The impact of the tumor microenvironment on the survival of penile cancer patients

Abstract

PeCa is a rare entity with rising incidence rates due to increased infections with human papillomaviruses (HPV). The distinct subtypes of PeCa with an individual pathogenesis demand biomarkers for a precise patient risk assessment regarding disease progression and therapeutic susceptibility. We recently identified promising candidates associated with an HPV-instructed tumor microenvironment (TME) using HPV-positive PeCa cell lines and tissue microarrays (TMA). The capacity of HPV + p63 + PeCa cells to release neutrophil-attracting CXCL-8 provided a molecular link explaining the infiltration of CD15 + myeloid cells in PeCa specimens. The candidate biomarkers HPV, p63, CD15, DKK1, and CD147 linked a tumor-promoting TME with a higher TNM classification reflecting more aggressive and metastasizing cancers. Based on immune-reactive scores (IRS) from TMA staining for these biomarkers, we calculated correlations and conducted association analyses to assess the degree of relationship between all biomarkers. We then conducted Kaplan-Meier survival estimates and Cox regression analyses to delineate the impact on PeCa patient survival. There is a notable predictive potential regarding the survival of patients with biomarker profiles beyond the potency of the individual biomarker. From all candidate biomarkers and biomarker profiles, the combination of CD147 and infiltrating CD15 + cells linked to an active HPV-driven transformation displayed cancer-immune dynamics with dismal prognosis for patients. After deciphering relevant interdependencies, the HPV + CD147 + CD15 + status was the most potent profile predicting metastasis-free survival of PeCa patients. The results of this report underscore the need for analysis of the TME and the development of multi-parameter composite scores that reflect fundamental cancer-immune relationships to tailor therapeutic interventions based on actual cancer immune dynamics.

Keywords: CD147; HPV; Neutrophils; Penile cancer; Survival; Tumor microenvironment.

Fig. 1

Schematic illustration of relationships between individual biomarkers.

Fig. 2

Survival estimation by Kaplan–Meier for the HPV-p63-CD15 status. Kaplan–Meier survival statistics were calculated using the Log-Rank test for HPV + (A–C), p63 + (D–F), CD15 + (G–I), p63 + CD15 + (J–L), HPV + p63 + (M–O), HPV + CD15 + (P–R) and HPV + p63 + CD15 + (S–U) PeCa specimens for OS (A,D,G,J,M,P,S), TSS (B,E,H,K,N,Q,T) and MFS (C,F,I,L,O,R,U). Red lines = positive, blue lines = negative.

Fig. 3

Survival estimation by Kaplan–Meier of the DKK1-p63-CD15 status. Kaplan–Meier survival statistics were calculated using the Log-Rank test for DKK1 + (A–C), DKK1 + p63 + (D–F), DKK1 + CD15 + (G–I), DKK1 + p63 + CD15 + (J–L) PeCa specimens for OS (A,D,G), TSS (B,E,H) and MFS (C,F,I). Red lines = positive, blue lines = negative.

Fig. 4

Survival estimation by Kaplan–Meier of the HPV-CD147-CD15 status. Kaplan–Meier survival estimates were calculated using the Log-Rank test for CD147 + (A–C), CD147 + CD15 + (D–F), HPV + CD147 + (G–I) and HPV + CD147 + CD15 + (J–L), HPV + CD147 + CD15 + DKK1 + (M–O), HPV + CD147 + CD15 + p63 + (P–R) and HPV + CD147 + CD15 + p63 + DKK1 + (S–U) PeCa specimens for OS (A,D,G,J,M,P,S), TSS (B,E,H,K,N,Q,T) and MFS (C,F,I,L,O,R,U). Red lines = positive, blue lines = negative.