Single-cell genomic analysis (SCG)—A Key to Decipher the Cancer Landscape

By Sarada Preeta Kalainayakan, Ph.D.


Cancer therapies have seen rapid growth in recent years, with 41 drug approvals in 2019 alone (1). Part of this progress could be attributed to the arrival of next-generation sequencing and subsequent advances in drug development. Sequencing identifies mutations present in tumor samples using normal cells for comparison. For instance, EGFR mutations are predominantly identified in non-small lung cancer samples than normal lung cells. These alterations can then be effectively targeted by EGFR inhibitors including gefitinib and erlotinib. Although targeted therapies increase patient survival transiently, they cannot prevent the ensuing drug resistance, metastasis or relapse due to clonal evolution. 

Bulk Genomic Analysis versus Single-Cell Genomic Analysis

Cancer cells constantly evolve to bolster cancer-promoting mechanisms and overcome therapies by accumulating survival mutations under drug selection. The resulting tumor heterogeneity of clonal populations influences the prognosis of the disease and the course of treatment. Therefore, monitoring tumor alterations during treatments is necessary to predict drug resistance and recurrence. Genomic analyses of a cluster of tumors average out all the mutations in the tumor microenvironment by including stromal cells, and immune cells. As a result, the small percentage of mutations that confer drug resistance or survival benefit becomes undetectable. Hence, studying single-cell mutations that develop into distinct and disparate clonal populations is vital to identifying new drug targets and make therapeutic decisions (2–5).

Rising Need for High-Resolution Genomic Analysis

Single-cell genomic analysis (SCG) provides insights into the mutational landscape within a single cell enabling isolation of survival mutations.

  • Single-cell DNA sequencing (scDNA-Seq) and single-cell RNA sequencing (scRNA-Seq) are the most common approaches of SCG. scDNA-seq assists in understanding tumor initiation, metastatic dissemination, and drug resistance (6).
  • Besides, intratumoral heterogeneity and clonal evolution, scRNA-Seq enables identification of rare malignant cells in minimal residual disease conditions due to its high sensitivity (3).

In addition to their stand-alone applications, SCGs can be complemented with various multi-omics approaches. For instance, SCGs in combination with epigenetic sequencing approaches like single-cell bisulfite sequencing (scBS-Seq) can be used to detect differences in methylation states and copy number variants (2).

 Limitations of Single-Cell Genomic Analysis

Although SCG has vast applications, there are also a few limitations such as the introduction of artifacts from DNA isolation and extraction (5), non-uniform rates of amplification, biased amplification, low mappability rates, and high levels of PCR duplication. These, however, do not outweigh the advantages and computational analyses have been used to overcome such constraints (2).

Single-Cell Genomic Analysis: A Potential Path to Personalized Medicine

SCG based drug discovery has the potential to make personalized medicine a possibility. There are at least a dozen types of multi-omics approaches that can be used with SCGs to profile for factors including neoantigen load, chromatin state, nucleosome status, and RNA transcriptome (2). Although SCG approaches are more accessible to research than clinical applications, there is increasing commercialization in recent times (6). For instance, Immunitas therapeutics, a single cell genomics-based drug discovery company, recently announced $39 million Series A financing led by Leaps by Bayer and Novartis Venture Fund to work on immune-modulatory drug targets (7). Considering the immense potential of SCG, it is not a surprise that the potential market of SCG is estimated to reach 2.49 billion USD by 2025 (8).


  2. Sierant MC, Choi J. Single-Cell Sequencing in Cancer: Recent Applications to Immunogenomics and Multi-omics Tools. Genomics Inform. 2018 ; 16(4):e17.
  3. Shalek AK, Benson M. Single-cell analyses to tailor treatments. Sci Transl Med. 2017; 9(408).
  4. Nangalia J, Campbell PJ. Genome Sequencing during a Patient’s Journey through Cancer. N Engl J Med. 2019;381(22):2145-2156.
  5. Navin NE. The first five years of single-cell cancer genomics and beyond. Genome Res. 2015; 25(10):1499-507.

© All rights reserved.