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J Cancer Metastasis Treat 2020;6:5 I http://dx.doi.org/10.20517/2394-4722.2020.13 Page 3 of 38

its growing role during the development of new drugs targeting metabolic vulnerabilities in multiple cancer
[3,4]
diseases . However, drug discovery is limited by the unsuitability of animal models for high-throughput
drug screening. Moreover, animal studies may not adequately predict the clinical efficacy of therapeutics in
humans. These limitations motivated researchers to develop new three-dimensional (3D) in vitro models to
[5]
better mimic the in vivo tumor microenvironment .

Experimental procedure: Here, we introduce a novel high-content metabolomics screen based on high-
resolution direct infusion mass spectrometry (DIMS) technology able to monitor the metabolic response
of drug-treated mammalian cells in 3D 96-well format. This rapid and systematic metabolomic method was
validated on multiple cancer and normal cells, cultured either in individual or in co-culture cell systems
using 13C- 15N labeled tracer analysis.

Results: Novel synergistic combination of drugs were identified utilizing the metabolic profiling obtained
using DIMS. These include chemotherapies targeting the metabolic reprogramming of cancer cells,
including mitochondrial oxidative phosphorylation and glutaminolysis.

Conclusion: Overall, the rapid data acquisition and improved detection limits of mass spectrometry are
[5,6]
paving the way for applications of metabolomics in preclinical screening , opening new opportunities in
drug discovery and personalized medicine.

REFERENCES
1. Luengo A, Gui DY, Vander Heiden MG. Targeting metabolism for cancer therapy. Cell Chem Biol 2017; 24:1161-80.
2. Molina JR, Sun Y, Protopopova M, Gera S, Bandi M, et al. An inhibitor of oxidative phosphorylation exploits cancer vulnerability. Nat
Med 2018;24:1036-46.
3. Tiziani S, Kang Y, Choi JS, Roberts W, Paternostro G. Metabolomic high-content nuclear magnetic resonance-based drug screening of a
kinase inhibitor library. Nat Commun 2011;2:545.
4. Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 2016;15:473-84.
5. Lu X, Lodi A, Konopleva M, Tiziani S. Three-dimensional leukemia co-culture system for in vitro high-content metabolomics screening.
SLAS Discov 2019;24:817-28.
6. Lodi A, Saha A, Lu X, Wang B, Sentandreu E, et al. Combinatorial treatment with natural compounds in prostate cancer inhibits prostate
tumor growth and leads to key modulations of cancer cell metabolism. NPJ Precis Oncol 2017;1:pii:18.

3. Clinical application of artificial intelligence in ovarian cancer

Se Ik Kim , Youngjin Han , Untack Cho , Yong Sang Song 1,2
1
1
2
1 Department of Obstetrics and Gynecology and Cancer Research Institute, College of Medicine, Seoul National
University, Seoul 03080, Korea.
2 Biomodulation, Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, Korea.

Among several types of gynecologic cancer, ovarian cancer is the most lethal type. Due to the absence
of specific symptoms and effective biomarkers, the survival rate of ovarian cancer is poor. Moreover,
platinum resistance is a major obstacle in ovarian cancer treatment. Thus, accurate biomarkers associated
with chemoresistance and recurrence of the cancer are necessitated. To establish personalized therapeutic
strategies for ovarian cancer patients, a prediction model that precisely predicts patient responses to
chemotherapy and diagnosis could be computed and incorporated. Although many prediction models
for cancer have been suggested, few models specific for ovarian cancer have been proposed. Thus, we
performed integrative analysis incorporating both clinico-pathologic and multi-omics data and developed
prediction models for diagnosis and prognosis of ovarian cancer. In addition, we conducted metagenome