To measure the IHC expression of the different markers and quantify the
inflammatory cells expressing the slides, containing whole-tumor sections or
immunohistochemically stained TMA sections, were digitally scanned at
×200 magnification using a ScanScope Aperio AT Turbo slide scanner
(Leica Microsystems). The images were visualized using the ImageScope software
program (Leica Microsystems) and analyzed using the Aperio Image Toolbox and
GENIE image analysis tool (Leica Microsystems). The pathologist who performed
the image analysis was blinded to patients’ outcome. After training, the
software by a pathologist (Supplementary Fig. S1 ), membranous PD-L1 expression in malignant
epithelial cells and macrophages was analyzed using a cell membrane staining
algorithm, and the staining intensity scored as 0 (no staining), 1+
(weak staining), 2+ (moderate staining), or 3+ (strong staining)
and extension (percentage) of expression were determined (Fig. 1 ). The PD-L1 H-scores for tumor
tissues were determined by multiplying the staining intensity and reactivity
extension values (range, 0–300). The densities of cells expressing CD3,
CD4, CD8, CD57, granzyme B, CD45RO, PD-1, and FOXP3 were evaluated using the
Aperio nuclear algorithm and CD68 using Aperio cytoplasmic algorithm (Fig. 2 and Supplementary Fig. S2 ) and counting
the cells positive for them in 5 random square areas (1 mm2 each) in
both intratumoral and peritumoral compartments. While 5 intratumoral regions
were available in all cases and 24 tumors did not have peritumoral regions for
analysis. Histologic assessment of each 1 mm2 was performed to ensure
that tumor tissue (at least 80% malignant cells and tumor stroma) was
included in the selected intratumoral region, and only non-malignant cells were
included in the peritumoral compartment. For this analysis, each area examined
was overlapped with the sequential IHC slides to quantify each marker at the
same location of the tumor specimens. The average total number of cells positive
for each marker in the 5 square areas was expressed in density per
mm2. Similar to PD-L1 H-score from 5
intratumoral areas, the TMA was scored as the median PD-L1
H-score average of all cores from each case. In addition, as
has been proposed by Teng and colleagues (24 (link)), 4 different types tumor microenvironment we were able to
identify based on the density of TILs and the expression of PD-L1, as follows:
type I (adaptive immune resistance), type II (immunologic ignorance), type III
(intrinsic induction), and type IV (tolerance). Combining PD-L1 expression in
malignant cells (>5% was considered positive) with the density of
cells expressing CD3 using 3 levels (tertile) divided on the basis of regular
values of distribution by the statistical software (moderate and severe density
were considered positive), we were able to identify the frequency of those four
subtypes of tumor’ microenvironment in our NSCLC cases.
inflammatory cells expressing the slides, containing whole-tumor sections or
immunohistochemically stained TMA sections, were digitally scanned at
×200 magnification using a ScanScope Aperio AT Turbo slide scanner
(Leica Microsystems). The images were visualized using the ImageScope software
program (Leica Microsystems) and analyzed using the Aperio Image Toolbox and
GENIE image analysis tool (Leica Microsystems). The pathologist who performed
the image analysis was blinded to patients’ outcome. After training, the
software by a pathologist (
epithelial cells and macrophages was analyzed using a cell membrane staining
algorithm, and the staining intensity scored as 0 (no staining), 1+
(weak staining), 2+ (moderate staining), or 3+ (strong staining)
and extension (percentage) of expression were determined (
tissues were determined by multiplying the staining intensity and reactivity
extension values (range, 0–300). The densities of cells expressing CD3,
CD4, CD8, CD57, granzyme B, CD45RO, PD-1, and FOXP3 were evaluated using the
Aperio nuclear algorithm and CD68 using Aperio cytoplasmic algorithm (
the cells positive for them in 5 random square areas (1 mm2 each) in
both intratumoral and peritumoral compartments. While 5 intratumoral regions
were available in all cases and 24 tumors did not have peritumoral regions for
analysis. Histologic assessment of each 1 mm2 was performed to ensure
that tumor tissue (at least 80% malignant cells and tumor stroma) was
included in the selected intratumoral region, and only non-malignant cells were
included in the peritumoral compartment. For this analysis, each area examined
was overlapped with the sequential IHC slides to quantify each marker at the
same location of the tumor specimens. The average total number of cells positive
for each marker in the 5 square areas was expressed in density per
mm2. Similar to PD-L1 H-score from 5
intratumoral areas, the TMA was scored as the median PD-L1
H-score average of all cores from each case. In addition, as
has been proposed by Teng and colleagues (24 (link)), 4 different types tumor microenvironment we were able to
identify based on the density of TILs and the expression of PD-L1, as follows:
type I (adaptive immune resistance), type II (immunologic ignorance), type III
(intrinsic induction), and type IV (tolerance). Combining PD-L1 expression in
malignant cells (>5% was considered positive) with the density of
cells expressing CD3 using 3 levels (tertile) divided on the basis of regular
values of distribution by the statistical software (moderate and severe density
were considered positive), we were able to identify the frequency of those four
subtypes of tumor’ microenvironment in our NSCLC cases.
