Diversity defines cancer.

Thanks to technological advances, we know that each person’s cancer is different. Just like a snowflake, no two are alike. This diversity between different individuals is termed intertumoral heterogeneity. Its corollary is that we need personalized therapy or individualized medicine, in order for cancer treatment to be effective.

Another layer of complexity is intratumoral heterogeneity, which means all cells in a single tumor are not necessarily the same (molecularly). Different groups of cells in a tumor (also called clones) likely have diverse molecular features. This is true in case of most cancers. Of these, glioblastoma is considered to be one of the most heterogeneous cancers.

An aggressive brain cancer, glioblastoma is very difficult to treat and recurs in most cases even after treatment. Out of every 100 patients with glioblastoma, 50 die in less than 15 months of diagnosis and very few live more than 5 years. An important reason for this dismal prognosis is the high degree of intratumoral heterogeneity. Individual cells within this tumor are different from each other, both genetically and functionally. Hence these cells respond to treatment differentially, making this tumor difficult to eradicate completely and more prone for recurrence.

Numerous previous studies have looked at genomic profiles of glioblastoma by analyzing chunks of tumors, each containing hundreds of thousands of tumor cells. One such landmark study, conducted by Verhaak and colleagues as part of The Cancer Genome Atlas (TCGA), used genomic analysis and found different tumors to have distinct genomic characteristics [1]. Based on these genomic profiles, they classified glioblastoma into 4 subtypes:

Classical Mesenchymal Proneural Neural

These different subtypes of glioblastoma can each have variable degree of intratumoral heterogeneity. However, the diverse cellularity has never been systematically quantified. A recent study published in Science on June 12, 2014 does just that. Researchers from Broad Institute and Harvard use next-generation sequencing of individual cells in the tumor [2] and show that glioblastoma cells are far more heterogeneous than “previously thought”.

RNA-seq analysis of Glioblastoma

These researchers took 430 individual glioblastoma tumor cells isolated freshly from five different patients, and analyzed each cell by RNA sequencing (RNA-seq), an approach that involves profiling the transcriptome of the cell. The transcriptome includes all RNA in the cell – total RNA, messenger or mRNA, and other RNAs such as microRNA. Transcriptome sequencing or RNA-seq is a highly sensitive technique to detect genomic abnormalities commonly associated with cancer, such as gene fusion events or mutations. Change in expression of genes (either over-expression or decreased expressed) is an anomaly frequently seen in cancer; RNA-seq identifies gene expression levels in cancer cells as well.

In the Science study, RNA-seq analysis of glioblastoma cells revealed a high degree of cell-to-cell variability. Cells had different expression profiles of tyrosine kinase receptors, which are important targets for therapy. A direct clinical implication of this is that any single targeted therapeutic agent, no matter how effective will not kill all tumor cells. This provides a strong rationale for the use of combinations therapy for this and possibly other cancers.

This study also used RNA-seq to determine what state individual cells are in. Each tumor comprised cells in different states:

Some were differentiated, mature and hence sensitive to therapeutic agents,

Some were stem cell-like (glioma stem cells), had the potential for self-renewal and were resistant to most treatments, and

Some were in different intermediate states and showed variable responses to treatments

Considering this level of diversity, no single drug can completely kill all cells. Also, there are subtypes of cells that can reform the tumor after therapy. Hence, almost all glioblastoma tumors eventually recur even following the most aggressive therapies.

Researchers also determined which TCGA subtype (listed above) the individual tumor cells belong to. Surprisingly, every glioblastoma tumor was a heterogeneous mixture of cells from these different subtypes, pointing to the true diversity in tumor cells that we would miss when analyzing data from whole tumor chunks.

Clinical Implications

Important from a clinical standpoint, this study showed that increased heterogeneity in tumors is associated with poor prognosis (decreased patient survival). Heterogeneity thus has direct translational relevance and need to be considered for therapy. Bradley Bernstein from the Broad Institute, one of the senior authors on this study said in the press release, “Understanding the cellular landscape can provide a blueprint for identifying new therapies that target each of the various sub­populations of cancer cells, and ultimately for tailoring such therapies to individual patient tumors.”

This study is probably the first to quantify the extreme heterogeneity of glioblastoma. It reveals glioblastoma to be a formidable disease to manage clinically. While it underscores the challenge in successfully treating a cancer like glioblastoma, knowing this diversity helps us understand its basic biology. An ideal approach would be to leverage data on intratumoral heterogeneity to design new and effective therapeutic strategies against this deadly disease.

References

Verhaak, R.G., et al., Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 2010. 17(1): p. 98-110. doi: 10.1016/j.ccr.2009.12.020 Patel, A.P., et al., Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 2014. DOI: 10.1126/science.1254257

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