Development of Multigene Expression Signature Maps at the Protein Level from Digitized Immunohistochemistry Slides

Molecular classification of diseases based on multigene expression signatures is increasingly used for diagnosis, prognosis, and prediction of response to therapy. Immunohistochemistry (IHC) is an optimal method for validating expression signatures obtained using high-throughput genomics techniques since IHC allows a pathologist to examine gene expression at the protein level within the context of histologically interpretable tissue sections. Additionally, validated IHC assays may be readily implemented as clinical tests since IHC is performed on routinely processed clinical tissue samples. However, methods have not been available for automated n-gene expression profiling at the protein level using IHC data. We have developed methods to compute expression level maps (signature maps) of multiple genes from IHC data digitized on a commercial whole slide imaging system. Areas of cancer for these expression level maps are defined by a pathologist on adjacent, co-registered H&E slides, allowing assessment of IHC statistics and heterogeneity within the diseased tissue. This novel way of representing multiple IHC assays as signature maps will allow the development of n-gene expression profiling databases in three dimensions throughout virtual whole organ reconstructions

Glossary of Terms.

<p>Glossary of Terms.</p

Generation of an IHC Signature Map.

<p>Displayed values of the IHC maps and IHC signature maps are in relative units (r. u.). (<b>A</b>) IHC Map for ENO2, shown in red since the weighting factor was positively signed (higher expression is associated with aggressive disease) and thus higher expression was shown as more intense red. (<b>B</b>) IHC Map for CD34 (also shown in red due to positively signed weighting factor). (<b>C</b>) IHC Map for MKI67 (also shown in red due to positively signed weighing factor). (<b>D</b>) IHC Map for ACPP, shown in blue since the weighting factor was negatively signed (higher expression is associated with non-aggressive disease) and thus higher expression was shown as more intense blue. (<b>E</b>) The weighted sum of IHC Scores (termed an IHC Signature Map) were projected in grid squares across annotated tumor areas of Gleason scores 3+3, 3+4, and 4+3 outlined in green, yellow and red, respectively.</p

Four-gene IHC signature map.

<p>The IHC signature map is displayed in relative units (r. u.), projected against a black background to illustrate the four-gene signature map in each pathologist-annotated area (tumor areas of Gleason sum scores 3+3, 3+4, and 4+3 outlined in green, yellow and red, respectively).</p

Alignment results using TurboReg.

<p>(<b>A</b>) Image of H&E-stained reference section image. (<b>B</b>) Image of section stained for ACPP previously brought into rough alignment with reference image by user. (<b>C</b>) Image of section stained for ENO2 previously brought into rough alignment with reference image by user. (<b>D</b>) Monochromatic images of A–C generated for purposes of overlay illustrations. (<b>E</b>) TurboReg translated and rotated (angle ⊖_A) the image of ACPP-stained section until a mean location error was minimized. (<b>F</b>) Similarly, TurboReg translated and rotated (angle ⊖_B) the image of ENO2-stained section to minimize mean location error between this image and the reference H&E-stained image. The final registered IHC image is overlaid on the reference H&E image in the bottom of panels (<b>E</b>) and (<b>F</b>). Values for translations and rotation determined by TurboReg along with the initial coarse registration steps were stored in the software for later use in transforming grid locations from the reference to the IHC images when generating IHC and signature maps.</p

The main SigMap program window.

<p>Within the main program window the user selects the reference H&E image (designated “Select H&E”), the IHC images, and the analysis macro to be used. The Aperio Positive Pixel Count algorithm was employed in this example using default threshold settings (designated as “Default Brown Staining PPC”). If desired, threshold settings may be adjusted by navigating to the algorithm settings menu by selecting “Set Algorithm Settings”.</p

Generation of an IHC Map.

<p>(<b>A</b>) Reference H&E-stained section. (<b>B</b>) Pathologist annotations drawn in XML markup format using a pen tablet screen. In this example, prostate cancer of Gleason sum score 3+3 is outlined in green, score 3+4 is outlined in yellow and 4+3 is outlined in red. (<b>C</b>) IHC Map overlaid the annotated image with a grid with dimensions 0.25×0.25 mm². (<b>D</b>) Grid squares that were within pathologist annotated areas were retained. (<b>E</b>) Contours of the annotated regions at the resolution of the analysis grid registered to the ACPP image. (<b>F</b>) The intensity of ACPP staining within each grid location in (F) was determined by using the results of the Positive Pixel Count algorithm (default settings) multiplied by the assigned weighting factor. The weighted value for each grid square was termed an IHC score. The two-dimensional depiction of IHC Score values was termed an IHC Map.</p

IHC signature scores from 11 annotated regions of cancer from 10 subjects.

<p>The benchmark (blue) and native (red) data for the same region are shown immediately adjacent to each other for comparison. For each region, the minimum, 1^st quartile, median, 3^rd quartile and the maximum values are shown. The data are sorted first by grade and then by the median of the benchmark data within each grade.</p

Registration errors between the IHC and H&E images.

<p>Registration errors between each of the 3 IHC datasets registered to the reference H&E data for all 10 subjects. The error estimate is based on the vector distance between landmarks placed on the same anatomic features present in the registered IHC and reference H&E slides. The histogram contains 5 measurements for each of 3 IHC datasets for 10 cases resulting in a total of 150 error estimates. The histogram shows the stain dependent errors for ACPP (blue), MKI67 (red) and CD34 (green).</p

SigMap's registration window.

<p>Shown is the registration window in which the user performs rough alignment of each IHC slide (the section stained with antibody directed against ACPP illustrated to the right) to the reference slide (H&E stained section illustrated to the left). Manipulations may include clockwise or counterclockwise rotations in 90 degree increments, or vertical or horizontal flipping to achieve rough image alignment prior to launching TurboReg which is done by selecting “OK”.</p