Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

Ruth Nussinov, Hyunbum Jang, Chung-Jung Tsai, Feixiong Cheng, Anna R R Panchenko
2019 PLoS Computational Biology  
At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor's genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses-all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the
more » ... enter of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community. generation sequencing [4] [5] [6] [7] [8] [9] [10] [11] [12][13][14]. That is, rather than select therapies based on the perceived type of cancer, for example, tissue or organ location, therapeutic selection is based on analysis of the individual's genomic sequence and the specific identified mutational aberrations [15][16][17][18]. It is the mutation-guided therapeutics, rather than the traditional cancer type-dependence classification, such as that based on classical anatomy and histology, that has etched a new context into this terminology. This concept has compelled a paradigm shift. Now patients with BRAF V600E mutations would be prescribed the same drug regimen irrespective of their cancer type and location, for example, acute myeloid leukemia, breast cancer, or melanoma [19]. Precision oncology was not always based on the individual's genomic sequence. Since its inception, exactly what precision oncology includes has been unclear. In 2015, Collins and Varmus proposed that blood-typing-based targeted therapies and immune therapy be included [20]. A 2017 analysis revealed that in the literature, "precision oncology" appears to have undergone an evolution [21]. Early on, therapies were disease and/or protein targeted, such as, for example, vascular endothelial growth factor (VEGF) inhibitors and Bcr-abl1 tyrosine kinase inhibitors (TKIs), bevacizumab (Avastin) and imatinib, respectively. Later, precision oncology treatments were referred to as selections of therapies based on analyses of biomarkers. Examples include crizotinib (Xalkori) in lung cancer with EML4-ALK rearrangements or adjuvant chemotherapy as in the Oncotype DX panel in breast cancer. The literature analysis observed that, only as of January 2016, precision oncology therapeutic selection appears to have been primarily based on next-generation sequencing data. As noted by Tsang and colleagues, various terms have been used to relate to precision genomic oncology, including simply precision oncology [22], genomics-driven oncology [23], genomic oncology, and personalized cancer medicine. All refer to high-throughput sequencing to inform clinical decisions at the point-of-care [24]. Even though the conceptual foundation of precision oncology is rational, thus stimulating broad enthusiasm, its current lack of demonstrated clear breakthroughs in clinical trials argues that in addition to more patient sequence data, critical components may be missing. Next-generation sequencing of patients with advanced cancers showed that less than 10% have actionable mutations [25, 26], and a randomized trial of precision medicine [27] did not observe improved outcomes with genome-based precision oncology. The biological complexity underlying target identification is challenging. The breakthrough of the next-generation sequencing delivered a new precision component: treatments might be tailored, not only to a certain illness but also to a specific person's genetic make-up. This notion of prescribing "the right drug to the right person at the right time" [28][29][30][31][32] has stimulated considerable research efforts, which have been pushed to the fore by the Precision Medicine Initiative (also called All of Us Research Program). But to date, it seems to still fall short, and the magnitude of the task is daunting. The mutational landscape is highly heterogeneous and challenging to decipher. Data indicate that the least mutated cancers have on average 0.28 mutations per megabase, with most presenting 8.15 mutations per megabase [21, 33]. Whole-exome analysis of pancreatic cancer, which is considered only moderately mutated, indicates 2.64 mutations per megabase. Further disconcerting is the low consistency among mutated genes. For example, certain mutations are observed fairly rarely in pancreatic cancer and are observed in other tissues as well [34]. Additional factors tied to cancer and individual complexities cast long and formidable shadows as well. Therefore, even though it is broadly believed that precision oncology can improve treatments and prognosis, and that precision data are essential for precision oncology, the consensus is that this may not be enough [35]. Current clinical results do not question the sequencebased hypothesis and strategies, but they do emphasize the need for considering their completeness [36]. The literature thrives with proposed additional clinical considerations, and vital PLOS Computational Biology | https://doi.org/10.
doi:10.1371/journal.pcbi.1006658 fatcat:ii47m4bybraz7edark3m6axcby