMLD: 0.01%-0.02%; fixed-panel NGS: Often 0.1% VAF). Often defined as ≥ 2 unique tumor-derived fragments	Lack of uniformity leads to results not being directly comparable across different platforms and laboratories
Key performance metrics	Sensitivity: 70%-95% (depends on assay and tumor shed); specificity: > 99%. Lead time: Median 8-9 months ahead of radiographic recurrence	High specificity ensures ctDNA-positivity is highly actionable. Sensitivity limitations mean a negative result cannot fully rule out MRD

PCR: Polymerase chain reaction; NGS: Next-generation sequencing; MTMLD: Multiplex polymerase chain reaction-based targeted deep sequencing; VAF: Variant allele frequency; ctDNA: Circulating tumor DNA; MRD: Minimal residual disease.