Article Figures & Data
Figures
- Fig. 1.
Diagram illustrating development of structural genomics and cancer genomes. (A) Number of tumor genomes sequenced by The Cancer Genome Atlas across 26 major cancer types from Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov). (B) Number of PDB structures of human proteins from 1988 to 2017 from PDB database (https://www.rcsb.org). (C) Genotyping by next-generation sequence technology. (D) Regulatory non-coding mutations in cancer. (E) Protein structural view of coding mutations in cancer.
- Fig. 2.
Survey of personalized oncology drug discovery. (A) Oncology vs. non-oncology phase transition success rate. (B) Biomarker-based phase transition success rates. (C) Success rate of personalized medicines approved by FDA from 2014 to 2017. BLA, Biologic License Application; NDA, New Drug Application. Data collected from BIO Industry analysis (https://www.bio.org/bio-industry-analysis-published-reports) and FDA website (https://blogs.fda.gov/fdavoice/index.php/2015/03/fda-continues-to-lead-in-precision-medicine).
- Fig. 3.
A diagram illustrating computational approaches for development of personalized immunotherapies. (A) Collection of patient samples (both tumor samples and matched samples). (B) An integrated approach for identification of actionable biomarkers using innovative genomics approaches, proteomics, and computational biophysics. (C and D) Guiding the application of personalized immunotherapies or combination immunotherapies that highly specifically target neoantigens derived from tumor somatic mutations. CTLA-4, cytotoxic T-lymphocyte-associated protein 4; MHC, major histocompatibility complex; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; TCR, T-cell receptor.
- Fig. 4.
Structural landscape of druggable proteome in oncology drug discovery. In total, 18 selected cancer genes with well-annotated driver mutations and available protein three-dimensional structures across five classic cancer pathways were illustrated: 1) PI3K/AKT/PTEN pathway, 2) EGF/EGFR signaling, 3) RAS pathway, 4) cell metabolism pathways, and 5) hormone (estrogen/androgen) pathways. Cancer driver mutations were collected from My Cancer Genome (https://www.mycancergenome.org/). Protein structures were collected from PDB database (http://www.rcsb.org/pdb/home/home.do).
- Fig. 5.
A personal cancer mutanome infrastructure for the development of personalized treatment using breast cancer as a case study. The entire infrastructure contains four core components: performing tumor genetic and genomic testing (A), identifying actionable biomarkers that may guide the personalized therapies using bioinformatics and computational biology tools (e.g., protein structure hotspot clustering shown in Table 3) (B), pre-clinical validation (in vitro or in vivo functional assays) (C), and guiding the application of monotherapies or combination therapies based on steps 1–3 (D).
Tables
Key Word Definitions Allosteric site A regulatory site on the protein’s surface is distinct from the substrate, ligand, or partner binding sites (Nussinov and Tsai, 2015). Clinically actionable mutation A mutation alters clinical responses (e.g., survival or drug responses) in patients harboring this mutation. Driver mutation A mutation directly or indirectly promotes a selective growth or survival to the cell in which it occurs. Edgetic alleles Genetic alterations (mutations) alter specific macromolecular interactions (“edges”) rather than affecting folding and stability of proteins (Sahni et al., 2015). Edgetic mutations include “node” removal by truncating mutations (Zhong et al., 2009) and in-frame edgetic mutations that disrupt interactions between proteins, DNA, or RNA. Network-attacking mutations Mutations alter signaling networks via different types of network perturbations: signaling network dynamics, network structure, and dysregulation of phosphorylation sites (Creixell et al., 2015). Orthosteric site The primary, unmodulated binding site (on a receptor) of a ligand, such as adenosine triphosphate (ATP) binding site of a kinase. Personal mutanome A personal mutanome is a portfolio of DNA sequencing data (e.g., whole exome or whole genome), protein structural genomics, and interpretation of mutational landscape of an individual patient. Personalized medication Interventions or/and products are tailored to the individual patients based on their predicted response or risk (e.g., particularly genomic or molecular profiles as clinically actionable biomarkers) of disease. Spliceosome mutations Hotspot somatic mutations affect genes encoding RNA splicing factors (Dvinge et al., 2016). Undruggable A protein could not be targeted pharmacologically. More appropriate terms might be “difficult to drug” or “yet to be drugged.” - TABLE 2
Lists of US FDA-approved personalized oncological medications during 2012–2017
Data comes from www.fda.gov/drugs.
Drug Name Company Active Ingredient Approval Date Indication Biomarkers Verzenio Eli Lilly Abemaciclib 09/28/2017 Breast cancer HR+ and HER2- Idhifa Celgene Enasidenib 08/01/2017 Acute myeloid leukemia (AML) IDH2 mutation Nerlynx Puma Biotechnology Neratinib 07/17/2017 Breast cancer HER2-amplified Rydapt Novartis Midostaurin 04/28/2017 AML FLT3+ Alunbrig Takeda Brigatinib 04/28/2017 Non-small cell lung cancer (NSCLC) ALK+ Zejula Tesaro Niraparib 03/27/2017 Recurrent epithelial ovarian, fallopian tube, or primary peritoneal cancer BRCA1, BRCA2 Kisqali Novartis Ribociclib 03/14/2017 Metastatic breast cancer HR+, HER2- Imfinzi AstraZeneca Durvalumab 03/01/2017 Metastatic urothelial carcinoma PD-L1 Rubraca Clovis Oncology Rucaparib 12/19/2016 Ovarian cancer BRCA1, BRCA2 Lartruvo Eli Lilly Olaratumab 10/19/2016 Soft tissue sarcoma PDGFRA+ Tecentriq Genentech Atezolizumab 5/18/2016 Bladder cancer PD-L1 Venclexta AbbVie and Genentech Venetoclax 4/11/2016 Chronic lymphocytic leukemia (CML) 17p-deleption Alecensa Roche and Genentech Alectinib 12/11/2015 Metastatic NSCLC ALK Cotellic Genentech Cobimetinib 11/10/2015 Metastatic melanoma BRAF-V600E/K Unituxin United Therapeutics Dinutuximab 3/10/2015 Neuroblastoma MYCN-amplification Tagrisso AstraZeneca Osimertinib 11/13/2015 NSCLC EGFR-T790M Ibrance Pfizer Palbociclib 2/3/2015 Metastatic breast cancer ER+/HR+, HER2- Opdivo Bristol-Myers Squibb Nivolumab 12/22/2014 Metastatic melanoma BRAF-V600 Lynparza AstraZeneca Olaparib 12/19/2014 Metastatic ovarian cancer BRCA1, BRCA2 Keytruda Merck Pembrolizumab 9/4/2014 Metastatic melanoma PD-L1 positive Zykadia Novartis Ceritinib 4/29/2014 Metastatic NSCLC ALK+ Imbruvica Janssen Ibrutinib 11/13/2013 Mantle cell lymphoma (MCL) 17p-deleption Gazyva Genentech Obinutuzumab 11/1/2013 Chronic lymphocytic leukemia (CLL) MS4A1 (CD20+) Gilotrif Boehringer Ingelheim Afatinib 7/12/2013 Metastatic NSCLC EGFR Tafinlar GlaxoSmithKline Dabrafenib 5/29/2013 Metastatic melanoma BRAF-V600E/K Mekinist GlaxoSmithKline Trametinib 5/29/2013 Metastatic melanoma BRAF-V600E/K Kadcyla Genentech Ado-trastuzumab emtansine 2/22/2013 Metastatic breast cancer HER2+ Iclusig Ariad Pharmaceuticals Ponatinib 12/14/2012 CML BCR-ABL1, T315I Cometriq Exelixis Cabozantinib 11/29/2012 Medullary thyroid cancer RET Synribo Teva Pharmaceutical Omacetaxine mepesuccinate 10/26/2012 CML BCR-ABL1 Bosulif Pfizer Bosutinib 9/4/2012 CML BCR-ABL1 Perjeta Genentech Pertuzumab 6/8/2012 Metastatic breast cancer HER2+ ALK, anaplastic lymphoma kinase; BRCA, breast cancer; FLT3, fms like tyrosine kinase 3; HR+, hormone receptor-positive; MS4A1, membrane spanning 4-domains A1; MYCN, N-myc proto-oncogene protein; PDGFRA, platelet-derived growth factor receptor A; RET, ret proto-oncogene.
- TABLE 3
Lists of computational tools and bioinformatics resources for analysis of personal mutanomes in cancer
Names Description Website Refs Cancer genomics resources TCGA and GDC The Cancer Genome Atlas and Genomic Data Commons Data Portal https://portal.gdc.cancer.gov Chin et al. (2011) ICGC The International Cancer Genome Consortium https://icgc.org Milius et al. (2014) CBioPortal Providing visualization, analysis and download of large-scale cancer genomics data sets. http://www.cbioportal.org Gao et al. (2013) COSMIC Comprehensive resources for curating somatic mutations http://cancer.sanger.ac.uk/cosmic Forbes et al. (2015) Bioinformatics resources for protein structures PDB The PDB is the global archive for experimentally determined, atomic-level three-dimensional structures of proteins, DNA, and RNA. https://www.rcsb.org/ Rose et al. (2017) Interactome3D Manually curated protein-protein interactions with known three-dimensional structure information. http://interactome3d.irbbarcelona.org Mosca et al. (2013) dSysMap dSysMap is a useful tool to study the network perturbations by genetic variants http://dsysmap.irbbarcelona.org/ Mosca et al. (2015) Interactome INSIDER An integrative structural and genomic resource for functional exploration of human disease mutations at multiple-scale, proteome-wide human interactome http://interactomeinsider.yulab.org Meyer et al. (2018) Mutational clustering tools on protein structures AlloDriver Detect druggable mutations via dysregulation of protein allosteric functions. NA Shen et al. (2017) Cancer3D A useful tool to explore potential cancer drivers or pharmacogenomic biomarkers using protein structure information. http://cancer3d.org/ Porta-Pardo et al. (2015) CLUMPS Assess the significance of mutational clustering in a given 3D structure. NA Kamburov et al. (2015) HotSpot3D Detect mutation–mutation and mutation–drug clusters using three-dimensional protein structures. NA Niu et al. (2016) KNMPx Detect a kinome-wide pharmacogenomic biomarkers via rewiring phosphorylation-related signaling networks and drug sensitivity/resistance NA Zhao et al. (2017) ReKINect Detect network-attacking mutations in phosphorylation-based signaling networks. http://rekinect.science/ Creixell et al. (2015) SGDriver A structural genomics-based method that detects mutation clustering on protein–ligand binding site residues via a Bayes inference statistical framework. NA Zhao et al. (2016)
In this issue
Jump to section
- Article
- Visual Overview
- Abstract
- I. Introduction: Personal Cancer Mutanome Defined
- II. Computational Resources and Tools for Personal Mutanomes
- III. Personal Mutanomes for Accelerating Modern Oncology Drug Development
- IV. A Personal Mutanome Infrastructure for Precision Oncology
- V. Conclusions and Perspectives
- Authorship Contributions
- Footnotes
- Abbreviations
- References
- Figures & Data
- Info & Metrics
- eLetters