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Background:
Review

Critical Evaluation of Treatment Response, Driver Mutations, and Circulating Tumor DNA as Markers of Tumor Biology in Colorectal Liver Metastasis

by
Mikel Madi
1,†,
Antony Haddad
1,2,†,
Kyoji Ito
1,
Neal Bhutiani
2,3 and
Jean-Nicolas Vauthey
1,*
1
Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
2
Department of Surgery, University of Louisville, Louisville, KY 40292, USA
3
Department of Microbiology and Immunology, University of Louisville, Louisville, KY 40292, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2026, 18(7), 1111; https://doi.org/10.3390/cancers18071111
Submission received: 11 February 2026 / Revised: 16 March 2026 / Accepted: 19 March 2026 / Published: 30 March 2026
(This article belongs to the Special Issue Multidisciplinary Treatment with Surgery and Chemotherapy of Liver Metastases from Colorectal Cancer)
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Simple Summary

Assessment of tumor biology has become increasingly relevant in the management of colorectal liver metastasis (CLM). Tumor biology reflects tumor aggressiveness and was previously assessed using clinical features including tumor size, number, and extent. More recently, tumor biology has expanded to include response to chemotherapy, genetic mutations, and tumor-specific biomarkers. This review explains the different approaches available for assessing tumor response to chemotherapy. In addition, we summarize the current evidence on the role of driver mutations in prognosis and informing liver-directed therapies. We discuss the role of circulating tumor DNA (ctDNA) as a true biomarker in CLM treatment and its future applications. Finally, we highlight the use of tumor biology for risk stratification, operative decision-making, and perioperative treatment strategies. Integrating tumor biology into routine clinical practice represents a critical step toward personalized management of CLM.

Abstract

Background: Improved assessment of tumor biology has contributed to better outcomes in colorectal liver metastasis (CLM). Previously, tumor biology was assessed based on clinical factors such as number and size of metastases, primary tumor characteristics, and extent of extrahepatic disease. Currently, tumor biology assessment includes response to chemotherapy, genetic mutations, and circulating tumor DNA (ctDNA). Methods: A review of the literature in Medline/Pubmed, Embase, and Cochrane Library was conducted using keywords and MeSH terms. Results: Tumor response to chemotherapy can be assessed using pathologic and radiologic criteria. Radiologic morphologic response has been associated with more accurate determination of outcomes compared with size-based criteria. Pathologic tumor response can be assessed by the percentage of cancer cells remaining within each tumor, the ratio of cancer cells to fibrosis, and the thickness of the tumor–normal liver interface. Six driver mutations are consistently associated with outcomes in CLM: RAS/BRAF, TP53, SMAD4, FBXW7, and APC. All are associated with decreased overall survival (OS) and recurrence-free survival (RFS) except for APC, which is associated with better survival. More than 50% of patients have co-mutations, and a three-tier pathway-centric risk score integrating these mutations offers a more comprehensive approach. While mutations should be considered when evaluating for locoregional therapy, it should not influence ablation margins, surgical margins, or parenchymal sparing approach. Preoperative ctDNA is associated with worse survival, but clearance after hepatectomy is associated with improved survival. Postoperative ctDNA status is associated with recurrence and has the potential to guide the choice of adjuvant chemotherapy. Conclusion: Tumor biology enables informed, precise, and personalized decision-making. Integration of response to chemotherapy, driver mutations, and ctDNA into routine practice is critical to improve CLM management.

1. Introduction

More than half of patients with colorectal cancer (CRC) present with liver metastasis [1]. Over the past two decades, long-term outcomes for patients with colorectal liver metastasis (CLM) have markedly improved, with 5-year overall survival (OS) exceeding 50% [2]. This is due to increased adoption of curative-intent approaches, advancements in surgical techniques, development of targeted systemic therapies, and personalized treatment decision-making based on tumor biology [3].
Tumor biology was classically assessed using number and size of metastases, primary tumor characteristics, extent of extrahepatic disease, and tumor markers [4]. More recently, assessment of tumor biology has evolved to include tumor response to chemotherapy, genetic mutations, and circulating tumor DNA (ctDNA) [5]. Currently, CLM tumor biology is a cornerstone for prognostic assessment, defining resectability, and determining indications for transplantation [6].
In this review, we discuss three aspects of CLM tumor biology: radiologic and pathologic approaches to assessing tumor response to chemotherapy, the impact of genetic mutations on long-term outcomes and local therapy, and the role of ctDNA in guiding personalized management.

2. Methods

A narrative review of the literature was performed using the Medline/PubMed, Embase, and Cochrane Library databases, up to October 2025, using MeSH terms and keywords. The terms “Colorectal Liver Metastasis”, “Colorectal Liver Metastases”, “Colorectal Liver Mets”, “Colon Cancer Metastasis to the Liver”, “CLM”, and “CRLM” were searched in the title and/or abstract. Only English language articles were considered for screening. No restrictions were made on publication date. All pertinent articles’ references were reviewed to identify additional relevant studies.

3. Response to Chemotherapy

Less than 20% of CLM are candidates for upfront resection, and around 40% of initially unresectable patients undergo resection after systemic chemotherapy [7,8]. Nowadays, most patients receive preoperative chemotherapy mainly consisting of FOLFOX or FOLFIRI, with or without anti-VEGF (vascular endothelial growth factor) and/or anti-EGFR agents [9]. It is crucial to evaluate tumor response to chemotherapy to guide further management, avoid unnecessary side effects, and reduce extra costs [10].

3.1. Radiologic Response

Given its availability, noninvasive nature, and relatively low cost, radiologic assessment based on computed tomography (CT) has played an essential role in CLM treatment. It is intuitive to evaluate tumor response to chemotherapy based on metastasis size, with tumor shrinkage indicating a good response. This assessment was first quantified by the World Health Organization (WHO) criteria, which relies on the tumor volume based on longest perpendicular and horizontal diameters multiplication (Table 1) [11]. Recently, a simpler single-parameter Response Evaluation Criteria in Solid Tumor (RECIST) approach was introduced based only on the diameter of the largest tumor, classifying patients into four groups (complete response, partial response, progressive disease, and stable disease) (Table 1) [12]. Despite its simplicity, this approach has many limitations. First, relying on tumor diameter is not representative of the volume, since most tumors are not spherical [13]. Furthermore, the shrinkage may occur in a non-uniform fashion without affecting the longest diameter [14]. In contrast to cytotoxic chemotherapy, many antineoplastic agents are cytostatic, such as bevacizumab and cetuximab, leading to cell growth arrest without directly killing tumor cells. Despite their proven long-term advantages, these drugs were not found to be associated with objective radiologic response based on the RECIST criteria [15].
These limitations of the RECIST criteria prompted better radiologic assessment. Many radiologists observed changes in tumor morphology without significant size reduction after targeted chemotherapy, especially with bevacizumab [10]. Chun et al. described these morphological changes as homogenous and low attenuation with a thin, sharply defined tumor–liver interface (Figure 1) [10]. Based on these changes, the radiologic morphologic criteria was put forth, stratifying patients into three groups irrespective of tumor size change (optimal response, incomplete response, and no response) (Table 1) [10]. Importantly, Shindoh et al. validated this criteria in 209 patients and showed that the morphologic criteria is sufficiently associated with OS regardless of the chemotherapy regimen [19]. This study showed no correlation between morphologic and RECIST criteria [19]. Nishioka et al. validated this association with OS and RFS on a Japanese cohort [20]. Similarly, Mazard et al. reported comparable outcomes in patients treated with bevacizumab-based chemotherapy for unresectable CLM. Notably, the RECIST criteria was not associated with RFS in this study [21]. Overall, RECIST provides a standardized and objective size-based assessment, whereas morphologic criteria better capture tumor appearance changes (Table 2). Therefore, integration of morphologic criteria and size criteria may be needed to improve accuracy of radiologic response to chemotherapy in patients with CLM [19].

3.2. Pathologic Response

On pathologic evaluation, untreated tumors are characterized by a mixture of viable tumor cells and necrosis. After exposure to chemotherapy, the cellular response is manifested by tumor cell death associated with fibrosis and mucin deposition, constituting the basis of chemotherapy response assessment [26]. This classifies patients into three groups (complete response, major response, and minor response) based on the mean of the percentage of cancer cells remaining within each tumor (Table 1) [16]. In a cohort of 305 patients who underwent irinotecan- or oxaliplatin-based preoperative chemotherapy regimens, Blazer et al. reported a significant difference in 5-year OS based on pathologic response (75% in complete response, 56% in major response, and 33% in minor) [16].
Another reliable approach to assess CLM response to chemotherapy is the tumor regression grade (TRG) scoring system, which relies on the ratio of cancer cells to fibrosis, classifying patients into five groups (Table 1). Rubbia-Brandt et al. showed that partial histological response (TRG3) and no histological response (TRG4 and 5) were associated with significant decrease in 3-year and 5-year OS compared with major histological response (TRG1 and 2) [17]. The majority of residual cancer cells post-chemotherapy aligns near the tumor–normal liver interface (TNI); thus, measuring tumor thickness at this interface was also reported as a tool to assess pathologic response in CLM (Table 1) [18]. In a cohort of 171 patients, increased TNI thickness was independently associated with a stepwise decrease in 5-year RFS between TNI < 0.5 mm (58%), TNI 0.5–5 mm (24%), and TNI ≥ 5 mm (11%) [27].

3.3. Radiologic–Pathologic Correlation

Despite differences in approach, both radiologic and pathologic measures were found to be reliable in assessing CLM response to chemotherapy [28]. In fact, studies have shown a correlation between both approaches, further validating their relevance. Maru et al. reported an excellent correlation between tumor thickness at TNI and radiologic response by morphologic criteria; however, a weak correlation was seen between tumor thickness at TNI and RECIST criteria [18]. Both RECIST and morphologic criteria were associated with the percentage of residual tumor cells on pathologic assessment [10]. Based on a cohort of 234 patients, Chun et al. reported a median residual tumor percentage of 20%, 50%, and 70% for morphologic non-responders, suboptimal responders, and optimal responders, respectively [10]. The median percentages of residual tumors were 30%, 50%, and 70% for RECIST partial response, stable disease, and progressive disease, respectively [10]. Shindoh et al. confirmed these findings with an emphasis on the superiority of morphologic criteria over RECIST in correlating with the pathologic response. Major pathologic response was present in 92% of patients with optimal morphologic response, in contrast to 83% of patients with partial response by RECIST criteria [19].
Despite the clinical utility of treatment response assessment in CLM, several limitations must be acknowledged. Notably, in the CAIRO5 trial, morphologic response was not associated with OS, and neither RECIST nor morphologic response was associated with recurrence [29]. Furthermore, morphologic and pathologic responses across multiple metastases in the same patient are often combined into a single category, without fully accounting for intrapatient tumor heterogeneity [29]. A major challenge in imaging-based assessment is disappearing liver metastases after chemotherapy, which occurs in approximately 20% of lesions and does not always correspond to a complete pathologic response [30]. Pathologic response assessment is only available postoperatively and therefore cannot inform preoperative decision-making. Moreover, chemotherapy-associated liver injury may further complicate pathologic evaluation, as sinusoidal obstruction syndrome has been associated with reduced pathologic response [31].

4. Genetic Mutations

Initial studies on genetic mutations in CLM aimed to understand the biological relationship between the primary tumor and metastatic disease [32]. These mutations were then used by medical oncologists to inform systemic therapy selection [33]. Currently, the evaluation of these mutations is critical to determining CLM tumor biology and informing biology-driven clinical management. Six driver mutations in five signaling pathways have been consistently associated with CLM tumor biology: RAS/BRAF, SMAD4, TP53, APC, and FBXW7, corresponding to the RTK–RAS, TGF-β, p53, Wnt, and Notch pathways, respectively [34].

4.1. Frequency, Presentation, and Prognosis

The RAS genes, including KRAS and NRAS, are proto-oncogenes, and they are part of the mitogen-activated protein kinase (MAPK) signaling pathway, which regulates cellular growth, apoptosis, and angiogenesis [35]. As a downstream effect, RAS mutations upregulate metastasis-promoting factors like ANGPT2 and CXCR4 that disturb the cytokine milieu of the tumors, possibly affecting tumor–stroma interactions, favoring liver metastasis formation [36]. RAS mutations also promote immune evasion by modulating tumor microenvironment, promoting metastatic dissemination [37]. The association of RAS mutation with decreased OS and recurrence-free survival (RFS) in patients with CLM was first reported in 2013 by Vauthey et al. in a cohort of 193 patients [38]. It was detected in 40–50% of patients with CLM and was associated with right-sided primary tumors and synchronous metastases [39,40]. A recent meta-analysis of 36 studies with a total of 15,766 patients supported these findings (Table 3) [41]. Given its clinical importance, Kawaguchi et al. developed an externally validated contour prognostic model based on the number of CLM, the largest tumor diameter, and RAS mutation status (Figure 2) [42]. In patients without recurrence two years after CLM resection, RAS mutation was the only factor associated with increased risk of recurrence [43]. Thus, these findings have prompted consideration of more intensive surveillance between 2 and 4 years in patients with RAS mutation at some high-volume centers such as The University of Texas MD Anderson Cancer Center, although broader validation is still needed before this approach can be generalized [43].
BRAF mutation, one of the most deleterious mutations in CRC, is rarely found in resectable CLM due to its aggressive biology and advanced disease at diagnosis [45]. BRAF is also a proto-oncogene and functions as another activator of MAPK signaling pathway and a member of the rapidly accelerated fibrosarcoma (RAF) protein kinases family [45]. BRAF mutations stimulate glutathione synthesis via upregulation of glutamate–cysteine ligase, which protects tumors from oxidative stress during distant metastasis to the liver and the lungs [46]. Furthermore, BRAF mutation may enhance metastasis by regulating CXCL16 expression and promoting angiogenesis in the tumor microenvironment [47]. A multi-institution matched case control study by Gagniere et al., including 1497 patients with CLM, showed that BRAF mutation is associated with lower median OS and RFS (Table 3) [48]. V600E BRAF mutation is specifically associated with worse survival compared with non-V600E BRAF and was reported to be the strongest predictor of OS and RFS in CLM [49]. BRAF mutation is also used as a contraindication to transplant for patients with CLM [6].
TP53 encodes p53, a tumor suppressor protein, and is the most commonly mutated gene in CLM, with a prevalence of up to 77% [34]. It plays a major role in maintaining genomic stability by regulating cell cycle arrest and apoptosis following DNA damage, and its mutations allows for proliferation of unstable tumor cells [50]. TP53 mutation is shown to negatively impact both long-term oncological outcomes, and its effect is more pronounced in the presence of RAS/TP53 co-mutation (Table 3) [34]. Furthermore, TP53 mutation is independently associated with worse pathologic response to chemotherapy, reflecting more aggressive tumor biology [51].
SMAD4 is a tumor suppressor gene in the TGF-β signaling pathway, which normally regulates cell differentiation and growth inhibition [52]. When SMAD4 is lost, TGF-β signaling shifts from tumor-suppressive to pro-metastatic through SMAD-independent pathways, particularly Rho/ROCK/LIMK pathways [53]. Furthermore, loss of SMAD4 promotes the accumulation of myeloid-derived suppressor cells through the CCL15–CCR1 chemokine axis, which facilitates invasion and creates an immunosuppressive microenvironment [54,55]. SMAD4 mutation is present in 13% of patients with CLM and is associated with worse oncological outcomes [56]. In a cohort of 278 patients with CLM, SMAD4 mutation was associated with significantly lower 3-year OS after hepatectomy [56]. These findings were validated in an external cohort of patients with stage IV rectal cancer treated with chemotherapy alone [56]. A meta-analysis including a total of 3020 patients also reported an increase in HR for OS and RFS in patients with SMAD4 mutation (Table 3) [41].
Recently, an additional mutation has been associated with poor prognosis in patients with CLM. FBXW7 mutations disrupt ubiquitin-mediated degradation of oncoproteins and cell cycle regulators within the Notch pathway, contributing to uncontrolled proliferation and metastatic potential [57]. Around 6% of patients with CLM have FBXW7 mutation, independently associated with decreased OS (Table 3) [44]. A similar association was observed in CRC patients based on a meta-analysis of ten studies including 4199 patients [58].
APC functions as a tumor suppressor gene by degrading β-catenin and regulating the WNT pathway [59]. Unlike the previously mentioned mutations, APC mutation is associated with improved oncologic outcomes in mCRC [60]. A study of 579 patients with CLM showed that APC mutation is independently associated with better OS (Table 3). These findings were validated in an external cohort of patients with unresectable metastatic CRC, with a comparable OS HR [34].

4.2. Co-Mutations and Pathway-Centric Risk Classification

More than 50% of patients with CLM harbor mutations in multiple genes, with some tumors carrying up to four driver mutations [34]. Due to its synergistic deleterious effect on oncologic outcomes, the RAS/TP53 co-mutation is the most studied for patients with CLM [61,62]. In a cohort of 401 patients with CLM, Chun et al. showed that RAS/TP53 co-mutation is present in 31.4% of patients and is independently associated with decreased OS [63]. Moreover, Maki et al. reported that KRAS mutation is associated with worse outcomes only when TP53 is co-mutated [64]. Interestingly, in a cohort of 507 patients, there was no difference in OS and RFS between patients with RAS mutation but wild-type TP53 and SMAD4 compared with patients with RAS wild-type [65]. Thus, a comprehensive multigene assessment of mutations is more reflective of tumor biology. Kawaguchi et al. developed a robust risk stratification model for resectable and unresectable metastatic colorectal cancer, accounting for the four most commonly altered pathways: TP53, APC, RAS/BRAF, and SMAD4. This pathway-centric risk classification stratifies patients into three grades based on their genetic profile. Grade 1 includes patients with no driver mutations or one adverse mutation in the presence of APC. Grade 2 includes patients with one adverse mutation or two adverse mutations in the presence of APC. Grade 3 includes patients with two or more adverse mutations or three driver mutations in the presence of APC. The covariate-adjusted 5-year OS is significantly higher in grade 1 (76.9%), compared with grade 2 (58.7%), which was higher than grade 3 (39.5%) (Figure 3) [34]. Another three-gene scoring system based on RAS, SMAD4, and APC mutational status was proposed in a Chinese cohort and showed consistent association with OS in patients with CLM [66].

4.3. Local Therapy Considerations

In addition to its prognostic value, tumor biology is also considered in guiding the selection and extent of local therapy for CLM. Odisio et al. showed that KRAS mutation is associated with an earlier and higher rate of local tumor progression after CLM ablation, implying that a 5 mm margin can be considered safe for KRAS wild-type, but a margin of 10 mm is needed when KRAS is mutated [67]. A subsequent follow-up study by the same group, using deformable CT image registration and autosegmentation, showed that a margin ≥ 5 mm was not associated with local tumor progression, irrespective of RAS mutational status [68].
Given its association with a worse overall prognosis, some investigators have linked KRAS mutation with positive surgical margins and increased local recurrence risk [69,70]. However, a recent meta-analysis of 19 studies and a total of 7391 patients did not support this association [71]. Some researchers argued that more extensive surgery, such as anatomic resection, is needed in patients with KRAS mutation, given their aggressive biology [72]. However, a propensity score-matched study of 622 patients with CLM showed that OS, RFS, and liver-specific RFS were similar between anatomic and non-anatomic resections for both mutated and wild-type KRAS patients [73]. Furthermore, in a cohort of 415 patients, Nishioka et al. showed that local recurrence is not associated with RAS/TP53, BRAF, SMAD4, or FBXW7 mutations [74]. More importantly, this study showed that neither driver mutations nor R1 resection are associated with an increase in local recurrence risk after R0 curative intent hepatectomy [74].
Taken together, these findings suggest that tumor biology alone should not determine surgical margins, and an R0 resection should always be attempted [75,76]. Given the increased risk of recurrence in patients undergoing curative intent treatment of CLM, the extent of resection should take into account the feasibility of repeat curative intent treatment in case of recurrence. For instance, parenchymal sparing hepatectomy (PSH) allows for increased salvageability in case of liver recurrence for patients with solitary small tumors [77]. Furthermore, PSH is not associated with increased recurrence despite the associated higher rate of R1 resection in patients with bilateral extensive CLM [78]. Accordingly, PSH is the recommended surgical approach for patients with CLM [75].
BRAF mutation is associated with unresectable CLM at presentation and lower rates of conversion to resectability after systemic therapy [79,80]. However, BRAF mutation alone is not considered a contraindication for surgery in resectable CLM [81]. When evaluating liver transplantation (LT) in unresectable cases, BRAF has been proposed as a contraindication to LT given its aggressive tumor biology [82]. A review of the ongoing clinical trials on LT in CLM reported that eight ongoing trials have excluded patients with BRAF mutation [83].
The clinical significance of somatic mutations in CLM is increasingly recognized, but several limitations remain present. Most of the discussed evidence is based on high-volume tertiary referral centers, potentially limiting the generalizability of these findings at the current timepoint. However, meta-analyses evaluating the effect of individual mutations on long-term survival have shown low-to-moderate heterogeneity across studies [41]. Although pathway-centric risk classification represents a more comprehensive and externally validated approach, it would still benefit from further validation across diverse cohorts. Finally, the clinical applicability of mutational profiling remains constrained by substantial cost and resources, limiting its availability across centers.

5. Circulating Tumor DNA

Recurrence rates in CLM remain high, exceeding 50%, and mostly occurring within the first two years [43]. Strategies for prediction or early detection of recurrence are highly emphasized for optimal treatment of patients with CLM. Well-established tumor markers such as CEA are helpful, especially when their dynamic response to preoperative chemotherapy and after CLM resection is studied [84]. Recently, ctDNA has emerged as an accurate biomarker in CRC, with promising applications in prognosis, response to systemic therapy, and early recurrence detection [85,86].
Serial ctDNA measurements may serve as a sensitive dynamic indicator of disease burden and treatment response [87]. For instance, a significant decline in ctDNA as early as four weeks after treatment initiation may help identify patients who are responding well to systemic therapy, whereas persistently elevated or rising ctDNA levels during treatment may suggest treatment resistance and may prompt consideration of a change in regimen [88]. In addition, longitudinal ctDNA sequencing may detect emergent actionable resistance mutations during active treatment, which could further inform therapy selection [89]. Furthermore, several studies have associated ctDNA status prior to liver resection with long-term survival in patients with CLM. Based on a cohort of 212 patients with solitary CLM, Kobayashi et al. reported that detection of ctDNA prior to hepatectomy was associated with significantly decreased RFS [90]. Furthermore, the detection of specific genes such as KRAS, and TP53 in preoperative ctDNA was associated with worse survival [91,92]. However, Newhook et al. showed that clearance of ctDNA after surgery is associated with outcomes comparable with those with undetectable preoperative ctDNA and that sustained postoperative ctDNA detection is associated with worse OS and RFS (Figure 4) [93].
After curative intent surgery, detection of ctDNA without any radiographic evidence of disease is termed minimal residual disease (MRD) and is associated with increased risk of recurrence [94]. In a cohort of 105 patients, Nishioka et al. showed that detection of ctDNA within 180 days after hepatectomy is associated with worse median RFS (6.3 months vs. 12.2 months) [95]. This study also showed that RAS/TP53 co-mutation is associated with increased risk of ctDNA detection postoperatively [95]. Furthermore, Marmorino et al. corroborated these findings by showing that postoperative detection of ctDNA is associated with significantly worse RFS on multivariate analysis [96]. At present, a detectable ctDNA postoperatively is not sufficient to start adjuvant chemotherapy (ACT) in patients with resected CLM. However, the GALAXY trial reported that ctDNA detection following the resection of stage II-IV CRC can identify patients who benefit from ACT based on an analysis of 644 patients [97]. In the same cohort, longitudinal assessment of ctDNA during ACT showed that sustained clearance is associated with better 2-year RFS (89.0% vs. 3.3%) and OS (100% vs. 82%) compared with transient clearance [98]. These findings constitute the basis of an ongoing clinical trial at the University of Texas MD Anderson Cancer Center (NCT05062317) using ctDNA to guide the ACT selection following CLM resection. This trial classifies patients into two groups based on ctDNA status several weeks after hepatectomy. This trial aims to evaluate whether ctDNA negative patients can safely receive a less intensive systemic therapy regimen than their ctDNA positive counterparts without increasing risk of recurrence (Figure 5). ctDNA shows considerable potential with several clinical applications, and further studies are needed to fully elucidate its role in CLM management.
While ctDNA has demonstrated strong prognostic value for detecting MRD and predicting recurrence after CLM resection, several shortcomings limit its clinical use. Most available studies are still limited by relatively small sample sizes [93,94]. A major challenge in ctDNA-based detection of MRD is the analytical sensitivity of current assays, with false-negative results being reported in up to 33%, particularly with tumor-agnostic platforms [94]. Furthermore, ctDNA is mostly interpreted as a binary variable (detectable vs. undetectable); however, further study of ctDNA as a continuous variable may further refine prognostication [93]. In addition, the costs associated with repeated testing further limits studies evaluating longitudinal ctDNA dynamics and its full clinical potential.

6. Future Directions in CLM Biology

Beyond the main biologic pillars discussed in this review, several emerging domains are likely to further refine CLM management. A deeper understanding of the tumor microenvironment, including its role in tumor initiation, progression, and metastasis, may uncover new therapeutic endeavors [100]. Clonal evolution is also gaining increasing interest in mCRC, as detection of emerging resistant clones may be helpful for adapting therapies [101]. Likewise, further insight into intrapatient tumor heterogeneity is needed to better understand its influence on tumor development, treatment response, and disease outcomes [102]. Multi-omics approaches integrating genomic, transcriptomic, proteomic, and epigenomic data also hold promise for more advanced treatment strategies [103]. In addition, investigation of hepatocyte metabolic reprogramming may reveal liver-specific mechanisms of metastasis [104]. Lastly, artificial intelligence may help integrate complex datasets to uncover patterns not detectable through conventional analyses, ultimately supporting more personalized patient care [105].

7. Conclusions

Tumor biology for CLM has been evolving to include more nuanced variables. Radiologic and pathologic assessments beyond size reduction better portray the effects of targeted therapies and tumor response. Somatic gene mutations reflect the tumor pathophysiology and help predict aggressiveness. ctDNA reflects ongoing tumor status and aids early detection of MRD and recurrence. Together, these domains support a more biology-informed approach for patient selection, perioperative decision-making, surveillance, and prognostication in CLM. Further validation of the current evidence across diverse patient populations is needed to broaden its applicability. Studies with in-depth genetic analysis and ctDNA dynamics may contribute to further advancing the field by improving oncologic risk stratification and patient outcomes.

Author Contributions

Article preparation: M.M., A.H., K.I., N.B. and J.-N.V.; article editing: M.M., A.H., K.I., N.B. and J.-N.V.; final approval: M.M., A.H., K.I., N.B. and J.-N.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

This study is a narrative review of previously published literature. No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Siegel, R.L.; Kratzer, T.B.; Giaquinto, A.N.; Sung, H.; Jemal, A. Cancer statistics, 2025. CA Cancer J. Clin. 2025, 75, 10–45. [Google Scholar] [CrossRef]
  2. Eng, C.; Yoshino, T.; Ruíz-García, E.; Mostafa, N.; Cann, C.G.; O’Brian, B.; Benny, A.; Perez, R.O.; Cremolini, C. Colorectal cancer. Lancet 2024, 404, 294–310. [Google Scholar] [CrossRef] [PubMed]
  3. Johannsen, I.R.; Boysen, A.K.; Nielsen, M.F.; Mortensen, F.V.; Kirkegård, J. Liver Metastases and Survival Among Patients with Colorectal Cancer. JAMA Netw. Open 2025, 8, e2550467. [Google Scholar] [CrossRef]
  4. Fong, Y.; Fortner, J.; Sun, R.L.; Brennan, M.F.; Blumgart, L.H. Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: Analysis of 1001 consecutive cases. Ann. Surg. 1999, 230, 309–318. [Google Scholar] [CrossRef]
  5. Kawaguchi, Y.; Kopetz, S.; Panettieri, E.; Hwang, H.; Wang, X.; Cao, H.S.T.; Tzeng, C.D.; Chun, Y.S.; Aloia, T.A.; Vauthey, J.N. Improved Survival over Time After Resection of Colorectal Liver Metastases and Clinical Impact of Multigene Alteration Testing in Patients with Metastatic Colorectal Cancer. J. Gastrointest. Surg. 2022, 26, 583–593. [Google Scholar] [CrossRef]
  6. Adam, R.; Piedvache, C.; Chiche, L.; Adam, J.P.; Salamé, E.; Bucur, P.; Cherqui, D.; Scatton, O.; Granger, V.; Ducreux, M.; et al. Liver transplantation plus chemotherapy versus chemotherapy alone in patients with permanently unresectable colorectal liver metastases (TransMet): Results from a multicentre, open-label, prospective, randomised controlled trial. Lancet 2024, 404, 1107–1118. [Google Scholar] [CrossRef]
  7. Lam, V.W.; Spiro, C.; Laurence, J.M.; Johnston, E.; Hollands, M.J.; Pleass, H.C.; Richardson, A.J. A systematic review of clinical response and survival outcomes of downsizing systemic chemotherapy and rescue liver surgery in patients with initially unresectable colorectal liver metastases. Ann. Surg. Oncol. 2012, 19, 1292–1301. [Google Scholar] [CrossRef]
  8. Okuno, M.; Hatano, E.; Toda, R.; Nishino, H.; Nakamura, K.; Ishii, T.; Seo, S.; Taura, K.; Yasuchika, K.; Yazawa, T.; et al. Conversion to complete resection with mFOLFOX6 with bevacizumab or cetuximab based on K-RAS status for unresectable colorectal liver metastasis (BECK study): Long-term results of survival. J. Hepatobiliary Pancreat. Sci. 2020, 27, 496–509. [Google Scholar] [CrossRef]
  9. Morris, V.K.; Kennedy, E.B.; Baxter, N.N.; Benson, A.B., 3rd; Cercek, A.; Cho, M.; Ciombor, K.K.; Cremolini, C.; Davis, A.; Deming, D.A.; et al. Treatment of Metastatic Colorectal Cancer: ASCO Guideline. J. Clin. Oncol. 2023, 41, 678–700. [Google Scholar] [CrossRef] [PubMed]
  10. Chun, Y.S.; Vauthey, J.-N.; Boonsirikamchai, P.; Maru, D.M.; Kopetz, S.; Palavecino, M.; Curley, S.A.; Abdalla, E.K.; Kaur, H.; Charnsangavej, C.; et al. Association of Computed Tomography Morphologic Criteria with Pathologic Response and Survival in Patients Treated with Bevacizumab for Colorectal Liver Metastases. JAMA 2009, 302, 2338–2344. [Google Scholar] [CrossRef] [PubMed]
  11. Miller, A.B.; Hoogstraten, B.; Staquet, M.; Winkler, A. Reporting results of cancer treatment. Cancer 1981, 47, 207–214. [Google Scholar] [CrossRef]
  12. Eisenhauer, E.A.; Therasse, P.; Bogaerts, J.; Schwartz, L.H.; Sargent, D.; Ford, R.; Dancey, J.; Arbuck, S.; Gwyther, S.; Mooney, M.; et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer 2009, 45, 228–247. [Google Scholar] [CrossRef]
  13. Mantatzis, M.; Kakolyris, S.; Amarantidis, K.; Karayiannakis, A.; Prassopoulos, P. Treatment response classification of liver metastatic disease evaluated on imaging. Are RECIST unidimensional measurements accurate? Eur. Radiol. 2009, 19, 1809–1816. [Google Scholar] [CrossRef]
  14. Fournier, L.; de Geus-Oei, L.F.; Regge, D.; Oprea-Lager, D.E.; D’Anastasi, M.; Bidaut, L.; Bäuerle, T.; Lopci, E.; Cappello, G.; Lecouvet, F.; et al. Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group—ESOI Joint Paper. Front. Oncol. 2021, 11, 800547. [Google Scholar] [CrossRef] [PubMed]
  15. Choi, H.; Charnsangavej, C.; de Castro Faria, S.; Tamm, E.P.; Benjamin, R.S.; Johnson, M.M.; Macapinlac, H.A.; Podoloff, D.A. CT evaluation of the response of gastrointestinal stromal tumors after imatinib mesylate treatment: A quantitative analysis correlated with FDG PET findings. AJR Am. J. Roentgenol. 2004, 183, 1619–1628. [Google Scholar] [CrossRef]
  16. Blazer, D.G.; Kishi, Y.; Maru, D.M.; Kopetz, S.; Chun, Y.S.; Overman, M.J.; Fogelman, D.; Eng, C.; Chang, D.Z.; Wang, H.; et al. Pathologic Response to Preoperative Chemotherapy: A New Outcome End Point After Resection of Hepatic Colorectal Metastases. J. Clin. Oncol. 2008, 26, 5344–5351. [Google Scholar] [CrossRef]
  17. Rubbia-Brandt, L.; Giostra, E.; Brezault, C.; Roth, A.D.; Andres, A.; Audard, V.; Sartoretti, P.; Dousset, B.; Majno, P.E.; Soubrane, O.; et al. Importance of histological tumor response assessment in predicting the outcome in patients with colorectal liver metastases treated with neo-adjuvant chemotherapy followed by liver surgery. Ann. Oncol. 2007, 18, 299–304. [Google Scholar] [CrossRef]
  18. Maru, D.M.; Kopetz, S.; Boonsirikamchai, P.; Agarwal, A.; Chun, Y.S.; Wang, H.; Abdalla, E.K.; Kaur, H.; Charnsangavej, C.; Vauthey, J.N.; et al. Tumor thickness at the tumor-normal interface: A novel pathologic indicator of chemotherapy response in hepatic colorectal metastases. Am. J. Surg. Pathol. 2010, 34, 1287–1294. [Google Scholar] [CrossRef]
  19. Shindoh, J.; Loyer, E.M.; Kopetz, S.; Boonsirikamchai, P.; Maru, D.M.; Chun, Y.S.; Zimmitti, G.; Curley, S.A.; Charnsangavej, C.; Aloia, T.A.; et al. Optimal morphologic response to preoperative chemotherapy: An alternate outcome end point before resection of hepatic colorectal metastases. J. Clin. Oncol. 2012, 30, 4566–4572. [Google Scholar] [CrossRef] [PubMed]
  20. Nishioka, Y.; Shindoh, J.; Yoshioka, R.; Gonoi, W.; Abe, H.; Okura, N.; Yoshida, S.; Oba, M.; Hashimoto, M.; Watanabe, G.; et al. Radiological Morphology of Colorectal Liver Metastases after Preoperative Chemotherapy Predicts Tumor Viability and Postoperative Outcomes. J. Gastrointest. Surg. 2015, 19, 1653–1661. [Google Scholar] [CrossRef] [PubMed]
  21. Mazard, T.; Boonsirikamchai, P.; Overman, M.J.; Asran, M.A.; Choi, H.; Herron, D.; Eng, C.; Maru, D.M.; Ychou, M.; Vauthey, J.N.; et al. Comparison of early radiological predictors of outcome in patients with colorectal cancer with unresectable hepatic metastases treated with bevacizumab. Gut 2018, 67, 1095–1102. [Google Scholar] [CrossRef] [PubMed]
  22. Karagkounis, G.; Horvat, N.; Danilova, S.; Chhabra, S.; Narayan, R.R.; Barekzai, A.B.; Kleshchelski, A.; Joanne, C.; Gonen, M.; Balachandran, V.; et al. Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy. Ann. Surg. Oncol. 2024, 31, 9196–9204. [Google Scholar] [CrossRef] [PubMed]
  23. Vera, R.; Gómez, M.L.; Ayuso, J.R.; Figueras, J.; García-Alfonso, P.; Martínez, V.; Lacasta, A.; Ruiz-Casado, A.; Safont, M.J.; Aparicio, J.; et al. Correlation of RECIST, Computed Tomography Morphological Response, and Pathological Regression in Hepatic Metastasis Secondary to Colorectal Cancer: The AVAMET Study. Cancers 2020, 12, 2259. [Google Scholar] [CrossRef]
  24. Suzuki, C.; Blomqvist, L.; Sundin, A.; Jacobsson, H.; Byström, P.; Berglund, Å.; Nygren, P.; Glimelius, B. The initial change in tumor size predicts response and survival in patients with metastatic colorectal cancer treated with combination chemotherapy. Ann. Oncol. 2012, 23, 948–954. [Google Scholar] [CrossRef]
  25. Adams, R.B.; Aloia, T.A.; Loyer, E.; Pawlik, T.M.; Taouli, B.; Vauthey, J.N. Selection for hepatic resection of colorectal liver metastases: Expert consensus statement. HPB 2013, 15, 91–103. [Google Scholar] [CrossRef]
  26. Poultsides, G.A.; Bao, F.; Servais, E.L.; Hernandez-Boussard, T.; DeMatteo, R.P.; Allen, P.J.; Fong, Y.; Kemeny, N.E.; Saltz, L.B.; Klimstra, D.S.; et al. Pathologic Response to Preoperative Chemotherapy in Colorectal Liver Metastases: Fibrosis, not Necrosis, Predicts Outcome. Ann. Surg. Oncol. 2012, 19, 2797–2804. [Google Scholar] [CrossRef]
  27. Brouquet, A.; Zimmitti, G.; Kopetz, S.; Stift, J.; Julié, C.; Lemaistre, A.I.; Agarwal, A.; Patel, V.; Benoist, S.; Nordlinger, B.; et al. Multicenter validation study of pathologic response and tumor thickness at the tumor-normal liver interface as independent predictors of disease-free survival after preoperative chemotherapy and surgery for colorectal liver metastases. Cancer 2013, 119, 2778–2788. [Google Scholar] [CrossRef]
  28. Paulatto, L.; Dioguardi Burgio, M.; Sartoris, R.; Beaufrère, A.; Cauchy, F.; Paradis, V.; Vilgrain, V.; Ronot, M. Colorectal liver metastases: Radiopathological correlation. Insights Imaging 2020, 11, 99. [Google Scholar] [CrossRef]
  29. Bond, M.J.G.; Mijnals, C.; Bolhuis, K.; van Amerongen, M.J.; Engelbrecht, M.R.W.; Hermans, J.J.; van Lienden, K.P.; May, A.M.; Swijnenburg, R.J.; Punt, C.J.A. Prognostic value of radiologic and pathological response in colorectal cancer liver metastases upon systemic induction treatment: Subgroup analysis of the CAIRO5 trial. ESMO Open 2024, 9, 104075. [Google Scholar] [CrossRef]
  30. Kataoka, K.; Mauer, M.; Shiozawa, M.; Marreaud, S.; Kishi, Y.; Cabrieto, J.; Onaya, H.; Ducreux, M.; Suto, T.; Kang, H.C.; et al. Diagnostic Accuracy of Imaging in Assessing Nonviability of Disappearing Colorectal Liver Metastasis. JAMA Surg. 2025, 160, 1212–1220. [Google Scholar] [CrossRef] [PubMed]
  31. Viganò, L.; Capussotti, L.; De Rosa, G.; De Saussure, W.O.; Mentha, G.; Rubbia-Brandt, L. Liver resection for colorectal metastases after chemotherapy: Impact of chemotherapy-related liver injuries, pathological tumor response, and micrometastases on long-term survival. Ann. Surg. 2013, 258, 731–740. [Google Scholar] [CrossRef]
  32. Albanese, I.; Scibetta, A.G.; Migliavacca, M.; Russo, A.; Bazan, V.; Tomasino, R.M.; Colomba, P.; Tagliavia, M.; La Farina, M. Heterogeneity within and between primary colorectal carcinomas and matched metastases as revealed by analysis of Ki-ras and p53 mutations. Biochem. Biophys. Res. Commun. 2004, 325, 784–791. [Google Scholar] [CrossRef] [PubMed]
  33. Lièvre, A.; Bachet, J.B.; Le Corre, D.; Boige, V.; Landi, B.; Emile, J.F.; Côté, J.F.; Tomasic, G.; Penna, C.; Ducreux, M.; et al. KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer. Cancer Res. 2006, 66, 3992–3995. [Google Scholar] [CrossRef] [PubMed]
  34. Kawaguchi, Y.; Kopetz, S.; Kwong, L.; Xiao, L.; Morris, J.S.; Tran Cao, H.S.; Tzeng, C.D.; Chun, Y.S.; Lee, J.E.; Vauthey, J.N. Genomic Sequencing and Insight into Clinical Heterogeneity and Prognostic Pathway Genes in Patients with Metastatic Colorectal Cancer. J. Am. Coll. Surg. 2021, 233, 272–284.e13. [Google Scholar] [CrossRef]
  35. Downward, J. Targeting RAS signalling pathways in cancer therapy. Nat. Rev. Cancer 2003, 3, 11–22. [Google Scholar] [CrossRef] [PubMed]
  36. Urosevic, J.; Blasco, M.T.; Llorente, A.; Bellmunt, A.; Berenguer-Llergo, A.; Guiu, M.; Cañellas, A.; Fernandez, E.; Burkov, I.; Clapés, M.; et al. ERK1/2 Signaling Induces Upregulation of ANGPT2 and CXCR4 to Mediate Liver Metastasis in Colon Cancer. Cancer Res. 2020, 80, 4668–4680. [Google Scholar] [CrossRef]
  37. Janssen, J.B.E.; Medema, J.P.; Gootjes, E.C.; Tauriello, D.V.F.; Verheul, H.M.W. Mutant RAS and the tumor microenvironment as dual therapeutic targets for advanced colorectal cancer. Cancer Treat. Rev. 2022, 109, 102433. [Google Scholar] [CrossRef]
  38. Vauthey, J.N.; Zimmitti, G.; Kopetz, S.E.; Shindoh, J.; Chen, S.S.; Andreou, A.; Curley, S.A.; Aloia, T.A.; Maru, D.M. RAS mutation status predicts survival and patterns of recurrence in patients undergoing hepatectomy for colorectal liver metastases. Ann. Surg. 2013, 258, 619–626. [Google Scholar] [CrossRef]
  39. Goffredo, P.; Utria, A.F.; Beck, A.C.; Chun, Y.S.; Howe, J.R.; Weigel, R.J.; Vauthey, J.N.; Hassan, I. The Prognostic Impact of KRAS Mutation in Patients Having Curative Resection of Synchronous Colorectal Liver Metastases. J. Gastrointest. Surg. 2019, 23, 1957–1963. [Google Scholar] [CrossRef]
  40. Díez-Alonso, M.; Mendoza-Moreno, F.; Jiménez-Álvarez, L.; Nuñez, O.; Blazquez-Martín, A.; Sanchez-Gollarte, A.; Matías-García, B.; Molina, R.; San-Juan, A.; Gutierrez-Calvo, A. Prognostic factors of survival in stage IV colorectal cancer with synchronous liver metastasis: Negative effect of the KRAS mutation. Mol. Clin. Oncol. 2021, 14, 93. [Google Scholar] [CrossRef]
  41. Roncato, R.; Polesel, J.; Tosi, F.; Peruzzi, E.; Brugugnoli, E.; Pantano, C.L.; Furfaro, M.; Girolamo, F.D.; Nani, A.; Pani, A.; et al. The challenge of molecular selection in liver-limited metastatic colorectal cancer for surgical resection: A systematic review and meta-analysis in the context of current and future approaches. Oncol. Res. 2024, 32, 1407–1422. [Google Scholar] [CrossRef]
  42. Kawaguchi, Y.; Kopetz, S.; Tran Cao, H.S.; Panettieri, E.; De Bellis, M.; Nishioka, Y.; Hwang, H.; Wang, X.; Tzeng, C.D.; Chun, Y.S.; et al. Contour prognostic model for predicting survival after resection of colorectal liver metastases: Development and multicentre validation study using largest diameter and number of metastases with RAS mutation status. Br. J. Surg. 2021, 108, 968–975. [Google Scholar] [CrossRef] [PubMed]
  43. Kawaguchi, Y.; Kopetz, S.; Lillemoe, H.A.; Hwang, H.; Wang, X.; Tzeng, C.D.; Chun, Y.S.; Aloia, T.A.; Vauthey, J.N. A New Surveillance Algorithm After Resection of Colorectal Liver Metastases Based on Changes in Recurrence Risk and RAS Mutation Status. J. Natl. Compr. Cancer Netw. 2020, 18, 1500–1508. [Google Scholar] [CrossRef] [PubMed]
  44. Kawaguchi, Y.; Newhook, T.E.; Tran Cao, H.S.; Tzeng, C.-W.D.; Chun, Y.S.; Aloia, T.A.; Dasari, A.; Kopetz, S.; Vauthey, J.-N. Alteration of FBXW7 is Associated with Worse Survival in Patients Undergoing Resection of Colorectal Liver Metastases. J. Gastrointest. Surg. 2021, 25, 186–194. [Google Scholar] [CrossRef]
  45. Yamashita, S.; Chun, Y.S.; Kopetz, S.E.; Vauthey, J.N. Biomarkers in colorectal liver metastases. Br. J. Surg. 2018, 105, 618–627. [Google Scholar] [CrossRef]
  46. Laoukili, J.; van Schelven, S.; Küçükköse, E.; Verheem, A.; Goey, K.; Koopman, M.; Borel Rinkes, I.; Kranenburg, O. BRAF(V600E) in colorectal cancer reduces sensitivity to oxidative stress and promotes site-specific metastasis by stimulating glutathione synthesis. Cell Rep. 2022, 41, 111728. [Google Scholar] [CrossRef] [PubMed]
  47. Deng, W.; Liu, X.; Huang, S.; Wu, Z.; Alessandro, F.; Lin, Q.; Cai, Z.; Zhang, Z.; Huang, Y.; Wang, H.; et al. CXCL16 promotes tumor metastasis by regulating angiogenesis in the tumor micro-environment of BRAF V600E mutant colorectal cancer. Transl. Oncol. 2024, 41, 101854. [Google Scholar] [CrossRef] [PubMed]
  48. Gagnière, J.; Dupré, A.; Gholami, S.S.; Pezet, D.; Boerner, T.; Gönen, M.; Kingham, T.P.; Allen, P.J.; Balachandran, V.P.; De Matteo, R.P.; et al. Is Hepatectomy Justified for BRAF Mutant Colorectal Liver Metastases?: A Multi-institutional Analysis of 1497 Patients. Ann. Surg. 2020, 271, 147–154. [Google Scholar] [CrossRef]
  49. Margonis, G.A.; Buettner, S.; Andreatos, N.; Kim, Y.; Wagner, D.; Sasaki, K.; Beer, A.; Schwarz, C.; Løes, I.M.; Smolle, M.; et al. Association of BRAF Mutations with Survival and Recurrence in Surgically Treated Patients with Metastatic Colorectal Liver Cancer. JAMA Surg. 2018, 153, e180996. [Google Scholar] [CrossRef]
  50. Levine, A.J. p53, the cellular gatekeeper for growth and division. Cell 1997, 88, 323–331. [Google Scholar] [CrossRef]
  51. Maki, H.; Haddad, A.; Ayabe, R.I.; Lendoire, M.; Khanduri, I.; Maru, D.M.; Vauthey, J.N. TP53 Alteration and Its Effect on Pathologic Response Are Associated with Survival after Resection of Colorectal Liver Metastases. J. Gastrointest. Surg. 2023, 27, 2597–2600. [Google Scholar] [CrossRef]
  52. Miyaki, M.; Kuroki, T. Role of Smad4 (DPC4) inactivation in human cancer. Biochem. Biophys. Res. Commun. 2003, 306, 799–804. [Google Scholar] [CrossRef]
  53. Voorneveld, P.W.; Kodach, L.L.; Jacobs, R.J.; Liv, N.; Zonnevylle, A.C.; Hoogenboom, J.P.; Biemond, I.; Verspaget, H.W.; Hommes, D.W.; de Rooij, K.; et al. Loss of SMAD4 Alters BMP Signaling to Promote Colorectal Cancer Cell Metastasis via Activation of Rho and ROCK. Gastroenterology 2014, 147, 196–208.e13. [Google Scholar] [CrossRef]
  54. Inamoto, S.; Itatani, Y.; Yamamoto, T.; Minamiguchi, S.; Hirai, H.; Iwamoto, M.; Hasegawa, S.; Taketo, M.M.; Sakai, Y.; Kawada, K. Loss of SMAD4 Promotes Colorectal Cancer Progression by Accumulation of Myeloid-Derived Suppressor Cells through the CCL15-CCR1 Chemokine Axis. Clin. Cancer Res. 2016, 22, 492–501. [Google Scholar] [CrossRef] [PubMed]
  55. Itatani, Y.; Kawada, K.; Fujishita, T.; Kakizaki, F.; Hirai, H.; Matsumoto, T.; Iwamoto, M.; Inamoto, S.; Hatano, E.; Hasegawa, S.; et al. Loss of SMAD4 from colorectal cancer cells promotes CCL15 expression to recruit CCR1+ myeloid cells and facilitate liver metastasis. Gastroenterology 2013, 145, 1064–1075.e11. [Google Scholar] [CrossRef]
  56. Mizuno, T.; Cloyd, J.M.; Vicente, D.; Omichi, K.; Chun, Y.S.; Kopetz, S.E.; Maru, D.; Conrad, C.; Tzeng, C.-W.D.; Wei, S.H.; et al. SMAD4 gene mutation predicts poor prognosis in patients undergoing resection for colorectal liver metastases. Eur. J. Surg. Oncol. 2018, 44, 684–692. [Google Scholar] [CrossRef] [PubMed]
  57. Yumimoto, K.; Nakayama, K.I. Recent insight into the role of FBXW7 as a tumor suppressor. Semin. Cancer Biol. 2020, 67, 1–15. [Google Scholar] [CrossRef]
  58. Shang, W.; Yan, C.; Liu, R.; Chen, L.; Cheng, D.; Hao, L.; Yuan, W.; Chen, J.; Yang, H. Clinical significance of FBXW7 tumor suppressor gene mutations and expression in human colorectal cancer: A systemic review and meta-analysis. BMC Cancer 2021, 21, 770. [Google Scholar] [CrossRef] [PubMed]
  59. Aghabozorgi, A.S.; Bahreyni, A.; Soleimani, A.; Bahrami, A.; Khazaei, M.; Ferns, G.A.; Avan, A.; Hassanian, S.M. Role of adenomatous polyposis coli (APC) gene mutations in the pathogenesis of colorectal cancer; current status and perspectives. Biochimie 2019, 157, 64–71. [Google Scholar] [CrossRef]
  60. Wang, C.; Ouyang, C.; Cho, M.; Ji, J.; Sandhu, J.; Goel, A.; Kahn, M.; Fakih, M. Wild-type APC Is Associated with Poor Survival in Metastatic Microsatellite Stable Colorectal Cancer. Oncologist 2021, 26, 208–214. [Google Scholar] [CrossRef] [PubMed]
  61. Li, J.; Waller, G.; Qin, Y.; Yu, A.T.; Li, T.; Li, D.; Srouji, R.; Correa-Gallego, C.; Hiotis, S.; Gunasekaran, G.; et al. Worse Survival in Co-Altered RAS-TP53 Patients with Resected Colorectal Liver Metastases. Ann. Surg. Oncol. 2026, 33, 1448–1459. [Google Scholar] [CrossRef] [PubMed]
  62. Datta, J.; Smith, J.J.; Chatila, W.K.; McAuliffe, J.C.; Kandoth, C.; Vakiani, E.; Frankel, T.L.; Ganesh, K.; Wasserman, I.; Lipsyc-Sharf, M.; et al. Coaltered Ras/B-raf and TP53 Is Associated with Extremes of Survivorship and Distinct Patterns of Metastasis in Patients with Metastatic Colorectal Cancer. Clin. Cancer Res. 2020, 26, 1077–1085. [Google Scholar] [CrossRef] [PubMed]
  63. Chun, Y.S.; Passot, G.; Nishioka, Y.; Katkhuda, R.; Arvide, E.M.; Benzerdjeb, N.; Lopez, J.; Kopetz, S.E.; Maru, D.M.; Vauthey, J.N. Colorectal Liver Micrometastases: Association with RAS/TP53 Co-Mutation and Prognosis after Surgery. J. Am. Coll. Surg. 2022, 235, 8–16. [Google Scholar] [CrossRef] [PubMed]
  64. Maki, H.; Ayabe, R.I.; Haddad, A.; Nishioka, Y.; Newhook, T.E.; Tran Cao, H.S.; Chun, Y.S.; Tzeng, C.D.; Vauthey, J.N. Associations of KRAS Point Mutations with Survival of Patients Who Underwent Curative-Intent Resection of Colorectal Liver Metastases. Ann. Surg. Oncol. 2025, 32, 2425–2434. [Google Scholar] [CrossRef]
  65. Kawaguchi, Y.; Kopetz, S.; Newhook, T.E.; De Bellis, M.; Chun, Y.S.; Tzeng, C.D.; Aloia, T.A.; Vauthey, J.N. Mutation Status of RAS, TP53, and SMAD4 is Superior to Mutation Status of RAS Alone for Predicting Prognosis after Resection of Colorectal Liver Metastases. Clin. Cancer Res. 2019, 25, 5843–5851. [Google Scholar] [CrossRef]
  66. Chen, W.J.; Wang, H.W.; Wang, L.J.; Xu, D.; Liu, M.; Xing, B.C. Mutational status of RAS, SMAD4 and APC predicts survival after resection of colorectal liver metastases in Chinese patients: Prognostic stratification based on genetic sequencing data of multiple somatic genes. World J. Surg. Oncol. 2025, 23, 172. [Google Scholar] [CrossRef]
  67. Odisio, B.C.; Yamashita, S.; Huang, S.Y.; Harmoush, S.; Kopetz, S.E.; Ahrar, K.; Shin Chun, Y.; Conrad, C.; Aloia, T.A.; Gupta, S.; et al. Local tumour progression after percutaneous ablation of colorectal liver metastases according to RAS mutation status. Br. J. Surg. 2017, 104, 760–768. [Google Scholar] [CrossRef]
  68. Lin, Y.M.; Paolucci, I.; O’Connor, C.S.; Anderson, B.M.; Rigaud, B.; Fellman, B.M.; Jones, K.A.; Brock, K.K.; Odisio, B.C. Ablative margins of colorectal liver metastases using deformable CT image registration and autosegmentation. Radiology 2023, 307, e221373. [Google Scholar] [CrossRef]
  69. Zhang, Q.; Peng, J.; Ye, M.; Weng, W.; Tan, C.; Ni, S.; Huang, D.; Sheng, W.; Wang, L. KRAS Mutation Predicted More Mirometastases and Closer Resection Margins in Patients with Colorectal Cancer Liver Metastases. Ann. Surg. Oncol. 2020, 27, 1164–1173. [Google Scholar] [CrossRef]
  70. Rhaiem, R.; Duramé, A.; Primavesi, F.; Dorcaratto, D.; Syn, N.; de la Hoz Rodríguez, Á.; Dupré, A.; Piardi, T.; Fernández, G.B.; Villaverde, A.P. Critical appraisal of surgical margins according to KRAS status in liver resection for colorectal liver metastases: Should surgical strategy be influenced by tumor biology? Surgery 2024, 176, 124–133. [Google Scholar] [CrossRef]
  71. Brandão, G.R.; Trindade, B.O.; Flores, L.H.F.; Motter, S.B.; Alves, C.B.; Remonti, T.A.P.; Lucchese, A.M.; Junior, A.D.P.; Kalil, A.N. Does RAS Status Increase the Prevalence of Positive Resection Margin in Colorectal Liver Metastasis? A Systematic Review and Meta-Analysis. Am. Surg. 2023, 89, 5638–5647. [Google Scholar] [CrossRef]
  72. Margonis, G.A.; Buettner, S.; Andreatos, N.; Sasaki, K.; Ijzermans, J.N.M.; van Vugt, J.L.A.; Pawlik, T.M.; Choti, M.A.; Cameron, J.L.; He, J.; et al. Anatomical Resections Improve Disease-free Survival in Patients with KRAS-mutated Colorectal Liver Metastases. Ann. Surg. 2017, 266, 641–649. [Google Scholar] [CrossRef]
  73. Joechle, K.; Vreeland, T.J.; Vega, E.A.; Okuno, M.; Newhook, T.E.; Panettieri, E.; Chun, Y.S.; Tzeng, C.-W.D.; Aloia, T.A.; Lee, J.E.; et al. Anatomic Resection Is Not Required for Colorectal Liver Metastases with RAS Mutation. J. Gastrointest. Surg. 2020, 24, 1033–1039. [Google Scholar] [CrossRef]
  74. Nishioka, Y.; Paez-Arango, N.; Boettcher, F.O.; Kawaguchi, Y.; Newhook, T.E.; Chun, Y.S.; Tzeng, C.D.; Tran Cao, H.S.; Lee, J.E.; Vreeland, T.J.; et al. Neither Surgical Margin Status nor Somatic Mutation Predicts Local Recurrence After R0-intent Resection for Colorectal Liver Metastases. J. Gastrointest. Surg. 2022, 26, 791–801. [Google Scholar] [CrossRef]
  75. Haddad, A.; Vauthey, J.N.; Newhook, T.E. A caution against KRAS-guided margin width during hepatectomy for colorectal liver metastases. Surgery 2024, 176, 134–136. [Google Scholar] [CrossRef] [PubMed]
  76. Pikoulis, E.; Papaconstantinou, D.; Pikouli, A.; Pararas, N.; Buettner, S.; Wang, J.; Stasinos, G.; Belias, M.; Dellaportas, D.; Pozios, I.; et al. Is Precision Surgery Applicable to Colorectal Liver Metastases? A Systematic Review and Meta-analysis of Studies that Investigate the Association of Surgical Technique with Outcomes in the Context of Distinct Tumor Biology. Ann. Surg. Oncol. 2024, 31, 1823–1832. [Google Scholar] [CrossRef] [PubMed]
  77. Mise, Y.; Aloia, T.A.; Brudvik, K.W.; Schwarz, L.; Vauthey, J.N.; Conrad, C. Parenchymal-sparing Hepatectomy in Colorectal Liver Metastasis Improves Salvageability and Survival. Ann. Surg. 2016, 263, 146–152. [Google Scholar] [CrossRef]
  78. Lendoire, M.; Maki, H.; Haddad, A.; Jain, A.J.; Tran Cao, H.S.; Chun, Y.S.; Tzeng, C.D.; Vauthey, J.N.; Newhook, T.E. Parenchymal-sparing hepatectomy vs. single-stage major hepatectomy for bilateral colorectal liver metastases: A higher rate of microscopically positive margins is not associated with increased local recurrence after parenchymal-sparing hepatectomy. J. Gastrointest. Surg. 2025, 29, 102070. [Google Scholar] [CrossRef]
  79. Uutela, A.; Osterlund, E.; Halonen, P.; Kallio, R.; Ålgars, A.; Salminen, T.; Lamminmäki, A.; Soveri, L.M.; Ristamäki, R.; Lehtomäki, K.; et al. Resectability, conversion, metastasectomy and outcome according to RAS and BRAF status for metastatic colorectal cancer in the prospective RAXO study. Br. J. Cancer 2022, 127, 686–694. [Google Scholar] [CrossRef]
  80. Tang, W.; Liu, Y.; Ji, M.; Liu, T.; Chen, Y.; Zhuang, A.; Mao, Y.; Chang, W.; Wei, Y.; Ren, L.; et al. Association of RAS/BRAF Status and Prognosis of Metastatic Colorectal Cancer: Analysis of 1002 Consecutive Cases. Ann. Surg. Oncol. 2022, 29, 3593–3603. [Google Scholar] [CrossRef] [PubMed]
  81. Margonis, G.A.; Wang, J.; Papakonstantinou, D.; Beyer, K.; Kreis, M.E.; D’Angelica, M. A critical appraisal of the current landscape of resectable BRAF mutated colorectal liver metastases: A systematic review. Chin. Clin. Oncol. 2024, 13, 51. [Google Scholar] [CrossRef]
  82. Smedman, T.M.; Line, P.D.; Hagness, M.; Syversveen, T.; Grut, H.; Dueland, S. Liver transplantation for unresectable colorectal liver metastases in patients and donors with extended criteria (SECA-II arm D study). BJS Open 2020, 4, 467–477. [Google Scholar] [CrossRef]
  83. Cigrovski Berkovic, M.; Mrzljak, A.; Melandro, F.; Lai, Q. Inclusion criteria for liver transplantation in patients with colorectal liver metastases: How to make the best selection? World J. Clin. Oncol. 2025, 16, 106629. [Google Scholar] [CrossRef] [PubMed]
  84. Haddad, A.; Lendoire, M.; Uppal, A.; Maki, H.; Folkert, I.; Wang, Y.; Ayabe, R.I.; Newhook, T.E.; Chun, Y.S.; Tzeng, C.D.; et al. CEA Rebound After Discontinuation of Pre-Hepatectomy Chemotherapy Predicts Worse Outcomes After Resection of Colorectal Cancer Liver Metastases. Ann. Surg. Oncol. 2025, 32, 1021–1032. [Google Scholar] [CrossRef]
  85. Tie, J.; Kinde, I.; Wang, Y.; Wong, H.L.; Roebert, J.; Christie, M.; Tacey, M.; Wong, R.; Singh, M.; Karapetis, C.S.; et al. Circulating tumor DNA as an early marker of therapeutic response in patients with metastatic colorectal cancer. Ann. Oncol. 2015, 26, 1715–1722. [Google Scholar] [CrossRef] [PubMed]
  86. Tie, J.; Wang, Y.; Tomasetti, C.; Li, L.; Springer, S.; Kinde, I.; Silliman, N.; Tacey, M.; Wong, H.L.; Christie, M.; et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl. Med. 2016, 8, 346ra92. [Google Scholar] [CrossRef] [PubMed]
  87. Ghidini, M.; Hahne, J.C.; Senti, C.; Heide, T.; Proszek, P.Z.; Shaikh, R.; Carter, P.; Hubank, M.; Trevisani, F.; Garrone, O.; et al. Circulating Tumor DNA Dynamics and Clinical Outcome in Metastatic Colorectal Cancer Patients Undergoing Front-Line Chemotherapy. Clin. Cancer Res. 2025, 31, 707–718. [Google Scholar] [CrossRef]
  88. Kim, S.; Lim, Y.; Kang, J.K.; Kim, H.P.; Roh, H.; Kim, S.Y.; Lee, D.; Bang, D.; Jeong, S.Y.; Park, K.J.; et al. Dynamic changes in longitudinal circulating tumour DNA profile during metastatic colorectal cancer treatment. Br. J. Cancer 2022, 127, 898–907. [Google Scholar] [CrossRef]
  89. Parikh, A.R.; Leshchiner, I.; Elagina, L.; Goyal, L.; Levovitz, C.; Siravegna, G.; Livitz, D.; Rhrissorrakrai, K.; Martin, E.E.; Van Seventer, E.E.; et al. Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers. Nat. Med. 2019, 25, 1415–1421. [Google Scholar] [CrossRef]
  90. Kobayashi, S.; Nakamura, Y.; Taniguchi, H.; Odegaard, J.I.; Nomura, S.; Kojima, M.; Sugimoto, M.; Konishi, M.; Gotohda, N.; Takahashi, S.; et al. Impact of Preoperative Circulating Tumor DNA Status on Survival Outcomes After Hepatectomy for Resectable Colorectal Liver Metastases. Ann. Surg. Oncol. 2021, 28, 4744–4755. [Google Scholar] [CrossRef]
  91. Nakamura, Y.; Yokoyama, S.; Matsuda, K.; Tamura, K.; Mitani, Y.; Iwamoto, H.; Mizumoto, Y.; Murakami, D.; Kitahata, Y.; Yamaue, H. Preoperative detection of KRAS mutated circulating tumor DNA is an independent risk factor for recurrence in colorectal cancer. Sci. Rep. 2021, 11, 441. [Google Scholar] [CrossRef]
  92. Narayan, R.R.; Goldman, D.A.; Gonen, M.; Reichel, J.; Huberman, K.H.; Raj, S.; Viale, A.; Kemeny, N.E.; Allen, P.J.; Balachandran, V.P. Peripheral circulating tumor DNA detection predicts poor outcomes after liver resection for metastatic colorectal cancer. Ann. Surg. Oncol. 2019, 26, 1824. [Google Scholar] [CrossRef]
  93. Newhook, T.E.; Overman, M.J.; Chun, Y.S.; Dasari, A.; Tzeng, C.D.; Cao, H.S.T.; Raymond, V.; Parseghian, C.; Johnson, B.; Nishioka, Y.; et al. Prospective Study of Perioperative Circulating Tumor DNA Dynamics in Patients Undergoing Hepatectomy for Colorectal Liver Metastases. Ann. Surg. 2023, 277, 813–820. [Google Scholar] [CrossRef]
  94. Tie, J.; Wang, Y.; Cohen, J.; Li, L.; Hong, W.; Christie, M.; Wong, H.L.; Kosmider, S.; Wong, R.; Thomson, B.; et al. Circulating tumor DNA dynamics and recurrence risk in patients undergoing curative intent resection of colorectal cancer liver metastases: A prospective cohort study. PLoS Med. 2021, 18, e1003620. [Google Scholar] [CrossRef] [PubMed]
  95. Nishioka, Y.; Chun, Y.S.; Overman, M.J.; Cao, H.S.T.; Tzeng, C.D.; Mason, M.C.; Kopetz, S.W.; Bauer, T.W.; Vauthey, J.N.; Newhook, T.E. Effect of Co-mutation of RAS and TP53 on Postoperative ctDNA Detection and Early Recurrence after Hepatectomy for Colorectal Liver Metastases. J. Am. Coll. Surg. 2022, 234, 474–483. [Google Scholar] [CrossRef]
  96. Marmorino, F.; Prisciandaro, M.; Giordano, M.; Ortolan, E.; Crucitta, S.; Manca, P.; Antoniotti, C.; Valenti, M.M.; Danesi, R.; Conca, V.; et al. Circulating Tumor DNA as a Marker of Minimal Residual Disease After Radical Resection of Colorectal Liver Metastases. JCO Precis. Oncol. 2022, 6, e2200244. [Google Scholar] [CrossRef]
  97. Kotani, D.; Oki, E.; Nakamura, Y.; Yukami, H.; Mishima, S.; Bando, H.; Shirasu, H.; Yamazaki, K.; Watanabe, J.; Kotaka, M.; et al. Molecular residual disease and efficacy of adjuvant chemotherapy in patients with colorectal cancer. Nat. Med. 2023, 29, 127–134. [Google Scholar] [CrossRef]
  98. Nakamura, Y.; Watanabe, J.; Akazawa, N.; Hirata, K.; Kataoka, K.; Yokota, M.; Kato, K.; Kotaka, M.; Kagawa, Y.; Yeh, K.-H.; et al. ctDNA-based molecular residual disease and survival in resectable colorectal cancer. Nat. Med. 2024, 30, 3272–3283. [Google Scholar] [CrossRef] [PubMed]
  99. Adams, A.M.; Vreeland, T.J.; Newhook, T.E. Circulating Tumor DNA: Towards More Individualized Treatment for Patients with Resectable Colorectal Cancer. J. Gastrointest. Cancer 2023, 54, 1071–1081. [Google Scholar] [CrossRef]
  100. Galindo-Pumariño, C.; Collado, M.; Herrera, M.; Peña, C. Tumor Microenvironment in Metastatic Colorectal Cancer: The Arbitrator in Patients’ Outcome. Cancers 2021, 13, 1130. [Google Scholar] [CrossRef] [PubMed]
  101. Saoudi González, N.; Salvà, F.; Ros, J.; Baraibar, I.; Rodríguez-Castells, M.; García, A.; Alcaráz, A.; Vega, S.; Bueno, S.; Tabernero, J.; et al. Unravelling the Complexity of Colorectal Cancer: Heterogeneity, Clonal Evolution, and Clinical Implications. Cancers 2023, 15, 4020. [Google Scholar] [CrossRef]
  102. Sveen, A.; Løes, I.M.; Alagaratnam, S.; Nilsen, G.; Høland, M.; Lingjærde, O.C.; Sorbye, H.; Berg, K.C.; Horn, A.; Angelsen, J.H.; et al. Intra-patient Inter-metastatic Genetic Heterogeneity in Colorectal Cancer as a Key Determinant of Survival after Curative Liver Resection. PLoS Genet. 2016, 12, e1006225. [Google Scholar] [CrossRef]
  103. Lee, D.; Park, Y.; Kim, S. Towards multi-omics characterization of tumor heterogeneity: A comprehensive review of statistical and machine learning approaches. Brief. Bioinform. 2021, 22, bbaa188. [Google Scholar] [CrossRef] [PubMed]
  104. Wang, S.; Ma, Z.; Lv, L.; Yu, Q.; Liu, S.; Lu, Y. Tumor microenvironment and metabolic reprogramming: Unraveling the complex interplay in gastrointestinal tumor liver metastasis. Crit. Rev. Oncol. Hematol. 2025, 216, 105001. [Google Scholar] [CrossRef] [PubMed]
  105. Rompianesi, G.; Pegoraro, F.; Ceresa, C.D.; Montalti, R.; Troisi, R.I. Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J. Gastroenterol. 2022, 28, 108–122. [Google Scholar] [CrossRef] [PubMed]
Cancers 18 01111 g001
Figure 1. Pre- and post-treatment CT scans showing RECIST stable disease and morphologic optimal response (A,B), characterized by homogeneous attenuation and sharp tumor–liver interface; morphologic incomplete response (C,D), with homogeneous attenuation but ill-defined tumor–liver interface remaining after treatment. Adapted from Chun et al. [10] with permission.
Figure 1. Pre- and post-treatment CT scans showing RECIST stable disease and morphologic optimal response (A,B), characterized by homogeneous attenuation and sharp tumor–liver interface; morphologic incomplete response (C,D), with homogeneous attenuation but ill-defined tumor–liver interface remaining after treatment. Adapted from Chun et al. [10] with permission.
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Figure 2. Contour plot of 5-year overall survival probability according to largest diameter and number of colorectal liver metastases for patients with RAS mutation (a) and RAS wild-type (b). The color gradient reflects the 5-year overall survival probability, with darker shades indicating lower survival and lighter shades indicating higher survival. Adapted from Kawaguchi et al. [42] with permission.
Figure 2. Contour plot of 5-year overall survival probability according to largest diameter and number of colorectal liver metastases for patients with RAS mutation (a) and RAS wild-type (b). The color gradient reflects the 5-year overall survival probability, with darker shades indicating lower survival and lighter shades indicating higher survival. Adapted from Kawaguchi et al. [42] with permission.
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Figure 3. Overall survival (OS) based on the number of mutations. (A) OS in 561 patients who underwent resection of CLM at MD Anderson Cancer Center. OS curves after adjustment for age, primary lymph node metastasis, number of CLM, and largest CLM diameter. (B) OS in 503 patients with unresectable CLM at Memorial Sloan Kettering Cancer Center. OS curves after adjustment for age and number of sites involved at initial diagnosis of metastasis. Adverse mutations listed as: RAS/BRAF, TP53, and SMAD4. Adapted and modified from Kawaguchi et al. [34] with permission.
Figure 3. Overall survival (OS) based on the number of mutations. (A) OS in 561 patients who underwent resection of CLM at MD Anderson Cancer Center. OS curves after adjustment for age, primary lymph node metastasis, number of CLM, and largest CLM diameter. (B) OS in 503 patients with unresectable CLM at Memorial Sloan Kettering Cancer Center. OS curves after adjustment for age and number of sites involved at initial diagnosis of metastasis. Adverse mutations listed as: RAS/BRAF, TP53, and SMAD4. Adapted and modified from Kawaguchi et al. [34] with permission.
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Figure 4. Kaplan–Meier survival analyses by perioperative ctDNA dynamics among patients who underwent curative intent surgical clearance of metastatic CRC including hepatectomy. (A) Recurrence-free survival. (B) Overall survival. Log-rank p values. Prehep/Postop indicates prehepatectomy/postoperative. Adapted and modified from Newhook et al. [93] with permission.
Figure 4. Kaplan–Meier survival analyses by perioperative ctDNA dynamics among patients who underwent curative intent surgical clearance of metastatic CRC including hepatectomy. (A) Recurrence-free survival. (B) Overall survival. Log-rank p values. Prehep/Postop indicates prehepatectomy/postoperative. Adapted and modified from Newhook et al. [93] with permission.
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Figure 5. NCT05062317: Risk-stratified post-hepatectomy chemotherapy for patients with resectable colorectal liver metastases. Arrows indicate the direction of clinical management and follow-up over time, while symbols denote ctDNA sampling time points. Adapted from Adams et al. [99] with permission.
Figure 5. NCT05062317: Risk-stratified post-hepatectomy chemotherapy for patients with resectable colorectal liver metastases. Arrows indicate the direction of clinical management and follow-up over time, while symbols denote ctDNA sampling time points. Adapted from Adams et al. [99] with permission.
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Table 1. Radiologic and pathologic criteria used to assess colorectal liver metastasis response to chemotherapy.
Table 1. Radiologic and pathologic criteria used to assess colorectal liver metastasis response to chemotherapy.
CriteriaAssessment ParameterClassification
RadiologicWHO [11]Decrease in tumor volumeComplete response: disappearance of all known disease
Partial response: ≥50% decrease in total tumor load
Progressive disease: ≥25% increase in the size of one lesion, emergence of new lesions
No change: neither partial response nor progressive disease
RECIST [12]Decrease in tumor largest diameterComplete response: disappearance of all known disease
Partial response: ≥30% decrease in the sum of diameters of all lesions
Progressive disease: ≥20% increase, emergence of new lesions, or unequivocal increase in diameter of non-target lesions
Stable disease: neither partial response nor progressive disease
Morphologic [10]Lower attenuation,
More homogeneous content
Sharper tumor–normal liver		Optimal response: a very pronounced change, like a hepatic cyst in the portal phase
Incomplete response: subtle morphological changes, not reaching a pseudocyst appearance
No response: no morphological changes between the pre-treatment and post-treatment imaging
PathologicTumor viability [16]Percentage of residual cancer cells in the total tumor areaComplete response: no residual cancer cells
Major response: 1% to 49% residual cancer cells
Minor response: ≥50% residual cancer cells
Tumor regression grade [17]Ratio of cancer cells to fibrosisTRG1: no cancer cells and major fibrosis
TRG2: rare cancer cells dispersed through pronounced fibrosis
TRG3: more cancer cells in the setting of predominant fibrosis
TRG4: residual cancer cells predominating fibrosis
TRG5: major residual cancer cells with minimal to no fibrosis
Tumor thickness [18]Thickness of tumor–normal liver interface (TNI)TNI < 0.5 mm
TNI 0.5–5 mm
TNI ≥ 5 mm
Abbreviations: WHO: World Health Organization; RECIST: response evaluation criteria in solid tumor; TRG: tumor regression grade; TNI: tumor–normal liver interface.
Table 2. Comparison of advantages and limitations of RECIST and morphologic criteria for response assessment in colorectal liver metastases.
Table 2. Comparison of advantages and limitations of RECIST and morphologic criteria for response assessment in colorectal liver metastases.
RECISTMorphologic
Assessment Unidimensional tumor size
measurements		Qualitative changes in tumor appearance including attenuation, enhancement, and tumor–liver interface
Advantages- Objective quantitative measurements
- Ease of implementation
- Standardized and widely accepted indicator of response [19]
- High interobserver agreement [22]		- Superior correlation with pathologic response [23]
- Superior correlation with OS regardless of chemotherapy regimen [19]
- Reflection of tumor biology (cell viability) [10]
- High interobserver agreement [10]
Limitations- Poor assessment of response to bevacizumab [10]
- Inability to capture qualitative tumor changes
- Inconsistent association with survival and pathologic response [21]
- Strict cutoff values limiting discriminatory ability [24]		- Subjective assessment requiring experienced radiologists
- Dependent on high-quality CT imaging and adequate enhancement
- Challenging for small tumors (<1–1.5 cm) [19]
- Limited validation with MRI [25]
Table 3. Frequency of the six driver mutations in colorectal liver metastases and their effect on overall survival and recurrence-free survival.
Table 3. Frequency of the six driver mutations in colorectal liver metastases and their effect on overall survival and recurrence-free survival.
MutationPathwayFrequencyOS HRRFS HR
RASRTK–RAS34.2%1.68 (1.54–1.84) a1.46 (1.33–1.61) b
BRAFRTK–RAS4.8%2.62 (2.14–3.20) c1.89 (1.32–2.73) d
TP53p5377.2%1.88 (1.3–2.74) e-
SMAD4TGF-β11.5%1.93 (1.56–2.38) f1.95 (1.31–2.91) g
FBXW7Notch5.7%1.99 (1.15–3.45) h-
APCWnt76.7%0.66 (0.49–0.89) i-
Abbreviations: OS: Overall survival; HR: hazard ratio; RFS: recurrence-free survival. a Data from a meta-analysis including 36 studies and a total of 15,766 patients [41]. b Data from a meta-analysis including 20 studies and a total of 8355 patients [41]. c Data from a meta-analysis including 13 studies and a total of 5831 patients [41]. d Data from a meta-analysis including 8 studies and a total of 3138 patients [41]. e Data from a retrospective study including a total of 395 patients [34]. f Data from a meta-analysis including 5 studies and a total of 222 patients [41]. g Data from a meta-analysis including 2 studies and a total of 125 patients [41]. h Data from a retrospective study including a total of 476 patients [44]. i Data from a retrospective study including a total of 326 patients [34].
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Madi, M.; Haddad, A.; Ito, K.; Bhutiani, N.; Vauthey, J.-N. Critical Evaluation of Treatment Response, Driver Mutations, and Circulating Tumor DNA as Markers of Tumor Biology in Colorectal Liver Metastasis. Cancers 2026, 18, 1111. https://doi.org/10.3390/cancers18071111

AMA Style

Madi M, Haddad A, Ito K, Bhutiani N, Vauthey J-N. Critical Evaluation of Treatment Response, Driver Mutations, and Circulating Tumor DNA as Markers of Tumor Biology in Colorectal Liver Metastasis. Cancers. 2026; 18(7):1111. https://doi.org/10.3390/cancers18071111

Chicago/Turabian Style

Madi, Mikel, Antony Haddad, Kyoji Ito, Neal Bhutiani, and Jean-Nicolas Vauthey. 2026. "Critical Evaluation of Treatment Response, Driver Mutations, and Circulating Tumor DNA as Markers of Tumor Biology in Colorectal Liver Metastasis" Cancers 18, no. 7: 1111. https://doi.org/10.3390/cancers18071111

APA Style

Madi, M., Haddad, A., Ito, K., Bhutiani, N., & Vauthey, J.-N. (2026). Critical Evaluation of Treatment Response, Driver Mutations, and Circulating Tumor DNA as Markers of Tumor Biology in Colorectal Liver Metastasis. Cancers, 18(7), 1111. https://doi.org/10.3390/cancers18071111

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