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This study applied a dual-agent, 13C-pyruvate and 13C-urea, hyperpolarized 13C magnetic resonance spectroscopic imaging (MRSI) and multi-parametric (mp) ¹H magnetic resonance imaging (MRI) approach in the transgenic adenocarcinoma of mouse prostate (TRAMP) model to investigate changes in tumor perfusion and lactate metabolism during prostate cancer development, progression and metastases, and after lactate dehydrogenase-A (LDHA) knock-out. An increased Warburg effect, as measured by an elevated hyperpolarized (HP) Lactate/Pyruvate (Lac/Pyr) ratio, and associated Ldha expression and LDH activity were significantly higher in high- versus low-grade TRAMP tumors and normal prostates. The hypoxic tumor microenvironment in high-grade tumors, as measured by significantly decreased HP 13C-urea perfusion and increased PIM staining, played a key role in increasing lactate production through increased Hif1α and then Ldha expression. Increased lactate induced Mct4 expression and an acidic tumor microenvironment that provided a potential mechanism for the observed high rate of lymph node (86%) and liver (33%) metastases. The Ldha knockdown in the triple-transgenic mouse model of prostate cancer resulted in a significant reduction in HP Lac/Pyr, which preceded a reduction in tumor volume or apparent water diffusion coefficient (ADC). The Ldha gene knockdown significantly reduced primary tumor growth and reduced lymph node and visceral metastases. These data suggested a metabolic transformation from low- to high-grade prostate cancer including an increased Warburg effect, decreased perfusion, and increased metastatic potential. Moreover, these data suggested that LDH activity and lactate are required for tumor progression. The lactate metabolism changes during prostate cancer provided the motivation for applying hyperpolarized 13C MRSI to detect aggressive disease at diagnosis and predict early therapeutic response.

Although normally dormant, hair follicle stem cells (HFSCs) quickly become activated to divide during a new hair cycle. The quiescence of HFSCs is known to be regulated by a number of intrinsic and extrinsic mechanisms. Here we provide several lines of evidence to demonstrate that HFSCs utilize glycolytic metabolism and produce significantly more lactate than other cells in the epidermis. Furthermore, lactate generation appears to be critical for the activation of HFSCs as deletion of lactate dehydrogenase (Ldha) prevented their activation. Conversely, genetically promoting lactate production in HFSCs through mitochondrial pyruvate carrier 1 (Mpc1) deletion accelerated their activation and the hair cycle. Finally, we identify small molecules that increase lactate production by stimulating Myc levels or inhibiting Mpc1 carrier activity and can topically induce the hair cycle. These data suggest that HFSCs maintain a metabolic state that allows them to remain dormant and yet quickly respond to appropriate proliferative stimuli.

The phosphoinositide 3-kinase (PI3K) pathway regulates multiple steps in glucose metabolism and also cytoskeletal functions, such as cell movement and attachment. Here, we show that PI3K directly coordinates glycolysis with cytoskeletal dynamics in an AKT-independent manner. Growth factors or insulin stimulate the PI3K-dependent activation of Rac, leading to disruption of the actin cytoskeleton, release of filamentous actin-bound aldolase A, and an increase in aldolase activity. Consistently, PI3K inhibitors, but not AKT, SGK, or mTOR inhibitors, cause a significant decrease in glycolysis at the step catalyzed by aldolase, while activating PIK3CA mutations have the opposite effect. These results point toward a master regulatory function of PI3K that integrates an epithelial cell's metabolism and its form, shape, and function, coordinating glycolysis with the energy-intensive dynamics of actin remodeling.

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.

Abstract Background First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.