Substantially modified ratios of effector to regulatory T cells during chemotherapy in ovarian cancer patients return to pre-treatment levels at completion: implications for immunotherapy

Anthony Park, Chindu Govindaraj, Sue D Xiang, Julene Halo, Michael Quinn, Karen Scalzo-Inguanti, Magdalena Plebanski, Anthony Park, Chindu Govindaraj, Sue D Xiang, Julene Halo, Michael Quinn, Karen Scalzo-Inguanti, Magdalena Plebanski

Abstract

Ovarian cancer is the leading cause of death from gynaecological malignancy. Despite improved detection and treatment options, relapse rates remain high. Combining immunotherapy with the current standard treatments may provide an improved prognosis, however, little is known about how standard chemotherapy affects immune potential (particularly T cells) over time, and hence, when to optimally combine it with immunotherapy (e.g., vaccines). Herein, we assess the frequency and ratio of CD8+ central memory and effector T cells as well as CD4+ effector and regulatory T cells (Tregs) during the first 18 weeks of standard chemotherapy for ovarian cancer patients. In this pilot study, we observed increased levels of recently activated Tregs with tumor migrating ability (CD4+CD25hiFoxp3+CD127-CCR4+CD38+ cells) in patients when compared to controls. Although frequency changes of Tregs as well as the ratio of effector T cells to Tregs were observed during treatment, the Tregs consistently returned to pre-chemotherapy levels at the end of treatment. These results indicate T cell subset distributions associated with recurrence may be largely resistant to being "re-set" to healthy control homeostatic levels following standard treatments. However, it may be possible to enhance T effector to Treg ratios transiently during chemotherapy. These results suggest personalized immune monitoring maybe beneficial when combining novel immuno-therapeutics with standard treatment for ovarian cancer patients.

Figures

Figure 1
Figure 1
Major subsets within the PBMCs of ovarian cancer patients over the course of standard chemotherapy. Peripheral blood from five ovarian cancer patients was collected over the course of the standard chemotherapeutic treatment. PBMCs of patients and controls (n = 10) were isolated and flow cytometry was performed to determine (A) the T cell (CD3+ CD56−) frequency within PBMCs; (B) the CD4+ T cell frequency within the T cell fraction (C) CD8+ T cell frequency within the T cell fraction. Comparison of the frequency of cell populations between patients and controls: ** p < 0.01.
Figure 2
Figure 2
Frequencies of CD8+ T cell subsets present within the peripheral blood of ovarian cancer patients over the course of chemotherapy. Flow cytometry was performed on PBMCS isolated from patients (n = 5) and controls (n = 6). (A) The gating strategy employed to identify effector and central memory CD8+ T cells; (B) Comparison of the CD8+ T cell subset frequencies between controls and patients; pre-chemo representing 0 rounds while post-chemo represents 6 rounds of chemotherapy; (C) Frequency fluctuations of CD8+ T cell subsets of patients over the course of chemotherapy. The dotted line represents the mean frequency of the healthy controls. Comparison of CD8+ T cell subset frequencies between controls and patients: not significant.
Figure 3
Figure 3
Frequency of recently activated CD8+ T cell subsets present within the PBMCs of patients during the course of chemotherapy. Flow cytometry was performed on PBMCS isolated from controls (n = 6) and patients undergoing chemotherapy (n = 5). (A) The gating strategy employed to identify recently activated effector and central memory CD8+ T cells present within the PBMCs; (B) Comparison of the CD8+ T cell subsets between controls and patients: pre-chemo representing 0 rounds while post-chemo represents 6 rounds of chemotherapy; (C) Frequency fluctuations of CD8+ T cell subsets of patients over the course of chemotherapy. The dotted line represents the mean frequency of the healthy controls. Comparison of CD8+ T cell subset frequencies between controls and patients: not significant.
Figure 4
Figure 4
Frequencies of regulatory cell subsets present within the peripheral blood of ovarian cancer patients. (A) The gating strategy employed to identify regulatory T cells (CD4+CD25hiFOXP3+CD127−) present in the PBMCs; (B) Comparison of regulatory T cell subset frequencies between healthy controls (n = 5) and ovarian cancer patients undergoing chemotherapy (n = 5); (C) Frequency fluctuations of regulatory T cell subsets of patients over the course of chemotherapy. The dotted line represents the mean frequency of the healthy controls. Comparison of regulatory T cell subset frequencies between controls and patients: * p < 0.05.
Figure 5
Figure 5
Frequency of CD4+ effector T cell subsets present within the PBMCs of patients. (A) The gating strategy employed to identify the different CD4+ T cell subsets present in the PBMCs; (B) Comparison of CD4+ T cell subset frequencies between healthy controls (n = 5) and ovarian cancer patients undergoing chemotherapy (n = 5); (C) Frequency fluctuations of CD4+ T cell subsets of patients over the course of chemotherapy. The dotted line represents the mean frequency of the healthy controls. Comparison of T cell subset frequencies between controls and patients: * p < 0.05.
Figure 6
Figure 6
Ratio of CD4+ and CD8+ T cell subsets to Treg subsets identified within PBMCs of cancer patients over standard chemotherapeutic treatment. The ratios of (A) CD4+ T cell subsets to their corresponding Treg subsets and (B) CD8+ T cell subset to conventional Tregs (CD4+CD25hiFOXP3+CD127−) for the individual patients before each round of chemotherapy are represented here. The ratios of all five cancer patients before the first and during the last round of chemotherapy for their corresponding subsets are also shown in concurrent bar graphs (not significant).

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