Single Cell Biology

Evolution of Tumor Cells: Insights into Multiple Myeloma's Clonal Dynamics through Single-Cell Genomics

Yogesh Goyal^* Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, United States
^*Correspondence: Yogesh Goyal, Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, United States, Email:

Received: 29-May-2023, Manuscript No. SCPM-23-22028; Editor assigned: 31-May-2023, Pre QC No. SCPM-23-22028 (PQ); Reviewed: 14-Jun-2023, QC No. SCPM-23-22028; Revised: 21-Jun-2023, Manuscript No. SCPM-23-22028 (R); Published: 28-Jun-2023, DOI: 10.35248/2168-9431.23.12.060

Description

Multiple myeloma remains a formidable challenge in the realm of cancer research and treatment due to its complexity and heterogeneity. Genomic profiling of bulk tumor samples has provided valuable insights into the disease, but it fails to capture the diverse cellular populations within the tumor. Single-cell genomics has revolutionized the field, enabling researchers to investigate the genomic heterogeneity of individual cells within the tumor microenvironment. In this article, we delve into how single-cell genomics has contributed to our understanding of clonal dynamics in multiple myeloma, opening new avenues for precision medicine and targeted therapies [1,2].

Single-cell genomics

Single-cell genomics involves the isolation and sequencing of individual cells, enabling the examination of the genetic landscape at unprecedented resolution. Techniques such as Single-Cell RNA Sequencing (scRNA-seq) and Single-Cell DNA Sequencing (scDNA-seq) have provided valuable insights into the heterogeneity of multiple myeloma tumors, offering a comprehensive view of tumor evolution [3].

Clonal heterogeneity in multiple myeloma

Multiple myeloma is characterized by the coexistence of multiple subpopulations of malignant plasma cells with distinct genetic and phenotypic profiles. These subclones can arise through various mechanisms, including mutational events, chromosomal rearrangements, and microenvironmental interactions. Singlecell genomics has revealed the presence of subclonal populations within a single patient, emphasizing the need to address intratumoral heterogeneity for effective treatment strategies.

Unraveling clonal evolution

By tracking the genetic alterations over time and treatment, single-cell genomics has shed light on the dynamic nature of clonal evolution in multiple myeloma. Longitudinal studies have demonstrated how specific genetic changes can influence disease progression and response to therapy. Understanding these evolutionary dynamics can aid in predicting treatment resistance and identifying novel therapeutic targets [4-6].

The role of microenvironment

The tumor microenvironment plays a significant role in supporting tumor growth and influencing clonal selection. Single-cell genomics has allowed researchers to investigate the interactions between malignant plasma cells and their neighboring stromal cells and immune cells. These interactions contribute to disease progression and therapeutic resistance, providing insights into potential immunotherapeutic strategies [7].

Implications for personalized medicine

Single-cell genomics has the potential to revolutionize multiple myeloma treatment by enabling personalized medicine approaches. Understanding the genomic landscape of individual patients' tumors can help personalized therapies that target specific driver mutations and vulnerabilities present in the clonal subpopulations, improving treatment efficacy and reducing the risk of relapse [8,9].

Challenges and future directions

While single-cell genomics has brought significant advancements, challenges remain, including the high cost and technical complexities associated with the technology. Additionally, the analysis of vast amounts of single-cell data requires sophisticated computational approaches. As the field progresses, efforts to make single-cell genomics more accessible and user-friendly will be vital to its widespread adoption in clinical settings [10].

Conclusion

Single-cell genomics has emerged as a transformative technology in our understanding of tumor evolution and clonal dynamics in multiple myeloma. By providing insights into the intricate genetic landscape and interactions within the tumor microenvironment, single-cell genomics comply with personalized treatment strategies and improved patient outcomes. As the technology continues to evolve, it is anticipated that single-cell genomics will play a pivotal role in advancing precision medicine in the fight against multiple myeloma.

References

1. Kumar S. K, Rajkumar V, Kyle RA, van Duin M, Sonneveld P, Mateos MV, et al. Multiple myeloma. Nat Rev Dis Primers. (2018); 4(1): 1-22.

   [Crossref] [Pub Med]

2. Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, et al. The evolutionary history of 2,658 cancers. Nature. 2020; 578(7793): 122-128.

   [Crossref] [Google Scholar] [Pub Med]

3. Dutta AK, Alberge JB, Sklavenitis-Pistofidis R, Lightbody ED, Getz G, Ghobrial IM. Single-cell profiling of tumour evolution in multiple myeloma: Opportunities for precision medicine. Nat Rev Clin Oncol. 2022;19(4):223-236.

   [Crossref] [Google Scholar] [Pub Med].

4. Najm FJ, Madhavan M, Zaremba A, Shick E, Karl RT, Factor DC, et al. Drug-based modulation of endogenous stem cells promotes functional remyelination in vivo. Nature. 2015;522(7555):216-220.

   [Crossref] [Google Scholar] [Pub Med].

5. Bianconi E, Piovesan A, Facchin, F, Beraudi A, Casadei R, Frabetti F, et al. An estimation of the number of cells in the human body. Ann Hum Biol. 2013; 40(6):463-471.

   [Crossref] [Google Scholar] [Pub Med].

6. Xu L, Duda DG, di Tomaso E, Ancukiewicz M, Chung DC, Lauwers GY, et al. Direct evidence that bevacizumab, an anti-VEGF antibody, up-regulates SDF1α, CXCR4, CXCL6, and neuropilin 1 in tumors from patients with rectal cancer. Cancer Res. 2009;69(20):7905-10.

   [Crossref] [Google Scholar] [Pub Med].

7. Stavropoulos N, Rowntree RK, Lee JT. Identification of developmentally specific enhancers for Tsix in the regulation of X chromosome inactivation. Mol Cell Biol. 2005;25(7):2757-69.

   [Crossref] [Google Scholar] [Pub Med].

8. Leshchiner I, Livitz D, Gainor JF, Rosebrock D, Spiro O, Martinez A, et al. Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment. biorxiv. 2018:508127.

   [Crossref] [Google Scholar].

9. Wang J, Fan HC, Behr B, Quake SR. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell. 2012;150(2):402-12.

   [Crossref] [Google Scholar] [Pub Med].

10. Wang Y, Waters J, Leung ML, Unruh A, Roh W, Shi X, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature. 2014;512(7513):155-60.

    [Crossref] [Google Scholar] [Pub Med].