Browsing by Subject "lactate"
Results Per Page
Sort Options
Item Open Access Lactate Metabolism in Cancer Cell Lines(2013) Kennedy, Kelly MariePathophysiologic lactate accumulation is characteristic of solid tumors and has been associated with metastases and poor overall survival in cancer patients. In recent years, there has been a resurgence of interest in tumor lactate metabolism. In the past, our group has shown that lactate can be used as a fuel in some cancer cell lines; however, survival responses to exogenous lactate alone are not well-described. We hypothesized that lactate utilization and cellular responses to exogenous lactate were varied and dynamic, dependent upon factors such as lactate concentration, duration of lactate exposure, and of expression of the lactate transporter, monocarboxylate transporter 1 (MCT1). We hypothesized that pharmacological inhibition of MCT1 with a small molecule, competitive MCT1 inhibitor, α-cyano-4-hydroxycinnamic acid (CHC), could elicit cancer cell death in high lactate conditions typical of that seen in breast cancer.
My work focused on defining: 1. Lactate levels in locally advanced breast cancer (LABC); 2. Lactate uptake and catabolism in a variety of cancer cell lines; 3. The effect of exogenous lactate on cancer cell survival; 4. Whether the lactate-transporters, MCT1 and MCT4 can be used as markers of cycling hypoxia.
Lactate levels in LABC biopsies were assessed ex vivo by bioluminescence. NMR techniques were employed extensively to determine metabolites generated from 13C-labeled lactate. Cell viability in response to extracellular lactate ( ± glucose and ± CHC) was measured with Annexin V / 7-AAD staining to assess acute survival responses and clonogenic assays to evaluate long-term colony forming ability after lactate treatment. MCT1 and MCT4 protein expression was evaluated in cancer cell lines with Western blots after exposure to chronic or cycling hypoxia. Immunofluorescence was employed to assess MCT1 and MCT4 expression in head and neck cancer biopsies, and the expression patterns of the transporters were correlated to areas of hypoxia, as indicated by hypoxia marker EF5.
Lactate concentrations in LABC biopsied ranged from 0 - 12.3 µmol/g of tissue. The LABC dataset was too small to derive statistical power to test if lactate accumulation in LABC biopsies was associated with poor patient outcome or other clinical parameters of known prognostic significance. All cell lines tested (normal and cancer) showed uptake and metabolism of labeled lactate, with dominant generation of alanine and glutamate; however, relative rates and the diversity of metabolites generated was different among cell lines. MCF7 cells showed greater overall lactate uptake (mean = 18mM) over five days than MDA-MB-231 cells (mean = 5.5mM). CHC treatment effectively prevented lactate uptake in cancer cells when lactate concentrations were ≤20mM.
Cell survival was dependent upon lactate concentration and glucose availability. Acute responses to exogenous lactate did not reflect the long-term consequences of lactate exposure. Acutely, HMEC and R3230Ac cells were tolerant of all lactate concentrations tested (0-40mM) regardless of presence or absence of glucose. MCF7 and MDA-MB-231 cells were tolerant of lactate within the concentration ranges seen in biopsies. Cytotoxicity was seen after 24 hr incubation with 40mM lactate (-glucose), but this concentration is three times higher than any measurement made in human biopsies of LABC. Similarly, HMEC and MCF7 cells showed significantly decreased colony formation in response to 40mM exogenous lactate (+ glucose) while R3230Ac and MDA-MB-231 cells showed no impairment in colony-forming abilities with any lactate concentration (+ glucose). 5mM CHC significantly increased cell death responses independent of lactate treatment, indicating off-target effects at high concentrations.
MCT1 was found to be expressed in a majority of the cell lines tested, except for MDA-MB-231 cells. Cancer cells exposed to exogenous lactate showed upregulation of MCT1 but not MCT4. Chronic hypoxia resulted in an increase in protein expression of MCT4 but a decrease in MCT1 expression in cancer cell lines. The time course of regulation of protein levels of each transporter suggested the possibility of expression of both transporters during cycling hypoxia. When cancer cells were exposed to cycling hypoxia, both transporters showed upregulation. In head and neck tumor biopsies, MCT1 expression was significantly positively correlated to aerobic tumor regions and inversely correlated to hypoxic tumor regions.
Cancer cell responses to exogenous lactate were not uniform. Some cell lines demonstrated a lactate-tolerant and/or a lactate-consuming phenotype while other cell lines demonstrated lactate-intolerant and/or non-lactate-consuming phenotype. My work indicates that exogenous lactate was well-tolerated at clinically relevant concentrations , especially in the presence of glucose. Evidence of glutamate metabolism from lactate indicated that exogenous lactate partially progresses through the TCA cycle, suggesting that lactate may be utilized for fuel. The cell death elicited from 5mM CHC treatment was not dependent upon presence of lactate, indicating that manipulation of lactate metabolism may not be the best option for targeting cancer metabolism. When attempting to manipulate lactate metabolism in tumors, microenvironmental factors, such as hypoxia and glucose, must be taken into account in order to ensure a predictable and favorable outcome. Together, these results illustrate the importance of characterizing tumor metabolism before therapeutic intervention.
Item Open Access Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.(Journal of personalized medicine, 2021-11) Pattharanitima, Pattharawin; Thongprayoon, Charat; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; Kaewput, Wisit; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; O'Corragain, Oisin A; Mao, Michael A; Garovic, Vesna D; Qureshi, Fawad; Dillon, John J; Cheungpasitporn, WisitLactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.Item Open Access Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.(Journal of clinical medicine, 2021-10) Pattharanitima, Pattharawin; Thongprayoon, Charat; Kaewput, Wisit; Qureshi, Fawad; Qureshi, Fahad; Petnak, Tananchai; Srivali, Narat; Gembillo, Guido; O'Corragain, Oisin A; Chesdachai, Supavit; Vallabhajosyula, Saraschandra; Guru, Pramod K; Mao, Michael A; Garovic, Vesna D; Dillon, John J; Cheungpasitporn, WisitLactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.Item Open Access Stereocomplexes Formed From Select Oligomers of Polymer d-lactic Acid (PDLA) and l-lactate May Inhibit Growth of Cancer Cells and Help Diagnose Aggressive Cancers-Applications of the Warburg Effect.(Perspect Medicin Chem, 2011-02-15) Goldberg, Joel SIt is proposed that select oligomers of polymer d-lactic acid (PDLA) will form a stereocomplex with l-lactate in vivo, producing lactate deficiency in tumor cells. Those cancer cells that utilize transport of lactate to maintain electrical neutrality may cease to multiply or die because of lactate trapping, and those cancer cells that benefit from utilization of extracellular lactate may be impaired. Intracellular trapping of lactate produces a different physiology than inhibition of LDH because the cell loses the option of shuttling pyruvate to an alternative pathway to produce an anion. Conjugated with stains or fluorescent probes, PDLA oligomers may be an agent for the diagnosis of tissue lactate and possibly cell differentiation in biopsy specimens. Preliminary experimental evidence is presented confirming that PDLA in high concentrations is cytotoxic and that l-lactate forms a presumed stereocomplex with PDLA. Future work should be directed at isolation of biologically active oligomers of PDLA.