Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health

Abstract

Recent remarkable advances in genome sequencing have enabled detailed maps of identified and interpreted genomic variation, dubbed "mutanomes." The availability of thousands of exome/genome sequencing data has prompted the emergence of new challenges in the identification of novel druggable targets and therapeutic strategies. Typically, mutanomes are viewed as one- or two-dimensional. The three-dimensional protein structural view of personal mutanomes sheds light on the functional consequences of clinically actionable mutations revealed in tumor diagnosis and followed up in personalized treatments, in a mutanome-informed manner. In this review, we describe the protein structural landscape of personal mutanomes and provide expert opinions on rational strategies for more streamlined oncological drug discovery and molecularly targeted therapies for each individual and each tumor. We provide the structural mechanism of orthosteric versus allosteric drugs at the atom-level via targeting specific somatic alterations for combating drug resistance and the "undruggable" challenges in solid and hematologic neoplasias. We discuss computational biophysics strategies for innovative mutanome-informed cancer immunotherapies and combination immunotherapies. Finally, we highlight a personal mutanome infrastructure for the emerging development of personalized cancer medicine using a breast cancer case study.

Graphical abstract

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).