Double genetic disruption of lactate dehydrogenases A and B is required to ablate the "Warburg effect" restricting tumor growth to oxidative metabolism.

Increased glucose consumption distinguishes cancer cells from normal cells and is known as the "Warburg effect" because of increased glycolysis. Lactate dehydrogenase A (LDHA) is a key glycolytic enzyme, a hallmark of aggressive cancers, and believed to be the major enzyme responsible for pyruvate-to-lactate conversion. To elucidate its role in tumor growth, we disrupted both the LDHA and LDHB genes in two cancer cell lines (human colon adenocarcinoma and murine melanoma cells). Surprisingly, neither LDHA nor LDHB knockout strongly reduced lactate secretion. In contrast, double knockout (LDHA/B-DKO) fully suppressed LDH activity and lactate secretion. Furthermore, under normoxia, LDHA/B-DKO cells survived the genetic block by shifting their metabolism to oxidative phosphorylation (OXPHOS), entailing a 2-fold reduction in proliferation rates in vitro and in vivo compared with their WT counterparts. Under hypoxia (1% oxygen), however, LDHA/B suppression completely abolished in vitro growth, consistent with the reliance on OXPHOS. Interestingly, activation of the respiratory capacity operated by the LDHA/B-DKO genetic block as well as the resilient growth were not consequences of long-term adaptation. They could be reproduced pharmacologically by treating WT cells with an LDHA/B-specific inhibitor (GNE-140). These findings demonstrate that the Warburg effect is not only based on high LDHA expression, as both LDHA and LDHB need to be deleted to suppress fermentative glycolysis. Finally, we demonstrate that the Warburg effect is dispensable even in aggressive tumors and that the metabolic shift to OXPHOS caused by LDHA/B genetic disruptions is responsible for the tumors' escape and growth.

Low-density lipoprotein balances T cell metabolism and enhances response to anti-PD-1 blockade in a HCT116 spheroid model.

Introduction: The discovery of immune checkpoints and the development of their specific inhibitors was acclaimed as a major breakthrough in cancer therapy. However, only a limited patient cohort shows sufficient response to therapy. Hence, there is a need for identifying new checkpoints and predictive biomarkers with the objective of overcoming immune escape and resistance to treatment. Having been associated with both, treatment response and failure, LDL seems to be a double-edged sword in anti-PD1 immunotherapy. Being embedded into complex metabolic conditions, the impact of LDL on distinct immune cells has not been sufficiently addressed. Revealing the effects of LDL on T cell performance in tumor immunity may enable individual treatment adjustments in order to enhance the response to routinely administered immunotherapies in different patient populations. The object of this work was to investigate the effect of LDL on T cell activation and tumor immunity in-vitro. Methods: Experiments were performed with different LDL dosages (LDLlow = 50 µg/ml and LDLhigh = 200 µg/ml) referring to medium control. T cell phenotype, cytokines and metabolism were analyzed. The functional relevance of our findings was studied in a HCT116 spheroid model in the context of anti-PD-1 blockade. Results: The key points of our findings showed that LDLhigh skewed the CD4+ T cell subset into a central memory-like phenotype, enhanced the expression of the co-stimulatory marker CD154 (CD40L) and significantly reduced secretion of IL-10. The exhaustion markers PD-1 and LAG-3 were downregulated on both T cell subsets and phenotypical changes were associated with a balanced T cell metabolism, in particular with a significant decrease of reactive oxygen species (ROS). T cell transfer into a HCT116 spheroid model resulted in a significant reduction of the spheroid viability in presence of an anti-PD-1 antibody combined with LDLhigh. Discussion: Further research needs to be conducted to fully understand the impact of LDL on T cells in tumor immunity and moreover, to also unravel LDL effects on other lymphocytes and myeloid cells for improving anti-PD-1 immunotherapy. The reason for improved response might be a resilient, less exhausted phenotype with balanced ROS levels.

Immunometabolic Markers in a Small Patient Cohort Undergoing Immunotherapy.

Although the discovery of immune checkpoints was hailed as a major breakthrough in cancer therapy, generating a sufficient response to immunotherapy is still limited. Thus, the objective of this exploratory, hypothesis-generating study was to identify potentially novel peripheral biomarkers and discuss the possible predictive relevance of combining scarcely investigated metabolic and hormonal markers with immune subsets. Sixteen markers that differed significantly between responders and non-responders were identified. In a further step, the correlation with progression-free survival (PFS) and false discovery correction (Benjamini and Hochberg) revealed potential predictive roles for the immune subset absolute lymphocyte count (rs = 0.51; p = 0.0224 *), absolute basophil count (rs = 0.43; p = 0.04 *), PD-1+ monocytes (rs = -0.49; p = 0.04 *), hemoglobin (rs = 0.44; p = 0.04 *), metabolic markers LDL (rs = 0.53; p = 0.0224 *), free androgen index (rs = 0.57; p = 0.0224 *) and CRP (rs = -0.46; p = 0.0352 *). The absolute lymphocyte count, LDL and free androgen index were the most significant individual markers, and combining the immune subsets with the metabolic markers into a biomarker ratio enhanced correlation with PFS (rs = -0.74; p ≤ 0.0001 ****). In summary, in addition to well-established markers, we identified PD-1+ monocytes and the free androgen index as potentially novel peripheral markers in the context of immunotherapy. Furthermore, the combination of immune subsets with metabolic and hormonal markers may have the potential to enhance the power of future predictive scores and should, therefore, be investigated further in larger trials.

Restricting Glycolysis Preserves T Cell Effector Functions and Augments Checkpoint Therapy.

Tumor-derived lactic acid inhibits T and natural killer (NK) cell function and, thereby, tumor immunosurveillance. Here, we report that melanoma patients with high expression of glycolysis-related genes show a worse progression free survival upon anti-PD1 treatment. The non-steroidal anti-inflammatory drug (NSAID) diclofenac lowers lactate secretion of tumor cells and improves anti-PD1-induced T cell killing in vitro. Surprisingly, diclofenac, but not other NSAIDs, turns out to be a potent inhibitor of the lactate transporters monocarboxylate transporter 1 and 4 and diminishes lactate efflux. Notably, T cell activation, viability, and effector functions are preserved under diclofenac treatment and in a low glucose environment in vitro. Diclofenac, but not aspirin, delays tumor growth and improves the efficacy of checkpoint therapy in vivo. Moreover, genetic suppression of glycolysis in tumor cells strongly improves checkpoint therapy. These findings support the rationale for targeting glycolysis in patients with high glycolytic tumors together with checkpoint inhibitors in clinical trials.

The Peripheral and Intratumoral Immune Cell Landscape in Cancer Patients: A Proxy for Tumor Biology and a Tool for Outcome Prediction.

Functional systemic and local immunity is required for effective anti-tumor responses. In addition to an active engagement with cancer cells and tumor stroma, immune cells can be affected and are often found to be dysregulated in cancer patients. The impact of tumors on local and systemic immunity can be assessed using a variety of approaches ranging from low-dimensional analyses that are performed on large patient cohorts to multi-dimensional assays that are technically and logistically challenging and are therefore confined to a limited sample size. Many of these strategies have been established in recent years leading to exciting findings. Not only were analyses of immune cells in tumor patients able to predict the clinical course of the disease and patients' survival, numerous studies also detected changes in the immune landscape that correlated with responses to novel immunotherapies. This review will provide an overview of established and novel tools for assessing immune cells in tumor patients and will discuss exemplary studies that utilized these techniques to predict patient outcomes.