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Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer.

Twyman-Saint Victor C, Rech AJ, Maity A, Rengan R, Pauken KE, Stelekati E, Benci JL, Xu B, Dada H, Odorizzi PM, Herati RS, Mansfield KD, Patsch D, Amaravadi RK, Schuchter LM, Ishwaran H, Mick R, Pryma DA, Xu X, Feldman MD, Gangadhar TC, Hahn SM, Wherry EJ, Vonderheide RH, Minn AJ - Nature (2015)

Bottom Line: Immune checkpoint inhibitors result in impressive clinical responses, but optimal results will require combination with each other and other therapies.Anti-CTLA4 predominantly inhibits T-regulatory cells (Treg cells), thereby increasing the CD8 T-cell to Treg (CD8/Treg) ratio.Addition of PD-L1 blockade reverses T-cell exhaustion to mitigate depression in the CD8/Treg ratio and further encourages oligoclonal T-cell expansion.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA [2] Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

ABSTRACT
Immune checkpoint inhibitors result in impressive clinical responses, but optimal results will require combination with each other and other therapies. This raises fundamental questions about mechanisms of non-redundancy and resistance. Here we report major tumour regressions in a subset of patients with metastatic melanoma treated with an anti-CTLA4 antibody (anti-CTLA4) and radiation, and reproduced this effect in mouse models. Although combined treatment improved responses in irradiated and unirradiated tumours, resistance was common. Unbiased analyses of mice revealed that resistance was due to upregulation of PD-L1 on melanoma cells and associated with T-cell exhaustion. Accordingly, optimal response in melanoma and other cancer types requires radiation, anti-CTLA4 and anti-PD-L1/PD-1. Anti-CTLA4 predominantly inhibits T-regulatory cells (Treg cells), thereby increasing the CD8 T-cell to Treg (CD8/Treg) ratio. Radiation enhances the diversity of the T-cell receptor (TCR) repertoire of intratumoral T cells. Together, anti-CTLA4 promotes expansion of T cells, while radiation shapes the TCR repertoire of the expanded peripheral clones. Addition of PD-L1 blockade reverses T-cell exhaustion to mitigate depression in the CD8/Treg ratio and further encourages oligoclonal T-cell expansion. Similarly to results from mice, patients on our clinical trial with melanoma showing high PD-L1 did not respond to radiation plus anti-CTLA4, demonstrated persistent T-cell exhaustion, and rapidly progressed. Thus, PD-L1 on melanoma cells allows tumours to escape anti-CTLA4-based therapy, and the combination of radiation, anti-CTLA4 and anti-PD-L1 promotes response and immunity through distinct mechanisms.

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Peripheral T cell exhaustion, reinvigoration, CD8/Treg ratio, and tumor PD-L1 predict response to RT + immune checkpoint blockadea) Heat map showing the relative proportions of PD-1+ CD8 T cells that are Ki67+GzmB+ or Eomes+ and the CD8/Treg ratio for each sample (columns) subtracted from the average values of untreated controls. Black hatches indicated CR and treatment with RT + anti-CTLA4 (C4) +/− anti-PD-L1 (P1). From these data, a multivariable RF predictor for CR was developed. Boxplot shows bootstrap distributions of variable importance scores (more predictive variables have higher values), and of b) minimal depth (MD), a statistic to measure predictiveness. Bar plot shows % bootstrap models for which the MD for the indicated variable was significant. Bootstrap mean +/− SD for the out-of-bag prediction error rate is listed on top. c) Probability of CR vs. change (treated vs. untreated control) in CD8/Treg ratio for mice with a high (blue dots) or low (red dots) change in % PD1+ splenic CD8 T cells that are Eomes+. d) Heat map similar to (a) except using T cells from peripheral blood. e) Percent peripheral blood PD-1+ CD8 T cells that are Eomes+ vs. Ki67+GzmB+ after RT + checkpoint blockade. Values are subtracted from average of untreated controls. Each circle represents a mouse. Probability of CR (proportional to circle size), prediction error rate, and quadrant boundaries are estimated from the RF model. f) Representative contour plots examining splenic CD8 T cells from B16-F10 or Res 499 tumors for PD-1 and Eomes (top), followed by examination of the PD-1+Eomes+ subset for Ki67 and GzmB (bottom). g) Ratios of PD-1+Eomes+ splenic CD8 T cells that are Ki67+GzmB+ (reinvigorated) compared to Ki67−GzmB− (exhausted) from mice with Res 499 tumors.
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Figure 9: Peripheral T cell exhaustion, reinvigoration, CD8/Treg ratio, and tumor PD-L1 predict response to RT + immune checkpoint blockadea) Heat map showing the relative proportions of PD-1+ CD8 T cells that are Ki67+GzmB+ or Eomes+ and the CD8/Treg ratio for each sample (columns) subtracted from the average values of untreated controls. Black hatches indicated CR and treatment with RT + anti-CTLA4 (C4) +/− anti-PD-L1 (P1). From these data, a multivariable RF predictor for CR was developed. Boxplot shows bootstrap distributions of variable importance scores (more predictive variables have higher values), and of b) minimal depth (MD), a statistic to measure predictiveness. Bar plot shows % bootstrap models for which the MD for the indicated variable was significant. Bootstrap mean +/− SD for the out-of-bag prediction error rate is listed on top. c) Probability of CR vs. change (treated vs. untreated control) in CD8/Treg ratio for mice with a high (blue dots) or low (red dots) change in % PD1+ splenic CD8 T cells that are Eomes+. d) Heat map similar to (a) except using T cells from peripheral blood. e) Percent peripheral blood PD-1+ CD8 T cells that are Eomes+ vs. Ki67+GzmB+ after RT + checkpoint blockade. Values are subtracted from average of untreated controls. Each circle represents a mouse. Probability of CR (proportional to circle size), prediction error rate, and quadrant boundaries are estimated from the RF model. f) Representative contour plots examining splenic CD8 T cells from B16-F10 or Res 499 tumors for PD-1 and Eomes (top), followed by examination of the PD-1+Eomes+ subset for Ki67 and GzmB (bottom). g) Ratios of PD-1+Eomes+ splenic CD8 T cells that are Ki67+GzmB+ (reinvigorated) compared to Ki67−GzmB− (exhausted) from mice with Res 499 tumors.

Mentions: To determine if treatment and resistance-related changes in peripheral T cells can constitute a biomarker for tumor response, we modeled the effects of reinvigoration, exhaustion, and the CD8/Treg ratio. Specifically, we used 1) the percent PD-1+ splenic CD8 T cells that are Eomes+ to integrate the burden that exhausted T cells might exert, 2) the percent PD-1+ CD8 T cells that are Ki67+GzmB+ as a measure of potential reinvigoration, and 3) the CD8/Treg ratio as a barometer for the suppressive potential of Tregs. The overall prediction accuracy of the model was 84%, and variables for T cell reinvigoration and exhaustion were the most predictive, followed by the CD8/Treg ratio (Extended Data Fig. 5a–b). Moreover, the percentage of PD-1+ CD8 T cells that were Eomes+ was a striking modifier of the likelihood of CR as nearly all observed CRs occurred when the percent Ki67+GzmB+ in PD-1+ CD8 T cells was high but the relative size of the PD-1+Eomes+ exhausted population was not (Fig. 4a). Similar relationships existed with the CD8/Treg ratio, and prediction using T cells from peripheral blood yielded highly similar results (Extended Data Fig. 5c–e). In total, immune parameters from peripheral T cells that relate the size of the exhausted T cell population, reinvigoration, and the CD8/Treg ratio can predict response to RT combined with immune checkpoint blockade.


Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer.

Twyman-Saint Victor C, Rech AJ, Maity A, Rengan R, Pauken KE, Stelekati E, Benci JL, Xu B, Dada H, Odorizzi PM, Herati RS, Mansfield KD, Patsch D, Amaravadi RK, Schuchter LM, Ishwaran H, Mick R, Pryma DA, Xu X, Feldman MD, Gangadhar TC, Hahn SM, Wherry EJ, Vonderheide RH, Minn AJ - Nature (2015)

Peripheral T cell exhaustion, reinvigoration, CD8/Treg ratio, and tumor PD-L1 predict response to RT + immune checkpoint blockadea) Heat map showing the relative proportions of PD-1+ CD8 T cells that are Ki67+GzmB+ or Eomes+ and the CD8/Treg ratio for each sample (columns) subtracted from the average values of untreated controls. Black hatches indicated CR and treatment with RT + anti-CTLA4 (C4) +/− anti-PD-L1 (P1). From these data, a multivariable RF predictor for CR was developed. Boxplot shows bootstrap distributions of variable importance scores (more predictive variables have higher values), and of b) minimal depth (MD), a statistic to measure predictiveness. Bar plot shows % bootstrap models for which the MD for the indicated variable was significant. Bootstrap mean +/− SD for the out-of-bag prediction error rate is listed on top. c) Probability of CR vs. change (treated vs. untreated control) in CD8/Treg ratio for mice with a high (blue dots) or low (red dots) change in % PD1+ splenic CD8 T cells that are Eomes+. d) Heat map similar to (a) except using T cells from peripheral blood. e) Percent peripheral blood PD-1+ CD8 T cells that are Eomes+ vs. Ki67+GzmB+ after RT + checkpoint blockade. Values are subtracted from average of untreated controls. Each circle represents a mouse. Probability of CR (proportional to circle size), prediction error rate, and quadrant boundaries are estimated from the RF model. f) Representative contour plots examining splenic CD8 T cells from B16-F10 or Res 499 tumors for PD-1 and Eomes (top), followed by examination of the PD-1+Eomes+ subset for Ki67 and GzmB (bottom). g) Ratios of PD-1+Eomes+ splenic CD8 T cells that are Ki67+GzmB+ (reinvigorated) compared to Ki67−GzmB− (exhausted) from mice with Res 499 tumors.
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Figure 9: Peripheral T cell exhaustion, reinvigoration, CD8/Treg ratio, and tumor PD-L1 predict response to RT + immune checkpoint blockadea) Heat map showing the relative proportions of PD-1+ CD8 T cells that are Ki67+GzmB+ or Eomes+ and the CD8/Treg ratio for each sample (columns) subtracted from the average values of untreated controls. Black hatches indicated CR and treatment with RT + anti-CTLA4 (C4) +/− anti-PD-L1 (P1). From these data, a multivariable RF predictor for CR was developed. Boxplot shows bootstrap distributions of variable importance scores (more predictive variables have higher values), and of b) minimal depth (MD), a statistic to measure predictiveness. Bar plot shows % bootstrap models for which the MD for the indicated variable was significant. Bootstrap mean +/− SD for the out-of-bag prediction error rate is listed on top. c) Probability of CR vs. change (treated vs. untreated control) in CD8/Treg ratio for mice with a high (blue dots) or low (red dots) change in % PD1+ splenic CD8 T cells that are Eomes+. d) Heat map similar to (a) except using T cells from peripheral blood. e) Percent peripheral blood PD-1+ CD8 T cells that are Eomes+ vs. Ki67+GzmB+ after RT + checkpoint blockade. Values are subtracted from average of untreated controls. Each circle represents a mouse. Probability of CR (proportional to circle size), prediction error rate, and quadrant boundaries are estimated from the RF model. f) Representative contour plots examining splenic CD8 T cells from B16-F10 or Res 499 tumors for PD-1 and Eomes (top), followed by examination of the PD-1+Eomes+ subset for Ki67 and GzmB (bottom). g) Ratios of PD-1+Eomes+ splenic CD8 T cells that are Ki67+GzmB+ (reinvigorated) compared to Ki67−GzmB− (exhausted) from mice with Res 499 tumors.
Mentions: To determine if treatment and resistance-related changes in peripheral T cells can constitute a biomarker for tumor response, we modeled the effects of reinvigoration, exhaustion, and the CD8/Treg ratio. Specifically, we used 1) the percent PD-1+ splenic CD8 T cells that are Eomes+ to integrate the burden that exhausted T cells might exert, 2) the percent PD-1+ CD8 T cells that are Ki67+GzmB+ as a measure of potential reinvigoration, and 3) the CD8/Treg ratio as a barometer for the suppressive potential of Tregs. The overall prediction accuracy of the model was 84%, and variables for T cell reinvigoration and exhaustion were the most predictive, followed by the CD8/Treg ratio (Extended Data Fig. 5a–b). Moreover, the percentage of PD-1+ CD8 T cells that were Eomes+ was a striking modifier of the likelihood of CR as nearly all observed CRs occurred when the percent Ki67+GzmB+ in PD-1+ CD8 T cells was high but the relative size of the PD-1+Eomes+ exhausted population was not (Fig. 4a). Similar relationships existed with the CD8/Treg ratio, and prediction using T cells from peripheral blood yielded highly similar results (Extended Data Fig. 5c–e). In total, immune parameters from peripheral T cells that relate the size of the exhausted T cell population, reinvigoration, and the CD8/Treg ratio can predict response to RT combined with immune checkpoint blockade.

Bottom Line: Immune checkpoint inhibitors result in impressive clinical responses, but optimal results will require combination with each other and other therapies.Anti-CTLA4 predominantly inhibits T-regulatory cells (Treg cells), thereby increasing the CD8 T-cell to Treg (CD8/Treg) ratio.Addition of PD-L1 blockade reverses T-cell exhaustion to mitigate depression in the CD8/Treg ratio and further encourages oligoclonal T-cell expansion.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA [2] Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

ABSTRACT
Immune checkpoint inhibitors result in impressive clinical responses, but optimal results will require combination with each other and other therapies. This raises fundamental questions about mechanisms of non-redundancy and resistance. Here we report major tumour regressions in a subset of patients with metastatic melanoma treated with an anti-CTLA4 antibody (anti-CTLA4) and radiation, and reproduced this effect in mouse models. Although combined treatment improved responses in irradiated and unirradiated tumours, resistance was common. Unbiased analyses of mice revealed that resistance was due to upregulation of PD-L1 on melanoma cells and associated with T-cell exhaustion. Accordingly, optimal response in melanoma and other cancer types requires radiation, anti-CTLA4 and anti-PD-L1/PD-1. Anti-CTLA4 predominantly inhibits T-regulatory cells (Treg cells), thereby increasing the CD8 T-cell to Treg (CD8/Treg) ratio. Radiation enhances the diversity of the T-cell receptor (TCR) repertoire of intratumoral T cells. Together, anti-CTLA4 promotes expansion of T cells, while radiation shapes the TCR repertoire of the expanded peripheral clones. Addition of PD-L1 blockade reverses T-cell exhaustion to mitigate depression in the CD8/Treg ratio and further encourages oligoclonal T-cell expansion. Similarly to results from mice, patients on our clinical trial with melanoma showing high PD-L1 did not respond to radiation plus anti-CTLA4, demonstrated persistent T-cell exhaustion, and rapidly progressed. Thus, PD-L1 on melanoma cells allows tumours to escape anti-CTLA4-based therapy, and the combination of radiation, anti-CTLA4 and anti-PD-L1 promotes response and immunity through distinct mechanisms.

Show MeSH
Related in: MedlinePlus