Evaluation of an RNAseq-Based Immunogenomic Liquid Biopsy Approach in Early-Stage Prostate Cancer

Leander Van Neste, Kirk J Wojno, Ricardo Henao, Shrikant Mane, Howard Korman, Jason Hafron, Kenneth Kernen, Rima Tinawi-Aljundi, Mathew Putzi, Amin I Kassis, Philip W Kantoff, Leander Van Neste, Kirk J Wojno, Ricardo Henao, Shrikant Mane, Howard Korman, Jason Hafron, Kenneth Kernen, Rima Tinawi-Aljundi, Mathew Putzi, Amin I Kassis, Philip W Kantoff

Abstract

The primary objective of this study is to detect biomarkers and develop models that enable the identification of clinically significant prostate cancer and to understand the biologic implications of the genes involved. Peripheral blood samples (1018 patients) were split chronologically into independent training (n = 713) and validation (n = 305) sets. Whole transcriptome RNA sequencing was performed on isolated phagocytic CD14+ and non-phagocytic CD2+ cells and their gene expression levels were used to develop predictive models that correlate to adverse pathologic features. The immune-transcriptomic model with the highest performance for predicting adverse pathology, based on a subtraction of the log-transformed expression signals of the two cell types, displayed an area under the curve (AUC) of the receiver operating characteristic of 0.70. The addition of biomarkers in combination with traditional clinical risk factors (age, serum prostate-specific antigen (PSA), PSA density, race, digital rectal examination (DRE), and family history) enhanced the AUC to 0.91 and 0.83 for the training and validation sets, respectively. The markers identified by this approach uncovered specific pathway associations relevant to (prostate) cancer biology. Increased phagocytic activity in conjunction with cancer-associated (mis-)regulation is also represented by these markers. Differential gene expression of circulating immune cells gives insight into the cellular immune response to early tumor development and immune surveillance.

Keywords: CD14+; CD2+; boosting; cancer; cells; gradient; immune; phagocytosis; transcriptomics.

Conflict of interest statement

Immunis.AI ownership interest: K.J.W., A.I.K., employee: K.J.W., consultant: P.W.K., L.V.N., R.H., A.I.K., research funding: H.K., J.H., K.K., M.P., K.J.W., R.T.-A., S.M., scientific advisory board: P.W.K., inventor on patents: A.I.K., K.J.W.

Figures

Figure 1
Figure 1
ROC curves for genomics only CD2, CD14, and CD14/CD2 ratio models. AUC values and confidence intervals are shown in the white area of Table 2.
Figure 2
Figure 2
ROC curves for CD14/CD2 ratio model compared to those models including age, PSA, and PSAD. AUC values and confidence intervals are shown in Table 2 above.
Figure 3
Figure 3
Top-ranked, enriched pathways and ontologies represented by the 120 genes in the best performing model according to MSigDB hallmark (A), KEGG (B), and gene ontology biological processes (C). Only terms that had a false discovery rate < 0.1 (or <0.01 for gene ontology (C)) are shown.

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