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Page 236 Crisafulli et al. Cancer Drug Resist 2019;2:225-41 I http://dx.doi.org/10.20517/cdr.2018.008

Another important issue is related to the creation of reference databases. Several efforts have been initiated
to reach this aim, such as the National Center for Biotechnology Information-sponsored data repository
ClinVar. Other large pharmacogenomic data repositories such as The Pharmacogenomics Knowledge Base
(PharmGKB) developed by Stanford University (www.pharmgkb.org) and other data sets, including those
available from the National Institutes of Health (NIH) GWAS collection (www.ncbi.nlm.nih.gov/snp),
[90]
through the use of electronic medical records have made it possible to computationally analyze personal
genomes for potential trans lation of pharmacogenomics into clinical practice.

SINGLE-CELL OMICS DEVELOPMENTS
Neoplasias are heterogeneous diseases that interact with complex microenvironments and batch analyses
inevitably “average” such target heterogeneity. Genomic sequencing at the single-cell level bears the potential
of identifying both distinct genetic cancer drivers and control networks/ druggable targets in heterogeneous
[91]
neoplastic populations . Single-cell genomic assays faithfully detect somatic mitochondrial DNA mutations,
track cellular relationships and hierarchies, and enable definition of clonal architecture in human cancer.

Combined single-cell RNA sequencing and genotyping can profile distinct subclones of the same tumor.
This allows to identify dysregulated transcriptional programs driven by potential drug-targetable genes,
with implications for targeted medicine. Single-cell DNA and RNA sequencing during neoadjuvant
chemotherapy can identify patients in which treatment leads to clonal extinction vs. those in which clones
persist after treatment. Notably, this analysis can reveal pre-existing resistant genotypes that could become
[92]
pharmacogenomic discovery targets . Improvements of single-cell RNA-sequencing procedures are
ongoing to correspondingly enhance the efficiency of such screening procedures.

Corresponding strategies can be applied to single-cell profiling and functional screening of long non-coding
[93]
RNA . These approaches will critically allow extending the identification of candidate diagnostic control
networks and therapeutic targets beyond the protein-coding regions of the genome.

Single cell proteomics by flow cytometry allow to track and analyse signalling events in individual cancer
cells and to create signalling network maps in each cell, to identify both common fundamental regulatory
themes and population heterogeneity. This can identify pathways that are activated in therapy-resistant cells
[94]
and can provide predictive, actionable cancer targets . Additional data can be provided by mass cytometry,
for high-dimensional, quantitative analysis of the effects of bioactive molecules on cell populations at single-
cell resolution. Correlation of proteomic, transcriptomic and mutagenomic profiles with intracellular
signaling molecules allows to correlate biological functions, such as metabolism, survival, DNA damage, cell
cycle and apoptosis, to provide determination of network states of heterogeneous populations of individual
cancer cells. Improvements in bioinformatic single-cell data comparison will be instrumental in taking the
[95]
greatest advantage of single-cell measurements .

CONClUSIONS AND FUTURE DIRECTIONS
Our current knowledge of rare and common somatic/genetic variants associated with cancer risk,
pharmacological treatment and disease outcome has led to significant progress, as well as to a number of
challenges associated with the clinical translation of these discoveries. Improved pharmacogenetic and
pharmacogenomic knowledge will extend known associations of genetic variabilily with drug responses. Key
limitations of traditional approaches, i.e., exclusive focus on already known sets of genes, will most likely
be overcome by global strategies, whereby the focus of the search will be extended to the entire genome,
transcriptome or proteome of an individual or groups of individuals.

Better characterization of a cancer entity will doubtless contribute to improved therapy. Additional
knowledge will be gathered by novel research strategies, such as those based on the concept of synthetic