2012 EDUCATIONAL BOOK - American Society of Clinical Oncology

GENOMIC AND PROTEOMIC TECHNOLOGIES AND BIOMARKERS
Fig 1. Analysis steps. Genomics and proteomics analyses from all measurement platforms should follow steps (a)–(f) to obtain clinically
relevant genomic signatures. (a) Sample experimental design accounting for technical artifacts in batches (e.g., processing date, lab technician,
etc). (b) Sample raw output from Affymetrix microarray. (c) Comparing the distribution of genomic data in each sample before and after
normalization, at which point measurements for each sample should be on the same scale. (d) Dendrogram for clustering of all data colored by
batch to identify artifacts in the data. (e) Heatmap of genes that the analysis identified as distinguishing tumor and normal samples (green
represents low measurement values and red high). (f) State predicted based on gene signature from the analysis in (e) for a new test set of tumor
and normal samples.
Approaches to Genomic and Proteomic Data Analyses
All genomic and proteomic analyses generally follow the
stages outlined in Figure 1. We will discuss these stages
using genomic analyses since they are better developed for
genomic data, although the various approaches apply to
proteomic datasets as well. First, careful experimental design
is required to ensure that the measurements collected
can quantify the hypothesized genomic state or differences
in tumors and have large enough number of samples to
provide sufficient statistical power. Moreover, technical elements
of the experimental design, including processing date,
lab technician, and processing center, can introduce significant
artifacts known as batch effects into the data. 11 These
batch effects are present in all genomics data, including
sequencing technologies. Therefore, the experimental design
should ensure that samples for each of the biologic conditions
are processed in each of the technical conditions (Fig.
1A). If such representation is impossible because of the size
or scope of the experiment, the experiments should adopt a
randomized block design. Once collected, the raw data (Fig.
1B) must be preprocessed to convert the raw output of the
platforms to identifiable genomic or proteomic measurements
that are comparable across the experiments (Fig. 1C),
including statistical removal of or control for batch effects
(Fig. 1D). Only after that can computational algorithms be
applied to infer gene-expression signatures pertinent to
cancer (Fig. 1E). 6 Finally, as with any statistical analyses,
genomic analyses require careful cross-validation to ensure
the relevance of the inferred signatures to future datasets
collected from different biologic samples in similar experimental
conditions (Fig. 1F). Further, directed experimentation
is required to prove the functional mechanisms
suggested by the inferred signatures.
In general, there are three main analysis approaches:
unsupervised, supervised, and class-prediction algorithms.
Unsupervised (or class-discovery) algorithms seek gene- or
protein-expression signatures that distinguish samples
without regard for the biologic conditions in the experimental
design. Hierarchical clustering has become perhaps the
most widely adopted algorithm for unsupervised genomic
analysis. 12 These algorithms identify distances between sets
of genes or samples. Visualization of these clustering across
samples in dendrograms (Fig. 2A) can compare biologic
samples measured under different technical conditions,
making them particularly useful for identifying batch effects
and success of algorithms removing those effects (Fig. 1D).
Once removed, the clustering will classify samples to ideally
distinguish patients with different clinical outcomes. Likewise,
genes that clustered together can be hypothesized to
function similarly. Similar utility can be gained from another
form of clustering, k-means clustering. This algorithm
divides genes or samples from the data into a predetermined
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