Single Nucleotide Polymorphisms as Biomarkers of Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Systematic Review
by
, , , ,
Katarzyna Połomska
1
,
Magda Rybicka
2,
Adrianna Jażdżewska
1,
Magdalena Prud
1,
Stefania Jackowska
2,
Jaroslaw Kobiela
1 and
Piotr Spychalski
1,* 1
Department of Surgical Oncology, Transplant Surgery and General Surgery, Medical University of Gdansk, Sklodowska 3A Str., 80-210 Gdansk, Poland
2
Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Abrahama 58, 80-307 Gdansk, Poland
*
Author to whom correspondence should be addressed.
Cancers 2025, 17(24), 3995; https://doi.org/10.3390/cancers17243995
Submission received: 11 November 2025
/
Revised: 7 December 2025
/
Accepted: 10 December 2025
/
Published: 15 December 2025
(This article belongs to the Section Cancer Biomarkers)
Simple Summary
Predicting the response of patients with rectal cancer to preoperative chemoradiotherapy is a major challenge in oncology. It has been hypothesized that variations in genes, called single nucleotide polymorphisms (SNPs), could explain why some patients achieve complete tumor regression while others do not. We systematically reviewed published studies that analyzed SNPs as possible markers of treatment response. We found no single genetic marker that consistently predicts how tumors respond to therapy. These results show that the current evidence is too weak to guide treatment decisions based on SNPs. Future research should focus on large, well-designed studies combining genetics with other biological and clinical data to build reliable prediction models.
Abstract
Background: Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer, but only 15–30% of patients achieve a pathological complete response. Single nucleotide polymorphisms represent stable genetic markers with potential predictive value for treatment response. This systematic review synthesizes current evidence on the association between SNPs and the response to nCRT in rectal cancer. Methods: PubMed and Web of Science databases were searched for relevant English studies. Two reviewers independently screened the titles and abstracts using the DistillerSR tool. Full-text articles were assessed for their eligibility. Data extraction followed the PRISMA guidelines, and the risk of bias was assessed. Results: Thirty-two studies (4116 patients) assessed 304 SNPs across 126 genes in 407 analyses. DNA repair genes (XRCC1, XRCC3, ERCC1, ERCC2) and folate metabolism genes (MTHFR, TYMS) were most frequently investigated. Only two SNPs demonstrated predictive value in multiple studies: rs25487 (XRCC1) and rs1801133 (MTHFR); however, the associations were inconsistent. The remaining SNPs showed isolated associations in single studies. No SNP demonstrated predictive value across independent cohorts. Conclusions: Current evidence does not support the clinical use of individual SNPs to predict nCRT response in rectal cancer patients. Although XRCC1 and MTHFR polymorphisms have been extensively studied, their predictive utility remains inconclusive. Future research should prioritize large, multicenter prospective studies with standardized treatment and outcome definitions, and consider polygenic risk models or integrated multi-omic approaches.
1. Introduction
According to the latest NCCN Guidelines, neoadjuvant therapy is standard for patients with locally advanced rectal cancer (stage II or III, T3–T4 or N+), typically involving neoadjuvant chemoradiotherapy (nCRT) or total neoadjuvant therapy (TNT), and is especially recommended for tumors with high-risk features such as threatened mesorectal fascia or low tumor location [1]. The recommended approach is either long-course chemoradiotherapy using fluoropyrimidine-based chemotherapy (5-fluorouracil or capecitabine) administered concurrently with pelvic radiation, or total neoadjuvant therapy (TNT), which involves the administration of systemic chemotherapy (commonly FOLFOX or CAPOX) and radiation before surgery. TNT can be administered as induction chemotherapy followed by CRT, or as CRT followed by consolidation chemotherapy, and is especially recommended for high-risk features.
A key indicator of efficacy is the achievement of a pathological complete response (pCR), defined as the absence of residual tumor cells in the resected specimen after neoadjuvant treatment. In clinical practice, the proportion of patients who achieve pCR varies between 15% and 30%, depending on initial tumor burden and the specific treatment regimen. The PRODIGE-23 [2] and RAPIDO [3] trials reported pCR rates of 27.8% and 27.4%, respectively. Thus, one quarter of patients undergoing surgery (resection or abdominoperineal resection) after neoadjuvant therapy were found to have no detectable tumor cells on histopathological examination of the operative specimen. This has led to increasing interest in non-operative management strategies such as the “watch-and-wait” approach [1].
However, the ability to accurately predict which patients will respond favorably to nCRT remains limited. Single nucleotide polymorphisms (SNPs), as stable and accessible genetic markers, have shown potential in predicting radiosensitivity and chemosensitivity by influencing DNA repair capacity, cell cycle regulation, and drug metabolism. Systematic analyses of previously published studies have been constrained by the limited number of articles published on this topic and their heterogeneity [4,5,6,7,8]. This systematic review aims to synthesize the current evidence on SNPs associated with the response to nCRT in rectal cancer. This may aid in identifying genetic predictors that could stratify patients by their likelihood of response, thereby supporting personalized treatment decisions and potentially sparing selected patients from unnecessary surgery and its associated morbidity.
This study aims to systematically evaluate and synthesize the evidence on the associations between specific SNPs and the response to nCRT in patients with rectal cancer.
2. Materials and Methods
2.1. Search Strategy
The systematic review was based on the patients, interventions, comparisons, outcomes (PICO) framework and was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [9]. A search of the PubMed and Web of Science databases was performed to identify relevant articles without the use of filters. Backward citation chaining of the reference lists of all eligible full-text articles was performed to identify additional studies. This study was registered with PROSPERO (Identifier: CRD420251054562).
2.2. Evidence Acquisition
Databases were searched on 17 May 2025 to identify relevant articles. The search was conducted without using filters. We used a combination of keywords and Medical Subject Headings (MeSH) terms for the search. The following query was created using Boolean search terms: (SNP OR polymorphism) AND (colorectal cancer OR rectal cancer) AND (chemotherapy OR radiotherapy OR neoadjuvant).
The screening protocol is shown in the PRISMA flowchart (Figure 1). The initial search yielded 1976 results.
Machine-learning–assisted screening tool was applied for record selection to reduce screening time and improve accuracy [10]. Records were deduplicated with the DistillerSR (Evidence Partners Inc., Ottawa, ON, Canada) web application [11], and 1424 records were further processed.
Screening was further performed using DistillerSR [11]. The algorithm was initially trained with five relevant and five irrelevant studies and was iteratively retrained based on new included and excluded studies. A total of 558 studies were manually labeled by two independent authors (KP and SJ). We adopted a stopping criterion of 150 subsequent irrelevant articles since the last relevant (van Dijk et al. proposed a criterion of 100 irrelevant articles since last relevant) [12]. We identified 73 articles for full-text screening.
Full-text screening was performed manually by two independent researchers (KP and AJ) to identify the eligible studies. Disagreements between the researchers were resolved through discussion and consultation with another author (PS). After full-text screening, 32 articles were included in this review.
The following data were retrieved from the included studies: first author, title, journal, year of publication, study location (institution and country), study design, recruitment period, and sample size. We collected detailed data on patient characteristics, including age, sex, clinical staging, and type of surgery (if performed). For each study, we extracted information on the specific single nucleotide polymorphisms (SNPs) evaluated, methods of SNP detection, and the outcomes assessed pCR and tumor regression grade (TRG). Additionally, we recorded the definitions of response criteria, statistical methods used, effect sizes (e.g., odds ratios, hazard ratios, confidence intervals), and whether statistically significant associations were found. For evidence synthesis, odds ratios were directionally harmonized by inverting estimates where necessary so that all effect sizes consistently reflected the association with favorable treatment response. Data were independently extracted by two reviewers using a standardized form.
2.3. PICO Framework: Inclusion and Exclusion Criteria
We included original studies involving adult patients (≥18 years) with locally advanced rectal cancer treated with nCRT or neoadjuvant radiotherapy and surgery. The eligibility criteria included studies that reported the presence of single nucleotide polymorphisms (SNPs) and their association with treatment response, defined as pathological complete response (pCR) or tumor regression grade, and studies that provided a comparison between different SNP genotypes (wild-type, heterozygous, and homozygous). Studies focusing on other types of genetic alterations (e.g., MSI, gene expression) without SNP analysis and non-original reports (e.g., reviews, case reports, abstracts) were excluded. The PICO-based strategy adopted to identify relevant studies is presented in Table 1. Studies published in languages other than English were excluded from the review. No time constraints were imposed.
2.4. Evidence Synthesis and Risk of Bias Assessment
The QGenie tool was used to assess the risk of bias in the included studies. The risk of bias was evaluated by two independent authors (KP, AJ), and any disagreement was resolved via discussion and consensus [13].
3. Results
After screening and full-text assessment (73 articles), 32 articles were included in this review. All studies were genetic association studies; 12 were retrospective cohorts, and 20 were prospective cohorts (3 studies were pooled cohorts from randomized clinical trials). A total of 304 SNPs in 126 genes were analyzed. The total number of patients was 4116, with individual sample sizes ranging from 21 to 316. Most patients had stage II or III rectal cancer. Across the included studies, the neoadjuvant treatment protocols demonstrated substantial heterogeneity in both radiotherapy (RT) dose and concurrent chemotherapy regimens. Most studies used long-course chemoradiotherapy with total RT doses ranging from 45 to 50.4 Gy, delivered in 1.8–2.0 Gy fractions, frequently with a boost dose of 5.4–10 Gy. Fluoropyrimidine-based chemotherapy was the most widely used approach, with regimens consisting of continuous infusion of 5-fluorouracil (5-FU), capecitabine, tegafur/uracil (UFT), leucovorin, or combinations thereof. Several protocols included oxaliplatin-based doublets such as XELOX, mFOLFOX6, or CAPOX (with or without cetuximab). Less commonly, studies adopted intensified RT schedules (e.g., 60–65 Gy) or combined external-beam RT with brachytherapy. Furthermore, several studies administered more than one treatment protocol to the same cohort.
Across the included studies, 304 SNPs in 126 genes were analyzed. The number of SNPs analyzed per study varied widely: most studies assessed a small panel (1–10 SNPs), often targeting SNPs with prior pharmacogenetic relevance rather than using genome-wide strategies; only one study employed genome-wide screening of over 690,000 SNPs [14,15].
The most frequent genotyping methods were PCR-RFLP (eight studies), TaqMan (five studies), Sanger sequencing (four studies), and SNaPshot (three studies). Biological material for genotyping was derived from peripheral blood (21 studies), pre-treatment tumor tissue (8 studies), tumor samples (4 studies), and non-tumor tissue samples (1 study). The investigated outcomes were pathological complete response (pCR) in 16 studies and TRG in 18 studies. TRG was assessed using different scoring systems (Mandard, Dworak, and AJCC). The proportion of patients achieving pCR ranged from 11% to 51% across studies, and the proportion of responders to neoadjuvant treatment ranged from 19% to 84%. Across studies using the Mandard TRG system, responder definitions varied: eight studies defined responders as Mandard TRG 1–2, one study as Mandard TRG 1, and one study as Mandard TRG 1–3. Heterogeneity was also observed among studies employing the Dworak TRG system, where responders were defined as Dworak TRG 2–4 in two studies, modified Dworak TRG 2–3 in one study, and Dworak TRG 3–4 in three studies. Of the 16 studies assessing pCR, 12 defined pCR as the absence of residual tumor cells. One study specified pCR as no evidence of residual carcinoma or only residual microfoci; two studies (on the same cohort) allowed radiological assessment of complete response in patients who did not undergo surgery; and one study did not provide a definition of pCR. The detailed baseline characteristics of the included studies are presented in Table 2.
Genes involved in DNA repair pathways were the most frequently studied, particularly XRCC1 (14 SNPs analyzed in 11 studies), XRCC3 (10 SNPs in 5 studies), ERCC1 (7 SNPs in 9 studies), and ERCC2 (9 SNPs in 7 studies). The second most common group was folate metabolism genes, particularly MTHFR (11 SNPs in 12 studies) and TYMS (11 SNPs in 7 studies). The other most frequently studied genes were EGFR (7 SNPs in 7 studies) and GSTP1 (7 SNPs in 6 studies). Other frequently analyzed SNPs were located in genes related to immune response, miRNAs, reactive oxygen species, angiogenesis, and cell cycle regulation (Table 3).
Of the 304 SNPs analyzed, only 2 SNPs were associated with a pathological response in more than one study (rs25487 XRCC1 and rs1801133 MTHFR); most (22 SNPs) associations were reported in single studies only (Table 4). For each SNP assessed in multiple studies, the cumulative number of patients from studies that did not demonstrate a significant association with pathological response greatly exceeded the number of patients from studies that did. Even for the most widely studied SNPs (XRCC1 rs25487 and MTHFR rs1801133), the number of patients in negative studies significantly outweighed the number of patients in positive studies. The list of SNPs that were not associated with pathological response in any of the included studies is presented in Supplement S1.
3.1. rs25487 (XRCC1)
This SNP was analyzed in 10 studies, of which 4 demonstrated its association with pathological response after nCRT. Studies analyzing rs25487 varied in terms of outcome measures, nCRT schemes, and genotyping methods. Furthermore, studies have identified different genotypes related to pathological responses. Balboa et al. [15] found that the AA genotype was significantly associated with improved treatment response after adjustment for sex, age, and cancer stage (odds ratio [OR] 7.93; 95% CI: 1.03–60.83; p = 0.036). In the cohort analyzed by Grimminger et al., the AG genotype was linked to a higher major response rate (47%) than the AA or GG genotypes (22%; p = 0.039). Conversely, Formica et al. reported a strong association between the GG genotype and better tumor regression (OR 25.8; p = 0.049), and Lamas et al. identified the GG genotype as predictive of a favorable response over the GA genotype (OR 4.18; 95% CI: 1.62–10.74; p = 0.003).
3.2. rs1801133 (MTHFR)
This SNP was analyzed in 10 studies, of which three demonstrated its association with pathological response after nCRT. Multivariable analysis by Cecchin et al. found that patients carrying at least one T allele (CT or TT genotypes) had a significantly lower likelihood of achieving tumor regression (TRG ≤ 2) than those with the CC genotype (OR = 0.48; 95% CI: 0.24–0.96; p = 0.034). Nikas et al. reported that the CC genotype was associated with a higher probability of pathological response relative to the CT and TT genotypes (OR = 2.91; 95% CI: 1.23–6.89; p = 0.015). Similarly, Terrazzino et al. demonstrated a greater response rate among CC homozygotes than among T allele carriers (responders: 57% vs. 34%; OR = 0.32; 95% CI: 0.14–0.71; p < 0.006).
3.3. Risk of Bias
The risk of bias analysis is presented in Table 5. Overall quality scores ranged from 34 to 65 out of a maximum possible score of 77, with a mean of 51.72 (SD = 6.79) and a median of 52. Only two studies (6.2%) achieved good quality ratings (≥60 points), while the majority (56.2%, n = 18) were classified as moderate quality (50–59 points), and over one-third (37.5%, n = 12) demonstrated poor methodological quality (<50 points). Several critical methodological domains consistently scored below acceptable thresholds across the included studies. The sample size and statistical power considerations were particularly weak, averaging only 3.47 out of 7 points, indicating that most studies were underpowered and lacked formal sample size calculations. The assessment and control of other sources of bias were similarly inadequate (mean score = 3.72). Four studies exhibited particularly severe methodological limitations with overall scores below 45: Spindler 2006 (score 34) [41], Stoehlmacher 2008 (score 38) [43], Xiao 2016 (score 41) [45], and Rampazzo 2020 (score 43) [37].
4. Discussion
This systematic review synthesizes evidence from 32 studies, including 4116 patients and evaluating 304 SNPs across 126 genes, assessing their association with the response to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. Despite the growing body of literature on this subject, no polymorphism has emerged as a reliable or consistently reproducible predictor of treatment response. Overall, the findings were largely inconsistent and non-reproducible. Our findings are consistent with recent meta-analyses and systematic reviews, which also report a lack of robust or reproducible associations between individual polymorphisms and response to nCRT [8,46,47]. These results collectively emphasize the need for validation in larger, independent cohorts and suggest that current candidate gene approaches may be insufficient. The most frequently studied polymorphisms were located within genes involved in DNA damage repair pathways, particularly XRCC1, XRCC3, ERCC1, and ERCC2, and in genes related to folate metabolism, such as MTHFR and TYMS. XRCC1, XRCC3, ERCC1, and ERCC2 encode proteins crucial for the repair of DNA damage induced by ionizing radiation, directly affecting tumor cell radiosensitivity. MTHFR and TYMS play key roles in folate metabolism and nucleotide synthesis, influencing the effectiveness of fluoropyrimidine-based chemotherapy. In addition, variants within genes related to oxidative stress, immune regulation, and cell cycle control, such as GSTP1, EGF, IL13, FPR1, and TERT, were reported to show favorable associations with pathological complete response or tumor regression grade in single studies, but these signals were rarely replicated and generally remained exploratory.
Most studies adopted a candidate-driven approach, focusing on SNPs in these pathways. However, most SNPs were assessed in isolated studies, with the most frequently reported associations involving rs25487 (XRCC1) and rs1801133 (MTHFR). Even for these commonly investigated SNPs, the results remain highly inconsistent. For example, different cohorts identified either the wild-type or the homozygous variant genotype of MTHFR rs1801133 as favorable, while other studies did not observe any significant association, which substantially weakens the biological plausibility of a true effect and points to methodological variability.
For all SNPs analyzed in multiple studies, reports indicating no significant association with pathological response outnumber those demonstrating any positive correlation. This inconsistency likely reflects considerable heterogeneity among the included studies regarding outcome definitions (pCR, response according to different TRG systems with varying response cut-offs), nCRT protocols (variable chemotherapy regimens and radiotherapy doses), sample sizes, and analytical models (dominant, recessive, and additive models). In addition, the included studies used a wide range of genotyping platforms, ranging from low-throughput, locus-specific assays such as PCR-RFLP and TaqMan to sequencing- or array-based approaches (Sanger sequencing, SNP arrays, SNaPshot, and MassARRAY), each characterized by distinct analytical sensitivities, multiplexing capacities, and susceptibility to technical artifacts, which may have introduced further between-study variability in SNP calls and effect estimates. Detailed quality control metrics for these assays, including reproducibility, call rates, and the systematic use of positive and negative controls, have only been sparsely reported or are entirely missing in many studies, limiting a rigorous appraisal of genotyping reliability. Nearly 90% of the included studies analyzed SNPs in Caucasian patients, while the remaining studies included Asian patients; other ethnic groups were not represented. This limits the generalizability of the findings due to potential population-specific effects, such as differences in allele frequency and linkage disequilibrium (LD) structure. It is important to note significant heterogeneity in cancer staging across the analyzed studies, particularly the varying proportions of patients with stage II rectal cancer according to the AJCC. Patients with stage II cancer tend to have a better response to neoadjuvant treatment, which could facilitate an analysis of the association of SNP and complete pathological response in some studies.
Another hypothesis explaining the non-reproducibility of the results is the type I error (false-positive findings). Of all SNP analyses conducted (with each SNP analysis in each publication treated as a separate test), positive results were found in 30 of 407 separate statistical analyses (approximately 7%). Considering the plausible publication bias, where positive results are preferentially published, the true proportion of significant findings is likely to be lower, probably below 5%, which aligns with the nominal Type I error rate. Given the relatively small and often underpowered cohorts and the large number of statistical tests performed without systematic correction for multiple comparisons, the small proportion of positive results is close to what would be expected by chance alone, particularly in the presence of publication bias favoring significant findings.
This aligns with important limitations emerging consistently across the current body of literature, including generally small sample sizes with no reported power calculations, insufficient adjustment for multiple comparisons, substantial variability in treatment protocols and definitions of treatment response, limited use of multivariable analytical models, and considerable population heterogeneity with minimal consideration of environmental and epigenetic influences on the results. A risk of bias assessment using QGenie, a tool specifically developed for genetic association studies, highlighted suboptimal reporting and study design in several publications. This is particularly evident in the justification of sample sizes, handling of confounders, and replication strategies. Moreover, five studies with the lowest risk of bias (highest QGenie score) reported no significant SNP associated with the response to nCRT.
Our data highlight the ongoing uncertainty regarding the clinical utility of SNPs in predicting the nCRT response in rectal cancer. No individual variant has been sufficiently validated for clinical implementation. Importantly, none of the available genetic markers have shown a superior predictive value over the current clinicopathological criteria. Consequently, treatment decisions for patients with locally advanced rectal cancer should continue to be based on established clinical and pathological factors, radiological staging, and patient preference. Germline SNP testing for predicting the response to nCRT cannot currently be recommended outside of research settings. Conversely, somatic mutations have been identified to play a role in predicting the response to nCRT. KRAS mutation is independently associated with a lower pCR rate in locally advanced rectal cancer [48,49]. Microsatellite instability is independently associated with a reduction in pCR for locally advanced rectal cancer after nCRT [50]. This is reflected in current guidelines identifying checkpoint inhibitors as the preferred initial therapy for patients with microsatellite instability [1].
Future progress in the field may depend on shifting from single SNP analyses toward polygenic risk models and the combination of genetic data with other-omic layers (e.g., transcriptomics and proteomics). The application of machine learning, coupled with standardized nCRT protocols and outcome definitions, could help improve reproducibility and predictive accuracy. Genome-wide or large-panel approaches, coupled with rigorous replication and external validation and integrated with tumor-intrinsic features (somatic alterations, gene expression, epigenetic markers), circulating biomarkers, and advanced imaging, may enable the development of multi-omic prediction tools that better capture the complex biology of chemoradiotherapy response.
Before any genetic biomarker can be applied in clinical practice, several barriers must be addressed: robust validation, demonstration of added predictive value, cost-effectiveness, and accessibility of testing. Large-scale prospective, multicenter studies with standardized methodologies, sufficient sample sizes, multivariable analyses, and appropriate statistical corrections are urgently needed to clarify the true potential of genetic variants in guiding personalized therapy for rectal cancer.
5. Conclusions
This systematic review, covering 32 studies and 4116 patients, indicates that no individual germline SNP has shown a consistent association with the response to nCRT in locally advanced rectal cancer. Although some variants in DNA repair, folate metabolism, and other pathways have been highlighted as potential predictors in isolated analyses, their effects have not been reproduced across independent cohorts or under uniform response definitions. At present, the evidence base does not justify using individual SNPs as standalone biomarkers to guide neoadjuvant treatment decisions in rectal cancer. Risk stratification and selection of candidates for organ-preserving strategies, including watch-and-wait, should therefore continue to rely on established clinicopathological factors together with high-quality imaging and standardized response assessment. Future work should focus on sufficiently powered, multicenter prospective studies using harmonized neoadjuvant treatment protocols and uniform response criteria, encompassing both pathological complete response and validated tumor regression grading systems. In this setting, polygenic models and integrative multi-omic strategies that combine germline variation with tumor molecular profiles, imaging-derived features, and key clinical variables are more likely than single markers to yield clinically useful tools for predicting chemoradiotherapy response in rectal cancer.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers17243995/s1, Supplement S1: Summary of SNPs not related to treatment response in at least one study.
Author Contributions
K.P.: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing—original draft preparation, Writing—review and editing, Funding acquisition; M.R.: Writing—original draft preparation, Writing—review and editing; A.J.: Investigation, Data curation; M.P.: Investigation, Writing—review and editing; S.J.: Investigation, Data curation; J.K.: Conceptualization, Writing—review and editing, P.S.: Writing—review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.
Funding
This systematic review is supported by a student research grant (SKN/SP/601051/2024), awarded by the Polish Ministry of Science and Higher Education (MNiSW).
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Benson, A.B.; Venook, A.P.; Adam, M.; Chang, G.; Chen, Y.-J.; Ciombor, K.K.; Cohen, S.A.; Cooper, H.S.; Deming, D.; Garrido-Laguna, I.; et al. NCCN Guidelines® Insights: Rectal Cancer, Version 3.2024: Featured Updates to the NCCN Guidelines. J. Natl. Compr. Cancer Netw. 2024, 22, 366–375. [Google Scholar] [CrossRef] [PubMed]
- Conroy, T.; Bosset, J.F.; Etienne, P.L.; Rio, E.; François, E.; Mesgouez-Nebout, N.; Vendrely, V.; Artignan, X.; Bouché, O.; Gargot, D.; et al. Neoadjuvant chemotherapy with FOLFIRINOX and preoperative chemoradiotherapy for patients with locally advanced rectal cancer (UNICANCER-PRODIGE 23): A multicentre, randomised, open-label, phase 3 trial. Lancet Oncol. 2021, 22, 702–715. [Google Scholar] [CrossRef] [PubMed]
- Bahadoer, R.R.; Dijkstra, E.A.; van Etten, B.; Marijnen, C.A.M.; Putter, H.; Kranenbarg, E.M.-K.; Roodvoets, A.G.H.; Nagtegaal, I.D.; Beets-Tan, R.G.H.; Blomqvist, L.K.; et al. Short-course radiotherapy followed by chemotherapy before total mesorectal excision (TME) versus preoperative chemoradiotherapy, TME, and optional adjuvant chemotherapy in locally advanced rectal cancer (RAPIDO): A randomised, open-label, phase 3 trial. Lancet Oncol. 2021, 22, 29–42. [Google Scholar] [CrossRef]
- Clancy, C.; Burke, J.P.; Coffey, J.C. KRAS mutation does not predict the efficacy of neo-adjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis. Surg. Oncol. 2013, 22, 105–111. [Google Scholar] [CrossRef]
- Zhao, Y.; Li, X.; Kong, X. MTHFR C677T Polymorphism is Associated with Tumor Response to Preoperative Chemoradiotherapy: A Result Based on Previous Reports. Med. Sci. Monit. 2015, 21, 3068. [Google Scholar] [CrossRef][Green Version]
- De Mattia, E.; Roncato, R.; Palazzari, E.; Toffoli, G.; Cecchin, E. Germline and Somatic Pharmacogenomics to Refine Rectal Cancer Patients Selection for Neo-Adjuvant Chemoradiotherapy. Front. Pharmacol. 2020, 11, 897. [Google Scholar] [CrossRef]
- Spolverato, G.; Pucciarelli, S.; Bertorelle, R.; de Rossi, A.; Nitti, D. Predictive factors of the response of rectal cancer to neoadjuvant radiochemotherapy. Cancers 2011, 3, 2176–2194. [Google Scholar] [CrossRef]
- Pezzolo, E.; Modena, Y.; Corso, B.; Giusti, P.; Gusella, M. Germ line polymorphisms as predictive markers for pre-surgical radiochemotherapy in locally advanced rectal cancer: A 5-year literature update and critical review. Eur. J. Clin. Pharmacol. 2015, 71, 529–539. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, 71. [Google Scholar] [CrossRef]
- Hamel, C.; Kelly, S.E.; Thavorn, K.; Rice, D.B.; Wells, G.A.; Hutton, B. An evaluation of DistillerSR’s machine learning-based prioritization tool for title/abstract screening–impact on reviewer-relevant outcomes. BMC Med. Res. Methodol. 2020, 20, 256. [Google Scholar] [CrossRef] [PubMed]
- DistillerSR|Systematic Review Software|Literature Review Software. Available online: https://www.distillersr.com/products/distillersr-systematic-review-software (accessed on 7 March 2025).
- Van Dijk, S.H.B.; Brusse-Keizer, M.G.J.; Bucsán, C.C.; Van Der Palen, J.; Doggen, C.J.M.; Lenferink, A. Artificial intelligence in systematic reviews: Promising when appropriately used. BMJ Open 2023, 13, e072254. [Google Scholar] [CrossRef] [PubMed]
- Sohani, Z.N.; Sarma, S.; Alyass, A.; de Souza, R.J.; Robiou-Du-Pont, S.; Li, A.; Mayhew, A.; Yazdi, F.; Reddon, H.; Lamri, A.; et al. Empirical evaluation of the Q-Genie tool: A protocol for assessment of effectiveness. BMJ Open 2016, 6, e010403. [Google Scholar] [CrossRef] [PubMed]
- Kim, J.C.; Ha, Y.J.; Roh, S.A.; Cho, D.H.; Choi, E.Y.; Kim, T.W.; Kim, J.H.; Kang, T.W.; Kim, S.Y.; Kim, Y.S. Novel single-nucleotide polymorphism markers predictive of pathologic response to preoperative chemoradiation therapy in rectal cancer patients. Int. J. Radiat. Oncol. Biol. Phys. 2013, 86, 350–357. [Google Scholar] [CrossRef]
- Balboa, E.; Duran, G.; Lamas, M.J.; Gomez-Caamaño, A.; Celeiro-Muñoz, C.; Lopez, R.; Carracedo, A.; Barros, F. Pharmacogenetic analysis in neoadjuvant chemoradiation for rectal cancer: High incidence of somatic mutations and their relation with response. Pharmacogenomics 2010, 11, 747–761. [Google Scholar] [CrossRef]
- Boige, V.; Mollevi, C.; Gourgou, S.; Azria, D.; Seitz, J.; Vincent, M.; Bigot, L.; Juzyna, B.; Miran, I.; Gerard, J.; et al. Impact of single-nucleotide polymorphisms in DNA repair pathway genes on response to chemoradiotherapy in rectal cancer patients: Results from ACCORD-12/PRODIGE-2 phase III trial. Int. J. Cancer 2019, 145, 3163–3172. [Google Scholar] [CrossRef]
- Cecchin, E.; Agostini, M.; Pucciarelli, S.; De Paoli, A.; Canzonieri, V.; Sigon, R.; De Mattia, E.; Friso, M.L.; Biason, P.; Visentin, M.; et al. Tumor response is predicted by patient genetic profile in rectal cancer patients treated with neo-adjuvant chemo-radiotherapy. Pharmacogenomics J. 2011, 11, 214–226. [Google Scholar] [CrossRef]
- Chiang, S.F.; Huang, K.C.Y.; Chen, W.T.L.; Chen, T.W.; Ke, T.W.; Chao, K.S.C. Polymorphism of formyl peptide receptor 1 (FPR1) reduces the therapeutic efficiency and antitumor immunity after neoadjuvant chemoradiotherapy (CCRT) treatment in locally advanced rectal cancer. Cancer Immunol. Immunother. 2021, 70, 2937–2950. [Google Scholar] [CrossRef] [PubMed]
- Dreussi, E.; Cecchin, E.; Polesel, J.; Canzonieri, V.; Agostini, M.; Boso, C.; Belluco, C.; Buonadonna, A.; Lonardi, S.; Bergamo, F.; et al. Pharmacogenetics biomarkers and their specific role in neoadjuvant chemoradiotherapy treatments: An exploratory study on rectal cancer patients. Int. J. Mol. Sci. 2016, 17, 1482. [Google Scholar] [CrossRef]
- Dreussi, E.; Pucciarelli, S.; De Paoli, A.; Polesel, J.; Canzonieri, V.; Agostini, M.; Friso, M.L.; Belluco, C.; Buonadonna, A.; Lonardi, S.; et al. Predictive role of microRNA-related genetic polymorphisms in the pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients. Oncotarget 2016, 7, 19781–19793. [Google Scholar] [CrossRef]
- Dzhugashvili, M.; Luengo-Gil, G.; García, T.; González-Conejero, R.; Conesa-Zamora, P.; Escolar, P.P.; Calvo, F.; Vicente, V.; de la Peña, F.A. Role of genetic polymorphisms in NFKB-mediated inflammatory pathways in response to primary chemoradiation therapy for rectal cancer. Int. J. Radiat. Oncol. Biol. Phys. 2014, 90, 595–602. [Google Scholar] [CrossRef]
- Formica, V.; Benassi, M.; Del Vecchio Blanco, G.; Doldo, E.; Martano, L.; Portarena, I.; Nardecchia, A.; Lucchetti, J.; Morelli, C.; Giudice, E.; et al. Hemoglobin level and XRCC1 polymorphisms to select patients with locally advanced rectal cancer candidate for neoadjuvant chemoradiotherapy with concurrent capecitabine and a platinum salt. Med. Oncol. 2018, 35, 83. [Google Scholar] [CrossRef] [PubMed]
- Garcia-Aguilar, J.; Chen, Z.; Smith, D.D.; Li, W.B.; Madoff, R.D.; Cataldo, P.; Marcet, J.; Pastor, C. Identification of a biomarker profile associated with resistance to neoadjuvant chemoradiation therapy in rectal cancer. Ann. Surg. 2011, 254, 486–493. [Google Scholar] [CrossRef] [PubMed]
- Grimminger, P.P.; Brabender, J.; Warnecke-Eberz, U.; Narumiya, K.; Wandhöfer, C.; Drebber, U.; Bollschweiler, E.; Hölscher, A.H.; Metzger, R.; Vallböhmer, D. XRCC1 gene polymorphism for prediction of response and prognosis in the multimodality therapy of patients with locally advanced rectal cancer. J. Surg. Res. 2010, 164, e61–e66. [Google Scholar] [CrossRef] [PubMed]
- Havelund, B.M.; Spindler, K.L.G.; Ploen, J.; Andersen, R.F.; Jakobsen, A. Single nucleotide polymorphisms in the HIF-1α gene and chemoradiotherapy of locally advanced rectal cancer. Oncol. Lett. 2012, 4, 1056–1060. [Google Scholar] [CrossRef]
- Ho-Pun-Cheung, A.; Assenat, E.; Bascoul-Mollevi, C.; Bibeau, F.; Boissière-Michot, F.; Thezenas, S.; Cellier, D.; Azria, D.; Rouanet, P.; Senesse, P.; et al. A large-scale candidate gene approach identifies SNPs in SOD2 and IL13 as predictive markers of response to preoperative chemoradiation in rectal cancer. Pharmacogenomics J. 2011, 11, 437–443. [Google Scholar] [CrossRef]
- Ho-Pun-Cheung, A.; Assenat, E.; Thezenas, S.; Bibeau, F.; Rouanet, P.; Azria, D.; Cellier, D.; Grenier, J.; Ychou, M.; Senesse, P.; et al. Cyclin D1 Gene G870A Polymorphism Predicts Response to Neoadjuvant Radiotherapy and Prognosis in Rectal Cancer. Int. J. Radiat. Oncol. Biol. Phys. 2007, 68, 1094–1101. [Google Scholar] [CrossRef]
- Hu-Lieskovan, S.; Vallbohmer, D.; Zhang, W.; Yang, D.; Pohl, A.; Labonte, M.J.; Grimminger, P.P.; Hölscher, A.H.; Semrau, R.; Arnold, D.; et al. EGF61 polymorphism predicts complete pathologic response to cetuximab-based chemoradiation independent of KRAS status in locally advanced rectal cancer patients. Clin. Cancer Res. 2011, 17, 5161–5169. [Google Scholar] [CrossRef]
- Hur, H.; Kang, J.; Kim, N.K.; Min, B.S.; Lee, K.Y.; Shin, S.J.; Keum, K.C.; Choi, J.; Kim, H.; Choi, S.H.; et al. Thymidylate synthase gene polymorphism affects the response to preoperative 5-fluorouracil chemoradiation therapy in patients with rectal cancer. Int. J. Radiat. Oncol. Biol. Phys. 2011, 81, 669–676. [Google Scholar] [CrossRef]
- Kim, S.Y.; Baek, J.Y.; Oh, J.H.; Park, S.C.; Sohn, D.K.; Kim, M.J.; Chang, H.J.; Kim, D.Y. A phase II study of preoperative chemoradiation with tegafur-uracil plus leucovorin for locally advanced rectal cancer with pharmacogenetic analysis. Radiat. Oncol. 2017, 12, 62. [Google Scholar] [CrossRef]
- Lamas, M.J.; Duran, G.; Gomez, A.; Balboa, E.; Anido, U.; Bernardez, B.; Rana-Diez, P.; Lopez, R.; Carracedo, A.; Barros, F. X-ray cross-complementing group 1 and thymidylate synthase polymorphisms might predict response to chemoradiotherapy in rectal cancer patients. Int. J. Radiat. Oncol. Biol. Phys. 2012, 82, 138–144. [Google Scholar] [CrossRef]
- Leu, M.; Riebeling, T.; Dröge, L.H.; Hubert, L.; Guhlich, M.; Wolff, H.A.; Brockmöller, J.; Gaedcke, J.; Rieken, S.; Schirmer, M.A. 8-oxoguanine dna glycosylase (Ogg1) cys326 variant: Increased risk for worse outcome of patients with locally advanced rectal cancer after multimodal therapy. Cancers 2021, 13, 2805. [Google Scholar] [CrossRef]
- Nicosia, L.; Gentile, G.; Reverberi, C.; Minniti, G.; Valeriani, M.; de Sanctis, V.; Marinelli, L.; Cipolla, F.; de Luca, O.; Simmaco, M.; et al. Single nucleotide polymorphism of gstp1 and pathological complete response in locally advanced rectal cancer patients treated with neoadjuvant concomitant radiochemotherapy. Radiat. Oncol. J. 2018, 36, 218–226. [Google Scholar] [CrossRef]
- Nikas, J.B.; Lee, J.T.; Maring, E.D.; Washechek-Aletto, J.; Felmlee-Devine, D.; A Johnson, R.; Smyrk, T.C.; Tawadros, P.S.; A Boardman, L.; Steer, C.J. A common variant in MTHFR influences response to chemoradiotherapy and recurrence of rectal cancer. Am. J. Cancer Res. 2015, 5, 3231–3240. Available online: https://pmc.ncbi.nlm.nih.gov/articles/PMC4656744/ (accessed on 28 July 2025).
- Páez, D.; Salazar, J.; Paré, L.; Pertriz, L.; Targarona, E.; del Rio, E.; Barnadas, A.; Marcuello, E.; Baiget, M. Pharmacogenetic study in rectal cancer patients treated with preoperative chemoradiotherapy: Polymorphisms in thymidylate synthase, epidermal growth factor receptor, GSTP1, and DNA repair genes. Int. J. Radiat. Oncol. Biol. Phys. 2011, 81, 1319–1327. [Google Scholar] [CrossRef]
- Peng, J.; Ma, W.; Zhou, Z.; Gu, Y.; Lu, Z.; Zhang, R.; Pan, Z. Genetic variations in the PI3K/PTEN/AKT/mTOR pathway predict tumor response and disease-free survival in locally advanced rectal cancer patients receiving preoperative chemoradiotherapy and radical surgery. J. Cancer 2018, 9, 1067–1077. [Google Scholar] [CrossRef]
- Rampazzo, E.; Cecchin, E.; Del Bianco, P.; Menin, C.; Spolverato, G.; Giunco, S.; Lonardi, S.; Malacrida, S.; De Paoli, A.; Toffoli, G.; et al. Genetic variants of the TERT gene, telomere length, and circulating TERT as prognostic markers in rectal cancer patients. Cancers 2020, 12, 3115. [Google Scholar] [CrossRef]
- Sclafani, F.; Chau, I.; Cunningham, D.; Peckitt, C.; Lampis, A.; Hahne, J.; Braconi, C.; Tabernero, J.; Glimelius, B.; Cervantes, A.; et al. Prognostic role of the LCS6 KRAS variant in locally advanced rectal cancer: Results of the EXPERT-C trial. Ann. Oncol. 2015, 26, 1936–1941. [Google Scholar] [CrossRef]
- Sclafani, F.; Chau, I.; Cunningham, D.; Lampis, A.; Hahne, J.C.; Ghidini, M.; Lote, H.; Zito, D.; Tabernero, J.; Glimelius, B.; et al. Sequence variation in mature microRNA-608 and benefit from neo-adjuvant treatment in locally advanced rectal cancer patients. Carcinogenesis 2016, 37, 852–857. [Google Scholar] [CrossRef]
- Sebio, A.; Salazar, J.; Páez, D.; Berenguer-Llergo, A.; del Río, E.; Tobeña, M.; Martín-Richard, M.; Sullivan, I.; Targarona, E.; Balart, J.; et al. EGFR ligands and DNA repair genes: Genomic predictors of complete response after capecitabine-based chemoradiotherapy in locally advanced rectal cancer. Pharmacogenomics J. 2015, 15, 77–83. [Google Scholar] [CrossRef]
- Spindler, K.L.G.; Nielsen, J.N.; Lindebjerg, J.; Brandslund, I.; Jakobsen, A. Prediction of response to chemoradiation in rectal cancer by a gene polymorphism in the epidermal growth factor receptor promoter region. Int. J. Radiat. Oncol. Biol. Phys. 2006, 66, 500–504. [Google Scholar] [CrossRef]
- Stanojevic, A.; Spasic, J.; Marinkovic, M.; Stojanovic-Rundic, S.; Jankovic, R.; Djuric, A.; Zoidakis, J.; Fijneman, R.J.A.; Castellvi-Bel, S.; Cavic, M. Methylenetetrahydrofolate reductase polymorphic variants C677T and A1298C in rectal cancer in Slavic population: Significance for cancer risk and response to chemoradiotherapy. Front. Genet. 2024, 14, 1299599. [Google Scholar] [CrossRef]
- Stoehlmacher, J.; Goekkurt, E.; Mogck, U.; Aust, D.E.; Kramer, M.; Baretton, G.B.; Liersch, T.; Ehninger, G.; Jakob, C. Thymidylate synthase genotypes and tumor regression in stage II/III rectal cancer patients after neoadjuvant fluorouracil-based chemoradiation. Cancer Lett. 2008, 272, 221–225. [Google Scholar] [CrossRef]
- Terrazzino, S.; Agostini, M.; Pucciarelli, S.; Pasetto, L.M.; Friso, M.L.; Ambrosi, A.; Lisi, V.; Leon, A.; Lise, M.; Nitti, D. A haplotype of the methylenetetrahydrofolate reductase gene predicts poor tumor response in rectal cancer patients receiving preoperative chemoradiation. Pharmacogenet. Genom. 2006, 16, 817–824. [Google Scholar] [CrossRef]
- Xiao, L.; Yu, X.; Zhang, R.; Chang, H.; Xi, S.; Xiao, W.; Zeng, Z.; Zhang, H.; Xu, R.; Gao, Y. Can an IL13-1112 C/T (rs1800925) polymorphism predict responsiveness to neoadjuvant chemoradiotherapy and survival of Chinese Han patients with locally advanced rectal cancer? Oncotarget 2016, 7, 34149–34157. [Google Scholar] [CrossRef][Green Version]
- Guo, C.X.; Yang, G.P.; Pei, Q.; Yin, J.Y.; Tan, H.Y.; Yuan, H. DNA repair gene polymorphisms do not predict response to radiotherapy-based multimodality treatment of patients with rectal cancer: A meta-analysis. Asian Pac. J. Cancer Prev. 2015, 16, 713–718. [Google Scholar] [CrossRef][Green Version]
- Systematic Review of Candidate Single-Nucleotide Polymorphisms as Biomarkers for Responsiveness to Neoadjuvant Chemoradiation for Rectal Cancer-PubMed. Available online: https://pubmed.ncbi.nlm.nih.gov/26124319/ (accessed on 10 November 2025).[Green Version]
- Chow, O.S.; Kuk, D.; Keskin, M.; Smith, J.J.; Camacho, N.; Pelossof, R.; Chen, C.T.; Chen, Z.; Avila, K.; Weiser, M.R.; et al. KRAS and Combined KRAS/TP53 Mutations in Locally Advanced Rectal Cancer Are Independently Associated with Decreased Response to Neoadjuvant Therapy. Ann. Surg. Oncol. 2016, 23, 2548–2555. [Google Scholar] [CrossRef] [PubMed]
- Aboelmaaty, S.; Gomaa, I.A.; Sileo, A.; Sassun, R.; Ng, J.C.; Keshk, N.O.; McKenna, N.P.; Perry, W.R.; Larson, D.W. The Impact of RAS/BRAF Mutation on Pathological Complete Response After Total Neoadjuvant Therapy in Rectal Cancer. Ann. Surg. Oncol. 2025, 32, 7326–7332. [Google Scholar] [CrossRef] [PubMed]
- Hasan, S.; Renz, P.; Wegner, R.E.; Finley, G.; Raj, M.; Monga, D.; McCormick, J.; Kirichenko, A. Microsatellite Instability (MSI) as an Independent Predictor of Pathologic Complete Response (PCR) in Locally Advanced Rectal Cancer: A National Cancer Database (NCDB) Analysis. Ann. Surg. 2020, 271, 716–723. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
PRISMA screening protocol.
Table 1.
PICO framework for the systematic review.
| Description | |
|---|---|
| P—Population | Adult patients (≥18 years) diagnosed with locally advanced non-metastatic rectal cancer who underwent neoadjuvant (chemo)radiotherapy (nCRT). |
| E—Exposure | Presence of specific single nucleotide polymorphisms (SNPs). |
| C—Comparator | Patients with alternative SNP variants or wild-type genotypes. |
| O—Outcomes | Pathological complete response (pCR) or Tumor regression grade (TRG) |
Table 2.
Baseline characteristics of the included studies.
| Author | Year | Study Design | Country | Number of Patients | Years of Recruitment | Initial Staging | Neoadjuvant Protocol | Median Age | Sample Examined | Number of SNPs Analyzed | Genes Investigated | Genotyping Method | Primary Outcome | Responder/pCR Definition and Rate | Positive Predictors of Tumor Response |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Balboa [15] | 2010 | Retrospective cohort | Spain | 65 | NR | stage II (31%), stage III (69%) | RT + Uracil/Capecitabine | 64 | peripheral blood sample, pre-treatment tumor material | 7 | XRCC1, ERCC1, ERCC2 (XPD), MTHFR, DPYD, TYMS, EGFR | SNaPshot | Mandard TRG | responders (TRG 1–2) 48% non-responders (TRG 3–5) 53% | XRCC1 - rs25487 AA |
| Boige [16] | 2019 | Prospective cohort (RCT subgroup) | France | 316 | 2005–2008 | T3–4 M0 (100%) | RT + Capecitabine (49%) or dose-intensified RT + Capecitabine + Oxaliplatin (51%) | 61 | peripheral blood sample | 66 | ERCC1, ERCC2 (XPD), ERCC4, XRCC1, XRCC3, XPA, GSTP1, MTHFR, TYMS, SOD2 | Illumina Infinium iSelect custom SNP genotyping | Dworak TRG (modified) | responders (TRG 2–3) 37.6% non-responders (TRG 0–1) 62.4% | ERCC2 - rs1799787 C>T ERCC1 - rs10412761 A>G |
| Cecchin [17] | 2011 | Retrospective cohort | Italy | 238 | 1993–2006 | staging not specified | RT 45–50.4 Gy + 5-FU ± others | 61 | peripheral blood sample | 21 | MTHFR, ABCB1, ABCC2, GSTA1*B, RAD51, MLH1, MSH2, OGG1, XRCC3, XRCC1, ERCC2 (XPD), ERCC1, GSTP1 | Pyrosequencing, TaqMan, Gel electrophoresis | Mandard TRG | good responders (TRG 1–2) 51% intermediate responders (TRG 3) 21% non-responders (TRG 4–5) 27% | hOGG1 - rs1052133 CC MTHFR - rs1801133 TT |
| Chiang [18] | 2021 | Retrospective cohort + animal model | Taiwan | 130 | 2006–2014 | cT3–4 or cN+ (100%) | RT 50.4 Gy + 5-FU/UFT/Capecitabine | 59.7 | non-tumor surgical material | 4 | FPR1, TIM3, P2RX7, TLR1 | MassARRAY | Dworak TRG | responders (TRG 3–4) 65% non-responders (TRG 1–2) 35% | FPR1 - rs867228 AC/AA |
| Dreussi [19] | 2016 | Retrospective cohort | Italy | 280 | 1993–2011 | T3–T4 and N0–2 M0 (100%) | RT 50.4 Gy/55.0 Gy + 5-FU/Capecitabine ± Oxaliplatin | 61 | peripheral blood sample | 30 | MTHFR, MSH6, XRCC1, OGG1, MDM2, MLH1, MGMT, GSTP1, SOD2, XRCC3, TP53, ATM, EGFR, PARP-1, EXO1, ERCC1, ERCC2 (XPD), EGF, VEGF, APEX1 | TaqMan PCR, Pyrosequencing, Gel electrophoresis | pCR | pCR (Mandard TRG 1) 28% | none |
| Dreussi [20] | 2016 | Prospective cohort | Italy | 265 | 1993–2011 | T3–T4 N0–2 M0 (100%) | RT 50.4/55.0 Gy + 5-FU/Capecitabine ± Oxaliplatin | NR | peripheral blood sample | 114 | SMAD3, TRBP, DROSHA, CNOT4, CNOT6, DDX20, DGCR8, DICER1, SMAD2, SMAD5, TNRC6A, TNRC6B, miR-196a-2, miR-371A (authors do not report all analyzed SNPs) | BeadXpress platform | pCR | pCR (Mandard TRG 1) 28% | SMAD3 - rs744910 AG/GG - rs745103 GG - rs17228212 TT TRBP - rs6088619 AG/GG DROSHA - rs10719 CC |
| Dzhugashvili [21] | 2014 | Retrospective cohort | Spain | 159 | 2004–2010 | cT0–2 (5.7%) cT3–4 (94.3%) cN+ (72.9%) | RT 50.4 Gy (1.8 Gy per fraction) + Capecitabine | 64 | peripheral blood sample | 6 | IL1B, PTGS1, PTGS2 | TaqMan | pCR | pCR (ypT0N0) 18.2% | none |
| Formica [22] | 2018 | Prospective cohort | Italy | 51 | NR | stage II (35%), stage III (65%) | RT 45 Gy (1.8 Gy per fraction) + 5.4 Gy boost + Cisplatin + Capecitabine | 63 | surgical tumor material | 5 | GSTP1, XRCC1, ERCC1, MTHFR, ABCB1 | Pyrosequencing | AJCC TRG | responders (AJCC TRG 0–1) 34% non-responders (AJCC TRG 2–3) 66% | XRCC1 - rs25487 GG |
| Garcia-Aguilar [23] | 2011 | Prospective cohort (Secondary analysis of phase II trial) | USA, Spain | 132 | 2004–2012 | stage II (28%), stage III (69%), unknown (3%) | RT 50.4 Gy, + 5-FU ± mFOLFOX-6 | 57 | pre-treatment tumor material | 2 | CCND1, MTHFR | Sanger sequencing | pCR | pCR (AJCC TRG 0) 25% | CCND1 - rs603965 AG/GG MTHFR - rs1801133 TC/CC |
| Grimminger [24] | 2010 | Prospective cohort | Germany | 81 | 1997–2008 | T3/4 Nx (100%) | RT 50.4 Gy + 5-FU | 59 | pre-treatment tumor material | 3 | XRCC1 | TaqMan | Viable residual tumor cells | major response (VRTC 3–4) 32% minor response (VRTC 1–2) 68% | XRCC1 - rs25487 AG |
| Havelund [25] | 2012 | Prospective cohort | Denmark | 198 | 2005–2009 | cT3 (83%), cT4 (17%); N+ (89%) | RT 50.4 Gy with or ± 10 Gy brachytherapy + UFT + Leucovorin | 63 | peripheral blood sample | 3 | HIF-1α | KASPar, Sanger sequencing, TaqMan | Mandard TRG | responders (TRG 1) 19% non-responders (TRG 2–4) 81% | none |
| Ho-Pun-Cheung [26] | 2011 | Prospective study | France | 71 | 2005–2008 | stage II (20%), stage III (72%), stage IV (9%) | RT 45/50 Gy (1.8–2 Gy per fraction) + Capecitabine ± Oxaliplatin | 61 | peripheral blood sample | 128 | ADPRT, CHEK1, CHEK2, ATM, BRCA1, BRCA2, ERCC1, ERCC2 (XPD), ERCC4, ERCC5, LIG3, LIG4, MBD4, MGMT, XPA, XRCC1, XRCC3, BAX, CASP3, CASP8, CASP10, CASP9, BCL2, CCND1, CDKN1A, MDM2, TP53, TP73, EGF, EGFR, ERBB2, IGF1, FGF2, FGFR4, IGF2R, TGFB1, VEGF, FCGR2A, FCGR3A, IL8, IL10, IL1B, IL4, IL6, IL13, LTA, NFKB1, TNFA, PTGS2, PPARG, NFE2L2, PARP-1, MPO, GPX1, SOD2, NOS2A, NOS3, HIF-1A, CYP1A1, GSTP1, GSTT1, MT-ND3, MTHFR, ICAM5, GSK3B, CTNNB1, APEX1, NBN, RECQL, ZNF350, RAD52, XRCC5 | SNPlex Genotyping System, TaqMan, PCR-RFLP | Dworak TRG | responders (TRG 3–4) 45% non-responders (TRG 0–2) 55% | SOD2 - rs4880 CC IL13 - rs1800925 CC |
| Ho-Pun-Cheung [27] | 2007 | Prospective cohort | France | 70 | 1996–2001 | stage I (16%), stage II (31%), stage III (36%), stage IV (17%) | RT (45/60 Gy) | 64 | peripheral blood sample | 1 | CCND1 | PCR-RFLP | Dworak TRG | responders (TRG 2–4) 50% non-responders (TRG 0–1) 43% NR 7% | CCND1 - positive G870A AA |
| Hu-Lieskovan [28] | 2011 | Prospective cohort from Phase I/II trials | Germany, Slovenia, Belgium | 130 | NR | stage II (3%), stage III (84%), stage IV (12%) | RT + Cetuximab + Capecitabine/Oxaliplatin/5-FU | 61 | surgical tumor material | 13 | EGFR, KRAS, IL8, MTHFR, FCGR2A, FCGR3A, XRCC3, VEGF, EGF, CCND1, PTGS2, RAD51 | PCR-RFLP | pCR | pCR (Dworak TRG 4) 15% | EGF - rs4444903 AG/GG |
| Hur [29] | 2011 | Prospective cohort | South Korea | 44 | 2007–2008 | T2(2.3%), T3 (40.9%), T4 (56.8%) | RT 45 Gy (1.8 Gy per fraction) + 5.4 Gy boost + 5-FU | 58 | pre-treatment tumor material | 1 | TYMS | Sanger sequencing | pCR, Mandard TRG | pCR (Mandard TRG 1) 14% responders (Mandard TRG 1–2) 41% non-responders (Mandard TRG 3–4) 59% | none |
| Kim [14] | 2013 | Prospective cohort | South Korea | 113 (genome-wide screening of 691,162 SNPs) | NR | stage II (9%), stage III (89%), stage IV (2%) | RT 45 Gy + 5.4 Gy boost + 5-FU + Leucovorin (80%)/Capecitabine (20%) | 59 | peripheral blood sample | 9 | FAM101A, CORO2A, USP20, ZNF281, OR2T4, SLC10A7, ASZ1, MED4, CDC42BPA | Genome-Wide Human SNP Array, Pyrosequencing | Mandard TRG | responders (Mandard TRG 1–3) 84% non-responders (Mandard TRG 4) 16% | CORO2A - rs1985859 CC (wild type) |
| Kim [30] | 2017 | Prospective cohort | South Korea | 91 | 2009–2012 | T3 (97%), T4 (3%), N+ (87%) | RT 50.4 Gy (1.8 Gy per fraction) + Tegafur + Uracil + Leucovorin | 59 | peripheral blood sample | 7 | UMPS, CYP2A6, ABCB1 | PCR-RFLP | pCR | pCR (not defined) 11% | none |
| Lamas [31] | 2012 | Prospective cohort | Spain | 93 | 2007–2008 | stage II (28%), stage III (72%) | RT 50.4 Gy + 5-FU | 67 | peripheral blood sample | 5 | XRCC1, TYMS, MTHFR, ERCC1 | SNaPshot | Mandard TRG | responders (TRG 1–2) 47% non-responders (TRG 3–4) 53% | XRCC1 - rs25487 GG/AA TYMS - 5′UTR VNTR 2R/3G, 3C/3G, 3G/3G |
| Leu [32] | 2021 | Prospective cohort | Germany | 287 | 1998–2016 | stage II (21%), stage III (79%) | RT 50.4 Gy (1.8 Gy per fraction) + 5-FU ± Oxaliplatin | 64.4 | peripheral blood sample | 8 | SOD2, SOD3, CAT, CYBA, GPX1, MPO, OGG1 | SNaPshot | pCR | pCR (not defined) 17% | none |
| Nicosia [33] | 2018 | Retrospective cohort | Italy | 80 | 2008–2015 | T2 (11%) T3 (84%) T4 (5%), N+ (54%) M0 (100%) | RT 45 Gy + 10 Gy boost + Capecitabine (60%)/5-FU (40%) | 64 | peripheral blood sample | 2 | GSTP1, XRCC1 | Pyrosequencing | pCR | pCR (Dworak TRG 4) 19% | GSTP1 - rs1695 AA (wild type) |
| Nikas [34] | 2015 | Prospective cohort | USA | 108 | NR | staging not specified | RT 50.4 Gy + 5-FU | NR | peripheral blood sample | 1 | MTHFR | High-resolution Melting Analysis | pCR | pCR (College of American Pathologists TRG 0) 33% non-responders (College of American Pathologists TRG 3–4) 67% | MTHFR - rs1801133 CC (wild type) |
| Paez [35] | 2011 | Prospective cohort | Spain | 128 | 1998–2009 | T2 (6%), T3 (81%), T4 13%, N+ (60%) | RT 45 Gy+ 5-FU/Capecitabine/Capecitabine + Oxaliplatin/5-FU + Oxaliplatin | 65 | peripheral blood sample | 10 | XRCC1, ERCC1, EGFR, GSTP1, ERCC2 (XPD), TYMS | TaqMan | pCR | responders (pCR Mandard TRG 1 plus residual microfoci) 43% non-responders 57% | none |
| Peng [36] | 2018 | Retrospective cohort | China | 97 | 2008–2011 | stage II (36.3%), stage III (63.7%) | RT 50 Gy (2 Gy per fraction) + XELOX | 58 | peripheral blood sample | 12 | PTEN, PIK3CA, AKT1, AKT2, FRAP1 | PCR-RFLP | pCR, Dworak TRG | pCR (Dworak TRG 4) 14.4% responders (TRG 2–4) 69.1% non-responders (TRG 1) 30.9% | none |
| Rampazzo [37] | 2020 | Prospective cohort | Italy | 194 | NR | stage I (3.2%), stage II (11.1%), stage III (84.1%), stage IV (1.6%) | RT + Fluoropyrimidine ± other drug | 65 | peripheral blood sample | 8 | TERT | TaqMan | Mandard TRG | responders (TRG 1–2) 46% non-responders (TRG 3–5) 54% | TERT - rs2736108 CC - rs2853690 GG/AA |
| Sclafani [38] | 2015 | Retrospective cohort | UK, Spain, Sweden, and others | 155 | 2005–2008 | staging not specified | CAPOX (±Cetuximab) + Capecitabine-RT 45 Gy + 5.4 Gy | 60 | surgical tumor material, pre-treatment tumor material | 1 | KRAS | TaqMan | pCR | pCR (pCR or, in patients who did not undergo surgery, radiologic CR) 14% | KRAS - rs61764370 TG |
| Sclafani [39] | 2016 | Retrospective cohort | UK, Spain, Sweden, and others | 155 | 2005–2008 | staging not specified | CAPOX (±Cetuximab) + Capecitabine-RT 45 Gy + 5.4 Gy | 60 | surgical tumor material, pre-treatment tumor material | 1 | miR-608 | TaqMan | pCR | pCR (pCR or, in patients who did not undergo surgery, radiologic CR) 14% | none |
| Sebio [40] | 2015 | Retrospective cohort | Spain | 84 | NR | stage II (27.4%), stage III (72.6%) | RT 45 Gy (1.8 Gy per fraction) + Capecitabine | 68 | peripheral blood sample | 28 | TYMS, XRCC1, ERCC1, AREG, EGF, EREG, EGFR, ERCC2 (XPD) | TaqMan | pCR | complete response (Mandard TRG 1) 20.2% | ERCC1 - rs11615 CT/TT AREG - rs11942466 CC (wild type) |
| Spindler [41] | 2006 | Prospective cohort | Denmark | 77 | 2003–2005 | T3N0M0 (22%) T3N1M0 (62%) T3N2M0 (16%) | RT 65 Gy + UFT + Leucovorin | 64 | peripheral blood sample | 1 | EGFR | TaqMan | Mandard TRG | responders (TRG 1–2) 49% non-responders (TRG 3–4) 51% | EGFR - rs712829 GT/TT |
| Stanojevic [42] | 2024 | Prospective cohort | Serbia | 97 | 2018–2019 | stage II (8%), stage III (92%) | RT 50.4 Gy (1.8 Gy per fraction) + 5-FU + Leucovorin | 61 | pre-treatment tumor material | 2 | MTHFR | PCR-RFLP | Mandard TRG | responders (TRG 1–2) 31% non-responders (TRG 3–5) 67% NR 2% | none |
| Stoehlmacher [43] | 2008 | Retrospective cohort | Germany | 40 | 1998–2001 | stage II or III (100%) | RT 50.4 Gy (1.8 Gy per fraction) + 5-FU | 60 | pre-treatment tumor material | 1 | TYMS | PCR-RFLP | TRG * | responders 84% non-responders 16% | none |
| Terrazzino [44] | 2006 | Retrospective cohort | Italy | 125 | 1994–2002 | T2 (8%), T3 (66%), T4 (24%); N1 (67%); M0 (100%) | RT 48.4 Gy (median) + 5-FU (35%)/5-FU + Oxaliplatin (22%)/Leucovorin (29%)/Carboplatin (14%) | 60 | peripheral blood sample | 2 | MTHFR | PCR-RFLP | Mandard TRG | responders (TRG 1–2) 39% non-responders (TRG 3–4) 61% | MTHFR - rs1801133 CC |
| Xiao [45] | 2016 | Prospective cohort | China | 58 | 2007–2012 | stage II (22%), stage III (78%) | RT 46 Gy (2 Gy per fraction) + XELOX (86%) or mFOLFOX6 (10%) | NR | pre-treatment tumor material | 1 | IL13 | Sanger sequencing | pCR, Dworak TRG | pCR (Dworak TRG 4) 28% good response (TRG 3–4) 48% non-responders (TRG 0–2) 52% | none |
* grade 0 = no regression; grade 1 = dominant tumor mass with obvious fibrosis or mucin; grade 2 = dominantly fibrotic or mucinous changes, with few tumor cells or groups; grade 3 = very few tumor cells in fibrotic or mucinous tissue; grade 4 = no tumor cells. NR—not reported.
Table 3.
Genes and corresponding SNPs analyzed in the included studies.
| Gene Function | Name | Studies | Number of Studies | SNP ID | Number of Different SNPs Analyzed |
|---|---|---|---|---|---|
| Folate metabolism | DPYD | Balboa [15] | 1 | rs3918290 | 1 |
| MTHFR | Balboa [15], Boige [16], Cecchin [17], Dreussi [19], Formica [22], Garcia-Aguilar [23], Ho-Pun-Cheung [26], Hu-Lieskovan [28], Lamas [31], Nikas [34], Stanojevic [42], Terrazzino [44] | 12 | rs3737967, rs3818762, rs3737964, rs7553194, rs17367504, rs9651118, rs4846052, rs1572151, rs1801133, rs1801131, rs17375901 | 11 | |
| UMPS | Kim [30] | 1 | rs1801019 | 1 | |
| TYMS | Balboa [15], Boige [16], Hur [29], Lamas [31], Páez [35], Sebio [40], Stoehlmacher [43] | 7 | rs2853542, rs2847153, rs2298582, rs2612101, rs10502290, rs2260821, rs3744962, rs1001761, rs2853741, VNTR/5′UTR | 11 | |
| DNA repair | ERCC5 | Ho-Pun-Cheung [26] | 1 | rs17655 | 1 |
| MSH6 | Dreussi [19] | 1 | rs3136228 | 1 | |
| CHEK2 | Ho-Pun-Cheung [26] | 1 | rs2267130 | 1 | |
| RAD51 | Cecchin [17], Hu-Lieskovan [28] | 2 | rs1801320, rs5030783, rs1801321 | 3 | |
| ERCC1 | Balboa [15], Boige [16], Cecchin [17], Dreussi [19], Formica [22], Ho-Pun-Cheung [26], Lamas [31], Páez [35], Sebio [40] | 9 | rs11615, rs10412761, rs2336219, rs3212986, rs2298881, rs4803823, rs3212948 | 7 | |
| MGMT | Dreussi [19], Ho-Pun-Cheung [26] | 2 | rs12917 | 1 | |
| EXO1 | Dreussi [19] | 1 | rs4149963 | 1 | |
| MLH1 | Cecchin [17], Dreussi [19] | 2 | rs1799977, rs1800734 | 2 | |
| MSH2 | Cecchin [17] | 1 | rs2303428 | 1 | |
| XRCC1 | Balboa [15], Boige [16], Cecchin [17], Dreussi [19], Formica [22], Grimminger [24], Ho-Pun-Cheung [26], Lamas [31], Nicosia [33], Páez [35], Sebio [40] | 11 | rs25487, rs2293036, rs3213334, rs2023614, rs1001581, rs2854496, rs3213266, rs3213255, rs304729, rs1799782, rs3213239, rs25489, rs861539, rs3213245 | 14 | |
| BAX | Ho-Pun-Cheung [26] | 1 | rs36017265, rs4645878 | 2 | |
| XPA | Boige [16], Ho-Pun-Cheung [26] | 2 | rs2773354, rs3176757, rs2808667, rs2805835, rs3176689, rs3176683, rs3176658, rs3176639, rs1800975 | 9 | |
| XRCC3 | Boige [16], Cecchin [17], Dreussi [19], Ho-Pun-Cheung [26], Hu-Lieskovan [28] | 5 | rs3212102, rs12432907, rs3212090, rs3212079, rs861531, rs861530, rs861528, rs1799794, rs861539, rs1799796 | 10 | |
| PARP-1 | Dreussi [19], Ho-Pun-Cheung [26] | 2 | rs11136410 | 1 | |
| ADPRT | Ho-Pun-Cheung [26] | 1 | rs1136410 | 1 | |
| ERCC4 | Boige [16], Ho-Pun-Cheung [26] | 2 | rs1364362, rs1800067, rs11075223, rs1799802, rs744154, rs1799801, rs1799800 | 7 | |
| LIG4 | Ho-Pun-Cheung [26] | 1 | rs1805388, rs1805386 | 2 | |
| CHEK1 | Ho-Pun-Cheung [26] | 1 | rs521102 | 1 | |
| LIG3 | Ho-Pun-Cheung [26] | 1 | rs1052536, rs3135967 | 2 | |
| ERCC2 (XPD) | Balboa [15], Boige [16], Cecchin [17], Dreussi [19], Ho-Pun-Cheung [26], Páez [35], Sebio [40] | 7 | rs13181, rs238415, rs50872, rs50871, rs1799793, rs11878644, rs28365048, rs1799787 | 9 | |
| MBD4 | Ho-Pun-Cheung [26] | 1 | rs10342, rs140693 | 2 | |
| APEX1 | Dreussi [19], Ho-Pun-Cheung [26] | 2 | rs1130409, rs1760944 | 2 | |
| NBN | Ho-Pun-Cheung [26] | 1 | rs1805794 | 1 | |
| RECQL | Ho-Pun-Cheung [26] | 1 | rs13035 | 1 | |
| ZNF350 | Ho-Pun-Cheung [26] | 1 | rs2278415, rs2278420 | 2 | |
| RAD52 | Ho-Pun-Cheung [26] | 1 | rs11226 | 1 | |
| XRCC5 | Ho-Pun-Cheung [26] | 1 | rs1051677, rs1051685, rs6941, rs2440 | 4 | |
| PTEN | Peng [36] | 1 | rs2299939, rs12569998 | 2 | |
| Immune regulation | TNFA | Ho-Pun-Cheung [26] | 1 | rs1800629 | 1 |
| LTA | Ho-Pun-Cheung [26] | 1 | rs2229094 | 1 | |
| IL8 | Ho-Pun-Cheung [26], Hu-Lieskovan [28] | 2 | rs4073 | 1 | |
| IL4 | Ho-Pun-Cheung [26] | 1 | rs2243250 | 1 | |
| FPR1 | Chiang [18] | 1 | rs867228 | 1 | |
| IL13 | Ho-Pun-Cheung [26], Xiao [45] | 2 | rs20541, rs1800925 | 2 | |
| IL10 | Ho-Pun-Cheung [26] | 1 | rs1800896 | 1 | |
| FAM101A | Kim [14] | 1 | rs7955740 | 1 | |
| TLR1 | Chiang [18] | 1 | rs5743618 | 1 | |
| IL6 | Ho-Pun-Cheung [26] | 1 | rs1800795 | 1 | |
| P2RX7 | Chiang [18] | 1 | rs3751143 | 1 | |
| NFKB1 | Ho-Pun-Cheung [26] | 1 | rs3774932, rs3774934, rs3774936, rs3774937 | 4 | |
| FCGR2A | Ho-Pun-Cheung [26], Hu-Lieskovan [28] | 2 | rs1801274 | 1 | |
| FCGR3A | Ho-Pun-Cheung [26], Hu-Lieskovan [28] | 2 | rs396991 | 1 | |
| IL1B | Dzhugashvili [21], Ho-Pun-Cheung [26] | 2 | rs16944, rs1143627, rs1143634 | 3 | |
| TIM3 | Chiang [18] | 1 | rs1036199 | 1 | |
| CORO2A | Kim [14] | 1 | rs1985859 | 1 | |
| Angiogenesis | HIF-1A | Havelund [25], Ho-Pun-Cheung [26] | 2 | rs11549465, rs11549467, rs2057482, rs2246350 | 4 |
| VEGF | Dreussi [19], Ho-Pun-Cheung [26], Hu-Lieskovan [28] | 3 | rs2010963, rs1570360, rs3025039, rs699947 | 4 | |
| Growth factor receptor | IGF2R | Ho-Pun-Cheung [26] | 1 | rs629849 | 1 |
| ERBB2 | Ho-Pun-Cheung [26] | 1 | rs1801200 | 1 | |
| EGFR | Balboa [15], Dreussi [19], Ho-Pun-Cheung [26], Hu-Lieskovan [28], Páez [35], Sebio [40], Spindler [41] | 7 | rs11568315, rs2227983, rs17290169, rs17335738, rs712830, rs712829, rs11543848 | 7 | |
| EGF | Dreussi [19], Ho-Pun-Cheung [26], Hu-Lieskovan [28], Sebio [40] | 4 | rs4444903, rs6533485, rs11568993, rs4698803, rs11568972, rs929446, rs2074390, rs6850557 | 8 | |
| EREG | Sebio [40] | 1 | rs7687621, rs1017733 | 2 | |
| TGFB1 | Ho-Pun-Cheung [26] | 1 | rs1982073, rs1800471, rs1800469 | 3 | |
| IGF1 | Ho-Pun-Cheung [26] | 1 | rs2229765 | 1 | |
| FGFR4 | Ho-Pun-Cheung [26] | 1 | rs351855 | 1 | |
| FGF2 | Ho-Pun-Cheung [26] | 1 | rs308447 | 1 | |
| AREG | Sebio [40] | 1 | rs11942466, rs28635876, rs13104811, rs1353295, rs3913032, rs6447003, rs10034692, rs11725706, rs2132065 | 9 | |
| Oncogene | BRCA2 | Ho-Pun-Cheung [26] | 1 | rs1799943, rs206143, rs144848 | 3 |
| BRCA1 | Ho-Pun-Cheung [26] | 1 | rs1799966, rs16941, rs16942, rs799917 | 4 | |
| KRAS | Hu-Lieskovan [28], Sclafani [38] | 2 | rs61764370 | 1 | |
| PIK3CA | Peng [36] | 1 | rs2699887, rs6443624, rs7621329, rs7651265 | 4 | |
| miRNA | miR-371a | Dreussi [20] | 1 | rs28461391 | 1 |
| SMAD5 | Dreussi [20] | 1 | rs1057898, rs6871224 | 2 | |
| DDX20 | Dreussi [20] | 1 | rs197412 | 1 | |
| TNRC6B | Dreussi [20] | 1 | rs139911 | 1 | |
| miR-608 | Sclafani [39] | 1 | rs4919510 | 1 | |
| DGCR8 | Dreussi [20] | 1 | rs417309 | 1 | |
| CNOT4 | Dreussi [20] | 1 | rs11772832 | 1 | |
| TNRC6A | Dreussi [20] | 1 | rs6497759 | 1 | |
| TRBP | Dreussi [20] | 1 | rs6088619 | 1 | |
| miR-196a-2 | Dreussi [20] | 1 | rs11614913 | 1 | |
| SMAD2 | Dreussi [20] | 1 | rs1792671 | 1 | |
| CNOT6 | Dreussi [20] | 1 | rs6877400 | 1 | |
| DICER1 | Dreussi [20] | 1 | rs1057035 | 1 | |
| DROSHA | Dreussi [20] | 1 | rs10719 | 1 | |
| Transporter | SLC10A7 | Kim [14] | 1 | rs41398848 | 1 |
| Tumor suppressor | TP53 | Dreussi [19], Ho-Pun-Cheung [26] | 2 | rs1642785, rs1042522, rs2602141, rs560191 | 4 |
| Drug transporters | ABCB1 | Cecchin [17], Formica [22], Kim [30] | 3 | rs1045642, rs1128503, rs2032582 | 3 |
| CYP2A6 | Kim [30] | 1 | rs5031016, rs28399433, rs28399468 | 4 | |
| ABCC2 | Cecchin [17] | 1 | rs2273697, rs717620 | 2 | |
| Detoxication | GSTA1*B | Cecchin [17] | 1 | rs3957357 | 1 |
| GSTP1 | Boige [16], Cecchin [17], Dreussi [19], Formica [22], Ho-Pun-Cheung [26], Nicosia [33], Páez [35] | 7 | rs7927381, rs6591256, rs1138272, rs947894, rs1695 | 6 | |
| CYP1A1 | Ho-Pun-Cheung [26] | 1 | rs1048943 | 1 | |
| GSTT1 | Ho-Pun-Cheung [26] | 1 | rs4630 | 1 | |
| Reactive oxygen species | SOD2 | Boige [16], Dreussi [19], Ho-Pun-Cheung [26], Leu [32] | 4 | rs5746136, rs5746141, rs2842980, rs2758329, rs4342445, rs4880 | 7 |
| MPO | Ho-Pun-Cheung [26], Leu [32] | 2 | rs7208693, rs2333227 | 2 | |
| SOD3 | Leu [32] | 1 | rs699473 | 1 | |
| NOS2A | Ho-Pun-Cheung [26] | 1 | rs2297518 | 1 | |
| CAT | Leu [32] | 1 | rs1001179, rs769214 | 2 | |
| MT-ND3 | Ho-Pun-Cheung [26] | 1 | rs2853826 | 1 | |
| NOS3 | Ho-Pun-Cheung [26] | 1 | rs179998 | 1 | |
| GPX1 | Ho-Pun-Cheung [26], Leu [32] | 2 | rs1050450 | 1 | |
| OGG1 | Cecchin [17], Dreussi [19], Leu [32] | 3 | rs1052133 | 1 | |
| CYBA | Leu [32] | 1 | rs1049255 | 1 | |
| Cell cycle regulator | AKT1 | Peng [36] | 1 | rs1130214, rs2494738, rs2498804 | 3 |
| TP73 | Ho-Pun-Cheung [26] | 1 | rs2273953, rs1801173 | 2 | |
| CDKN1A | Ho-Pun-Cheung [26] | 1 | rs1801270 | 1 | |
| CCND1 | Garcia-Aguilar [23], Ho-Pun-Cheung [26], Ho-Pun-Cheung [27], Hu-Lieskovan [28] | 4 | rs603965, rs9344 | 2 | |
| ATM | Dreussi [19], Ho-Pun-Cheung [26] | 2 | rs1801516, rs189037, rs1800057 | 3 | |
| MDM2 | Dreussi [19], Ho-Pun-Cheung [26] | 2 | rs2279744, rs1470383 | 2 | |
| AKT2 | Peng [36] | 1 | rs8100018 | 1 | |
| FRAP1 | Peng [36] | 1 | rs2295080, rs11121704 | 2 | |
| TERT | Rampazzo [37] | 1 | rs2736108, rs2735940, rs2736098, rs2736100, rs35241335, rs11742908, rs2736122, rs2853690 | 8 | |
| Other | CDC42BPA | Kim [14] | 1 | rs192986 | 1 |
| ASZ1 | Kim [14] | 1 | rs7808424 | 1 | |
| SMAD3 | Dreussi [20] | 1 | rs17228212, rs2289791, rs744910, rs745103, rs8025774, rs8028147 | 6 | |
| OR2T4 | Kim [14] | 1 | rs1538704 | 1 | |
| USP20 | Kim [14] | 1 | rs2274507 | 1 | |
| ICAM5 | Ho-Pun-Cheung [26] | 1 | rs1056538, rs2228615 | 2 | |
| Apoptosis | BCL2 | Ho-Pun-Cheung [26] | 1 | rs2279115 | 1 |
| CASP9 | Ho-Pun-Cheung [26] | 1 | rs1052576 | 1 | |
| CASP8 | Ho-Pun-Cheung [26] | 1 | rs1045485, rs13113 | 2 | |
| CASP10 | Ho-Pun-Cheung [26] | 1 | rs13010627 | 1 | |
| CASP3 | Ho-Pun-Cheung [26] | 1 | rs6948, rs1049216 | 2 | |
| Transcription factors | ZNF281 | Kim [14] | 1 | rs4244146 | 1 |
| PPARG | Ho-Pun-Cheung [26] | 1 | rs1801282 | 1 | |
| MED4 | Kim [14] | 1 | rs1571256 | 1 | |
| NFE2L2 | Ho-Pun-Cheung [26] | 1 | rs5031039, rs35652124 | 3 | |
| β-catenin pathway | GSK3B | Ho-Pun-Cheung [26] | 1 | rs334558, rs3755557, rs6721961 | 2 |
| CTNNB1 | Ho-Pun-Cheung [26] | 1 | rs4135385, rs13072632 | 2 | |
| Cyclooxygenase | PTGS2 | Dzhugashvili [21], Ho-Pun-Cheung [26], Hu-Lieskovan [28] | 3 | rs5275, rs20417 | 2 |
| PTGS1 | Dzhugashvili [21] | 1 | rs1213266, rs5789 | 2 |
Table 4.
Summary of SNPs related to treatment response in at least one study.
| Name | Genes | Number of Studies with Effect/All Studies | Studies Showing Significance | Studies Not Showing Significance | Number of Patients in Studies Showing Significance/Total Number of Patients | Positive Predictors of Pathological Response | Allele Frequency | Effect Size * |
|---|---|---|---|---|---|---|---|---|
| Folate metabolism pathways | ||||||||
| rs2853542 G>C | TYMS | 1/6 | Lamas [31] | Balboa [15], Hur [29], Páez [35], Sebio [40], Stoehlmacher [43] | 93/454 | rs2853542 2R/3G, 3C/3G, 3G/3G | 39% | OR: 2.65; 95% CI: 1.10–6.39, p = 0.02 |
| rs1801133 C>T | MTHFR | 3/10 | (1) Cecchin [17], (2) Nikas [34], (3) Terrazzino [44] | Boige [16], Dreussi [19], Garcia-Aguilar [23], Ho-Pun-Cheung [26], Hu-Lieskovan [28], Lamas [31], Stanojevic [42] | 471/1590 | (1,2,3) rs1801133 CC | (1) 64% (2) 54% (3) 33% | (1) OR: 2.00; 95% CI: 1.03–4.00; p < 0.05 (2) OR: 2.91; 95% CI: 1.23–6.89; p = 0.0150 (3) OR: 3.13; 95% CI: 1.39–7.14; p = 0.002 |
| DNA repair pathway | ||||||||
| rs25487 A>G | XRCC1 | 4/10 | (1) Balboa [15], (2) Formica [22], (3) Grimminger [24], (4) Lamas [31] | Cecchin [17], Dreussi [19], Ho-Pun-Cheung [26], Nicosia [33], Páez [35], Sebio [40] | 239/1120 | (1) rs25487 AA, (2) rs25487 GG, (3) rs25487 AG, (4) rs25487 GG | (1) 10% (2) NR (3) 40% (4) 47% | (1) OR: 7.93 95% CI: 1.03–60.83; p = 0.006 (2) OR: 25.8; 95% CI: 1.02–653.85; p = 0.049 (3) OR: NR, 95% CI: NR; p = 0.039 (4) GG vs. GA: OR: 4.18; 95% CI: 1.62–10.74; p = 0.003 |
| rs11615 T>C | ERCC1 | 1/8 | Sebio [40] | Balboa [15], Cecchin [17], Dreussi [19], Formica [22], Ho-Pun-Cheung [26], Lamas [31], Páez [35] | 84/959 | rs11615 TT | 20% | TT vs. CT OR: 2.27; 95% CI: 0.76–7.69; p = 0.0235 |
| rs10412761 A>G | ERCC1 | 1/1 | Boige [16] | 316/361 | rs10412761 AG/GG | 44% | OR: 1.75, 95% CI: 1.02–2.94, p = 0.042 | |
| rs1799787 C>T | ERCC2 (XPD) | 1/1 | Boige [16] | 316/361 | rs1799787 CT/TT | 44% | OR: 1.82, 95% CI: 1.08–3.13, p = 0.027 | |
| Reactive oxygen species pathways | ||||||||
| rs1695 A>G | GSTP1 | 1/5 | Nicosia [33] | Dreussi [19], Formica [22], Ho-Pun-Cheung [26], Páez [35] | 80/559 | rs1695 AA | NR | OR: NR, 95% CI: NR; p = 0.04 |
| rs4880 C>T | SOD2 | 1/3 | Ho-Pun-Cheung [26] | Dreussi [19], Leu [32] | 71/638 | rs4880 CC | 32% | OR: 5.26; 95% CI: 1.56–16.67; p = 0.005 |
| rs1052133 C>G | OGG1 | 1/4 | Cecchin [17] | Dreussi [19], Ho-Pun-Cheung [26], Leu [32] | 238/876 | rs1052133 CC | 69% | OR: 2.13; 95% CI: 1.06–4.17; p < 0.05 |
| Growth factor receptor pathways | ||||||||
| rs4444903 A>G | EGF | 1/4 | Hu-Lieskovan [28] | Dreussi [19], Ho-Pun-Cheung [26], Sebio [40] | 130/565 | rs4444903 AG/GG | 54% | OR: 16.68; 95% CI: 2.1–130.8; p = 0.007 |
| rs712829 | EGFR | 1/3 | Spindler [41] | Ho-Pun-Cheung [26], Sebio [40] | 77/232 | rs712829 GT/TT | 54% | OR: NR, 95% CI: NR; p = 0.023 |
| rs11942466 C>A | AREG | 1/1 | Sebio [40] | 84/84 | rs11942466 CC | 20% | CC vs. CA OR: 2.33; 95% CI: 0.75–7.14; p = 0.0018 | |
| Cell cycle regulator pathways | ||||||||
| rs603965 G>A | CCND1 | 1/3 | Ho-Pun-Cheung [27] | Garcia-Aguilar [23], Ho-Pun-Cheung [26] | 70/273 | rs603965 AA | 14% | OR: 10.0; 95% CI: 1.2–84.7; p = 0.034 |
| Immune regulation pathways | ||||||||
| rs1985859 C>T | CORO2A | 1/1 | Kim [14] | 113/113 | rs1985859 CC | 34% | OR: 4.88; 95% CI: 1.06–22.73; p = 0.03 | |
| rs867228 T>C | FPR1 | 1/1 | Chiang [18] | 130/130 | rs867228 AC/AA | 42% | OR: 2.521; 95% CI: 1.162–5.473; p = 0.017 | |
| rs1800925 C>T | IL13 | 1/2 | Ho-Pun-Cheung [26] | Xiao [45] | 71/129 | rs1800925 CC | 63% | OR: 7.14; 95% CI: 2.04–25.00; p = 0.0008 |
| Oncogenic pathways | ||||||||
| rs61764370 T>G | KRAS | 1/2 | Sclafani [38] | Hu-Lieskovan [28] | 155/285 | rs61764370 TG | 21% | OR: NR, 95% CI: NR; p = 0.02 |
| Telomere length pathways | ||||||||
| rs2736108 C>T | TERT | 1/1 | Rampazzo [37] | 194/194 | rs2736108 CC | NR | CC vs. TT OR: 4.6; 95% CI: 1.1–19.1; p = 0.034 | |
| rs2853690 G>A | TERT | 1/1 | Rampazzo [37] | 194/194 | rs2853690 GG/AA | NR | AA/GG vs. AG OR: 3.0; 95% CI: 1.3–6.9; p = 0.008 | |
| Other pathways | ||||||||
| rs17228212 C>T | SMAD3 | 1/1 | Dreussi [20] | 265/265 | rs17228212 TT | NR | OR: 2.01; 95% CI: 1.22–3.31; p = 0.0064 | |
| rs744910 A>G | SMAD3 | 1/1 | Dreussi [20] | 265/265 | rs744910 AG/GG | NR | OR: 2.22; 95% CI: 1.18–4.17; p = 0.0135 | |
| rs745103 T>C | SMAD3 | 1/1 | Dreussi [20] | 265/265 | rs745103 GG | NR | OR 2.08; 95% CI: 1.06–4.00; p = 0.0316 | |
| rs6088619 C>T | TRBP | 1/1 | Dreussi [20] | 265/265 | rs6088619 AG/GG | NR | OR: 2.56; 95% CI: 1.27–5.26; p = 0.0089 | |
| rs10719 T>C | DROSHA | 1/1 | Dreussi [20] | 265/265 | rs10719 CC | NR | OR: 3.0; 95% CI: 1.3–6.9; p = 0.008 | |
* Effect size refers to comparisons between the indicated genotype(s)/variant(s) and all other genotypes combined, unless specified otherwise. OR—odds ratio; CI—confidence interval; NR—not reported.
Table 5.
Risk of bias in the included studies according to the QGenie tool.
| Author | Year | Rationale for Study | Selection and Definition of Outcome of Interest | Selection and Comparability of Comparison Groups (If Applicable) | Technical Classification of the Exposure | Non-Technical Classification of the Exposure | Other Sources of Bias | Sample Size and Power | A Priori Planning of Analyses | Statistical Methods and Control for Confounding | Testing of Assumptions and Inferences for Genetic Analyses | Appropriateness of Inferences Drawn from Results | Overall |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Balboa [15] | 2010 | 6 | 6 | 4 | 6 | 5 | 5 | 3 | 5 | 6 | 5 | 6 | 57/77 |
| Boige [16] | 2019 | 6 | 6 | 5 | 6 | 6 | 5 | 4 | 6 | 6 | 4 | 4 | 58/77 |
| Cecchin [17] | 2011 | 6 | 6 | 4 | 6 | 5 | 5 | 5 | 5 | 5 | 5 | 6 | 58/77 |
| Chiang [18] | 2021 | 6 | 6 | 5 | 6 | 4 | 6 | 4 | 5 | 5 | 3 | 6 | 56/77 |
| Dreussi [20] | 2016 | 6 | 6 | 5 | 6 | 5 | 4 | 5 | 5 | 5 | 5 | 6 | 58/77 |
| Dreussi [19] | 2016 | 6 | 6 | 5 | 5 | 4 | 4 | 5 | 5 | 6 | 3 | 6 | 55/77 |
| Dzhugashvili [21] | 2014 | 6 | 6 | 4 | 6 | 4 | 5 | 5 | 5 | 6 | 4 | 6 | 57/77 |
| Formica [22] | 2018 | 5 | 6 | 4 | 3 | 2 | 4 | 2 | 5 | 6 | 3 | 6 | 46/77 |
| Garcia-Aguilar [23] | 2011 | 6 | 6 | 5 | 6 | 5 | 5 | 5 | 5 | 6 | 3 | 6 | 58/77 |
| Grimminger [24] | 2010 | 6 | 6 | 4 | 6 | 5 | 4 | 3 | 5 | 6 | 3 | 6 | 54/77 |
| Havelund [25] | 2012 | 6 | 6 | 6 | 6 | 5 | 3 | 4 | 5 | 5 | 4 | 6 | 56/77 |
| Ho-Pun-Cheung [27] | 2011 | 6 | 6 | 5 | 5 | 4 | 3 | 3 | 5 | 6 | 3 | 6 | 52/77 |
| Ho-Pun-Cheung [26] | 2007 | 5 | 6 | 4 | 5 | 5 | 3 | 2 | 5 | 6 | 3 | 5 | 49/77 |
| Hu-Lieskovan [28] | 2011 | 6 | 6 | 3 | 4 | 5 | 4 | 4 | 5 | 6 | 3 | 5 | 51/77 |
| Hur [29] | 2011 | 6 | 6 | 5 | 4 | 6 | 2 | 2 | 5 | 6 | 3 | 4 | 49/77 |
| Kim [14] | 2013 | 6 | 6 | 4 | 5 | 3 | 3 | 4 | 4 | 6 | 4 | 6 | 51/77 |
| Kim [30] | 2017 | 5 | 5 | 5 | 3 | 3 | 6 | 4 | 5 | 5 | 5 | 5 | 51/77 |
| Lamas [31] | 2012 | 6 | 6 | 5 | 4 | 3 | 3 | 4 | 5 | 6 | 4 | 6 | 52/77 |
| Leu [32] | 2021 | 7 | 6 | 5 | 4 | 3 | 5 | 6 | 5 | 7 | 4 | 7 | 59/77 |
| Nicosia [33] | 2018 | 5 | 6 | 5 | 6 | 3 | 2 | 3 | 5 | 5 | 4 | 5 | 49/77 |
| Nikas [34] | 2015 | 7 | 7 | 4 | 4 | 5 | 1 | 5 | 6 | 5 | 2 | 6 | 52/77 |
| Páez [35] | 2011 | 5 | 6 | 4 | 5 | 5 | 3 | 3 | 4 | 4 | 4 | 5 | 48/77 |
| Peng [36] | 2018 | 6 | 5 | 4 | 4 | 3 | 4 | 3 | 5 | 5 | 3 | 6 | 48/77 |
| Rampazzo [37] | 2020 | 6 | 4 | 3 | 5 | 3 | 2 | 3 | 5 | 4 | 4 | 4 | 43/77 |
| Sclafani [38] | 2015 | 5 | 3 | 4 | 5 | 5 | 3 | 3 | 4 | 4 | 5 | 6 | 47/77 |
| Sclafani [39] | 2016 | 7 | 6 | 6 | 6 | 7 | 4 | 3 | 6 | 5 | 6 | 6 | 62/77 |
| Sebio [40] | 2015 | 6 | 4 | 4 | 6 | 2 | 2 | 3 | 6 | 5 | 5 | 4 | 47/77 |
| Spindler [41] | 2006 | 6 | 3 | 4 | 3 | 2 | 3 | 2 | 2 | 3 | 2 | 4 | 34/77 |
| Stanojevic [42] | 2024 | 6 | 7 | 7 | 7 | 5 | 5 | 3 | 6 | 5 | 7 | 7 | 65/77 |
| Stoehlmacher [43] | 2008 | 6 | 3 | 3 | 4 | 2 | 4 | 1 | 4 | 3 | 2 | 6 | 38/77 |
| Terrazzino [44] | 2006 | 6 | 6 | 4 | 6 | 5 | 2 | 3 | 6 | 5 | 6 | 5 | 54/77 |
| Xiao [45] | 2016 | 5 | 3 | 2 | 3 | 2 | 5 | 2 | 6 | 3 | 4 | 6 | 41/77 |
| Criterium average | 5.88 | 5.50 | 4.41 | 5.00 | 4.09 | 3.72 | 3.47 | 5.00 | 5.19 | 3.91 | 5.56 |
Green means the highest score (lowest risk of bias) and red means the lowest score (highest risk of bias).
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MDPI and ACS Style
Połomska, K.; Rybicka, M.; Jażdżewska, A.; Prud, M.; Jackowska, S.; Kobiela, J.; Spychalski, P. Single Nucleotide Polymorphisms as Biomarkers of Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Systematic Review. Cancers 2025, 17, 3995. https://doi.org/10.3390/cancers17243995
AMA Style
Połomska K, Rybicka M, Jażdżewska A, Prud M, Jackowska S, Kobiela J, Spychalski P. Single Nucleotide Polymorphisms as Biomarkers of Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Systematic Review. Cancers. 2025; 17(24):3995. https://doi.org/10.3390/cancers17243995
Chicago/Turabian StylePołomska, Katarzyna, Magda Rybicka, Adrianna Jażdżewska, Magdalena Prud, Stefania Jackowska, Jaroslaw Kobiela, and Piotr Spychalski. 2025. "Single Nucleotide Polymorphisms as Biomarkers of Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Systematic Review" Cancers 17, no. 24: 3995. https://doi.org/10.3390/cancers17243995
APA StylePołomska, K., Rybicka, M., Jażdżewska, A., Prud, M., Jackowska, S., Kobiela, J., & Spychalski, P. (2025). Single Nucleotide Polymorphisms as Biomarkers of Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Systematic Review. Cancers, 17(24), 3995. https://doi.org/10.3390/cancers17243995
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