Despite the fact that Otto H. Warburg discovered the Warburg effect almost one hundred years ago, why cancer cells waste most of the glucose carbon as lactate remains an enigma. Warburg proposed a connection between the Warburg effect and cell dedifferentiation. Hypoxia is a common tumor microenvironmental stress that induces the Warburg effect and blocks tumor cell differentiation. The underlying mechanism by which this occurs is poorly understood, and no effective therapeutic strategy has been developed to overcome this resistance to differentiation. Using a neuroblastoma differentiation model, we discovered that hypoxia repressed cell differentiation through reducing cellular acetyl-CoA levels, leading to reduction of global histone acetylation and chromatin accessibility. The metabolic switch triggering this global histone hypoacetylation was the induction of pyruvate dehydrogenase kinases (PDK1 and PDK3). Inhibition of PDKs using dichloroacetate (DCA) restored acetyl-CoA generation and histone acetylation under hypoxia. Knocking down PDK1 induced neuroblastoma cell differentiation, highlighting the critical role of PDK1 in cell fate control. Importantly, acetate or glycerol triacetate (GTA) supplementation restored differentiation markers expression and neuron differentiation under hypoxia. Moreover, ATAC-Seq analysis demonstrated that hypoxia treatment significantly reduced chromatin accessibility at RAR/RXR binding sites, which can be restored by acetate supplementation. In addition, hypoxia-induced histone hypermethylation by increasing 2-hydroxyglutarate (2HG) and reducing α-ketoglutarate (αKG). αKG supplementation reduced histone hypermethylation upon hypoxia, but did not restore histone acetylation or differentiation markers expression. Together, these findings suggest that diverting pyruvate flux away from acetyl-CoA generation to lactate production is the key mechanism that Warburg effect drives dedifferentiation and tumorigenesis. We propose that combining differentiation therapy with acetate/GTA supplementation might represent an effective therapy against neuroblastoma.

Cancer cells have been found to frequently express genes that are normally restricted to the testis, often referred to as cancer/testis (CT) antigens or genes^{1, 2}. Because germ cell specific antigens are not recognized as “self” by the innate immune system³, CT-genes have previously been suggested as ideal candidate targets for cancer therapy⁴. The use of CT- genes in cancer therapy has thus far been unsuccessful, most likely because their identification has relied on gene expression in whole testis, including the testicular somatic cells, precluding the detection of true germ cell specific genes. By comparing the transcriptomes of micro-dissected germ cell subtypes, representing the main developmental stages of human spermatogenesis⁵, with the publicly accessible transcriptomes of 2.617 samples from 49 different healthy somatic tissues⁶ and 9.232 samples from 33 tumor types⁷, we here discover hundreds of true germ cell specific cancer expressed genes. Strikingly, we found these germ cell cancer genes (GC-genes) to be widely expressed in all analyzed tumors. Many GC-genes appeared to be involved in processes that are likely to actively promote tumor viability, proliferation and metastasis. Targeting these true GC-genes thus has the potential to inhibit tumor growth with infertility being the only possible side effect. Moreover, we identified a subset of GC-genes that are not expressed in spermatogonial stem cells. Targeting of this GC-gene subset is predicted to only lead to temporary infertility, as untargeted spermatogonial stem cells can recover spermatogenesis after treatment. Our GC- gene dataset enables improved understanding of tumor biology and provides multiple novel targets for cancer treatment.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer and the fastest-growing cause of cancer-related deaths in the United States. Most HCC cases are attributed to chronic hepatitis C virus infection, which affects nearly 3 million Americans and 170 million globally. Although surveillance for HCC in hepatitis C patients can improve survival, the optimal surveillance policies remain unknown. In this study, we develop a mixed-integer programming (MIP)-based framework to systematically analyze a rich set of policies and determine the optimal HCC surveillance policies with the maximum societal net benefit. Our MIP-based framework captures two problem features that make dynamic programming-based formulation computationally intractable. In particular, our proposed framework allows to (1) explicitly formulate M-switch policies that are practical for implementation, and (2) tailor surveillance policies for each subpopulation by stratifying surveillance intervals based on the observable disease states. We theoretically analyze the HCC surveillance problem, characterize when the surveillance policies should be adapted to populations with different disease progression rates, and quantify the trade-off between decreasing HCC incidence and increasing treatment outcomes. We carefully parameterize our model using clinical trial data, a previously validated simulation model, and published clinical studies. Our numerical analyses lead to three main results with important policy implications. First, we find that, in addition to cirrhotic patients, expanding surveillance to patients in earlier stage of hepatitis C infection improves the cost-effectiveness of HCC surveillance. Second, compared with the one-size-fits-all type routine policies, we find that it is cost-effective to stratify surveillance strategies based on the stage of hepatitis C infection with less frequent cancer surveillance in earlier stages of infection. Lastly, we find that a little flexibility in the policy structure as captured by M-switch policies is sufficient to capture almost as much benefit as complex fully dynamic policies.