Platinum resistance in gynecologic malignancies: Response, disease free and overall survival are predicted by biochemical signature: A metabolomic analysis.

OBJECTIVE: Platinum resistance, defined as the lack of response or relapse within six months of platinum-based chemotherapy, is an important determinant of survival in gynecologic cancer. We used quantitative Mass Spectrometry to identify metabolic signatures that predict platinum resistance in patients receiving chemotherapy for gynecologic cancers. METHODS: In this study 47 patients with adenocarcinoma of the ovary or uterus who were candidates for carboplatin plus paclitaxel submitted blood for quantitation of metabolites and surgical specimens for the isolation 3-dimensional organoids used to measure individual patient platinum resistance, ex vivo. Results were correlated with response, time to progression and survival. RESULTS: Of 47 patients, 27 (64.3%) achieved complete remission with a mean time to progression of 1.9 years (± 1.5), disease-free survival of 1.7 years (± 1.4) and overall survival of 2.6 years (± 1.6) and a mean cisplatin lethal concentration 50% (LC50) = 1.15 µg/ml (range 0.4-3.1). Cisplatin LC50's correlated with a non-significant decrease in complete remission (RR [95% CI] =0.76 [0.46-1.27]), diminished disease-free survival (median: 1.15 vs. 2.99 years, p = 0.038) and with biochemical signatures of 186 metabolites. Receiver operating curves (ROC) of lipid ratios, branched chain amino acids and the tryptophan to kynurenine ratio identified patients at the highest risk of relapse and death (AUC = 0.933) with a sensitivity of 92.0% and specificity of 86.0% (p < 0.001). CONCLUSIONS: Metabolic signatures in gynecologic cancer identify patients at the highest risk of relapse and death offering new diagnostic and prognostic tools for management of the advanced gynecologic tumors.

Influence of the interaction between nodal fibroblast and breast cancer cells on gene expression.

Our aim was to evaluate the interaction between breast cancer cells and nodal fibroblasts, by means of their gene expression profile. Fibroblast primary cultures were established from negative and positive lymph nodes from breast cancer patients and a similar gene expression pattern was identified, following cell culture. Fibroblasts and breast cancer cells (MDA-MB231, MDA-MB435, and MCF7) were cultured alone or co-cultured separated by a porous membrane (which allows passage of soluble factors) for comparison. Each breast cancer lineage exerted a particular effect on fibroblasts viability and transcriptional profile. However, fibroblasts from positive and negative nodes had a parallel transcriptional behavior when co-cultured with a specific breast cancer cell line. The effects of nodal fibroblasts on breast cancer cells were also investigated. MDA MB-231 cells viability and migration were enhanced by the presence of fibroblasts and accordingly, MDA-MB435 and MCF7 cells viability followed a similar pattern. MDA-MB231 gene expression profile, as evaluated by cDNA microarray, was influenced by the fibroblasts presence, and HNMT, COMT, FN3K, and SOD2 were confirmed downregulated in MDA-MB231 co-cultured cells with fibroblasts from both negative and positive nodes, in a new series of RT-PCR assays. In summary, transcriptional changes induced in breast cancer cells by fibroblasts from positive as well as negative nodes are very much alike in a specific lineage. However, fibroblasts effects are distinct in each one of the breast cancer lineages, suggesting that the inter-relationships between stromal and malignant cells are dependent on the intrinsic subtype of the tumor.

Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection.

This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management.

Inborn-like errors of metabolism are determinants of breast cancer risk, clinical response and survival: a study of human biochemical individuality.

Breast cancer remains a leading cause of morbidity and mortality worldwide yet methods for early detection remain elusive. We describe the discovery and validation of biochemical signatures measured by mass spectrometry, performed upon blood samples from patients and controls that accurately identify (>95%) the presence of clinical breast cancer. Targeted quantitative MS/MS conducted upon 1225 individuals, including patients with breast and other cancers, normal controls as well as individuals with a variety of metabolic disorders provide a biochemical phenotype that accurately identifies the presence of breast cancer and predicts response and survival following the administration of neoadjuvant chemotherapy. The metabolic changes identified are consistent with inborn-like errors of metabolism and define a continuum from normal controls to elevated risk to invasive breast cancer. Similar results were observed in other adenocarcinomas but were not found in squamous cell cancers or hematologic neoplasms. The findings describe a new early detection platform for breast cancer and support a role for pre-existing, inborn-like errors of metabolism in the process of breast carcinogenesis that may also extend to other glandular malignancies. Statement of Significance: Findings provide a powerful tool for early detection and the assessment of prognosis in breast cancer and define a novel concept of breast carcinogenesis that characterizes malignant transformation as the clinical manifestation of underlying metabolic insufficiencies.