In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy.
Sara CorvignoSunil BadalMeredith L SpradlinMichael KeatingIgor PereiraElaine SturEmine BayraktarKatherine I FosterNicholas W BatemanWaleed BarakatKathleen M DarcyThomas P ConradsG Larry MaxwellPhilip L LorenziSusan K LutgendorfYunfei WenLi ZhaoPremal H ThakerMichael J GoodheartJinsong LiuNicole FlemingSanghoon LeeLivia S EberlinAnil K SoodPublished in: NPJ precision oncology (2023)
In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients' tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets.
Keyphrases
- mass spectrometry
- locally advanced
- ms ms
- neoadjuvant chemotherapy
- high grade
- machine learning
- end stage renal disease
- emergency department
- gene expression
- rectal cancer
- bone marrow
- liquid chromatography
- squamous cell carcinoma
- multiple sclerosis
- ejection fraction
- deep learning
- high resolution
- chronic kidney disease
- radiation therapy
- breast cancer cells
- clinical trial
- gas chromatography
- high performance liquid chromatography
- single cell
- neural network
- simultaneous determination
- solid phase extraction