Standardized Approach for Microsatellite Instability Detection in Colorectal Carcinomas

Isabel González-García, Víctor Moreno, Matilde Navarro, Juan Martí-Ragué, Eugenio Marcuello, Carmen Benasco, Olga Campos, Gabriel Capellà, Miguel A. Peinado

Affiliations of authors: I. González-García, M. Navarro, O. Campos (Institut Català d'Oncologia), J. Martí-Ragué (Servei de Cirurgia), C. Benasco (Servei d'Anatomia Patològica), M. A. Peinado (Institut de Recerca Oncològica), Ciutat Sanitària i Universitària de Bellvitge, L'Hospitalet, Barcelona, Spain; V. Moreno, Institut Català d'Oncologia and Laboratori de Bioestadística i Epidemiologia, Universitat Autònoma de Barcelona; E. Marcuello, Servei d'Oncologia Médica, Hospital de la Santa Creu i Sant Pau, Barcelona; G. Capellà, Institut Català d'Oncologia and Laboratori d'Investigació Gastrointestinal, Hospital de la Santa Creu i Sant Pau.

Correspondence to: Miguel A. Peinado, Ph.D., Institut de Recerca Oncològica, Hospital Duran i Reynals, Autovia Castelldefels km 2,7, L'Hospitalet, 08907 Barcelona, Spain (e-mail: mpeinado{at}iro.es).


    ABSTRACT
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 Notes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Ubiquitous mutations in microsatellite DNA sequences define a specific type of genetic instability, termed microsatellite instability (MSI). Various approaches have been used to identify the presence and degree of MSI. To define standard diagnostic criteria for MSI, we developed and tested a mathematical model. Methods: We designed an algorithm for the efficient characterization of MSI and used it to analyze data on six microsatellite markers in colorectal carcinoma and normal tissues from 415 patients. Theoretical models considering one, two, or three populations were tested against the data collected. Results: The observed frequencies of MSI in our series of samples best fit a two-population model, stable and unstable, defined by instability in two or more of four to six markers analyzed. MSI was observed in 7.5% of the tumors. The misclassification rate was less than 5% when any four loci were analyzed and less than 1% when the six markers were used. A stepwise strategy, consisting first of a bulk screening of two loci and then a second screening of two to four additional markers, provided excellent sensitivity (>=97%) and specificity (100%). Tumors with MSI had distinctive genetic and clinicopathologic features, including better patient survival. Conclusion: To assess the presence of MSI in colorectal cancer, we have developed a simple, sensitive, and specific approach based on the apparent good fit of the data to a two-population model. Its application to a prospective series of patients with colorectal carcinomas demonstrates that the presence of MSI characterizes a subset of less aggressive tumors.



    INTRODUCTION
 Top
 Notes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
A subset of colorectal cancers and other types of human tumors exhibit ubiquitous microsatellite instability (MSI). This discrete type of genomic instability is characterized by small deletions or insertions within short tandem repeats in tumor DNA when compared with the corresponding DNA from normal tissue (13). MSI is a symptom of defective DNA repair and characterizes a distinctive tumor phenotype known as the microsatellite mutator phenotype (4,5). Hereditary nonpolyposis colorectal cancer is caused by a nonfunctional DNA repair gene, resulting in tumors displaying MSI (2,6). Therefore, assessment of MSI may be used in a first screening to identify individuals who may have hereditary nonpolyposis colorectal cancer and should receive more extensive genetic analysis (79).

The ubiquitous nature of the MSI in colorectal cancer and the intrinsic somatic instability of microsatellite sequences (10,11) have led to use of heterogeneous approaches to define the presence of this microsatellite mutator phenotype in tumor specimens. Differences in the type and number of markers analyzed, as well as the cutoff criteria, to define microsatellite mutator phenotype have produced conflicting results (5,12). Recently, a panel of experts (5) recommended that alterations in two or more markers from a reference panel of five loci (two mononucleotide repeats and three dinucleotide repeats) be used to identify tumors with high MSI. Alternatively, if more markers are analyzed, MSI-positive tumors should display mutations in 30%–40% of the loci. In addition, the panel of experts hinted that algorithms for efficient characterization of MSI were needed and that these algorithms should be validated in prospective studies.

The aim of our study was to develop a mutational model to determine the presence and degree of MSI and to evaluate this model in a prospectively collected series of more than 400 patients with colorectal carcinoma. This method analyzes whether the observed frequencies of mutations in microsatellite markers characterize distinct tumor populations regardless of MSI prevalence. In addition, we have investigated the nature and number of microsatellites to be analyzed, as well as the cutoff criteria, to define MSI by a robust, simple, standard, and reproducible approach.


    MATERIALS AND METHODS
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 Notes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Patients

Two hundred seventeen patients preoperatively diagnosed with colorectal cancer at the Hospital de la Santa Creu i Sant Pau (HSCSP), Barcelona, Spain, and 216 patients preoperatively diagnosed with colorectal cancer at the Ciutat Sanitària i Universitària de Bellvitge (CSUB), Barcelona, were prospectively included. Patients from consecutive series collected during the period from July 1991 to June 1994 (HSCSP patients) and the period from January 1996 to December 1998 (CSUB patients) were selected according to the inclusion criteria described below. Studies were originally designed to evaluate the prognostic value of specific genetic alterations (HSCSP collection) and the relationships between diet and molecular alterations (CSUB collection). Inclusion criteria were electively resected primary adenocarcinomas, availability of fresh paired normal mucosa and tumor samples within 2 hours after tumor removal, and no postoperative death. Inclusion in the study did not influence the adjuvant treatment given. Study protocols were approved by the respective ethics committees. Written informed consent was obtained from the CSUB patients. No chemotherapy or radiotherapy was given to these patients before surgery. However, for four patients who received previous radiotherapy, DNA analysis was performed on biopsy specimens obtained by colonoscopy to obviate genetic changes appearing after treatment. DNA amenable for genetic analysis was obtained from paired normal and tumor tissues from 415 patients (199 from HSCSP and 216 from CSUB) of the 433 patients considered. Therefore, 415 patients were included in this study. Distributions for all clinicopathologic and genetic parameters considered were indistinguishable between excluded and included patients, and no differences were observed between series of patients from each hospital. The 415 patients included 255 males and 160 females, and the mean age of all patients in the study was 67.3 years ± 11.7 years (± standard deviation) (range = 23–96 years). One hundred nineteen tumors were located in the right colon, and 296 tumors were in the left colon, including the rectum. The distribution of the adenocarcinomas by Dukes' classification was as follows: 55 adenocarcinomas of grades A + B1, 164 of grades B2 + B3, 144 of grade C, and 52 of grade D. Thirty-one tumors were poorly differentiated; the rest were well or moderately differentiated. Follow-up information was available for the 199 HSCSP patients. At the end point of the follow-up (January 1999), 105 patients were alive without disease, 13 had died without disease, three were alive with disease, and 78 had died of disease. The mean follow-up was 71 months ± 9 months (± standard deviation) (range = 56–89 months). Unfortunately, a validated family history was available for only a minority of patients; therefore, its relationship with MSI could not be evaluated.

Microsatellite Analysis

No microdissection of the tumor samples was performed. However, we estimated that specimens contained a minimum of 50% tumor cells according to previous genetic analyses [(13); Capella G, Peinado MA: unpublished results]. Six microsatellite DNA regions were amplified by polymerase chain reaction (PCR) from paired normal and tumor tissues, and products were resolved on denaturing polyacrylamide sequencing gels. The stability of each microsatellite was scored according to the absence (stable) or the presence (unstable) of mobility-shifted bands or additional bands in tumor DNA compared with normal DNA. When the band pattern was difficult to interpret or no amplification product from the normal or tumor DNA was obtained, the sample was considered uncertain and scored as not analyzed. All experiments were evaluated by at least two of three observers (I. González-García, O. Campos, and M. A. Peinado). PCR primer sequences for the microsatellite regions were as follows: D21S415, CCTGATTTGTCTTTCATCTCG and TGCCTGCTGTTGGACTTACT; D21S1235, CTTTCATGTGTGTCTACGGAT and GGCTACTCTCTGCCCAGAT; D12S95, AAGGTGCAATGGGCTA and CAACTGTGTGTGTTTATATGTG; D4S2948, TGCAGGCTAAGTATGTTCCA and TTCCCGGCTCTGTAACAC; SIT2, CTCACTGATGATCATATTGAAAT and GTGAAATCCTGTCTCTACTAAAAA; and BAT26, TGACTACTTTTGACTTCAGCC and AACCATTCAACATTTTTAACCC. PCR conditions were 40 cycles of a denaturing step for 30 seconds at 94°C; an annealing step for 30 seconds at 55°C (D12S95, D4S2948, and SIT2), 57°C (D21S415), or 60°C (D21S1235 and BAT26); and an extension step for 30 seconds at 72°C. Before the PCR, tubes were preheated at 95°C for 1 minute. A 5-minute extension step (72°C) was performed at the end of the PCR. Two microsatellites contained mononucleotide sequences, BAT26 (14) and SIT2 [modified from AP{Delta}2 (1)], and four contained CA repeats, D21S415, D21S1235, D12S95, and D4S2948. The four CA repeat markers were selected as indicators of instability because they were the most frequently mutated in the analysis of 13 samples previously characterized as MSI positive [(1,15); Capella G, Peinado MA: unpublished data] with an array of 26 markers. This panel of 26 markers was composed of 22 CA repeats (D12S43, D12S77, D12S78, D12S79, D12S84, D12S95, D21S365, D21S368, D21S369, D21S406, D21S415, D21S416, D21S1235, D4S405, D4S418, D4S1551, D4S1587, D4S2912, D4S2946, D4S2948, D4S3001, and D4S3022), an ATA repeat D4S2397, a TTA repeat in the PLA-2 gene, an AAAT repeat intragenic to the NF-1 gene, and a TCTA repeat in the WFII-3 gene. This panel was initially selected for gene mapping studies that were not related to the assessment of MSI in cancer cells. Information on primer sequences and PCR conditions for this analysis may be obtained from the authors upon request.

p53 and K-ras Mutation Analysis

Mutations in the p53 and K-ras genes were found in a high proportion of specimens. p53 mutations in exons 4–9 were analyzed by single-strand conformation polymorphism and cycle sequencing. Mutations at codons 12 and 13 of the K-ras gene were detected and characterized by single-strand conformation polymorphism, restriction fragment length polymorphism, PCR, and cycle sequencing (13). p53 mutations were identified in 48% of tumors (153 of 319 tumors tested), and K-ras mutations at codons 12 and 13 were identified in 40% of tumors (153 of 378 tested).

Statistical Analysis

The associations between qualitative variables were analyzed by Fisher's exact test. The association between MSI, age, and sex was modeled with the use of logistic regression. Ordinal variables were assessed with the exact two-sample Wilcoxon test. Means of quantitative variables were compared with a two-sample t test. Disease-free and overall survival distributions were estimated by the Kaplan–Meier method and were analyzed with the log-rank test. Hazard ratios and their 95% confidence intervals (CIs) were derived from Cox's proportional models. All P values are from two-sided statistical tests.

Mutational Population Models

To estimate the prevalence of MSI in this population, we considered the following three scenarios: the two working hypotheses used to classify tumors with regard to the degree of MSI (a two-population model, stable versus unstable, or a three-population model, stable versus low instability versus high instability) and also the unlikely possibility that all tumors belong to a unique population (a one-population model). Three different mixture models of binomial distributions were fit to observed data.

Premise of the Model

All models assume that all loci tested have the same probability of mutation that is conditional on the population (stable or unstable) to which the tumor belongs. To test for this assumption, we compared the distribution of mutations at each locus for marginal homogeneity with a likelihood ratio test. This test was derived from a logistic regression model, where the probability of mutation was related to a categorical variable indicating the locus. In this analysis, a robust estimation of the covariance matrix proposed by Harrell (16) was used to take into account the correlation of the observations because loci were clustered within tumors. This approach is equivalent to fitting a generalized estimating equation logistic model.

Options Considered

Two-population model. This model assumes that two homogeneous populations of tumors exist, one that is stable with low probability of mutation in microsatellite DNA and the other that is unstable with a higher probability of mutation. This model has the following likelihood function:

where x is the number of unstable loci observed of m tested in one specific tumor. The model has three parameters: p is the prevalence of tumors in the stable population and, thus, 1 - p is the proportion of tumors with MSI. r0 and r1 are the probability that a given loci is mutated for the supposed stable and unstable populations, respectively. Bi(x,m,r) is the binomial probability function:

The likelihood function allows us to estimate the parameters and to test the goodness of fit. These models are identifiable only if the number of loci tested for each tumor is at least the number of parameters (17). Thus, for this model, tumors with informative results from at least three loci were used for the analysis.

Three-population model. This model, although more complicated, postulates that there is an additional intermediate group with a probability of mutation halfway between the stable and unstable populations. The model might be supported by the empirical observation of a relatively high frequency of tumors that have MSI in only one locus. This model would help in deciding whether this group represents a truly distinct population.


To estimate the five parameters of this model, we used only tumors with results from five or more loci.

One-population model. Finally, a one-population model, assuming the existence of a single population with moderate levels of instability, was also assayed in spite of distinct lines of evidence dismissing this possibility.

Covariate Analysis

Because we have worked with populations of two hospitals, these models have also been adapted to allow for different probabilities according to hospital or other covariates (z). For example, the two-population model with different prevalence of MSI between hospitals would be modeled by


where p0 represents the prevalence of the stable population in hospital coded with z = 0, and p1 represents the prevalence of the stable population in hospital coded with z = 1. Models can be extended in a similar way to test and to estimate the effects of additional covariates.

Parameter Estimation and Assessment of Model Adequacy

Parameters of the mixtures of binomial distributions were estimated by maximum likelihood with an Expectation Maximization (EM) algorithm (17) within the S-Plus statistical program package. Random initial values were tested, and convergence to the same solution was attained without exception. Three methods were used to calculate the corresponding 95% CIs. First, 95% CIs were derived from the inverse of the information matrix with numerical methods. Second, bootstrap techniques after 10000 replications of the fitting procedure to samples with replacement of the data were used. Finally, profile likelihood CIs were calculated. Only the latter are reported because they are slightly more conservative.

Goodness of fit of the two-population model was assessed by exploring residuals and comparing observed frequencies with those expected from the fitted model with a Pearson's {chi}2 test. In addition, likelihood ratio tests were used to compare this model with other models that have different numbers of populations and to test for hospital effects.

Model Utility

Number of loci. Once a valid model was chosen, a decision rule to diagnose MSI was derived from the model properties. Because the probability of mutation in a locus is not 100% in an MSI tumor, a combination of loci is mandatory to improve sensitivity. Expected values of the proportion of loci showing mutation were tabulated, and sensitivity and specificity for diagnosis of MSI were calculated for different numbers of loci tested and distinct cutoff points. Areas under the Receiver Operating Characteristic (ROC) curves were also calculated. Because no external gold standard exists for MSI diagnosis, the predictions of the chosen model were assumed to be true. Thus, in this context, sensitivity is the probability of locus mutation given the tumor belongs to the MSI population, and specificity is the probability of normal locus given the tumor belongs to the stable population. Bootstrap methods were used to calculate 95% CIs of the sensitivity and specificity values. This was done to take into account the uncertainty derived from the use of estimated values of the model parameters.

Type of loci. In an attempt to validate the diagnostic accuracy of different combinations of loci, we considered tumors to have MSI when more than two of the tested loci were mutated (see "Results" section). Sensitivity and specificity of this classification were estimated and subsequently validated through bootstrap replications, sampling at random from the loci assessed. We chose a bootstrap validation over other alternatives, such as cross-validation or split-sample validation, because it uses all of the tumors and allows us to correct for overfitting (16). Moreover, to test for different combinations of loci in one step, a stepwise strategy was assayed by use of the same bootstrap technique. First, two markers were analyzed. If none was mutated or if both were mutated, the sample was considered to be stable (MSI negative) or unstable (MSI positive) for microsatellites, respectively. Any other combination (including PCR failure to amplify one of the sequences) would require further analysis of a minimum of two more markers. To choose the best pair of loci for initial analysis and other loci for subsequent analyses, in case of disagreement, all possibilities were considered. To interpret results obtained, a {kappa} statistic was calculated to assess the agreement between pairs of loci.


    RESULTS
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 Notes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Distribution of Mutations in the Six Microsatellite Sequences

Analysis of the six markers was attempted in normal and tumor specimens from the 415 patients included in the study. Complete information was obtained from 306 specimens (74%). In the remaining specimens, information was available from three (n = 7), four (n = 33), or five (n = 69) markers. Data for each marker and individual mutation frequencies are summarized in Table 1Go. Differences in the distribution of mutations for all microsatellite sequences were not statistically significant (test for homogeneity, P = .08), confirming the premise of the model. Three hundred fifty-one (85% of all) tumors had no instability, 33 (8%) tumors had instability in a single microsatellite, and 31 (7.5%) tumors had instabilities in two or more microsatellite loci.


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Table 1. Summary of mutations in microsatellite sequences
 
Types of MSI: Model Fits

Data were correctly fitted by the mixture model of two populations, one stable and the other unstable, with binomial distribution (P for lack of fit = .38). The fit was not improved with one- and three-population models; therefore, these models were dismissed. In the two-population model, the unstable group had an estimated prevalence of 7.1% (95% CI = 4.0%–11.1%). The mutation probability of a loci was estimated as 75.7% (95% CI = 64.4%–85.3%), which corresponds to the average sensitivity of each individual locus (Table 2Go). For the stable group (MSI negative), the mutation probability per locus was 1.7% (95% CI = 1.0%–2.6%), which corresponds to one minus specificity in Table 2Go. Results were almost identical when the populations from the two medical centers were analyzed separately, and differences in prevalence by center were not statistically significant. Finally, model parameters were very similar, irrespective of the microsatellites considered.


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Table 2. Microsatellite instability assessment using a different number of markers*
 
Number of Loci To Be Analyzed

If the model is assumed to be correct, the sensitivity and specificity of various numbers of loci were estimated from the model parameters and an optimal cutpoint was chosen (Table 2Go). The analysis of a combination of four microsatellite markers rendered a misclassification rate that was lower than 5%; analysis of six microsatellite markers rendered a misclassification rate that was lower than 1%. We conclude that unstable tumors (MSI positive) are those characterized by the presence of mutations in two or more markers from a minimum of four analyzed (Table 2Go).

Type of Loci

Once we established the requirement of at least four markers, the issue of the relative informativeness of the distinct loci analyzed remained. The frequency of a "unique" loci mutated (mutation in a single marker but not in the rest) was similar for all markers. BAT26 was always mutated in concurrence with at least another marker (Table 1Go); alone, BAT26 yielded 100% specificity, as previously reported (18). This marker, however, shows a limited diagnostic sensitivity (86%). The next best unique loci were SIT2 (93% sensitivity) and D12S95 (82% sensitivity).

To define and validate a simple and efficient two-step analytic approach, we performed bootstrap simulations using different combinations of microsatellite markers. Best results were obtained when one of the two markers containing runs of adenines (BAT26 and SIT2) was used in combination with a CA repeat: BAT26/D12S95 pair (sensitivity = 97%; specificity = 100%; proportion of samples needing a second-step analysis = 20%) and SIT2/D12S95 pair (sensitivity = 98%; specificity = 100%; proportion of samples needing a second-step analysis = 10%) were the two best options considered.

It is noteworthy that the selected combinations of loci did not correspond to those markers showing the highest agreement (BAT26/SIT2, {kappa} = 0.82; BAT26/D4S2948, {kappa} = 0.76), probably because of redundancy. Conversely, only a moderate to good concordance ({kappa} = 0.6) was observed among the selected pair of loci.

Molecular and Clinicopathologic Correlations

Tumors with mutations in a single microsatellite displayed characteristics similar to those of tumors without mutations and were grouped together in the stable class in agreement with the mutational model. Distinctive features of tumors displaying MSI included low rates of ras and p53 gene mutations, a location in the right colon, and poor differentiation (Table 3Go). It is noteworthy that a striking sexual dimorphism was observed regarding the patients' age and MSI status: MSI was associated with younger male patients (MSI prevalence according to age in men: <=45 years old = 40%; 46–65 years old = 11%; >65 years old = 4%; P<.001). In contrast, only one woman younger than 65 years had a tumor with MSI (2%) compared with 10 (10%) older women (P = .058). The test for this three-way interaction was statistically significant (P = .007).


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Table 3. Molecular and phenotypic characteristics of tumors with and without microsatellite instability (MSI)
 
Because of the prospective collection of samples and the availability of complete follow-up information for the 199 HSCSP patients, we could analyze patient survival as a function of tumor MSI status, as assessed by our algorithm. Only one of 12 patients with an MSI tumor died of cancer (8%), whereas 77 (41%) of 187 patients with no MSI died of cancer (Fig. 1Go). Analysis with log-rank statistic showed that patients with MSI tumors had higher overall survival (P = .046; hazard ratio of stable versus MSI-positive tumors = 5.8; 95% CI = 0.8–41.8). However, the association was not statistically significant when adjusted for Dukes' classification.



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Fig. 1. Kaplan–Meier curves for overall survival in tumors with and without microsatellite instability (MSI). A single event was recorded in patients with MSI (n = 12) at 6 months of follow-up; the survival rate was 92% (95% confidence interval [CI] = 77%–100%). The number of patients at risk for this group at 3 and 5 years of follow-up were 11 and 6, respectively. The main parameters of the survival curves for patients without MSI were as follows: follow-up = 3 years, n = 123, survival = 92% (95% CI = 60%–74%); follow-up = 5 years, n = 85, and survival = 59% (95% CI = 52%–66%). Only patients from the Hospital de la Santa Creu i Sant Pau were included in this analysis.

 

    DISCUSSION
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 Notes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Accurate diagnosis of MSI appears to select patients who are good candidates for a more complex genetic analysis of mismatch repair genes (68) and to design specific therapeutic and/or preventive strategies (1921) for patients with colorectal cancer. The potential clinical usefulness of MSI definition relies on the development of standard criteria that can be reproduced in various settings. We have used statistical algorithms to assess the presence of distinct tumor populations in a large series of patients with colorectal cancer. These populations were defined by using the mutation frequencies obtained with a panel of six markers. Our approach provides simple but comprehensive diagnostic criteria not bound to a specific type and number of microsatellite markers. Therefore, our method is a way of determining whether MSI is present in colorectal or other cancers, even when heterogeneous approaches are used to detect MSIs.

The observed frequency of microsatellite mutations in our series of tumors yields the best fit for a two-population model, questioning the definition of an intermediate group [usually with a single unstable locus (5)] between stable and unstable tumors. This result is in agreement with the conclusions reached by Olschwang et al. (22), who analyzed more than 100 microsatellite loci in a smaller series of colorectal tumors. The excellent fitting of our results to a two-population model does not rule out the possible existence of different mutator phenotypes characterized by the nature of the mutated sequences or by the degree of instability. However, the relatively uniform genetic and phenotypic characteristics of MSI tumors [(1,3); this study] suggest that a single tumorigenic pathway in colorectal cancer is defined by ubiquitous mutations in adenine repeats and/or CA repeats (4). Therefore, no applications of the differential diagnostic of distinct microsatellite mutator phenotypes are envisioned.

Using this approach, we have observed a low prevalence of the MSI phenotype in our tumor collection of 7.5%. This percent is in line with a recent report using a similar approach (23) but is lower than percents described in earlier studies (13). This low proportion of MSIs cannot be attributed to the low sensitivity of our assay or to the limited number of microsatellites analyzed because samples from other tumor collections that displayed MSI (1,15,24,25) were analyzed with our panel of markers and the microsatellite mutator phenotype was confirmed in all samples. Ethnic or geographic differences might account for these differences in MSI prevalence, as it has been previously reported for this (1) and other genetic alterations (26).

Simple approaches to detect MSI have been proposed based on the reported highest specificity of some markers, mainly BAT26 (18,27). In addition, substantial criticism has been raised against the exclusive use of dinucleotide repeats to assess MSI, proposing a combination with mononucleotide runs (12). In our series of 415 tumors and corresponding normal tissues, BAT26 analysis alone was sufficient to detect unmistakably 25 of the 31 high MSI tumors, and the combination of two adenine runs provided the highest specificity. However, because two tumors displayed mutations in two CA repeats but not in any of the poly(A) markers, the highest sensitivity and overall diagnostic accuracy were achieved by the combination mononucleotide and dinucleotide repeats. The vulnerability of dinucleotide microsatellites to the increased mutation rate in MSI tumors is reinforced by the observation that all MSI tumors, but one, showed mutations in at least one dinucleotide microsatellite.

The high specificity and sensitivity of certain microsatellites suggest that they are suitable for simpler procedures (28). In this regard, a simple approach seems feasible with the panel that we have used. A stepwise strategy (first step is two markers; if nonconclusive, the second step is two to four additional markers) is likely to be cost-effective and will facilitate its application to large series of samples.

The statistical model that we used can be applied to other studies that use different microsatellite markers and will allow the evaluation of results reported so far. This may be especially useful when applied to cancers other than colorectal cancer. It should be noted that mutational patterns might differ in other tumor types and that these differences might subsequently affect how informative the various markers are.

The observed association between MSI tumors and various clinicopathologic and molecular characteristics, in agreement with the reports identifying this phenotype, further confirms the utility of our approach. Namely, patients who have tumors with MSI are usually younger at age of diagnosis (1), and the tumors are preferentially located in the right colon (13), tend to be undifferentiated (1), are mostly diploid (2,3), rarely contain mutations in the K-ras and p53 genes (1), and are less invasive (1). We have also confirmed, in a prospective setting with long-term follow-up, that this phenotype is associated with a better survival (3). Later on, other studies have found essentially the same association with sparse conflicting findings. The sexual dimorphism related to the patient's age and the incidence of MSI have also been reported in another study (29).

In summary, we have developed a simple, sensitive, and specific approach to assess the presence of MSI in colorectal cancer based on the apparent good fit of the data to a two-population model. The application of the experimental design reported herein may facilitate the use of MSI analysis as a routine diagnostic tool in cancer. Moreover, this strategy is easily adaptable to the retrospective analysis of other series of samples even when the type and number of markers determined are different from the ones that we used.


    NOTES
 
The authors of this article (except E. Marcuello) are members of the Writing Committee of the Bellvitge Colorectal Cancer Study Group. Members of the Bellvitge Colorectal Cancer Study Group are J. M. Badosa, C. Benasco, X. Bosch, M. Cambray, O. Campos, G. Capellà, A. Fabra, J.R. Germà, S. González, I. González-García, E. Guinó, B. Lloveras, J. Martí-Ragué, V. Moreno, M. Navarro, J. de Oca, A.M. Osorio, M.A. Peinado, C. del Rio, and F. Vilardell, Ciutat Sanitària i Universitària de Bellvitge and Institut de Recerca Oncològica, L'Hospitalet, Barcelona, Spain.

Supported by grants from Fondo de Investigación Sanitaria (FIS 94/37 and 97/787), Comisión Interministerial de Ciencia y Tecnología (CICYT SAF95/285, SAF96/187, SAF98/42, and SAF99/103), and Fundació La Marató de TV3 (95/48).

We thank Sara González, Silvia Tórtola, and Rosa-Ana Risques for technical support.


    REFERENCES
 Top
 Notes
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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Manuscript received August 23, 1999; revised December 17, 1999; accepted January 12, 2000.


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