Although the classification of breast carcinomas into molecular or immunohistochemical subtypes has contributed to a better categorization of women into different therapeutic regimens, breast cancer nevertheless still progresses or recurs in a remarkable number of patients. Identifying women who would benefit from chemotherapy could potentially increase treatment effectiveness, which has important implications for long-term survival. Metabolomic analyses of fluids and tissues from cancer patients improve our knowledge of the reprogramming of metabolic pathways involved in resistance to chemotherapy. This review evaluates how recent metabolomic approaches have contributed to understanding the relationship between breast cancer and the acquisition of resistance. We focus on the advantages and challenges of cancer treatment and the use of new strategies in clinical care, which helps us comprehend drug resistance and predict responses to treatment.

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.

There is a limited number of established ovarian cancer cell lines matching the low-grade serous histotype available for research purposes. Three-dimensional (3D) culture systems provide in vitro models with better tissue-like characteristics than two-dimensional (2D) systems. The goal in the study was to characterize the growth of a given low-grade serous ovarian carcinoma cell line in a 3D culture system conducted in a magnetic field. Moreover, the culture system was evaluated in respect to the assembly of malignant cell aggregates containing lymphocytes. CAISMOV24 cell line alone or mixed with human peripheral blood mononuclear cells (PBMC) were cultured using a commercially available 3D culture system designed for 24 well plates. Resulting cell aggregates revealed the intrinsic capacity of CAISMOV24 cells to assemble structures morphologically defined as papillary, and reflected molecular characteristics usually found in ovarian carcinomas. The contents of lymphocytes into co-cultured cell aggregates were significantly higher (p < 0.05) when NanoShuttle-conjugated PBMC were employed compared with non-conjugated PBMC. Moreover, lymphocyte subsets NK, T-CD4, T-CD8 and T-regulatory were successfully retrieved from co-cultured cell aggregates at 72h. Thus, the culture system allowed CAISMOV24 cell line to develop papillary-like cell aggregates containing lymphocytes.

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.