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Precision medicine: cancer genome and beyond

Publish Date

14 DEC 2021


Anna Tafuri


Precision medicine has created a totally new paradigm in cancer therapy. Single-cell multi-OMIC analysis is a novel tool which is bound to open new frontiers in cancer drug development and further advance our current treatment options.

Identifying DNA alterations driving tumor development and progression has enabled us to tailor care, predict outcomes, and design targeted treatments for multiple cancers. Yet, we are only at the beginning of the precision medicine journey, as we look beyond the tumor as a whole to understand the behavior of tumor cells and the tumor microenvironmenta at the single-cell level.


Transforming the lives of patients
Precision medicine is revolutionizing the care of cancer patients, shifting the therapeutic paradigm from a “one size fits all” treatment to a set of approaches specifically matching the tumor characteristics of each patient.1 By targeting tumor-specific alterations, precision medicine approaches aim to maximize the chances of eradicating the tumor, while reducing unwanted side effects on normal tissues.

In recent years, the field of precision medicine has significantly advanced, particularly with respect to the development of tumor-agnostic drugs, non-invasive methods of tumor detection, and cellular immunotherapy.

Tumor-agnostic treatments2: The discovery that key gene alterations have the potential to drive the growth of different cancer types has led to a conceptual change in drug development and the approval of the so-called tumor-agnostic drugs. These treatments have been shown to be potentially effective against tumors bearing the molecular alterations targeted by the drug, irrespective of their tissue of origin.

Non-invasive methods of tumor cell assessment: New technological advances have led to the development of the so-called liquid biopsies. These highly sensitive methods are capable of detecting circulating tumor cells (CTC) and/or cell-free tumor DNA (ctDNA) shed by the tumor in the patient’s bloodstream.3 Therefore, these methods can be used as a powerful tool to detect tumors, identify actionable mutations and monitor tumor responses.

Merck KGaA, Darmstadt, Germany has championed the development of liquid biopsies, which have dramatically changed the patient’s diagnostic journey, lowering the risks inherent to the invasiveness of tumor biopsies, while ensuring real-time capability and potentially obtaining a more complete picture of the tumor lesions.

Personalized immunotherapy: Genetic engineering of a patient’s own immune cells has led to the generation of T cells capable of reacting against tumor cells,4 which have been very successful in the treatment of some hematologic malignancies. The next challenge for immune cell therapy is to treat solid tumors and generate off-the-shelf immune cell products readily available for patients, overcoming the hurdles of generating individualized cell products.5

Moving towards single-cell multi-omics
Why do tumor cells remain unresponsive and/or eventually develop resistance to anti-cancer treatment? The key to this question is probably in the molecular mechanisms governing at the single-cell level, the evolution dynamics of tumor cells, and their interactions with other cell types in their immediate environment (tumor microenvironment [TME]).

Such information could not be obtained from conventional methods of molecular analysis, because they are based on the analysis of molecular components derived from whole (bulk) tumor fragments after lysis, i.e. chemical destruction, of all cellular constituents. Thus, bulk tumor lysatesb cannot capture the molecular features of the different cellular components at the “single cell” level.

Single-cell multi-OMICS is based on ground-breaking technological advances, which have transformed the molecular assessments in populations of millions of cells into new high-throughput methods, capable of detecting simultaneously multiple molecular signals at the single-cell level.6

Multi-OMICS tumor profiling: DNA alterations (genome) are key hallmarks of tumor cells. However, other factors, such as alterations of DNA methylationc (epigenome), mRNA transcriptiond and stability (transcriptome), protein generation (proteome), production of low-molecular-weight metabolites (metabolome), are critical to sustain tumor cell survival and proliferation.7 Integrated multi-omics analyses are a powerful tool to shed light on “omics” interactions and how they may drive clonal evolution and cellular cross-talk, ultimately regulating tumor responsiveness and resistance to specific treatments.

Understanding the tumor heterogeneitye: Most tumors are multiclonal, which means that they are composed of cell subsets, or clones, with different genetic and functional characteristics (clonal heterogeneity).8 Under the selective pressure of the environment, “the survival of the fittest clones” enables the tumor to thrive. Identifying patterns of clonal evolution in response to various insults will be crucial to design new drugs or combination therapies and prevent or overcome drug resistance.

Dissecting the role of the tumor microenvironment (TME): Emerging evidence suggests that the TME plays a crucial role in promoting tumor progression and modulating drug resistance.9 Understanding the molecular cross-talk and spatial relationship between tumor cells and the different cellular components of the TME will be essential to interfere with tumor growth. The use of molecular pathology and advanced imaging technologies, as well as clinical trials testing new TME-targeted agents, should help us leverage the TME function to enhance tumor responses.

The power of computational analyses and machine learning
The analysis of tumor multi-omics datasets is a crucial step to gain a better understanding of the tumor features that can be used as a target for therapeutic interventions.3,10 To this end, multi-disciplinary teams, composed of biologists, clinicians, statisticians, and bioinformaticians, work together to develop specific computational and machine learning approaches.

The unprecedented wealth of information generated through multi-omics approaches has created new challenges, due to the inherent complexity of such a large volume of data. First, normal variations occurring among individuals need to be distinguished from abnormal ones, related to pathological alterations. Second, molecular patterns or “signatures” resulting from the molecular interactions within one cell and/or the cross-talk between neighboring cells need to be defined. Finally, time and space have to be accounted for to model the molecular and cellular dynamics characterizing tumor evolution.

In summary, multi-OMICS analyses represent the next biological frontier of experimental oncology, a rapidly growing field, holding great promise to unravel actionable aspects of cancer biology, which may be translated into the identification of new therapeutic targets, improved patient selection approaches, and enhanced precision medicine efficacy.
Explore more about our personalized medicine approach here. Interested to learn more about the tools helping to drive R&D? Read our latest blogs on artificial intelligence and model-informed drug discovery and development.

Glossary of Terms

  1. Tumor microenvironment: “Normal” non-tumor cells, molecules, and blood vessels that surround and feed a tumor cell. Tumor cells influence the characteristics of their microenvironment, in turn the microenvironment affects how a tumor grows and spreads.11
  2. Bulk tumor lysates: A preparation containing the products of lysis (disintegration of a cell by rupture of the cell wall or membrane) of tumor cells and/or of tumor tissue, including components of the tumor microenvironment.6,12
  3. DNA methylation: A biological process by which methyl groups are added to certain regions of the DNA molecule, resulting in the alteration of the expression of certain genes and of the biological functions controlled by these genes13
  4. DNA transcription: Describes the process by which a cell makes an RNA copy (mes-senger RNA) of the genetic information contained in a stretch of DNA coding for a gene. The mRNA carries this genetic information from the DNA in the nucleus to the cytoplasm, where the proteins are made.14
  5. Tumor heterogeneity: A term used to describe the differences which exist between tumors of the same type in different patients, between cancer cells within a single tu-mor lesion in a single patient, or between the primary (original) tumor and secondary tumor lesions (metastasis) in a single patient. These differences may be due to genetic alterations or protein expression and may explain why some tumor cells respond to a specific treatment and others do not.8
  6. Protein translation: The process by which a cell makes proteins using the genetic in-formation carried in messenger RNA (mRNA).15


1 Nassar SF, et al. Precision Medicine : Steps Along the Road to Combat Human Cancer. Cells 2020;9:2056. doi:10.3390/cells9092056.
2 Looney AM, et al. Tumour-agnostic therapies. Nat Rev Drug Discov 2020;19:383-4. doi: 10.1038/d41573-020-00015-1.
3 Yang C, et al. Circulating tumor cells in precision oncology: clinical applications in liquid biopsy and 3D or-ganoid model. Cancer Cell Int. 2019;19:341. doi: 10.1186/s12935-019-1067-8.
4 Larson RC, Maus MV. Recent advances and discoveries in the mechanisms and functions of CAR T cells. Nat Rev Cancer 2021;21:145–61. doi: 10.1038/s41568-020-00323-z.
5 Aftab BT et al. Toward “off-the-shelf” allogeneic CAR T cells. Adv Cell Gene Ther. 2020;3:e86. doi: 10.1002/acg2.86.
6 Lee J et al. Single-cell multiomics: technologies and data analysis methods. Exp Mol Med 2020;52:1428–1442. doi: 10.1038/s12276-020-0420-2.
7 Chakraborty et al. Onco-Multi-OMICS Approach: A New Frontier in Cancer Research. BioMed Research Inter-national 2018; Article ID 9836256. doi: 10.1155/2018/9836256.
8 Ramón y Cajal S, et al. Clinical implications of intratumor heterogeneity: challenges and opportunities. J of Mol Med 2020; 98:161–77. doi: 10.1007/s00109-020-01874-2.
9 Baghban et al. Tumor micoenvironment complexity and therapeutic implications at a glance. Cell Commun and Signal 2020; 18:59. doi: 10.1186/s12964-020-0530-4.
10 Nicora G, et al. Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools. Front Oncol 2020;10:1030. doi: 10.3389/fonc.2020.01030.
11 National Cancer Institute. Tumor Microenvironment. Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/tumor-microenvironment. Last accessed August 2021.
12 ThermoFisher Scientific. Overview of Cell Lysis and Protein Extraction. Available from: https://www.thermofisher.com/uk/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-cell-lysis-and-protein-extraction.html. Last accessed August 2021.
13 National Cancer Institute. Methylation. Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/methylation. Last accessed August 2021.
14 National Cancer Institute. Transcription. Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/transcription. Last accessed August 2021.
15 National Cancer Institute. Translation. Available at: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/translation. Last accessed August 2021.

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