Casey G, Lindor NM, Papadopoulos N, Thibodeau SN, Moskow J, Steelman S, Buzin CH, Sommer SS, Collins CE, Butz M, Aronson M, Gallinger S, Barker MA, Young JP, Jass JR, Hopper JL, Diep A, Bapat B, Salem M, Seminara D, Haile R, Colon Cancer Family Registry FT. Conversion Analysis for Mutation Detection in MLH1 and MSH2 in Patients With Colorectal Cancer. JAMA. 2005;293(7):799-809. doi:10.1001/jama.293.7.799
Author Affiliations: Department of Cancer Biology,
Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio (Dr Casey); Departments
of Medical Genetics (Dr Lindor) and Laboratory Medicine and Pathology (Dr
Thibodeau and Ms Butz), Mayo Foundation, Rochester, Minn; GMP Genetics, Waltham,
Mass (Drs Papadopoulos, Moskow, Steelman, and Salem and Ms Collins); Clinical
Molecular Diagnostic Laboratory, City of Hope National Medical Center, Duarte,
Calif (Drs Buzin and Sommer); Departments of Surgery and Pathology, Mt Sinai
Hospital, Toronto, Ontario (Ms Aronson and Drs Gallinger and Bapat); Cojoint
Gastroenterology Laboratory, Bancroft Centre, Herston, Australia (Ms Barker
and Dr Young); Department of Pathology, McGill University, Montreal, Quebec
(Dr Jass); Centre for Genetic Epidemiology, University of Melbourne, Melbourne,
Australia (Dr Hopper); Department of Preventive Medicine, University of Southern
California–Norris Cancer Center, Los Angeles (Ms Diep and Dr Haile);
National Cancer Institute, National Institutes of Health, Bethesda, Md (Dr
Context The accurate identification and interpretation of germline mutations
in mismatch repair genes in colorectal cancer cases is critical for clinical
management. Current data suggest that mismatch repair mutations are highly
heterogeneous and that many mutations are not detected when conventional DNA
sequencing alone is used.
Objective To evaluate the potential of conversion analysis compared with DNA sequencing
alone to detect heterogeneous germline mutations in MLH1, MSH2, and MSH6 in
colorectal cancer patients.
Design, Setting, and Participants Multicenter study with patients who participate in the Colon Cancer
Family Registry. Mutation analyses were performed in participant samples determined
to have a high probability of carrying mismatch repair germline mutations.
Samples from a total of 64 hereditary nonpolyposis colorectal cancer cases,
8 hereditary nonpolyposis colorectal cancer–like cases, and 17 cases
diagnosed prior to age 50 years were analyzed from June 2002 to June 2003.
Main Outcome Measures Classification of family members as carriers or noncarriers of germline
mutations in MLH1, MSH2,
or MSH6; mutation data from conversion analysis compared
with genomic DNA sequencing.
Results Genomic DNA sequencing identified 28 likely deleterious exon mutations,
4 in-frame deletion mutations, 16 missense changes, and 22 putative splice
site mutations. Conversion analysis identified all mutations detected by genomic
DNA sequencing—plus an additional exon mutation, 12 large genomic deletions,
and 1 exon duplication mutation—yielding an increase of 33% (14/42)
in diagnostic yield of deleterious mutations. Conversion analysis also showed
that 4 of 16 missense changes resulted in exon skipping in transcripts and
that 17 of 22 putative splice site mutations affected splicing or mRNA transcript
stability. Conversion analysis provided an increase of 56% (35/63) in the
diagnostic yield of genetic testing compared with genomic DNA sequencing alone.
Conclusions The data confirm the heterogeneity of mismatch repair mutations and
reveal that many mutations in colorectal cancer cases would be missed using
conventional genomic DNA sequencing alone. Conversion analysis substantially
increases the diagnostic yield of genetic testing for mismatch repair mutations
in patients diagnosed as having colorectal cancer.
Hereditary nonpolyposis colorectal cancer (HNPCC) is a clinically heterogeneous
disease that has historically been diagnosed based on family history (Amsterdam
and Bethesda criteria).1- 3 Based
on family history, approximately 70% of HNPCC cases and a proportion of cases
that do not fit these criteria can be accounted for by mutations in any one
of several genes involved in DNA mismatch repair. In those cases with defective
mismatch repair, approximately 95% have alterations in 1 of 3 of the mismatch
repair genes MLH1, MSH2,
and MSH6, with a smaller proportion attributable
to mutations in other mismatch repair genes.4- 6 Identification
of a mutation may prompt genetic counseling, screening, and surveillance of
relatives to reduce morbidity and mortality. It has been proposed that screening
of all patients with colorectal cancer for mismatch repair gene mutations
is both feasible and desirable.5
The accurate identification and interpretation of mismatch repair mutation
carriers is essential for clinical management of colorectal cancer patients
and for scientific studies in which the mutation status of participants is
an important variable. Currently, most genetic testing is performed by genomic
DNA sequence analysis, but certain classes of gene mutations are not detected
using this approach, particularly large genomic deletions, genomic rearrangements,
and loss of expression mutations.7- 9 Studies
suggest that large genomic deletions may account for a substantial proportion
of HNPCC cases in the United States.10 Furthermore,
whereas conventional genomic DNA sequencing methods may identify putative
mutations in splice-site regions, additional studies are required to establish
their pathogenic effect.8
The heterogeneous nature of mismatch repair mutations in colorectal
cancer has not been well characterized, but there is evidence that large genomic
mutations may account for a substantial proportion of cases.7,9 Recent
studies have suggested that conversion analysis, in which alleles are separated
in hybrids prior to mutation screening, represents a more sensitive mutation
detection method than conventional DNA sequencing for identifying such mutations.7,9,11,12 To
date, only a limited number of studies have used conversion analysis to identify
mutations in MLH1 and MSH2 in
individuals for whom DNA sequencing failed to detect any mutations.7,9 There have been no studies comparing
the relative accuracy and specificity of conversion analysis with other mutation
testing methods in a rigorous way, which is essential if the method is to
be widely used clinically and in research.
In this study, we performed a blinded comparison of conventional DNA
sequencing and conversion analysis to identify mutations in MLH1, MSH2, and MSH6 in
89 colorectal cancer cases. These cases had a high probability of carrying
a mutation in mismatch repair genes. We sought to define the full complement
of mismatch repair mutations and to provide a strategy for the development
of a comprehensive test for the identification of germline mismatch repair
Participants were recruited through the Colon Cancer Family Registry,
which is supported by the National Cancer Institute. The Colon Cancer Family
Registry Consortium was initiated in 1997 and is dedicated to the establishment
of a comprehensive collaborative infrastructure for interdisciplinary studies
in the genetic epidemiology of colorectal cancer. The participating centers
of the Colon Cancer Family Registry are the University of Hawaii (Honolulu);
Fred Hutchinson Cancer Research Center (Seattle, Wash); Mayo Clinic (Rochester,
Minn); University of Southern California (Los Angeles); University of Queensland
(Brisbane, Australia); University of Melbourne (Melbourne, Australia); Cancer
Care Ontario (Toronto). The 6 registries (and collaborating institutions)
use standardized instruments and protocols to collect family history information,
epidemiological and clinical data, screening behavior, and related biological
specimens. Quality-control measures were in place throughout the collection,
processing, and storing of data and samples. All study participants provided
written consent and institutional review board approval for this study was
obtained from all participating centers. Additional information about the
Colon Cancer Family Registry can be found at http://www.cfr.epi.uci.edu/.
Participants were entered into the study through the Mayo Clinic, the
University of Southern California Consortium, Cancer Care Ontario, and the
University of Queensland. Participants had a prior diagnosis of colorectal
cancer and an available Epstein-Barr virus–transformed cell line. A participant was defined as an individual who belonged to a family that
met the Amsterdam 1 criteria for HNPCC; had at least 2 first- or second-degree
relatives with colorectal cancer or 1 relative with endometrial cancer and
at least 1 other relative with colorectal cancer (who did not meet Amsterdam
1 criteria and were referred to as HNPCC-like); or was diagnosed with colorectal
cancer prior to age 50 years but did not meet Amsterdam 1 criteria and was
not HNPCC-like. Importantly, in addition to these criteria, the majority of
cases (85 of 89) were selected because they also had prior evidence of a defect
in mismatch repair due to having either a tumor with high microsatellite instability
or loss of expression of a mismatch repair protein by immunohistochemistry.
Sixty-four HNPCC cases, 8 HNPCC-like, and 17 colorectal cancer cases diagnosed
prior to age 50 years were entered into the study. The identity of the participant
samples, family history of cases, and data on microsatellite instability or
immunohistochemistry were blinded until the end of the study.
Each center conducted its own microsatellite instability testing according
to a defined protocol involving testing tumors for instability at 10 specific
loci (BAT25, BAT26, ACTC, D5S346, D17S250, MYCL, BAT40, BAT34C4, D18S55, D10S197).13 This microsatellite instability
marker set included the National Cancer Institute recommended markers.14 A tumor was defined as having high microsatellite
instability if instability was seen in more than 30% of the markers tested.
Detailed immunohistochemistry staining methods have been described elsewhere.13 Briefly, 5-μm tissue sections from formalin-fixed,
paraffin-embedded tissue were stained using the avidin-biotin complex method
of Ventana Medical Systems (Tucson, Ariz) (buffer kit and DAB detection kit,
BioTek Solutions, Carpenteria, Calif) and using the Tech Mate 500 (Ventana)
automated immunohistochemical stain. Staining was performed using antibodies
to MLH1 (clone G168-728, 1/250; Pharmingen, San Diego, Calif), MSH2 (clone
FE11, 1/50; Oncogene Research Products, Cambridge, Mass), MSH6 (clone 44,
1/500; Transductions Laboratories, Lexington, Ky), and PMS2 (clone A16-4,
1/100; Pharmingen). When tumor cells showed an absence of nuclear staining
in the presence of positive staining in surrounding cells, absence of protein
expression was interpreted.13 Not all cases
were screened for MSH6 and PMS2 protein staining.
DNA Sequencing. DNA sequencing was performed
on all cases for mutations in MLH1 and MSH2. MSH6 sequencing was performed only on
those cases (n = 23) that were negative for deleterious mutations
in MLH1 or MSH2. Genomic
DNA was isolated from lymphoblastoid cell lines from each participant using
a blood kit (Qiagen Inc, Valencia, Calif). Samples were coded and submitted
to a clinical molecular diagnostic laboratory (City of Hope National Medical
Center, Duarte, Calif) for MLH1 and MSH2 sequencing and to the molecular genetics laboratory (Mayo Clinic)
for MSH6 sequencing.
Polymerase chain reaction (PCR) amplification was performed on genomic
DNA for all exons and adjacent intronic splice regions (19 fragments for MLH1, 16 for MSH2, and 16 for MSH6). All gene segments were sequenced in both directions
using an ABI 377 or ABI 3100 automated DNA sequencer (Applied Biosystems,
Foster City, Calif). Sequence changes were detected using Sequencher software
(Gene Codes, Ann Arbor, Mich); all chromatograms were analyzed by at least
2 individuals. Mutations were confirmed by repeating the PCR amplification
reaction and sequencing. All sequencing primers are available on request.
Conversion Analysis. Lymphoblastoid cell lines
from all participants were coded and submitted to GMP Genetics (Waltham, Mass)
for conversion analysis.7,15 Hybrid
cell lines were generated following conversion technology protocols by fusing
lymphoblastoid cells from participants with E2 mouse cells. The E2 cell line
is an immortal mouse embryonic cell line. This cell line and properties are
described.7 E2 cells were mixed with lymphoblastoid
cells, washed in Hanks balanced salt solution, resuspended in fusion media,
and transferred to a cuvette. Following electrofusion at 280V, the cells were
plated in plastic tissue culture multiwell plates and grown in a Dulbecco
modified eagle medium with 10% fetal bovine serum. Fusions were performed
on a BTX electro cell manipulator (Genetronic, San Diego, Calif). An electro
cell manipulator is an instrument that generates an electric current that
facilitates cell fusion.
During the next day, the cells were fed (using a Dulbecco modified eagle
medium) 10% fetal bovine serum supplemented with 1× hypoxanthine-aminopterin-thymidine
and 0.5 mg/mL of G418 (selection has been described7).
Two weeks later, approximately 42 hybrid clones were further expanded. Cells
were trypsinized and an aliquot was used for the isolation of DNA with reagents
and protocols from blood kits (Qiagen).
A minimum of 2 hybrids for each allele of chromosome 2 and chromosome
3 were selected for isolation of RNA and further analysis. GMP Genetics Inc
generated at least 2 monoallelic hybrids per allele for chromosome 2 and 3
from all live cell lines included in this study. Hybrids that contained human
chromosomes 2 (for MSH2 and MSH6) or 3 (for MLH1) were identified by genotyping
DNA prepared from the hybrids using 16 highly polymorphic microsatellite DNA
markers—8 each on human chromosomes 2 or 3. Hybrids containing chromosome
2 were identified by genotyping with the markers D2S2211, D2S162, D2S367,
D2S337, D2S117, D2S325, D2S2382, and D2S206. Hybrids containing chromosome
3 were identified by genotyping with the markers D3S1297, D3S1304, D3S2338,
D3S1289, D3S1614, D3S1565, D3S1262, and D3S1580. The markers used for genotyping
were derived from the linkage mapping set (version 2.5, MD10; Applied Biosystems).
Polymerase chain reaction amplification of the DNA markers was performed with
Taq and reaction buffer (Invitrogen, Carlsbad, Calif). The PCR products were
fractionated by capillary electrophoresis using an ABI 3100 automated DNA
sequencer. Total RNA was isolated using the RNeasy Kit (Qiagen) and cDNA generated
using SuperScript II reverse transcriptase (Invitrogen). Both positive and
negative reverse transcriptase cDNA reactions were performed from each RNA
Conversion analysis combines the separation of alleles into hybrids
along with analysis of cDNA sequence changes and effects on mRNA expression.
Changes in mRNA transcript size or levels of mRNA expression of MSH2 and MLH1 genes were determined by reverse
transciptase PCR from hybrids containing chromosomes 2 or 3. The coding region
of each gene was amplified by PCR with overlapping fragments. Each fragment
was amplified by PCR twice using 2 independent PCR reactions. To ensure the
integrity of cDNA and to verify the presence of human allele, PCR fragments
of other human genes of equivalent length to MSH2, MSH6, or MLH1 were amplified. The PCR fragments
were amplified from the same cDNA source. Specifically, human T-cell leukemia
virus–enhanced factor (HTLF) was used as a control on hybrids with chromosome
2 alleles and human transferrin receptor (TFRC) on hybrids with chromosome
3 alleles. Conversion analysis of MSH6 was performed
only on those cases negative for deleterious mutations in MLH1 or MSH2.
The primer pairs used to amplify the 2 fragments of MSH2 were MSH2-6 (5′-GCGCATTTTCTTCAACCAGG-3′) and MSH2-18
(5′-TAATCTGTTTGCCAGGGTCC -3′) at an annealing temperature of 65°C
and MSH2-20 (5′-CTGACTTCTCCAAGTTTCAGG -3′) and MSH2-4 (5′-TGGGCACTGACAGTTAACAC-3′)
at an annealing temperature of 60°C.
The primer pairs used to amplify the 2 fragments of MLH1 were MLH1-14 (5′-CACTTCCGTTGAGCATCTAG-3′) and MLH1-2
(5′-GCTGCAGAAATGCATCAAGC-3′) at an annealing temperature of 60°C
and MLH1-17 (5′-CAGCACATCGAGAGCAAGC -3′) and MLH1-18 (5′-ATCACACTTTGATACAACACTTTG
-3′) at an annealing temperature of 63°C.
The primer pairs used to amplify the 6 fragments of MSH6 had either M13 forward (5′ primers) or M13 reverse (3′
primers) to facilitate sequencing after amplification. The 6 pairs were: MSH6-31(5′-
GGGCCTTGCCGGCTGTC GGT-3′) and MSH6-4 (5′-CTAGATCCTTGTGTCTTAGGCTGT-ACTTCC-3′);
MSH6-6 (5′-CTCAGAGCCAGAGAAGAGGAAGA AGAGATG-3′) and MSH6-8 (5′-CTGACTCCAATAAGAGCATCCATGTGTACAG-3”);
MSH6-10 (5′-AGTCTCAGAACTTTGATCTTGTC ATCTGTTAC-3′) and MSH6-11
(5′-CAATAAGGCATTTTTTGAGGTAGAAGACAC-3′); MSH6-14 (5′-GGGAAAAGCTAAGTGATGGCAT
TGGGG-3′) and MSH6-15 (5′-TGGTCAAAGGCTGTATCCCATCGG-3′);
MSH6-18 (5′-GGTTTTAAGTCTAAAATCCTTAAGCAGGTC-3′) and MSH6-19 (5′-CAGCTAATAAGCCAGCCTGTCTCATAAGC-3′);
and MSH6-21 (5′-ATGACATTCTAATAGGCTGTGAGGAAGAG-3′) and MSH6-24
(5′-GTTGTCTGAATTTACCACCTTTGGTCAG-3′). The annealing temperature
was 69°C for all 6 fragments.
The primer pairs used to amplify TFRC were TFRC-1 (5′-ATTCTGCTCGTGGAGACTTC
-3′) and TFRC-3 (5′-CTTATCTGGTCAGTGCTCGC -3′) at an annealing
temperature of 63°C. The primer pairs used to amplify HTLF were HTLF-3 (5′-GACTCCAGATAAGAGAGCTG -3′) and HTLF-4
(5′-TTAGTATCCCTTCCCTACCC -3′) at an annealing temperature of 63°C.
The PCR reaction conditions were 10.0 μL of cDNA; 0.2 μL of 50 μM
for primer 1; 0.2 μL of 50 μM for primer 2; 5.0 μL of 10 ×
PCR buffer (included with Taq polymerase); 0.25 μL of 100 μM of nucleotide
mix (Invitrogen); 1.5 μL of 50 μM of magnesium chloride (included with
Taq polymerase); 1.0 μL of platinum TAQ DNA polymerase (Invitrogen); and
water to 50.0 μL. The PCR cycle conditions were 94°C (1 cycle) for
3 minutes, followed by 94°C for 30 seconds; primer-specific annealing
temperature for 30 seconds; 72°C for 2 minutes (35 cycles); and 72°C
for 5 minutes (1 cycle). The amplified fragments were resolved by running
between 7 and 9 μL of the PCR reaction on a 1% agarose gel.
Sequencing of cDNA Products. The PCR fragments
were purified using the AMPure purification system (Agencourt, Beverly, Mass).
Sequencing reactions were performed using BigDye terminator cycle sequencing
kit (version 3.1, Applied Biosystems). Sequencing products were resolved using
an Aurora DNA sequencer (Spectrumedix Corp, State College, Pa). Sequencing
data were analyzed using the LaserGene (DNASTAR Inc, Madison, Wis) and the
Mutation Surveyor (Soft Genetics Inc, State College, Pa).
Southern Blot Analysis of Large Genomic Deletions. This method has been described in detail elsewhere.16 Genomic
DNA from each patient was digested separately with 3 restriction endonucleases,
EcoR I, BglII, and Hind III. Each individual digested genomic DNA (2.5 mg)
was then loaded onto a 0.8% agarose gel for overnight electrophoresis at 55
V. After standard capillary gel transfer to the hybridization membrane (approximately
10 ng for each of the purified probes; 3 probes for hMSH2 capable of identify individual exons) was radioactively labeled with
[a-32P]-dCTP using the high prime kit (Roche, Basel, Switzerland). The radioactive
probes were added to 20 mL of hybridization solution at a concentration of
approximately 1 × 106 counts per minute. Membranes were placed
in the probe-hybridization solution and hybridization took place overnight
at 45°C. Following hybridization, the membranes were washed 3 times in
2 × sodium chloride sodium citrate buffer and 0.1% sodium dodecyl sulfate
at 60°C for 30 minutes; and then once in 0.2 × sodium chloride sodium
citrate buffer and 0.1% sodium dodecyl sulfate at 60°C for 30 minutes.
The radioactive membranes were then exposed to PhosphorImager screens (Amersham
Biosciences, Piscataway, NJ). Following exposure, the PhosphorImager screens
were scanned and the results were analyzed using ImageQuant software (version
5.0, Amersham Biosciences).
Cases were selected for study based on a number of clinical characteristics
and the presence of defective mismatch repair either by microsatellite instability
testing or immunohistochemistry, or both. Summaries of cancer family histories
for each of the 3 groups are provided in Table
1 and mutation data are summarized in Table 2. Mutations were considered pathogenic if the change met
any of the following criteria: a frameshift mutation that would be predicted
to result in a truncated protein; nonsense mutations; missense mutations if
additional mRNA expression data revealed aberrant splicing or exon skipping;
splice site mutations if additional data revealed aberrant splicing; large
genomic deletions that removed at least 1 exon; or duplication of exons. Four
in-frame deletions and missense mutations of unknown clinical significance
were not classified as deleterious because additional data are needed.
Detailed mutation data for the 64 colorectal cancer cases from HNPCC
Amsterdam 1 Criteria Families appear in Table 3 and Table 4. Table 5 presents mutation data for the 8 colorectal cases from HNPCC-like
families. Table 6 presents mutation
data for the 17 colorectal cancer cases who were diagnosed prior to age 50
years. Conventional genomic DNA sequence analyses identified 28 pathogenic
coding domain mutations, 16 missense mutations of unknown clinical significance,
4 in-frame deletion mutations, and 22 mutations in splice-site regions within
introns. Conversion analysis identified all 28 likely pathogenic coding domain
mutations, plus 14 additional pathogenic mutations, including 1 exon mutation,
12 large genomic deletions, and 1 exon duplication. This represents an increase
of 33% (14/42) in the number of likely pathogenic mutations detected by conversion
analysis compared with those detected by conventional DNA sequencing. Further
studies are needed to determine whether any of the in-frame deletion mutations
To confirm that conversion analysis correctly identified large genomic
deletions, we performed Southern blot analyses on a subset of 5 cases with
this type of mutation (those cases are identification numbers 3, 16, 56, 62,
and 94). Southern blot and conversion analysis data were consistent for all
Using reverse transcriptase PCR analysis of gene expression in hybrids,
conversion analysis also provided evidence of a likely pathogenic role for
4 of the 14 missense mutations that could not be interpreted on the basis
of the conventional genomic DNA sequencing alone. All 4 mutations are in coding
regions adjacent to splice sites of the deleted exons. One mutation (MLH1 793C>T) identified in 2 different families (identification numbers
28 and 29) was located 3 base pairs from the 5′ end of exon 10 and was
associated with skipping of exons 9 and 10 in cDNA. One missense mutation
(MSH2 1660A>G in identification number 57) was located 2 base
pairs from the 3′ end of exon 10 and was associated with skipping of
exon 10 in cDNA. A fourth mutation (MLH1 883A>G) in identification
number 50 was located 2 base pairs from the 3′ end of exon 10 and was
associated with skipping of exons 9 and 10 in cDNA.
Conversion analysis studies also clarified the pathogenic effect of
the 22 mutations within intron splice site regions (IVS+1 to IVS+5, IVS–2,
and IVS–8). With the exception of the 5 MLH1 IVS11-8 mutations
and 2 MLH1 IVS07-2 mutations, all the splice-site mutations affected
splicing. The 2 cases with the MLH1 IVS07-2 mutation showed loss
of mRNA expression of the corresponding MLH1 transcript.
This change (MLH1 IVS07-2) has been reported twice in the International
Collaborative Group on Hereditary Non-Polypsis Colorectal Cancer (INSIGHT)6 mutation database and would be predicted to result
in a splicing defect. Both cases showed loss of mRNA transcript expression,
suggesting that this mutation may result in unstable mRNA. Further studies
are needed to confirm this finding. The overall diagnostic yield of detecting
clinically relevant mutations using conversion analysis compared with conventional
DNA sequencing was 56% (35/63).
In this study of 89 colorectal cancer patients, who were selected because
of high probabilities of carrying a mutation in a mismatch repair gene due
to their family history, age at diagnosis, microsatellite instability, and/or
loss of MLH1, MSH2, or MSH6 protein expression of their tumors, we identified
likely pathogenic mutations in MLH1, MSH2, or MSH6 in 63 (71%) of the 89 cases.
Among the 3 groups with defective mismatch repair evaluated, likely deleterious
mutations were identified in 77% (49/64) of the Amsterdam I criteria HNPCC
cases, 88% (7/8) of HNPCC-like cases, and 41% (7/17) of colorectal cancer
cases diagnosed prior to age 50 years.
Overall, we identified 29 likely pathogenic coding domain mutations,
17 mutations that affected splicing or mRNA transcript stability, 12 large
genomic deletions, 1 exon duplication, 4 in- frame deletion mutations, and
12 missense mutations of unknown clinical significance. We do not report the
4 in-frame deletions as deleterious because additional studies are required
to confirm their pathogenic effect.
Together, these data imply that the great majority of HNPCC and HNPCC-like
colorectal cancer cases can be attributed to germline mutations in MLH1 or MSH2 when cases are preselected on
the basis of tumor characteristics for harboring a likely mismatch repair
defect. This frequency is higher than what would be anticipated from testing
clinically selected cases based on family history alone. Our finding of 1 MSH6 mutation carrier family in this population is consistent with MSH6 mutations accounting for only a low percentage of colorectal cancer
cases.4- 6 Note
that colorectal cancer cases in this study were selected based on tumor microsatellite
instability status and loss of MLH1 or MSH2 staining only.
Mutations in MLH1 and MSH2 (34% and 42%, respectively) accounted for a similar proportion of
HNPCC Amsterdam 1 cases. This frequency is consistent with that reported by
Wagner et al9 (42% and 41% for MLH1 and MSH2
mutations, respectively) in their study of 49 Amsterdam 1 criteria HNPCC families
and 10 HNPCC-like families. In contrast, we found that the majority of mutations
identified in our HNPCC-like cases and colorectal cancer cases diagnosed prior
to age 50 years were in the MSH2 gene (11 in MSH2, 2 in MLH1, and 1 in MSH6), suggesting a greater variability in family history
presentation in MSH2 than MLH1 mutation-related
The high number of large genomic deletions seen in our study is consistent
with the data from the study by Wagner et al,9 reporting
a high frequency of these mutations in 49 Amsterdam 1 criteria HNPCC families
and 10 HNPCC-like families. However, in the study by Wagner et al,9 50% (7/14) of the cases with large genomic alterations
had the same founder mutation (deletion of exons 1-6 of MSH2 ),
with the majority of cases belonging to a single-extended lineage arising
from a common European ancestor. This mutation was found only once in our
study population in a HNPCC-like family from North America and the relationship
between this case and the extended family described by Wagner et al9 and others17 is not
known. Our data imply that not only do large genomic deletions occur frequently
in colorectal cancer cases, but also that this type of mutation is highly
The highly heterogeneous nature of germline mismatch repair mutations
in HNPCC and other colorectal cancer patients presents serious challenges
for accurate genetic testing. We have shown that mutation testing using genomic
DNA sequencing alone would result in a high frequency of false-negatives within
samples chosen because they were highly likely to carry a mutation. Additional
analytical approaches are required to detect all of the mutations likely to
occur in HNPCC cases with defective mismatch repair.
Conversion analysis in combination with cDNA sequencing and mRNA expression
analysis offers a comprehensive approach for the detection of mismatch repair
mutations that occur in HNPCC. Conversion analysis has been adapted to use
blood samples (the main clinical material for a reference laboratory) rather
than lymphoblastoid cell lines and has been shown to have high efficiency
in generating hybrids, have a high capacity, and a turnaround time that is
acceptable to a reference laboratory. Furthermore, the results of this study
and previously published work indicated that conversion analysis (conversion
technology coupled with analysis of cDNA) can be used as a clinical platform
for mutation screening of HNPCC and other diseases.
Mutations that were not detected by DNA sequencing were predominantly
large genomic deletions that would be masked due to the presence of the remaining
normal allele. The separation of alleles through conversion analysis allowed
for the unmasking and detection of these mutations and also provided im portant
information for interpreting the clinical significance (ie, the pathogenic
nature) of both missense mutations and putative splice-site mutations. It
should be noted that genomic DNA sequencing when used in combination with
other analytical approaches, such as Southern blotting, multiplex ligation-dependent
probe amplification,18- 21 or
cDNA sequencing and reverse transcriptase PCR expression analysis, has the
potential to provide the same information as conversion analysis. These other
approaches, however, were not the subject of this analysis. Our study confirms
the highly heterogeneous nature of mismatch repair mutations in colorectal
cancer cases. Any mutation testing strategy that is adopted must take this
heterogeneity into account. Our study warrants additional studies comparing
conversion analysis with DNA sequencing used in combination with Southern
blotting and multiplex ligation–dependent probe amplification.
We found the lowest frequency of mismatch repair mutations (41%) in
the 17 colorectal cancer cases diagnosed as having tumors with high microsatellite
instability prior to age 50 years without an HNPCC-like family history. Nevertheless,
a mismatch repair gene mutation cannot be altogether excluded. Our findings
are similar to those of Liu et al22 who found
pathogenic mutations in 5 (42%) of 12 participants with high microsatellite
instability colorectal cancer who were diagnosed at age 35 years or younger.
These data emphasize the importance of high microsatellite instability status
as a marker for HNPCC in younger persons with no family history, especially
if he/she shows a loss of protein expression. Such cases are sometimes erroneously
dismissed as “sporadic” colorectal cancer, with the implication
that they are not present in individuals with a predisposing germline mutation.23
We have previously reported on the correlation between microsatellite
instability and immunohistochemistry results,13 and
the current study supports these findings. For those cases with likely deleterious
mutations and available immunohistochemistry staining, data were consistent
for 51 (96%) of 53 HNPCC and HNPCC-like cases. In this multicenter study,
we were unable to reevaluate discordant immunohistochemistry and mutation
data. Overall, this study supports the use of immunohistochemistry as a rapid,
reasonably sensitive, and specific tool for triaging a specific mismatch repair
gene for germline testing.
In conclusion, we have shown that DNA sequencing alone is not sufficiently
sensitive to detect the types of mutations in MLH1 and MSH2 genes found in colorectal cancer cases. Conversion
analysis provided a 33% improvement in the detection of mismatch repair mutations
in 89 colorectal cancer cases selected as highly likely to have a mutation.
The overall increase in clinically important information provided by conversion
analysis was 56% (35/63). These results have important implications for genetic
testing of individuals for both clinical and research purposes. Testing strategies,
whether conversion analysis, as validated herein, or a combination of other
approaches, must take into account the highly heterogeneous nature of mismatch
repair mutations in colorectal cancer.
Corresponding Author: Graham Casey, PhD,
Department of Cancer Biology, ND50, Cleveland Clinic Lerner College of Medicine,
9500 Euclid Ave, Cleveland, OH 44195 (email@example.com).
Author Contributions: Dr Casey had full access
to all of the data in the study and takes responsibility for the integrity
of the data and the accuracy of the data analysis.
Study concept and design: Casey, Lindor, Papadopoulos,
Thibodeau, Moskow, Steelman, Sommer, Jass, Hopper, Diep, Bapat, Salem, Seminara,
Acquisition of data: Casey, Lindor, Papadopoulos,
Thibodeau, Moskow, Steelman, Buzin, Sommer, Collins, Butz, Aronson, Gallinger,
Hopper, Diep, Bapat.
Analysis and interpretation of data: Casey,
Lindor, Papadopoulos, Thibodeau, Moskow, Steelman, Buzin, Sommer, Butz, Barker,
Young, Hopper, Bapat, Haile.
Drafting of the manuscript: Casey, Lindor,
Papadopoulos, Moskow, Steelman, Hopper, Haile.
Critical revision of the manuscript for important
intellectual content: Casey, Lindor, Papadopoulos, Thibodeau, Steelman,
Buzin, Sommer, Collins, Butz, Aronson, Gallinger, Barker, Young, Jass, Hopper,
Diep, Bapat, Salem, Seminara.
Statistical expertise: Hopper, Haile.
Obtained funding: Papadopoulos, Gallinger,
Jass, Hopper, Salem, Seminara.
Administrative, technical, or material support:
Casey, Lindor, Papadopoulos, Thibodeau, Moskow, Steelman, Collins, Butz, Gallinger,
Barker, Young, Diep, Salem, Seminara.
Study supervision: Casey, Papadopoulos, Steelman,
Buzin, Sommer, Aronson, Diep, Salem.
Financial Disclosures: None reported.
Funding/Support: GMP Genetics Inc sponsored
this study and funded all the mutation analyses performed at GMP Genetics
and City of Hope. This study was also funded by National Institutes of Health
(NIH) grant U01 CA74800. All other work was supported by funds from the National
Cancer Institute and the NIH under RFA contract CA-95-011 and through cooperative
agreements with the members of the Colon Cancer Family Registry and pricipal
Role of the Sponsor: GMP Genetics Inc had input
in the design of the study. GMP Genetics generated monoallelic hybrids for
chromosome 2 and 3 from all live cell lines included in this study and performed
the mutation analysis on nucleic acids isolated from these cells.
Collaborating Centers: Australian Colorectal
Cancer Family Registry (NIH grant UO1 CA097735), the USC Familial Colorectal
Neoplasia Collaborative Group (NIH grant UO1 CA074799), Mayo Clinic Cooperative
Family Registry for Colon Cancer Studies (NIH grant UO1 CA074800), Ontario
Registry for Studies of Familial Colorectal Cancer (NIH grant UO1 CA074783),
Seattle Colorectal Cancer Family Registry (NIH grant UO1 CA074794), University
of Hawaii Colorectal Cancer Family Registry (NIH grant UO1 CA074806), and
University of California, Irvine Informatics Center (NIH grant UO1 CA078296).
Disclaimer: This article does not necessarily
reflect the views or policies of the National Cancer Institute or any of the
collaborating centers in the Cancer Family Registries, nor does mention of
trade names, commercial products, or organizations imply endorsement by the
US government or the Cancer Family Registry.