Integrative Clinical Genomics of Metastatic Cancer

By D. Robinson, Yi-Mi Wu, R. Lonigro, P. Vats, E. Cobain, Jessica N Everett, Xuhong Cao, Erica Rabban, Chandan Kumar-Sinha, V. Raymond and 23 more. Nature, 2017

772 citations recorded by the source corpus.

Metastasis is the primary cause of cancer-related deaths. Although The Cancer Genome Atlas has sequenced primary tumour types obtained from surgical resections, much less comprehensive molecular analysis is available from clinically acquired metastatic cancers. Here we perform whole-exome and -transcriptome sequencing of 500 adult patients with metastatic solid tumours of diverse lineage and biopsy site. The most prevalent genes somatically altered in metastatic cancer included TP53, CDKN2A, PTEN, PIK3CA, and RB1. Putative pathogenic germline variants were present in 12.2% of cases of which 75% were related to defects in DNA repair. RNA sequencing complemented DNA sequencing to identify gene fusions, pathway activation, and immune profiling. Our results show that integrative sequence analysis provides a clinically relevant, multi-dimensional view of the complex molecular landscape and microenvironment of metastatic cancers.

Subfields: Cancer Research, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/05c7f309c55364ad18c76d24693e6c0c9e5cb188

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Citations in the corpus

PI3K/Akt/mTOR Pathway and Its Role in Cancer Therapeutics: Are We Making Headway? (2022, background): “Moreprevalent amplification of the oncogene AKT3 has been detected in many cancers, such as breast carcinoma, endometrial carcinoma, melanoma, ovarian epithelial tumor, cholangiocarcinoma, and non-small cell lung cancer (Table 2) (22, 23).”
Genomic correlates of clinical outcome in advanced prostate cancer (2019, methodology): “Fusions in ETS genes (ERG, ETV1, ETV4, ETV5, FLI1) and RAF1/ BRAF were detected using CODAC (41) and assessed manually in all cases where RNA-sequencing data were available.”
Systems Biology of Cancer Metastasis (2019, background): “A comprehensive patient cohort study of metastasis revealed that proliferative cancers were associated with increased metabolism and stress response, while the ones with more EMT like phenotype were more inflamed(Robinson et al., 2017).”
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation (2018, methodology): “MET500 - Metastatic solid tumors (Robinson et al., 2017) Database of Genotypes and Phenotypes (dbGaP) accession number phs000673.”
Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal (2018, neutral): “Similar large-scale studies of metastases have been limited (Robinson et al., 2017; Zehir et al., 2017) and have not included matched primary tumors.”
Artificial intelligence (AI) and big data in cancer and precision oncology (2020, background): “Through their approach of precision oncology, the study also identiﬁed predictive biomarkers for immune therapy for metastatic cancers [90].”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “Transcriptomic Data Processing—Transcriptomic data were analyzed as described previously (Robinson et al., 2017), using the Clinical RNA-seq Pipeline (CRISP) developed at University of Michigan (https://github.”
Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research (2022, background): “An elevated cell proliferation rate and cell cycle abnormalities have been reported to be caused by inactivation of tumor suppressor genes such as CDKN2A, PTEN and TP53 [85, 86].”