Prof. Wu Jiarui and Prof. Zeng Rong from the CAS KeyLaboratory of Systems Biology are building a scientist lab in HIAS, UCAS. The lab, together with the CAS Center for Excellence in Molecular Cell Science (CEMCS), the First Affiliated Hospital of Naval Medical University (Changhai Hospital of Shanghai), and other units, published online a paper entitled "Integrated Omics of Metastatic Colorectal Cancer" in the international academic journal Cancer Cell on September 3. The study integrates systems biology theory, omics analysis technology, and clinical basis, develops the first multi-omics integral map concerning metastatic colorectal cancer among Chinese patients, and proposes molecular subtypes and a new strategy of personalized therapy for metastatic colorectal cancer.
In recent years, with the rapid development of surgery, targeted therapy and immunotherapy, the survival rate of patients with early diagnosis of cancer has been greatly improved, but the treatment of advanced and metastatic cancer remains a common problem. Recent studies on pan-cancer genomes show little difference between in situ and metastatic genomes. The result leads to an urgent question: In what molecular aspects are the characteristics of metastasis shown? In addition, most targeted drug therapies are currently screened based on gene mutations, and patients without relevant genomic characteristics may therefore lose therapeutic opportunities, which undoubtedly means a worse prognosis for patients with advanced and metastatic cancer. Obviously, in order to solve these difficulties and challenges, it is necessary to carry out precision medical research on metastatic cancer based on systems biology.
Therefore, the research group collected 480 clinical samples from patients with metastatic colorectal cancer (mCRC), including primary lesions and corresponding metastatic lesions, proximal tumor-adjacent tissues, and distal normal tissues. These samples were then subjected to multi-omics data collection such as whole exon sequencing, MeDIP-chip, quantitative proteomics, and phosphoproteomics. By analyzing these clinical big data and comparing them with western datasets, researchers developed the first multi-omics integral map concerning mCRC among Chinese patients.
The first multi-omics integral map concerning mCRC among Chinese patients
The most prominent result of this study is that it systematically reveals the molecular signature spectrum of mCRC among Chinese patients for the first time. First, quantitative proteomic profiling differentiates three CRC subtypes characterized by different functional preferences and distinct prognoses. In particular, the three subtypes based on quantitative proteomics enable effective prognosis differentiation only in patients with advanced cancer, which provides an important indicator for clinical decision-making and timely treatment of patients with advanced cancer. Second, quantitative phosphoproteomic profiling based on primary lesions effectively distinguishes CRC cases with metastasis, suggesting a high correlation between protein phosphorylation abnormality and metastasis. The study also finds that there is no significant difference in genomic level between metastatic lesions and primary lesions in the same patient, which is consistent with the results of a recent metastatic pan-cancer study published by Nature that covers 2,520 cases of whole-genome sequencing data, exhibiting high similarities in genomic characteristics between metastatic lesions and primary lesions. More importantly, the metastatic lesions and primary lesions of mCRC are significantly different in terms of protein, phosphorylation modification, and kinase-substrate network, indicating that both primary and metastatic lesions should be taken into account in the analysis of metastatic tumors. The study based on multi-omics data integration provides abundant resources for deepening the understanding of mCRC in the Chinese population.
The first Chinese colorectal cancer (CCRC) protein molecular typing
Another important achievement of the study is that it gets rid of the dependence of targeted therapy in precision medicine on gene mutations and enables direct screening of proteins as drug targets. Researchers first made a quantitative analysis of kinase-substrate networks based on the paired proteome/phosphoproteome data of primary and metastatic lesions, and analyzed the heterogeneity between protein subtypes and between primary and metastatic lesions through kinase activity enrichment and kinase-substrate networks. On this basis, researchers established 31 mini patient-derived xenograft (MiniPDX) models for the tissues in 22 CRC patients, and conducted drug sensitivity tests for three kinase-targeted drugs (afatinib, gefitinib and regorafenib), verifying that primary and metastatic lesions from the same patient show different responses to the same drug. The results show that the treatment of metastatic tumors is not only personalized, but even site-specific. More importantly, in the tumor tissues corresponding to the miniPDX models, there were few associated mutations in the three drug-targeted genes. However, some tumor tissues showed high drug sensitivity, suggesting that kinase-substrate networks may be more sensitive and direct than the presence of mutations for indicating drug sensitivity. By constructing a machine-learning model of kinase-substrate networks, this study provides accurate predictions for pharmacodynamic discrimination, thus proposing a new direction beyond genomic analysis for personalized cancer medicine.
Targeted drug response discrimination model based on in-vivo drug sensitivity tests and machine learning.
Based on the work, the research group is building a larger-scale tumor medication database based on omics big data. The database is expected to be released for clinical application as soon as possible, thus benefiting cancer patients.
Under the joint guidance of Research Fellow Zeng Rong, Prof. Zhang Wei and Research Fellow Wu Jiarui, the study was mainly completed by Li Chen, an associate research fellow at Zeng Rong's research group, Sun Yidi, a PhD candidate at Zeng Rong's research group, Yu Guanyu, an attending doctor at the Colorectal Surgery Department of Changhai Hospital, Cui Jingru, research assistant of Wu Jiarui's research group, and Lou Zheng, deputy director of the Colorectal Surgery Department of Changhai Hospital. The project was funded by the CAS Strategic Priority Research Program, the Key R&D Program of the Ministry of Science and Technology of the People's Republic of China, and the National Natural Science Foundation of China.
Bibliography
1.Dekker, E., et al. (2019). Colorectal cancer.Lancet 394, 1467-1480.
2.Punt, C.J., et al. (2017). From tumourheterogeneity to advances in precision treatment of colorectal cancer. Nat RevClin Oncol 14, 235-246.
3. Priestley, P., et al. (2019). Pan-cancerwhole-genome analyses of metastatic solid tumours. Nature 575,210–216.
4.Knapp, S. (2018). New opportunities for kinasedrug repurposing and target discovery. Br J Cancer 118, 936-937.
5. Wu, X., et al. (2019). Integratingphosphoproteomics into kinase-targeted cancer therapies in precision medicine.J Proteomics 191, 68-79.
6.Zhang, F., et al. (2018). Characterization ofdrug responses of mini patient-derived xenografts in mice for predicting cancerpatient clinical therapeutic response. Cancer Commun (Lond) 38, 60.
7.Zhang, W., et al. (2015). Diagnosing phenotypesof single-sample individuals by edge biomarkers. J Mol Cell Biol 7, 231-241.
8.Sun, Y., et al. (2020). Kinase-substrate edgebiomarkers provide a more accurate prognostic prediction in ER-negative breastcancer. Genomics, Proteomics & Bioinformatics.
Editor | Jiang Xuchen
