Using drug response data to identify molecular effectors, and molecular “omic” data to identify candidate drugs in cancer

Abstract

The current convergence of molecular and pharmacological data provides unprecedented opportunities to gain insights into the relationships between the two types of data. Multiple forms of large scale molecular data, including but not limited to gene and microRNA transcript expression, DNA somatic and germline variations from Next-Generation DNA and RNA Sequencing, and DNA copy number from array comparative genomic hybridization are all potentially informative when one attempts to recognize the panoply of potentially influential events both for cancer progression and therapeutic outcome. Concurrently, there has also been a substantial expansion of the pharmacological data being accrued in a systematic fashion. For cancer cell lines, the National Cancer Institute cell line panel (NCI-60), the Cancer Cell Line Encyclopedia (CCLE), and the collaborative Genomics of Drug Sensitivity in Cancer (GDSC) databases all provide subsets of these forms of data. For the patient derived data, The Cancer Genome Atlas (TCGA) provides analogous forms of genomic information along with treatment histories. Integration of these data in turn relies on the fields of statistics and statistical learning. Multiple algorithmic approaches may be chosen from, depending on the data being considered, and the nature of the question being asked. Combining these algorithms with prior biological knowledge, the results of molecular biological studies, and the consideration of genes as pathways or functional groups provides both the challenge and the potential of the field. The ultimate goal is to provide a paradigm shift in the way that drugs are selected to provide a more targeted and efficacious outcome for the patient.

Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges

“…The cell lines have been selected over many passages for rapid 2-D growth on plastic in serum-containing media. The NCI60 call line panel (DeVita and Chu, 2008; Talmadge et al, 2007) provided a valuable resource from which most CDXs were generated, and recent efforts have greatly expanded the repertoire (Reinhold et al, 2015). These models are easily established in a wide variety of laboratory settings and have been successfully used to identify an abundance of cytotoxic drugs leading to chemotherapy treatments that still dominate clinical cancer management (Fig.…”

Durability of Kinase-Directed Therapies—A Network Perspective on Response and Resistance

“…These cell lines are well characterized (e.g., gene transcription expression, DNA copy number, DNA methylation, single nucleotide polymorphisms, and mutations). Genomic analysis of resistant and sensitive cell lines can produce candidate markers for innate sensitivity or resistance that can be confirmed chemogenomically through cellular studies (54,(56)(57)(58) Another challenge is that the tumor cell panels are typically comprised of immortalized cell lines that are adapted to grow in 2D culture, which alters the tumor cell biology (55,63). Recently, more clinically relevant tumor cells have been used to enhance the predictive power of the approach.…”

RNA Sequencing and Genetic Disease

“…Although the ML field is still emerging, analyses of RNA-seq data from patients, using various ML approaches, have been found to be robust for estimating disease susceptibility [57 • , 58 • , 59 •• ], recurrence [60], survival [57 • ], evaluation of treatment plans and drug sensitivity prediction [55,61,62], disease subtype differentiation [39,63], and biomarker identification [57 • , 58 • , 64]. Cancer genomics is a field in which ML holds great promise, particularly for classification and outcome assessment.…”