The details in the differences – painting a clearer picture of tumor heterogeneity

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To say tumor biology is complex is an understatement. Not only can tumors vary genetically, phenotypically and metabolically, there are even differences in cells within a single tumor. Collectively, this is referred to as tumor heterogeneity, and is used as a basis for the classification of tumors into subtypes, predominantly based on cancer-specific genomic or transcriptomic profiles. Tumor subtyping can be used to identify key biomarkers for personalized medicine.

What causes tumor heterogeneity? It is well-documented that distinct mutations or aberrations in “driver” oncogenes lead to tumor heterogeneity. However, some tumor subtypes seem to be the result of adaptation to the disrupted microenvironment caused by abnormal tumor vasculature triggering metabolic switches. In certain types of cancer, existing subtypes have metabolic and transcriptomic profiles that are similar to those of normal differentiated cells, while in others, they reflect the profiles of stem or mesenchymal cells. It is therefore thought that the cell of origin may play a vital role in tumor heterogeneity. Generally speaking, tumor heterogeneity can present itself in two ways – one way is through the occurrence of major genetic events such as somatic copy number aberrations and mutations and another way is through phenotypic variations in transcript and protein expression levels, as well as major metabolic rewiring. These processes are often mediated by epigenetic programming.

Intertumor vs. intratumor heterogeneity

What is the difference between intertumor and intratumor heterogeneity? Genetic and phenotypic variations between tumors, also known as intertumor heterogeneity, are well-documented and indicated by the transcriptional profiles used to classify multiple types of cancers, such as glioblastoma, leukemia, breast, pancreatic and colorectal tumors into their molecular subtypes (1–6). Intratumor heterogeneity, or the heritable genetic and phenotypic variations in cells within the same tumor, is not as well understood. Each tumor is a complex environment where different cell populations coexist. Each of these populations has its own genetic fingerprint, which leads to different phenotypes. This diversity may explain why cancer is so resistant to therapy, including more targeted therapeutic approaches and may also explain why when patients relapse; the therapy that was successful in the first instance often becomes ineffective. In this case, the therapeutic agent puts a selective pressure on the evolution of the tumor, killing clones that carry a particular mutation but not others, allowing them to proliferate.

In a recent review, Eason and Sadanandam classified the origins of tumor heterogeneity and subtypes into three broad categories, based on metabolic, genetic and/or molecular changes (7). The first category involves initial tumorigenic driver genetic or metabolic aberrations resulting in different tumor subtypes. In these cases, an initial aberration is most likely followed by subsequent secondary genetic or metabolic aberrations that further accelerate progression and affect patient prognosis. In the second category, heterogeneity can arise depending on the cell of origin, which determines the subtypes.

In certain cases, tumors that arise from well-differentiated cells can result in a subtype that maintains the metabolic and genetic characteristics of the original cell of origin, and mostly have favorable prognosis. Tumors originating from stem/precursor cells probably have fewer metabolic and genetic characteristics of their parental cells and consequently have a poorer prognosis. The third category involves epithelial-mesenchymal transition, which can lead to distinct tumor subtypes, with different prognosis.

How can we unravel this complexity?

Recent advances in high-throughput genomic analyses of multiple biopsies from individual tumors and developments in single-cell analysis technologies are helping researchers elucidate tumor heterogeneity with increased clarity. The most direct way to evaluate intratumor heterogeneity is to compare DNA sequences among regions of a tumor. NGS technologies, in particular exome sequencing, as well as whole genome amplification technologies to sequence the genomes of single cells, can help unravel the underlying complexity of tumor heterogeneity.

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Analyzing single cells can also aid in resolving a tumor’s genomic and transcriptomic profile by looking beyond bulk cellular averages. However, isolation methods tend to be either expensive and involve heavy instrument investment or are unreliable, and downstream analysis can be difficult due to the low amount of available nucleic acids from an individual cell. With the QIAscout system and QIAseq whole genome and transcriptome amplification tools, single cell analysis is now accessible to any lab looking to investigate tumor heterogeneity using single cells. Check out our complete single-cell workflow, from isolation to bioinformatics!


  1. 1. Golub, T.R. et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531–537.
  2. 2. Perou, C.M. et al. (2000) Molecular portraits of human breast tumours. Nature, 406, 747–752.
  3. 3. Collisson, E.A. et al. (2011) Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med 17, 500–503.
  4. 4. Sadanandam A. et al. (2013) A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat Med 19, 619–625.
  5. 5. Sadanandam, A. et al. (2015) A cross-species analysis in pancreatic neuroendocrine tumors reveals molecular subtypes with distinctive clinical, metastatic, developmental, and metabolic characteristics. Cancer Discov 5, 1296–1313.
  6. 6. Verhaak, R.G.W. et al. (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110.
  7. 7. Eason, K. and Sadanandam, A. (2016) Molecular or Metabolic Reprograming: What Triggers Tumor Subtypes? Cancer Res 76 (18) 5195–5200.
Devika Mathur

Devika Mathur is a Senior Technical & Marketing Writer at QIAGEN. Devika joined QIAGEN in 2008 and has been responsible for creating literature for numerous QIAGEN products, including the REPLI-g and QIAseq product lines, and has written extensively on various scientific topics ranging from next-generation sequencing and single cell analysis to PCR and sample preparation. Devika is a graduate from University College Cork, Ireland, and has a microbiology research background, focusing primarily on the molecular characterization of the replication module of the lactococcal bacteriophage Tuc2009.

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