Expression of mTOR, CD163, α-SMA, FOXp3 as survival predictors and its significance in patients with oral squamous cell carcinoma

Abstract

Background: Oral cancer; categorised under head and neck cancers (HNC) predominantly originates from squamous cells and is referred as oral squamous cell carcinoma (OSCC). Various factors (internal and external) causes OSCC. PI3K/AKT/mTOR pathways are known to be primarily mutated in HNC. mTOR remains as a key regulator for various physiological and developmental processes in normal and cancer cells. Cancer cells are surrounded by tumor microenvironment, majorly composed of immune cells. Cancer associated fibroblasts, macrophages and regulatory T cells controlled by mTOR, plays an important role in the progression of cancer.

Methods: Two hundred sixty retrospective patient samples were collected along with their demographical and clinic-pathological data. Here, we have analysed expression of mTOR, α-SMA, CD163 and FOXp3 using immunohistochemistry and their survival outcomes were calculated using Kaplan-Meier statistical method.

Results: Overexpression of CD163 and α-SMA was detected in samples of patients compared to mTOR and FOXp3. Their expression was compared with clinico-oncological parameters. We also observed two and three combinations of markers and its association with the prognosis of the cancer. The results suggest, higher the expression of all the four markers in combination correlated to poor prognosis of patients and vice-versa.

Conclusion: The study reveals that over expression of CD163 and α-SMA is strongly associated with disease outcome. The combinations of all the four marker expression profile will be emerging strategy towards prognosis and also to determine survival outcomes in patients. This is a pioneering observation of these combinations of markers in OSCC despite certain limitations.

Keywords: Head and Neck cancer; Immunohistochemical Markers; Kaplan-Meier; Prognosis; Retrospective.

Fig. 1

Role of mTOR signaling in activating TME cells. This image illustrates the activation of TME cells via mTOR signaling pathway emphasising importance of mTOR in cancer progression and development. a it depicts that growth factors or high energy levels involve in dimerization of RTK receptor in-turn recruit PI3K thereby phosphorylating PIP2 to PIP3. This results in the activation of AKT by PDK1. It promotes function of mTORC1. AKT also negatively modulated by TSC with phosphorylation of RHEB-GDP to RHEB-GTP. Activated mTOR in TME initiates expression of PD-L1 in T cells which causes decrease in T-cell based immune function in cancer cells. Additionally, mTOR also promotes differentiation of T cells to Tregs leading to immunosuppression and cancer progression. b Receptor-ligand binding occur in CAF, recruits PI3K which can promote cascade of PIP2 to PIP3 thereby activating mTOR by PDK1/AKT. mTOR is then transferred to nuclei following with differentiation of CAF. CAF derived chemokines and cytokines binds to the receptor in cancer cell promoting PI3K/AKT/mTOR cascade which regulates cell proliferation, progression of cancer. c TME is mainly composed of M2 type of macrophages. Polarization of M1 and M2 have distinct activating mechanism where this depicts only M2 mechanism. Polarization of M2 occurs through binding of Type I- IL-4 to JAK/STAT receptor thereby activating IRSs which cannot be translocated to nucleus induces PI3K/AKT/mTOR pathway and trigger gene expression that promotes macrophage activation. In contrast STAT3 can also be produced which has ability to translocate itself into nucleus and induce macrophage activation (www.biorender.com)

Fig. 2

mTOR role in characterization and regulation of TME cells. This image illustrates the cells involved in TME and its regulation by mTOR. mTOR involved in various modulating effects of the cells of TME that are essential for cancer cells to proliferate and progress. Here, we have highlighted cells that are essentially included in present study; CAFs, TAMs, regulatory T cells, CD4 and CD8 T cells (www.biorender.com)

Fig. 3

Survival curve against clinicopathological parameters - (A) Overall survival, (B) Site, (C) Grade, (D) T status, (E) N status, (F) Therapy

Fig. 4

Receiver Operating Characteristics curve (ROC) - Tissue markers expression against survival status

Fig. 5

Survival graphs based on high and low expression of markers (A) CD163, (B) α-SMA, (C) mTOR, (D) FOXp3

Fig. 6

Two marker combination survival curve (A) CD163 & mTOR (B) α-SMA & FOXp3 (C) CD163 & FOXp3 (D) CD163 & α-SMA (E) mTOR & FOXp3 (F) α-SMA & mTOR

Fig. 7

Three marker combination survival curve(A) CD163, α-SMA & mTOR (B) CD163, mTOR & FOXp3 (C)α-SMA, mTOR & FOXp3 (D) CD163, α-SMA & FOXp3