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Keywords:

  • colorectal neoplasm;
  • adjuvant chemotherapy;
  • tumor recurrence;
  • 5-fluoruracil;
  • prognosis

Abstract

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Although receiving adjuvant chemotherapy after radical surgery, a disappointing proportion of patients with colorectal cancer will develop tumor recurrence. Probability of relapse is currently predicted from pathological staging, there being a need for additional markers to further select high-risk patients. This study was aimed to identify a gene-expression signature to predict tumor recurrence in patients with Stages II and III colon cancer treated with 5′fluoruracil (5FU)-based adjuvant chemotherapy. Two-hundred and twenty-eight patients diagnosed with Stages II–III colon cancer and treated with surgical resection and 5FU-based adjuvant chemotherapy were included. RNA was extracted from formalin-fixed, paraffin-embedded tissue samples and expression of 27 selected candidate genes was analyzed by RT-qPCR. A tumor recurrence predicting model, including clinico-pathological variables and gene-expression profiling, was developed by Cox regression analysis and validated by bootstrapping. The regression analysis identified tumor stage and S100A2 and S100A10 gene expression as independently associated with tumor recurrence. The risk score derived from this model was able to discriminate two groups with a highly significant different probability of tumor recurrence (HR, 2.75; 95%CI, 1.71–4.39; p = 0.0001), which it was maintained when patients were stratified according to tumor stage. The algorithm was also able to distinguish two groups with different overall survival (HR, 2.68; 95%CI, 1.12–6.42; p = 0.03). Identification of a new gene-expression signature associated with a high probability of tumor recurrence in patients with Stages II and III colon cancer receiving adjuvant 5FU-based chemotherapy, and its combination in a robust, easy-to-use and reliable algorithm may contribute to tailor treatment and surveillance strategies.

Colorectal cancer (CRC) is a major public health problem, being the third most common cancer and the second leading cause of cancer-related death in Western countries.1 The risk of developing this neoplasm in the general population is around 5–6% at the age of 70, and it rises exponentially with age.2

To date, tumor stage at diagnosis remains the best predictor of recurrence and survival. Accordingly to the American Joint Committee on Cancer TNM staging system, Stage III patients, who account for approximately 40% of all CRC, have a 5-year overall survival of less than 50%. In contrast, stage II patients, who represent approximately one quarter of CRC patients, have a relatively good prognosis after curative resection, with a 5-year survival ranging from 72% for pT4N0 to 85% for pT3N0 cases.3

At diagnosis, 75–80% of patients with CRC present with localized disease. However, even after curative surgery, there is a noteworthy probability of developing tumor recurrence.4 Indeed, nearly 40% of patients with an initially nonmetastatic disease will experience locoregional relapse or develop distant metastases, leading to significant morbidity and, eventually, mortality. This high probability of tumor recurrence after surgery provides the rationale for adjuvant chemotherapy.5–8

The role of adjuvant chemotherapy in Stage III has been supported by large randomized studies performed by the National Surgical Adjuvant Breast and Bowel Project and National Cancer Institute sponsored cooperative groups.9 These trials have consistently demonstrated improvement in disease-free and overall survival, which dictates the current standard of care with FOLFOX for stage III CRC. Conversely, the value of adjuvant chemotherapy in stage II CRC remains controversial, because no conclusive data supporting its routine use are available yet. Nevertheless, it is increasingly recognized that a subset of stage II patients are likely to benefit from adjuvant chemotherapy, and the decision to treat those patients is often made by pooling together the perceived additional clinico-pathological risk factors.10 In fact, several high-risk factors, i.e. pT4, vascular or perineural invasion, intestinal obstruction or perforation, poorly differentiated tumors, or less than 12 removed lymph nodes, have been identified. Tumor registry data showed that patients with Stage II CRC exhibiting these features have a 5-year survival of 60%, similar to the figures obtained in Stage III patients and, therefore, adjuvant chemotherapy is usually offered to them.9

Although the above-mentioned, unquestionable benefit of adjuvant chemotherapy in most nonmetastatic CRC patients, it is also certain that some of them will experience tumor recurrence after treatment. Identification of this subgroup of refractory patients would allow optimizing the therapeutic approach, thus avoiding potential toxicity associated with noneffective drugs and favoring alternative regimens.11

In the last few years, molecular markers derived from tumor gene-expression profiling have emerged as a potential tool to select patients who would benefit from adjuvant treatment, thus enabling a more effective and tailored therapy.12–15 However, most studies carried out so far have shown important methodological limitations, such as small sample size, short follow-up, use of heterogeneous chemotherapeutic schedules and retrospective design, to draw definite conclusions. On the other hand, other well-designed, adequately powered analysis have been able to identify gene signatures associated with treatment response but their translation to clinical practice is still lamentably scarce, probably due to partial external validation. Finally, gene-expression profiling has been limited by the lack of reliable technology for RNA extraction from formalin-fixed, paraffin-embedded (FFPE) samples, thus restraining analyses to short series of patients from whom frozen tissue was available. All these considerations emphasize the need of more consistent, reliable and robust data supporting the transition from criteria based exclusively on clinico-pathological parameters to others incorporating molecular markers that further refine the selection of CRC patients who may benefit the most from adjuvant chemotherapy.

This study was aimed to identify a gene-expression signature to predict tumor recurrence in Stages II and III colon cancer patients treated with 5′fluoruracil (5FU)-based adjuvant chemotherapy, the overwhelming most common drug used in this setting.

Patients and Methods

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Since 1990, all patients with CRC diagnosed and treated at the Colorectal Cancer Unit of the Hospital Clínic of Barcelona were prospectively registered in a database including baseline (demographic, clinical, and tumor-related characteristics) and follow-up (primary and secondary treatments, recurrence and survival) data.4, 16 This database was approved by the Institutional Review Board and it is fully accessible to all members of the Unit using an intranet, password-protected, customized-profiled access with the final goal of systematically collecting clinical data from all patients attended in our center. For this study, we selected consecutive patients with Stages II and III colon adenocarcinoma submitted to curative-intent surgical resection from 1998 to 2005, receiving 5FU-based adjuvant chemotherapy, and with complete follow-up in our Unit. Adjuvant chemotherapy consisted of six cycles of 5FU (425 mg/m2) plus leucovorin (20 mg/m2) by rapid intravenous injection daily for 5 consecutive days every 4 weeks (in patients older than 70, the dose of 5FU was reduced to 370 mg/m2). Exclusion criteria were personal or family history of polyposis or Lynch syndromes, personal history of inflammatory bowel disease, R1 or R2 resections (microscopic or macroscopic neoplastic involvement of surgical margins, respectively), and lack of available FFPE block. Patients with rectal cancer were intentionally excluded since the therapeutic approach usually differs from the one employed in patients with colon cancer; indeed, most patients with rectal cancer are treated with neoadjuvant radio-chemotherapy and, based on current knowledge, molecular factors involved in the response to radiation therapy may be different to those involved in chemotherapy sensitivity/resistance. The study was approved by the institutional Ethics Committee of our hospital.

Demographic, clinical and tumor-related parameters included: age at diagnosis, gender, tobacco habit, personal and family history of neoplasia, presenting symptoms, baseline serum carcinoembryonic antigen concentration, presence of synchronous colorectal neoplasms, tumor location and size, histological type and grade, presence of vascular and/or perineural infiltration, perforation, microscopic tumor extension, pathologic TNM stage (American Joint Committee on Cancer, AJCC/UICC TNM, 7th edition),17 and treatment.

RNA extraction from formalin-fixed, paraffin-embedded tissue samples

Hematoxilin and eosin slides and FFPE blocks of all patients included in the study were retrieved from the Pathology Department archives. All slides were re-evaluated by a unique pathologist (MC).

Four 10 μM thick slices were obtained from each FFPE block and used to isolate total nucleic acids using a fully automated method of iron oxide beads coated with a nanolayer of silica on a modified VERSANT° kPCR Molecular System* (Siemens Healthcare Diagnostics, Tarrytown, NY).18, 19 Briefly, FFPE sections were heat lysed for 30 minutes at 80°C followed by 30 min at 65°C in the presence of proteinase K and detergent. Residual debris was removed from the lyses fluid through unspecific binding to silica-coated iron oxide beads. Beads were subsequently separated on a magnet and lysates were transferred to a 2-mL deep-well plate. During magnetization, the melted paraffin separated and formed a ring around the tube wall via hydrophobic interactions. Total RNA and DNA were bound to a fresh volume of beads under chaotropic conditions in the deep-well plate. Then, beads were magnetically separated and supernatants were discarded. Surface-bound nucleic acids were washed 3 times and eluted by incubation of the beads with 100 μL of elution buffer for 10 min at 70°C with shaking. Subsequently, a modified automated pipetting protocol was programmed, which allowed splitting of the 100 μL of eluate into 2 aliquots of 50 μL each. One aliquot containing total nucleic acid was separated from the beads and collected into a 96-place rack of 0.75-mL round-bottom tubes. The second 50 μL was incubated in 2-mL deep-well plates with 12 μL of DNase I mix (Applied Biosystems, Darmstadt, Germany) to remove genomic DNA for subsequent mRNA expression analysis. After incubation for 30 min at 37°C, DNA-free total RNA solution was obtained and collected in the same collection plate as was used for the undigested fraction (second 48 wells) and stored at -80°C until analysis.

Gene expression analysis

Target genes were selected because of previous published information associating them with CRC tumor recurrence, tumor progression or tumor response to chemotherapy. All of them correspond to genes involved in pivotal pathways including damage repair, survival/apoptosis, intracellular signaling, cell proliferation and differentiation and drug metabolism (Supporting Information Table 1).

Relative expression of evaluated genes, as well as RPL37A and CALM2 used for normalization, was assessed in triplicate by kinetic reverse transcriptase polymerase chain reaction using the SuperScript III Platinum One-Step Quantitative RT-PCR System with ROX (Invitrogen, Karlsruhe, Germany) in an ABI PRISM 7900HT (Applied Biosystems). Relative expression levels of selected genes were calculated for each sample as ΔCt values [ΔCt = Ct of target gene – geometric average Ct of the two control genes]. Sequences of primers and probes are available upon request.

Mismatch repair status assessment

Mismatch repair (MMR) status, a molecular characteristic associated with tumor response to 5FU,20, 21 was initially assessed by microsatellite instability testing. For this purpose, tumor DNA was extracted from FFPE tissue samples using the previously mentioned method to isolate total nucleic acids. Microsatellite instability status was assessed using five mononucleotide markers22: BAT25, BAT26, NR21, NR24 and MONO27 (MSI Analysis System, Version 1.2 Promega, Madison, WI) according to the manufacturers' instructions. PCR products were analyzed in the 3130 Genetic Analyzer (Applied Biosystems). Tumors with instability at ≥3 of these markers were classified as microsatellite unstable and those showing instability at ≤2 markers as microsatellite stable. Since the impact of MMR status in predicting response to 5FU depends on tumor stage,21 interaction between tumor TNM stage and MMR status was tested.

In those cases with inconclusive results, immunostaining for MMR proteins was performed. For this purpose, tumor tissue was evaluated using mouse monoclonal antibodies anti-MLH1, anti-MSH2, anti-MSH6 and anti-PMS2 (BD PharMingen, San Diego, CA), according to standard protocols.23 Tumor cells were considered to be negative for protein expression only if they lacked staining in a sample in which healthy colonocytes, lymphocytes and stromal cells were stained. If no immunostaining of healthy tissue could be repeatedly demonstrated, the results were considered undetermined.

Statistical methods

Probability of tumor recurrence was calculated from surgical resection to confirmation of either locoregional relapse and/or distant metastases. Development of metachronous colorectal lesions was not considered. Patients without tumor recurrence were censored at the last follow-up contact. Overall survival was calculated from surgical resection to death. Patients alive were censored at the last follow-up contact.

Initially, expression values of evaluated genes were subjected to a received operating curve (ROC) analysis in order to choose an individual threshold for each gene. The cut-off value was selected to minimize the distance between the curve and the upper left corner of the graph.24 Thus, continuous data were converted to binary form. Univariate Cox regression analysis was performed on each covariate to examine its influence on tumor recurrence, computing hazard ratio (HR) with the corresponding 95% confidence interval (95% CI). Thereafter, a multivariate stepwise Cox regression analysis was performed by including all variables achieving a p value <0.2 in the univariate analysis. A Wald statistic p value <0.05 was used as the criterion for selecting independent variables and including them in the final multivariate model, adjusting by age and gender. Both univariate and multivariate Cox proportional hazards were done using the coxph function from Survival R Package.

After establishing the multivariate model, a risk score (RS) was calculated for each patient according to the general form RS = exp Σβixis, where i = 1, …, k index variables, βi represents the coefficient for each variable estimated from the Cox regression model, and xis the corresponding value for each variable in a given patient. RS was subjected to a ROC analysis in order to choose the most appropriate threshold for predicting tumor recurrence. For this approach, specificity was prioritized over sensitivity. Thereafter, Kaplan-Meier curves were generated using the selected cut-off point and compared according to the log-rank test.

Finally, robustness of the mathematical algorithm resulting from the multivariate analysis was evaluated by bootstrapping with 1,000 resamples.25 For this purpose, validate function from RMS Package was used and the concordance index was computed.

Results

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

During the study period, 228 patients diagnosed with Stage II (78 patients) and III (150 patients) colon cancer and treated with surgical resection and 5FU-based adjuvant chemotherapy were included. Demographic, clinical and tumor-related characteristics are shown in Table 1.

Table 1. Characteristics of patients included in the analysis
inline image

After a median follow-up of 42 months (range, 6–152 months), 79 patients (34.8%) exhibited tumor recurrence, either as locoregional relapse (8 patients) or distant metastases (71 patients). Moreover, at the end of this period, 57 patients (25.0%) had died, 48 of them (84.2%) due to colon cancer.

RNA could be extracted and correctly analyzed for target genes from all FFPE tissue samples (data not shown).

Univariate Cox regression analysis identified tumor TNM stage (HR, 1.99; 95% CI, 1.21–3.28; p = 0.007), S100A10 (HR, 1.78; 95% CI, 1.14–2.77; p = 0.01), S100A2 (HR, 1.99; 95% CI, 1.17–3.42; p = 0.01), S100A3 (HR, 1.67; 95% CI, 1.03–2.69; p = 0.03), and SPON1 (HR, 1.64; 95%CI, 1.01–2.66; p = 0.04) as predictors of tumor recurrence (Table 2). Of note, neither MMR status (HR, 1.326; 95% CI, 0.609–2.881; p = 0.476) nor the interaction between MMR status and TNM tumor stage achieved statistical significance (HR, 4.280; 95% CI, 0.480–37.386; p = 0.191).

Table 2. Univariate analysis of predictors of tumor recurrence
inline image

After including the above-mentioned predictors as well as those genes achieving near significance in the univariate analysis (i.e., OAZ1, ABCC6, S100A4 and CXCL9) in the multivariate stepwise regression analysis, adjusted by age and gender, only tumor stage, S100A2 and S100A10 gene expression were identified as independently associated with tumor recurrence (Table 3).

Table 3. Independent predictors of tumor recurrence
inline image

The RS was calculated for each patient according to the following mathematical algorithm: RS = exp(0.7106*S100A2 + 0.5291*S100A10 + 0.7516*TNM stage – 0.0148*age – 0.2462*gender). In this equation, S100A2 and S100A10 gene expression, TNM stage and gender were introduced as dichotomous variables (S100A2 expression: ≥-9.68=1, <-9.68=0; S100A10 expression: ≥-1.53=1, <-1.53=0; tumor TNM: stage III=1, stage II=0; gender: male=1, female=0). The median value of this RS was 1.04 (range, 0.23-4.35). Thereafter, a ROC analysis allowed selecting a cut-off value of 1.70 to classify patients in a high-risk group (47 patients, 20.6%) and a low-risk group (181 patients, 79.4%) for tumor recurrence (specificity, 0.86). Figure 1 depicts Kaplan-Meier curves generated using the selected cut-off point. As it is shown, risk score generated by the multivariate model was able to discriminate two groups with a highly significant different probability of tumor recurrence (HR, 2.75; 95% CI, 1.71-4.39; p=0.0001). Moreover, it is important to point out that when patients were stratified according to tumor stage, the risk score continued discriminating two subgroups of different probability of relapse in both stage II (HR, 5.21; 95% CI, 2.48–10.95; p = 0.0001) and III (HR, 1.85; 95% CI, 1.02–3.47; p = 0.05) colon cancer patients (Fig. 2).

thumbnail image

Figure 1. Kaplan-Meier estimates of probability of being free of tumor recurrence according to the identified gene expression signature. Continuous line represents low-risk patients (RS < 1.7), and dotted line high-risk patients (RS > 1.7).

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thumbnail image

Figure 2. Kaplan-Meier estimates of probability of being free of tumor recurrence according to the identified gene expression signature in Stage II (A) and Stage III (B) colon cancer patients. Continuous line represents low-risk patients (RS < 1.7), and dotted line high-risk patients (RS > 1.7).

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Although it did not constitute a primary outcome, accuracy of the risk score was also evaluated regarding the probability of being a predictor of overall survival. Using the same cut-off point, the mathematical algorithm was also able to distinguish two groups with significantly different overall survival (HR, 2.68; 95% CI, 1.12–6.42; p = 0.03).

Finally, robustness of the mathematical model was evaluated by bootstrapping with 1000 resamples, obtaining a C-index of 0.6239.

Discussion

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

The results of this study demonstrate that S100A2 and S100A10 gene expression independently predict tumor recurrence in patients with stage II and III colon cancer after 5FU-based adjuvant chemotherapy. This gene signature, along with tumor TNM stage, provides a robust, easy-to-use and reliable mathematical algorithm to identify a subgroup of patients with higher probability of tumor relapse and shorter survival that may eventually benefit from a more aggressive therapeutic regimen and more intensive surveillance.

Strengths of this study rely on the fact that it includes a large cohort of patients attended in a single center, homogeneously treated, with prospective data collection and long follow-up period. Furthermore, pathological evaluation involved reexamination of all samples by a unique gastrointestinal pathologist, as well as assessment of the MMR status. Finally, use of archival FFPE samples to obtain the gene-expression signature allowed an easy translation of the obtained results to clinical practice.

We are aware, however, of some limitations of the study. First, because of our interest in magnifying the probability to identify differentially expressed target genes, all available patients were included in the evaluation set, thus preventing an independent validation. To overcome this drawback, we strictly ascertained robustness of the mathematical algorithm by bootstrapping, obtaining a high concordance index. Second, the use of FFPE samples precluded the employment of high-throughput techniques. Although this methodology benefit from a nontargeted, exploratory approach, it is also certain that it may result in a high proportion of spurious associations. In that sense, the candidate gene approach used in this study, although limited by its reliance on the knowledge of physiological and functional roles of target genes, favor a prompt validation of the most attractive candidates. Taken together, our study, rather than establishing a definitive gene-expression signature, may contribute to select a subset of genes which warrant further prospective evaluation in carefully and specifically designed studies.26

At present, risk of recurrence and prognosis are mainly predicted by pathological tumor staging. However, this system is limited since it is based on macro- and micro-morphology. To overcome this limitation, many molecular markers have been investigated.27 Initially, individual genetic characteristics, including TP53,28 KRAS29 and BRAF29 mutations, 18q loss of heterozygosity,30 and microsatellite instability31 have been reported as potential predictive and/or prognostic factors in patients with CRC.11 However, because of their limited accuracy or the lack of an adequate validation, they have not become routinely used in clinical practice. In the last few years, research in predictive biomarkers has been focused on tumor gene expression profiling. Most of these studies resulted in complex gene signatures including a quite large number of selected genes and with scarce concordance among them, two facts which limit their potential translation into clinical practice. Indeed, ChipDx° includes a 163 probe-based signature able to discriminate disease-free survival in patients with stage II and III colon cancer,12 Coloprint° includes 18 genes able to predict risk of relapse in patients of similar characteristics,13 OncotypeDx° includes 13 cancer-related and 5 reference genes for predicting recurrence in both treated and untreated stage II colon cancer patients,15, 32 and ColDx° includes a 634-probe set signature to predict recurrence in untreated, stage II colon cancer patients.33 On the other hand, by refining a metastasis-associated gene expression profile, originally identified in a mouse model of colon cancer metastasis, Smith et al. developed a 34-gene score associated with increased risk of metastasis and death from CRC.14 All these signatures have provided essential information about genes involved in carcinogenesis and related to tumor recurrence, survival and response to treatment, but they are not still routinely used in a clinical setting because of the lack of reliable prospective, independent validation. In addition, it is important to mention that most of them were either specifically designed to identify stage II colon cancer patients at low risk for tumor recurrence, who may be safely managed without chemotherapy, rather than predicting response to treatment (i.e., OncotypeDx°, ColoPrint° and ColDx°),13, 15, 32, 33 or they failed to demonstrate any value in predicting benefit from chemotherapy in the subset of patients receiving adjuvant treatment.32

Rather than selecting patients who may be safely managed without chemotherapy, the aim of our study was to establish a gene signature for predicting lack of benefit from 5FU-based adjuvant chemotherapy in patients with Stages II and III. As it is shown, our model refines the currently used clinico-pathological-based approach by adding a limited number of genetic markers. As a result, the RS derived from our multivariate model was able to discriminate two groups with a highly significant different probability of tumor recurrence (HR, 2.75; 95% CI, 1.71-4.39; p=0.0001), which it was maintained when patients were stratified according to tumor stage. Finally, our mathematical algorithm was also able to distinguish two groups with significantly different overall survival (HR, 2.68; 95% CI, 1.12–6.42; p = 0.03), thus reinforcing the validity of our model.

Regarding the genetic markers included in our algorithm, two genes coding for S100 proteins, S100A2 and S100A10, demonstrated to be independent biomarkers of CRC recurrence after 5FU-based adjuvant treatment. The human S100A10 and S100A2 are members of the S100 family of EF-hand Ca2+-binding proteins. This multigenic family is exclusively expressed in vertebrates and has been reported to play intracellular and extracellular regulatory activities on protein phosphorylation, dynamics of cytoskeleton components and Ca2+ homeostasis. In fact, members of the S100 family are involved in many pathological processes, including inflammation34, 35 and tumorigenesis.36–39 Over the past few years, they have received large attention because of their capacity to promote tumor progression and metastasis in many human neoplasms by modulating cell cycle, motility and invasion.40 However, the biologic functions of several S100 proteins in carcinogenesis have not been fully elucidated to date. Interestingly, Watanabe et al.,41 in a small study aimed to identify genetic biomarkers that could predict recurrence in stage III CRC, identified a subset of 45 discriminating genes including S100A10. S100A10 protein is a key plasminogen receptor of the extracellular cell surface that is over-expressed in many cancer cells and, indeed, it plays an important role in CRC invasiveness.42 Regarding S100A2, this protein has a controversial role in carcinogenesis, acting as a tumor suppressor in some cancer entities and, conversely, as a tumor promoter in others.43 The different patterns of S100A2 expression in distinct cancer types might be explained by the control through multiple factors with effects varying from tumor to tumor. Over-expression of S100A2 protein has been reported in lung, brain and several gastrointestinal cancers, including pancreatic, esophageal and gastric adenocarcinoma.40 Importantly, moderate/high expression of S100A2 gene in pancreatic cancer contributes to gemcitabine resistance and inhibition of apoptosis due to BNIP3 repression activity.44 In addition, in a second study on patients with pancreatic cancer, S100A2 over-expression was also associated with a poor prognosis,39 which remained prognostic following interrogation in a validation cohort.45 On the other hand, the ability of S100A proteins to predict response to chemotherapy was also observed in patients with stage IB-IIA cervical cancer, with S100A8 and S100A9 immunohistochemical expression correlating with tumor response.38 Finally, the role of S100A2 in CRC has been poorly established but copy number gains and high expression levels have been reported, thus suggesting its putative implication in tumor development.46 In such a neoplasm, to our knowledge, the present study is the first to identify S100A2 and S100A10 as potential markers of tumor recurrence. Unfortunately, the lack of a control group of untreated patients precludes distinguishing their predictive value on tumor response to 5FU-based chemotherapy from a potential effect on patients' prognosis.

Identification of a new gene-expression signature associated with a high probability of tumor recurrence in patients with Stages II and III colon cancer receiving 5FU-based chemotherapy after surgical resection, and its combination in a robust, easy-to-use and reliable mathematical algorithm may contribute to tailor adjuvant treatment and surveillance strategies. Independent validation of this model, as well as evaluation in patients treated with other regimens could identify individuals who might eventually benefit from a more aggressive treatment regimen or intensive follow-up. In that sense, although cross-resistance between 5FU and FOLFOX can not be ruled out, ascertainment of the validity of our mathematical algorithm in colon cancer patients treated with this latter schedule would merit a prospective evaluation.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

The work was carried out in part at the Esther Koplowitz Centre, Barcelona. The authors are indebted to the HCB-IDIBAPS Biobank-Tumor Bank and Xarxa de Bancs de Tumors de Catalunya-Pla Director d'Oncologia (XBTC-PDO) for sample and data procurement. Dr. Luis LaSalvia is employed in Siemens Healthcare and owns shares of Siemens AG. Dr. Christoph Petry was a former employee of Siemens Healthcare and inventor of a corresponding patent application.

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  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
IJC_27747_sm_SuppTab1.doc47KSupporting Information Table 1. Candidate genes evaluated in the analysis.

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