Overexpressed Aurora-A kinase promotes tumor growth through various pathways, but whether Aurora-A is also involved in metabolic reprogramming-mediated cancer progression remains unknown. Here, we report that Aurora-A directly interacts with and phosphorylates lactate dehydrogenase B (LDHB), a subunit of the tetrameric enzyme LDH that catalyzes the interconversion between pyruvate and lactate. Aurora-A-mediated phosphorylation of LDHB serine 162 significantly increases its activity in reducing pyruvate to lactate, which efficiently promotes NAD⁺ regeneration, glycolytic flux, lactate production and bio-synthesis with glycolytic intermediates. Mechanistically, LDHB serine 162 phosphorylation relieves its substrate inhibition effect by pyruvate, resulting in remarkable elevation in the conversions of pyruvate and NADH to lactate and NAD⁺. Blocking S162 phosphorylation by expression of a LDHB-S162A mutant inhibited glycolysis and tumor growth in cancer cells and xenograft models. This study uncovers a function of Aurora-A in glycolytic modulation and a mechanism through which LDHB directly contributes to the Warburg effect.

The Aurora A inhibitor alisertib shows encouraging activities in clinical trials against advanced breast cancer. However, it remains unclear whether and how the inflammatory microenvironment is involved in its efficacy. Here, we demonstrated that inhibition of Aurora A directly reshaped the immune microenvironment through removal of tumor-promoting myeloid cells and enrichment of anticancer T lymphocytes, which established a tumor-suppressive microenvironment and significantly contributed to the regression of murine mammary tumors. Mechanistically, alisertib treatment triggered apoptosis in myeloid-derived suppressor cells (MDSC) and macrophages, resulting in their elimination from tumors. Furthermore, alisertib treatment disrupted the immunosuppressive functions of MDSC by inhibiting Stat3-mediated ROS production. These alterations led to significant increases of active CD8+ and CD4+ T lymphocytes, which efficiently inhibited the proliferation of tumor cells. Intriguingly, alisertib combined with PD-L1 blockade showed synergistic efficacy in the treatment of mammary tumors. These results detail the effects of Aurora A inhibition on the immune microenvironment and provide a novel chemo-immunotherapy strategy for advanced breast cancers. Significance: These findings show that inhibition of Aurora A facilitates an anticancer immune microenvironment, which can suppress tumor progression and enhance anti–PD-L1 therapy in breast cancer. See related commentary by Rivoltini et al., p. 3169

Aurora-A kinase functions mainly in centrosome maturation, separation and spindle formation. It has also been found to be amplified or overexpressed in a range of solid tumors, which is linked with tumor progression and poor prognosis. Importantly, Aurora-A inhibitors are being studied in a number of ongoing clinical trials. However, whether and how Aurora-A has a role in the regulation of the mitotic checkpoint is controversial. Additionally, the function of nuclear-accumulated Aurora-A in late G2 phase is not clear. Here we show that knockout, inhibition or blockade of the nuclear entry of Aurora-A severely decreased the centromere localization of Aurora-B and the phosphorylation of histone H3 threonine 3 (H3T3-ph) mediated by the kinase Haspin in late G2 phase. We further reveal that nuclear-accumulated Aurora-A phosphorylates Haspin at multiple sites at its N-terminus and that this promotes H3T3-ph and the rapid recruitment to the centromere of the chromosomal passenger complex. In addition, Aurora-A facilitates the association of Aurora-B with their common substrates: Haspin and Plk1. Notably, these functions of Aurora-A are mostly independent of Plk1. Thus we demonstrate that, in late G2 and prophase, Aurora-A phosphorylates Haspin to trigger the Haspin-H3T3-ph-Aurora-B positive feedback loop that supports the timely establishment of the chromosomal passenger complex and the mitotic checkpoint before spindle assembly.

The three-dimensional (3D) tumor microenvironment (TME) comprises multiple interacting cell types that critically impact tumor pathology and therapeutic response. Efficient 3D imaging assays and analysis tools could facilitate profiling and quantifying distinctive cell–cell interaction dynamics in the TMEs of a wide spectrum of human cancers. Here, we developed a 3D live-cell imaging assay using confocal microscopy of patient-derived tumor organoids and a software tool, SiQ-3D (single-cell image quantifier for 3D), that optimizes deep learning (DL)–based 3D image segmentation, single-cell phenotype classification, and tracking to automatically acquire multidimensional dynamic data for different interacting cell types in the TME. An organoid model of tumor cells interacting with natural killer cells was used to demonstrate the effectiveness of the 3D imaging assay to reveal immuno-oncology dynamics as well as the accuracy and efficiency of SiQ-3D to extract quantitative data from large 3D image datasets. SiQ-3D is Python-based, publicly available, and customizable to analyze data from both in vitro and in vivo 3D imaging. The DL-based 3D imaging analysis pipeline can be employed to study not only tumor interaction dynamics with diverse cell types in the TME but also various cell–cell interactions involved in other tissue/organ physiology and pathology. Significance: A 3D single-cell imaging pipeline that quantifies cancer cell interaction dynamics with other TME cell types using primary patient-derived samples can elucidate how cell–cell interactions impact tumor behavior and treatment responses.