Page 81 - Read Online

Page 14 of 19 Shi et al. J Cancer Metastasis Treat 2018;4:47 I http://dx.doi.org/10.20517/2394-4722.2018.32

benefit to treatment. ITH arising from diverse cell subpopulations with distinct molecular features pro-
[16]
duce varying levels of drug sensitivity and resistance . Retrospective analysis of CTCs from patients who
had developed resistance to inhibitors of the androgen receptor (AR) showed higher activation of non-
canonical Wnt pathway beside altered expression and mutations in AR compared to untreated patients [136,137] .
In castrate-resistant prostate cancers high content single-cell longitudinal profiling of CTCs from a patient
undergoing chemotherapy and targeted therapy revealed a selective clonal expansion of cells with AR am-
plification supporting the adaptive model of therapy resistance evolution [137] . Similar observation of selective
clonal persistence was seen in breast cancer patients treated with chemotherapy. In this study, single-cell se-
quencing post-chemotherapy revealed transcriptional reprogramming of resistant signatures, elucidating the
[32]
mechanism of therapy resistance .
Based on aforementioned studies, an accurate assessment of ITH by single-cell sequencing using multire-
gional, longitudinal sampling is essential to understand the mechanism of drug resistance and facilitate the
development of more effective therapies.

FUTURE DIRECTIONS
With the development of precision microfluidic devices and sequencing technologies, single-cell analysis has
transformed our understanding of ITH and clonal evolution. Single-cell genomics promises to deconvolute
complex biological processes in cancer, reveal epigenetic alterations and monitor the evolution of metastatic
and treatment resistance clones. By applying single-cell sequencing to different experimental systems, such
as cells in culture, patient-derived xenografts, murine models and analysis of human tumors, novel diagnos-
tics and therapies can be developed. A major hurdle in single-cell sequencing is the high cost of the technol-
ogy. Moreover, the volume and complexity of single-cell sequencing datasets exceed that of the traditional
bulk sequencing, calling for better statistical algorithms to deconvolute the data. Additional caution should
be given on the transcriptome coverage and number of cells taken for single-cell analysis to ensure the accu-
racy of gene expression distribution estimates. Future breakthroughs in developing cost-effective sequencing
methods and powerful data analysis pipeline for single-cell sequencing are likely to expand the scope of this
technology beyond cancer to other diseases.

DECLARATIONS
Authors’ contributions

Manuscript drafting: Shi X, ChakrabortyP, Chaudhuri A

Availability of data and materials
Not applicable.

Financial support and sponsorship
This work was supported by MedGenome Inc.

Conflicts of interest
All authors are full-time employees of MedGenome Inc.

Ethical approval and consent to participate
Not applicable

Consent for publication
Not applicable.

Copyright
© The Author(s) 2018.