Increased tumor glycolysis is associated with decreased immune infiltration across human solid tumors

Abstract

Response to immunotherapy across multiple cancer types is approximately 25%, with some tumor types showing increased response rates compared to others (i.e. response rates in melanoma and non-small cell lung cancer (NSCLC) are typically 30-60%). Patients whose tumors are resistant to immunotherapy often lack high levels of pre-existing inflammation in the tumor microenvironment. Increased tumor glycolysis, acting through glucose deprivation and lactic acid accumulation, has been shown to have pleiotropic immune suppressive effects using in-vitro and in-vivo models of disease. To determine whether the immune suppressive effect of tumor glycolysis is observed across human solid tumors, we analyzed glycolytic and immune gene expression patterns in multiple solid malignancies. We found that increased expression of a glycolytic signature was associated with decreased immune infiltration and a more aggressive disease across multiple tumor types. Radiologic and pathologic analysis of untreated estrogen receptor (ER)-negative breast cancers corroborated these observations, and demonstrated that protein expression of glycolytic enzymes correlates positively with glucose uptake and negatively with infiltration of CD3⁺ and CD8⁺ lymphocytes. This study reveals an inverse relationship between tumor glycolysis and immune infiltration in a large cohort of multiple solid tumor types.

Keywords: glycolysis; immune infiltration; immunotherapy; solid tumors; tumor metabolism; tumor microenvironment.

Figure 1

The expression of glycolysis- and immune-related genes is negatively correlated across multiple solid tumor types. (A, B) The correlation between expression of selected glycolysis and immune genes was plotted for individual tumor types in the TCGA dataset (A) Basal/Her2 Breast Cancer (BRCA), Skin Cutaneous Melanoma (SKCM), Lung Adenocarcinoma (LUAD), and in the independent datasets (B) ER-negative METABRIC, GSE65904 Melanoma, GSE119267 LUAD). (C) The correlation between protein abundance of specific glycolysis and immune proteins was plotted for specific tumor types in the CPTAC cohort (BRCA, LUAD) and the GSE140343 LUAD cohort. Red = positive correlation; blue = negative correlation. The size and intensity of the circles are proportional to the Pearson r coefficient. Pearson correlation coefficients that were not statistically significant (p>0.05) are marked with an X.

Figure 2

Expression of the glycolysis signature FDGScore is inversely correlated with multiple estimates of T cell infiltration across solid tumors. (A–D) The correlation profiles of the FDG uptake signature (FDGScore) and the estimates of T cell subset abundance (as measured by ssGSEA) were calculated and plotted for the entire TCGA Pan Cancer cohort (A) and for individual tumor types within TCGA (B–D) (left). The expression of the FDGScore vs. CD8 (middle) and Tcm (right) T cell estimates is also shown with the calculated Pearson and Spearman coefficients. Red = positive correlation; blue = negative correlation. The size and intensity of the circles are proportional to the Pearson r coefficient. Pearson correlation coefficients that were not statistically significant (p>0.05) are marked with an X.

Figure 3

Overall survival by FDGScore and the CD8 T cell estimate in solid tumors. (A–D) Kaplan-Meier survival analysis was performed for the entire TCGA cohort (A), as well as individually for the TCGA SKCM (B), TCGA LUAD (C) and METABRIC (D) cohorts. The disease-specific survival probability of patients was measured in the top tertile vs the bottom tertile of expression of either FDGScore (left) or CD8 T cell estimate (right) for each cancer type, and the Hazard Ratio (HR) was calculated.

Figure 4

IHC staining of primary breast tumor samples reveals an inverse association between expression of glycolytic and immune markers. (A) Representative micrographs of immunohistochemical staining for CD8, GLUT1 and LDHA in our cohort of 49 primary, untreated ER-negative breast tumor samples. Shown are selected sections of tumors with high LDHA expression and low stromal CD8⁺ T-cell infiltrate (top), and with low LDHA expression and high stromal CD8⁺ T-cell infiltration (bottom). (B) The extent of stromal lymphocytic infiltration (sTILs) was quantified (by H&E staining, left; or by IHC staining of CD3⁺ (middle) or CD8⁺ (right) T cells) and plotted in tumors in the top tertile of LDHA expression vs. tumors in the bottom 2 tertiles of LDHA expression, as measured by the H-Score. (C) The percentage of stromal CD8⁺ TILs was plotted against the LDHA H-Score, and data was color coded according to whether the sample was in CD8 High (blue), LDHA High (red) or Neither (black) group. The Odds Ratio for CD8 High and LDHA High was calculated and displayed. (D) Recurrence-free survival was calculated and Kaplan-Meier plots were plotted for all tumors according to their phenotype as described in (C).