Effect of oncogene activating mutations and kinase inhibitors on amino acid metabolism of human isogenic breast cancer cells

Diagnostic spectro-cytology revealing differential recognition of cervical cancer lesions by label-free surface enhanced Raman fingerprints and chemometrics

Herein we have stepped-up on a strategic spectroscopic modality by utilizing label free ultrasensitive surface enhanced Raman scattering (SERS) technique to generate a differential spectral fingerprint for the prediction of normal (NRML), high-grade intraepithelial lesion (HSIL) and cervical squamous cell carcinoma (CSCC) from exfoliated cell samples of cervix. Three different approaches i.e. single-cell, cell-pellet and extracted DNA from oncology clinic as confirmed by Pap test and HPV PCR were employed. Gold nanoparticles as the SERS substrate favored the increment of Raman intensity exhibited signature identity for Amide III/Nucleobases and carotenoid/glycogen respectively for establishing the empirical discrimination. Moreover, all the spectral invention was subjected to chemometrics including Support Vector Machine (SVM) which furnished an average diagnostic accuracy of 94%, 74% and 92% of the three grades. Combined SERS read-out and machine learning technique in field trial promises its potential to reduce the incidence in low resource countries.

Numerical analysis of intracellular amino acid profiles of breast cancer cells with K-Ras or PI3K mutation in response to kinase inhibitors

BACKGROUND

Various efforts to understand the relationship between biological information and disease have been done using many different types of highthroughput data such as genomics and metabolomics. However, information obtained from previous studies was not satisfactory, implying that new direction of studies is in need. Thus, we have tried profiling intracellular free amino acids in normal and cancerous cells to extract some information about such relationship by way of the change in IFAA levels in response to the treatment of three kinase inhibitors. We define two measures such as relative susceptibility (RS) and relative efficacy (RE) to numerically quantify susceptibility of cell line to treatment and efficacy of treatment on cell line, respectively.

METHODS

We applied principal component analysis (PCA) to the intracellular free amino acids (IFAAs) of isogenic breast cells with oncogenic mutation in K-Ras or PI3K genes to investigate the change in IFAA levels in response to the treatment of three kinase inhibitors. Two-dimensional plot, which was graphically represented by using the first two principal components (PCs), enabled us to evaluate the treatment efficacy in cancerous cells in terms of the quantitative distance of two IFAA profiles from cancerous and normal cells with the same treatment condition.

RESULTS

The biggest change in metabolic states in K-Ras mutant cell was caused by REGO for both treatment time (RS=2.31 (24 h) and 1.64 (48 h)). Regarding RE, REGO was the most effective on K-Ras/PI3K mutant cell line for treatment time 24h (RE=1.28) while PI3K inhibitor had good effect on K-Ras mutant cell line for 48h (RE=1.1).

CONCLUSIONS

Numerical study on the link between amino acid profile and cancer has been done in two different dimensions. We then summarized such link in terms of two new metrics such as RS and RE, which we first define in this work. Although our study based on those metrics seems to work, we think that the usefulness of the metrics in cancer study of this kind need to be further investigated.

mTOR Complexes as a Nutrient Sensor for Driving Cancer Progression

Recent advancement in the field of molecular cancer research has clearly revealed that abnormality of oncogenes or tumor suppressor genes causes tumor progression thorough the promotion of intracellular metabolism. Metabolic reprogramming is one of the strategies for cancer cells to ensure their survival by enabling cancer cells to obtain the macromolecular precursors and energy needed for the rapid growth. However, an orchestration of appropriate metabolic reactions for the cancer cell survival requires the precise mechanism to sense and harness the nutrient in the microenvironment. Mammalian/mechanistic target of rapamycin (mTOR) complexes are known downstream effectors of many cancer-causing mutations, which are thought to regulate cancer cell survival and growth. Recent studies demonstrate the intriguing role of mTOR to achieve the feat through metabolic reprogramming in cancer. Importantly, not only mTORC1, a well-known regulator of metabolism both in normal and cancer cell, but mTORC2, an essential partner of mTORC1 downstream of growth factor receptor signaling, controls cooperatively specific metabolism, which nominates them as an essential regulator of cancer metabolism as well as a promising candidate to garner and convey the nutrient information from the surrounding environment. In this article, we depict the recent findings on the role of mTOR complexes in cancer as a master regulator of cancer metabolism and a potential sensor of nutrients, especially focusing on glucose and amino acid sensing in cancer. Novel and detailed molecular mechanisms that amino acids activate mTOR complexes signaling have been identified. We would also like to mention the intricate crosstalk between glucose and amino acid metabolism that ensures the survival of cancer cells, but at the same time it could be exploitable for the novel intervention to target the metabolic vulnerabilities of cancer cells.

Nonessential amino acid metabolism in breast cancer

Interest in studying cancer metabolism has risen in recent years, as it has become evident that the relationship between cancer and metabolic pathways could reveal novel biomarkers and therapeutic targets. Metabolic starvation therapy is particularly promising due to its low toxicity. Nonessential amino acids are promising metabolites for such therapy because they become essential in many tumor cells, including breast cancer cells. This review will focus on four nonessential amino acid metabolism pathways: glutamine-glutamate, serine-glycine, cysteine, and arginine-proline metabolism. Recent studies of these amino acids have revealed metabolic enzymes that have the potential to be effective as cancer therapy targets or biomarkers for response to metabolic starvation therapy. The review will also discuss features of nonessential amino acid metabolism that merit further investigation to determine their relevancy to breast cancer treatment.