2.5.2 Single-Cell DNA and RNA Sequencing
More recently, advances in fields such as microfluidics and nanotechnology have resulted
in a number of methodologies for assessing genome sequence, messenger RNA levels,
protein abundance, and chromatin accessibility at the single-cell level [22]. For the cancer
field, these exciting developments have meant that the cell subpopulations conforming
tumors have become apparent, analyses have revealed rare cell subtypes, and they have
also helped pinpoint which ones of these are able to re-establish growth after
treatment [22].
One of the most exciting observations stemming from these types of analyses is the
discovery that many of the mutations in a tumor are subclonal, therefore, meaning that
different sections within the same tumor may have different genomic alterations [22]—
therefore adding to the notion that a single biopsy may not be enough to identify all tumor
drivers. As another example of the power of these studies, researchers based at the Broad
Institute of MIT and Harvard identified, in melanoma tumors, a cellular program associated
with immune evasion and T-cell exclusion that is present from before therapy, and that is
able to predict responses to this type of treatment in an independent cohort of patients
[23]. But perhaps the most striking discovery has been the identification of non-genetic
mechanisms underlying the emergence of drug treatment resistance, which we will discuss
in detail in Sect. 2.6.
Although the number of methodologies published for single-cell DNA and RNA
analysis has been growing steadily over the last few years, the field still has numerous
challenges to overcome: The development of methods to efficiently and non-disruptively
isolate single cells from the tissue of origin, the amplification of that individual cell’s
biological material for downstream processing, and the subsequent analysis of that material
to identify the variation of interest as well as taking into account the potential errors and
biases [24]. However, these methodologies are set to become standard in the near future, as
large consortia such as The Human Cell Atlas are already undertaking a number of studies
to fulfill their aim of defining all human cell types at the molecular and morphological
levels by sequencing at least 10 billion single cells from healthy tissues [25]. It is not
difficult to envision, thus, that the technological advances stemming from this and related
projects may be exploited in a large follow-up project to TCGA where the focus will be
characterizing individual cells, the abundances of cell states in distinct tumors, and the
identification of therapy-resistant subclones.
2.5.3 Exploring Intra-Tumor Heterogeneity Through Sequencing
It has also been recognized that tumors are highly heterogeneous masses of distinct cell
types, clonal and subclonal genomic mutations as well as a mixture of distinct transcriptional cell states [26]. Bulk DNA and RNA sequencing can be exploited to investigate this
heterogeneity by specialized bioinformatics analyses. One early study using bulk genome
2 Opportunities and Perspectives of NGS Applications in Cancer Research
27
More recently, advances in fields such as microfluidics and nanotechnology have resulted
in a number of methodologies for assessing genome sequence, messenger RNA levels,
protein abundance, and chromatin accessibility at the single-cell level [22]. For the cancer
field, these exciting developments have meant that the cell subpopulations conforming
tumors have become apparent, analyses have revealed rare cell subtypes, and they have
also helped pinpoint which ones of these are able to re-establish growth after
treatment [22].
One of the most exciting observations stemming from these types of analyses is the
discovery that many of the mutations in a tumor are subclonal, therefore, meaning that
different sections within the same tumor may have different genomic alterations [22]—
therefore adding to the notion that a single biopsy may not be enough to identify all tumor
drivers. As another example of the power of these studies, researchers based at the Broad
Institute of MIT and Harvard identified, in melanoma tumors, a cellular program associated
with immune evasion and T-cell exclusion that is present from before therapy, and that is
able to predict responses to this type of treatment in an independent cohort of patients
[23]. But perhaps the most striking discovery has been the identification of non-genetic
mechanisms underlying the emergence of drug treatment resistance, which we will discuss
in detail in Sect. 2.6.
Although the number of methodologies published for single-cell DNA and RNA
analysis has been growing steadily over the last few years, the field still has numerous
challenges to overcome: The development of methods to efficiently and non-disruptively
isolate single cells from the tissue of origin, the amplification of that individual cell’s
biological material for downstream processing, and the subsequent analysis of that material
to identify the variation of interest as well as taking into account the potential errors and
biases [24]. However, these methodologies are set to become standard in the near future, as
large consortia such as The Human Cell Atlas are already undertaking a number of studies
to fulfill their aim of defining all human cell types at the molecular and morphological
levels by sequencing at least 10 billion single cells from healthy tissues [25]. It is not
difficult to envision, thus, that the technological advances stemming from this and related
projects may be exploited in a large follow-up project to TCGA where the focus will be
characterizing individual cells, the abundances of cell states in distinct tumors, and the
identification of therapy-resistant subclones.
2.5.3 Exploring Intra-Tumor Heterogeneity Through Sequencing
It has also been recognized that tumors are highly heterogeneous masses of distinct cell
types, clonal and subclonal genomic mutations as well as a mixture of distinct transcriptional cell states [26]. Bulk DNA and RNA sequencing can be exploited to investigate this
heterogeneity by specialized bioinformatics analyses. One early study using bulk genome
2 Opportunities and Perspectives of NGS Applications in Cancer Research
27