Multiplex Immunofluorescence in Formalin-Fixed Paraffin-Embedded Tumor Tissue to Identify Single-Cell-Level PI3K Pathway Activation

Abstract

Purpose: Identifying cancers with high PI3K pathway activity is critical for treatment selection and eligibility into clinical trials of PI3K inhibitors. Assessments of tumor signaling pathway activity need to consider intratumoral heterogeneity and multiple regulatory nodes.

Experimental design: We established a novel, mechanistically informed approach to assessing tumor signaling pathways by quantifying single-cell-level multiplex immunofluorescence using custom algorithms. In a proof-of-concept study, we stained archival formalin-fixed, paraffin-embedded (FFPE) tissue from patients with primary prostate cancer in two prospective cohort studies, the Health Professionals Follow-up Study and the Physicians' Health Study. PTEN, stathmin, and phospho-S6 were quantified on 14 tissue microarrays as indicators of PI3K activation to derive cell-level PI3K scores.

Results: In 1,001 men, 988,254 tumor cells were assessed (median, 743 per tumor; interquartile range, 290-1,377). PI3K scores were higher in tumors with PTEN loss scored by a pathologist, higher Gleason grade, and a new, validated bulk PI3K transcriptional signature. Unsupervised machine-learning approaches resulted in similar clustering. Within-tumor heterogeneity in cell-level PI3K scores was high. During long-term follow-up (median, 15.3 years), rates of progression to metastases and death from prostate cancer were twice as high in the highest quartile of PI3K activation compared with the lowest quartile (hazard ratio, 2.04; 95% confidence interval, 1.13-3.68).

Conclusions: Our novel pathway-focused approach to quantifying single-cell-level immunofluorescence in FFPE tissue identifies prostate tumors with PI3K pathway activation that are more aggressive and may respond to pathway inhibitors.

Figure 1.. Methods overview and virtual tissue microarrays for visualization of multiplex immunofluorescence results.

(A) An overview of the PI3K pathway, PTEN, stathmin, and phospho-S6 (pS6). (B) Design of the prospective prostate cancer cohorts within the Health Professionals Follow-up Study (HPFS) and the Physicians’ Health Study (PHS). (C)Example raw images from immunofluorescence imaging for PTEN, stathmin, and pS6 as markers of PI3K pathway activity and of AMACR for tumor masking, all from the same tumor core, as well as a fused image of all fluorescence channels. (D)Hematoxylin–eosin appearance of the core highlighted from the virtual tissue microarray in (E). (E)Virtual tissue microarray, an algorithm-based reconstruction of the multiplex immunofluorescence data. Cell type assignments from histology-based machine learning (round, epithelial cells; crosses, non-epithelial cells) and tumor cell recognition based on AMACR (colored, tumor cells; gray, non-tumor cells). Cell colors of tumor cells indicate PI3K activity scores.

Figure 2.. Cell counts and construct validity of the PI3K scores.

(A) Distribution of cell counts per tumor by cell type: tumor epithelial cells (yellow), non-tumor epithelial cells (gray), and non-epithelial cells (blue), from all cores of each patient. (B) Tumor-level PTEN status based on a genomically validated immunohistochemistry and PI3K scores. (C)Tumor-level PTEN status and immunofluorescence scores consisting of pS6 and stathmin. (D) PI3K scores and the newly developed prostate cancer PI3K/PTEN signature. (E)Gleason grade and PI3K scores. In panels B–E, individual data points are shown, with boxes indicating interquartile ranges and the central line the median. Whiskers extend 1.5 interquartile ranges beyond the inner quartiles. In B–G and F–H, color encodes the number of tumor cells per tumor from blue (low) to yellow (high).

Figure 3.. PI3K activation and lethal prostate cancer.

(A) Kaplan-Meier plot for PI3K scores in quartiles and time from cancer diagnosis to lethal disease. For adjusted estimates, see Table 2. (B) Upper panel: PI3K scores (x axis), modelled continuously using restricted cubic splines, and hazard ratios for lethal disease (y axis). The solid line with gray 95% CI bands is from an unadjusted model; the dotted line with blue 95% CI bands is from a model adjusted for tumor-level PTEN status by immunohistochemistry. The reference value (hazard ratio, 1) is set to the 25^th percentile of the PI3K score, a value of 40. Lower panel: Distribution of PI3K scores, according to tumor-level PTEN status by immunohistochemistry.

Figure 4.. Intratumoral heterogeneity and machine learning.

(A) Within-tumor heterogeneity. The heterogeneity is expressed as the proportion of cells that do not belong into the quartile of cell-level PI3K scores that each tumor is assigned to based on its median. Left inset: An example core with low heterogeneity. Right inset: An example core with high heterogeneity, including areas of low PI3K activity (blue) and high PI3K activity (red/yellow). (B) PI3K scores and within-tumor heterogeneity. Color encodes the number of tumor cells. (C) Loading plot from principal components analysis. Both principal components explain similar proportions of the variance, and the second principal component is positively loaded with stathmin and pS6 and negative loaded with PTEN, corresponding to the subject matter-informed approach of defining the PI3K score. (D) Subject matter-based PI3K scores (x axis) and a machine learning-based score (principal component 2, y axis). (E)Unsupervised clustering of cell-level PTEN, pS6, and stathmin values based on k-medoids, resulting in 5 clusters, indicated by different colors, visualized here along axes of a two-dimensional t-SNE dissimilarity decomposition.