Gene Expression Profiling of Human Tumors Using Frozen Surgical Pathology Material: Experience and Issues
Memorial Sloan-Kettering Cancer Center
New York, NY
Expression profiling refers to the process of measuring the expression of thousands of individual
genes simultaneously in a given tissue sample. The resulting patterns of gene expression reflect the
molecular basis of the sample phenotype and can be used for sample comparisons and classification. In
broad terms, there are two main methods of expression profiling: hybridization-based and
sequencing-based. In the former, RNA is extracted from the sample, converted to cDNA or cRNA, and
hybridized to a DNA microarray. DNA microarrays are either nylon membranes, glass slides, or synthetic
"chips", to which are attached nucleic acid probes as cDNA clones or cDNA clone-specific oligonucleotides
corresponding to hundreds to tens of thousands of genes
. The general concepts
of tumor gene expression profiling and its potential impact on clinical oncology and pathology have been
explored in detail in recent commentaries and reviews
Investigators in the field of expression profiling of human tumors initially performed
proof-of-principle studies, in which tumors of different morphology or different primary sites were
shown, perhaps not surprisingly, to have clearly distinguishable patterns of gene expression 
. In the jargon of the field, this type of diagnostic
classification by analysis of expression profiles is called "class prediction". These proof-of-principle
studies served to validate cDNA microarray technology, and investigators soon shifted their focus to
identification of molecularly defined tumor entities (usually with associated clinical relevance) that
were inapparent by conventional pathologic analysis ("class discovery"). Indeed, new unsuspected
biological subsets were thus detected first among cutaneous melanomas  , breast carcinomas
 , and pediatric acute lymphoblastic leukemias  , then in many other tumor
groups. In other cases, there has been what could be termed "class re-discovery" or "class
confirmation". Initial expression profiling experiments in breast cancers  led to the
rediscovery of the previously described but subsequently neglected immunohistochemical distinction of
basal vs luminal cell type breast carcinoma
. Studies of B-lineage diffuse
large cell lymphoma (DLCL) have confirmed the presence of a subset of germinal center-derived cases
(associated with a favorable prognosis) as anticipated by older data on the prognostic significance in
B-cell DLCL of BCL2 expression or the follicular lymphoma-associated BCL2 rearrangement due to the
. Likewise, it has by now been well
established that translocation-associated leukemias and sarcomas are robustly clustered by expression
profiling using cDNA microarrays
. This is perhaps not unexpected because the
aberrant transcriptional proteins encoded by translocation-derived fusion genes act primarily through
changes in gene expression.
There is a considerable gulf between the practical world of hospital-based molecular
diagnostic laboratories and the hopeful predictions coming out of high-throughput genomics laboratories,
such as one made in 1999 that "doctors will be offering gene expression profiles to some patients in the
next three years "  , failed to consider the many issues in moving complex assays from
research labs to clinical labs. Because of regulatory, billing, quality control, and test validation
concerns combined with limited resources, academic molecular diagnostic laboratories are extremely
limited and selective in their test menus. Furthermore, it is not clear that, considering the present
cost of microarrays, large scale expression profiling for "class prediction" is more cost-effective than
established diagnostic approaches, i.e. histopathology, supplemented in selected cases by
immunohistochemistry, cytogenetics, or specific molecular assays, although such an argument has been made
in acute leukemias  . Microarray-based assays present completely new quality assurance and
quality control (QA/QC) issues and these are beginning to be addressed
Aside from these important considerations, there are two critical issues facing the implementation of
microarray-based assays in surgical pathology: the adequacy of frozen surgical pathology material for
these assays and their reproducibility. These will be the focus of this presentation.
Suitability of frozen surgical pathology material
The adequacy of frozen surgical pathology material for microarray analyses can vary
substantially. With the advent of linear RNA amplification protocols, the adequacy issue relates more to
quality than quantity of material. Indeed, needle biopsy material can be used for microarray-based
. It is RNA quality that is a critical determinant of the reliability
of microarray-based expression profiling results. Conventionally handled material that is frozen at the
time of gross examination of the specimen or at the time of frozen section if one was performed (and the
piece was sufficient for procurement) may often show too much RNA degradation to provide an adequate
sample for expression profiling (even though it is usually adequate for RT-PCR based analyses such as
fusion transcript testing). The failure rates vary widely depending on the time intervals and type of
tissue. Although the ability to obtain adequate RNA for microarray analysis in only 50% samples may be
workable in a research setting, if the microarray data are needed for clinical reasons, this situation is
not acceptable. Procurement and snap-freezing of tissue in the operating room can decrease failure rates
to less than 10%. However, this may mean that a portion of the material bypasses gross and microscopic
examination by a surgical pathologist, a situation with potential political and medicolegal implications.
The pressure for intra-operative procurement and snap-freezing of tissue for microarray research studies
is increasing and this trend is likely to accelerate as clinical tests using microarray-based expression
profiling become established and accepted.
Inter-laboratory reproducibility of gene expression profiles
Major sources of poor reproducibility include different tissue handling and RNA extraction methods,
different microarray platforms, different statistical analytic methods, different study designs (e.g
different comparison groups), and the variability introduced by relatively small patient numbers. This
variability is perhaps more notable in prognostic studies than in class prediction studies. For most
tumor types, the issues of inter-laboratory and inter-platform reproducibility in the expression
profiling of human tumors are only beginning to be addressed.
Using replicate hybridizations of a reference RNA sample that met strict quality-control criteria,
Dumur et al.  found that showed that the Affymetrix Genechip system is highly reproducible,
with the main source of the minimal variations being the day-to-day variability, and not variability
between chips. Likewise, in a study of Affymetrix chips coordinated by multiple institutional microarray
facilities, the major source of variability was between laboratories and not between chips (even from
different chip lot numbers)  . This "operator-introduced" variability relates largely to
subtle differences in RNA extraction methods and probe preparation and labeling techniques between labs.
Performing replicates (e.g. triplicates) can substantially increase the reliability of gene expression
data  but are not likely to be possible clinically.
Different microarray platforms pose a special challenge for reproducibility. For instance, in a
comparison of genes most differentially expressed in pancreatic cancers as compared with nonneoplastic
tissues as defined by either Affymetrix oligonucleotide chips or custom spotted cDNA microarrays, it was
found that only 32 of 381 differentially expressed genes (8.3%) identified by these two platforms
overlapped  . In general, it was noted that differentially expressed genes whose expression
levels were high were more likely to be identified across different platforms. Other studies have also
found (non-commercial) custom spotted cDNA microarrays to show poor concordance with Affymetrix chips
 . There is only a slightly better concordance between different commercial products. A
comparison of Affymetrix (oligonucleotide), Agilent (cDNA), and Amersham (oligonucleotide) chips found
that while replicates on a given platform had excellent correlation coefficients (r>0.9), matched
measurements across platforms had consistently poorer correlation coefficients (r=0.48 to 0.59), with no
single manufacturer being distinctly superior  . Thus, at the present time, differences in
results obtained on different types of chips or microarrays appear largely due to the arrays themselves.
The flip side of this finding is that different platforms may still play a useful complementary role in
the research setting.
Another possible source of poor reproducibility between different microarray platforms is the problem
of incorrect annotations of individual elements (probes or cDNAs) on the microarrays. This may in rare
cases reflect simple errors. However, in most cases, it is due to the uncertain identification of some
genes at the time the array was designed. The human genome sequence, although largely complete, has
still not reached the point where all exons are defined unambiguously. Thus, Mecham et al.
 found that probes containing the most reliable sequence information showed higher
reproducibility on the same platform (both on the same chips and different versions of chips), as well as
higher reproducibility on the other platforms. This has also been observed by others  .
In terms of statistical analytic methods, microarray studies face a huge multiple testing problem
which makes the standard p<0.05 cutoff unusable. Typically, smaller microarray studies employ methods
to identify potentially biologically significant genes (for instance by using the False Discovery Rate
method) that might not be judged statistically significant after a strict Bonferroni adjustment of the p
value. This inevitably introduces "noise" in the differentially expressed gene lists. The variability
introduced by relatively small patient numbers cannot be underestimated. As the field of expression
profiling matures, it has become increasingly obvious that profiles based on small numbers of samples,
e.g. less than 10 per group compared, should be viewed with great caution, regardless of their apparent
significance as defined by often complex statistical methods.
Different study designs are also of obvious importance in comparing expression profiles across
studies. It is self-evident that the profile of differentially expressed genes identified for a given
tumor entity in a microarray-based study is partly dependent on the tumor types (or normal tissues) in
the comparison group. However, the choice of comparison group is often dictated at least partly by
convenience and does not always make biological sense (although interesting insights may still be
gained). Few if any genes in any given tumor are expressed solely in that tumor, hence the impact of
different comparison groups on the resulting lists of differentially expressed genes.
In the area of microarray-based prognostic predictors, statistical issues are more complex and there
are more potential causes of poor reproducibility. A recent meta-analysis of 30 published studies of
this type led to some interesting observations  . First, and not surprisingly, the authors
found that significant associations were 3.5 times more likely per doubling of sample size and
approximately 10 times more likely per 10-fold increase in the number of genes represented on the
microarrays used. However, when they examined the reported performance of prognostic predictors as
assessed by cross-validation (a method which tests a given prognostic predictor on portions of the
original dataset that was used to develop it), they noted a paradoxical trend for smaller studies to show
better sensitivity and specificity of their predictors compared to larger studies. This clearly
highlights the statistical artifacts introduced into predictive expression profiling studies by
relatively small patient numbers. They and others  note that cross-validation approaches
are often incomplete, i.e. incorrectly performed, which can lead to inflated estimates of the sensitivity
and specificity of a given microarray-based prognostic predictor. Other data analysis and interpretation
pitfalls are discussed in detail by Simon et al.  .
The different results obtained by different groups may be due to technical factors but the
variability may also have a biological component in that the differences may also reflect the likely
"multidimensional" nature of prognostically significant genes or pathways, i.e. the presence of a matrix
of several independent and additive prognostic categories for given cancer types. Larger more systematic
and more standardized studies will be needed to sort out these variability issues. Nonetheless, for
robust high quality gene expression signatures, such as those defined in acute leukemias  ,
reproducibility appears excellent, as demonstrated in a recent study that found excellent accuracy for
microarray-based classification of acute myeloid leukemias at a central referral laboratory, regardless
of differences in sample shipment time, storage time, or preparation period  . Finally, a
major recently completed inter-laboratory comparability study of expression profiling using Affymetrix
chips is also cause for optimism  . Four laboratories studied the same set of samples on
Affymetrix U133A chips and found excellent interlaboratory correlations, only marginally weaker than the
intralaboratory correlations. As expected, correlations were the weakest for genes with low expression
levels and low variability across samples. They also noted that interlaboratory variability in
microarray results was due less to handling or RNA extraction than to probe preparation and array
hybridization and processing. Overall, their findings are encouraging and suggest that reproducibility
issues will be effectively overcome by standardization of methods, careful QA/QC, and the use of a common
microarray platform, allowing the clinical implementation of these assays on frozen surgical pathology
material in the near future.
The author thanks Dr. William Gerald for sharing pre-publication data.
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