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Unraveling Cancer Evolution

In recent years, single cell omics, including single-cell genomes, epigenomes, transcriptomes, proteomics, and multi-omics, have been applied to explore cancer research. Exciting discoveries have been investigated in the field of cancer evolution, such as resistance to treatment and the secrets of the tumor microenvironment. With senior scientists and multi-omics advanced platforms, Creative Biolabs provides a comprehensive suite of single cell omics services for unraveling cancer evolution.

Investigating Clonal Diversity and Evolution in Primary Tumors

The continuous updating of next-generation sequencing technologies has applied gene sequencing technology to individual cell levels. It is well known that malignant tumors are a molecular disease, and the activation of oncogenes based on changes in gene levels and the inactivation of tumor suppressor genes play a crucial role in their development. Moreover, different levels and different types of tumor cells may exist between one tumor or multiple tumors. Therefore, the use of single-cell sequencing technology to conduct an in-depth study of malignant tumors can reveal the diversity that leads to the occurrence and evolution of malignant tumors, thus helping us to understand the development and evolution of tumors at the individual level.

Schematic Outline of the Experimental Design and Main Findings by Eirew.Fig.1 Schematic outline of the experimental design and main findings by eirew. (Janiszewska, 2015)

Investigating Chromosome Evolution in Cancer

Scientists have found in recent years that single-cell genome sequencing is of great value for the detection of somatic mutations, especially during tumor evolution. Most cancers have an abnormal number of chromosomes or DNA copy number aberrations (CNAS) in their genome. Preliminary data from researchers using single-cell gene sequencing in cancers such as prostate cancer, colon cancer, liver cancer, and lung cancer suggest that a discontinuous model of copy number evolution may also play a role in other solid tumors. This model has important implications for our evolutionary understanding of tumor growth dynamics and the clinical diagnosis and treatment of cancer patients.

Tracking chromosomal changes in human colorectal cancer.Fig.2 Tracking chromosomal changes in human colorectal cancer. (Johnson, 2019)

Understanding the Evolution of Resistance to Therapy

In recent years, metastatic melanoma immunological checkpoint inhibitors have achieved clinical efficacy. However, immuno-targeted drug treatments often lead to drug resistance in patients. Moreover, patients who lack BRAF mutations exhibit inapplicability to immunotherapy. Therefore, an understanding of the mechanisms behind an effective anti-tumor immune response is critical to future progress. By single-cell sequencing of 4645 cells from 19 melanoma patients, the scientists found that tumor tissues with high expression levels of MIFT also contained elevated levels of AKT kinase. The ecosystem profile of melanoma tumor cells was described, a subset of drug-resistant cells was identified, and the mechanism of drug resistance formation was explored.

Dissection of melanoma with single-cell RNA-seq.Fig.3 Dissection of melanoma with single-cell RNA-seq. (Tirosh, 2016)

Understanding Tumor Evolution and Metastasis

The single-cell transcriptome sequencing technology for various cell types of the tumor microenvironment enables the fine classification of the cell types of tumor tissues. It is generally accepted in the research community that the presence of a large number of immune cells CD3+ T cells represents a stronger antigenic experience and an anti-tumor immune response. High levels of tumor-infiltrating lymphocytes (TILs) in primary and metastatic breast cancer are associated with better prognosis, so the number of TILs can predict early triple-negative breast cancer, which is the primary subtype of immunotherapy. Subtype mutations in breast cancer with the highest TIL are most likely to produce new tumor antigens.

Workflow of both single-cell and bulk RNA-seq of human isolated T cells.Fig.4 Workflow of both single-cell and bulk RNA-seq of human isolated T cells. (Savas, 2018)


  • End-to-end single cell omics solutions
  • Fast turn-around time from receipt of sample to data delivery
  • Accurate and publishable data

With extensive experience and scientific knowledge, Creative Biolabs is committed to helping our clients navigate and expedite the single cell analysis program. Empowered by combining scientific knowledge, single cell sample preparation, single cell sequencing technologies, and informatics expertise, we are dedicated to extending your capabilities of exploring the cancer revolution via single cell data. Please contact us to know more.


  1. Janiszewska, M.; et al. Clonal Evolution in Cancer: A Tale of Twisted Twines. Cell stem cell. 2015, 16(1): 11-12.
  2. Johnson, S.C.; et al. Watching cancer cells evolve through chromosomal instability. Nature. 2019.
  3. Tirosh, I.; et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016, 352(6282): 189-196.
  4. Savas, P.; et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nature Medicine. 2018, 24: 986-993.
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