Science

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:

  1. Classical
  2. Mesenchymal
  3. Proneural
  4. 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.

RNA-seq glioblastoma
Single-cell RNA-seq reveals glioblastoma heterogeneity and may help design new, more effective therapies (Image Credit: cancer.gov)

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

  1. 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
  2. Patel, A.P., et al., Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 2014. DOI: 10.1126/science.1254257

 

Revolution! It drives radical transformation.

The omics revolution over the past decade has been a tour de force leading to unprecedented advances in biomedical sciences. Omics is a generic term for all fields of biomedicine with the suffix –omics. For instance, genomics indicates study of genome, epigenomics indicates study of epigenetic modifications, and so on for other fields such as proteomics, transcriptomics, microbiomics, metabolomics, etc. (each of these words deserves a separate blog post and will get one in due course). Advances in these areas have arguably been the most disruptive innovations of our time.

Breakthroughs in Biomedical Sciences

Technological innovations in the nineties spurred rapid development of the omics field, leading to a never-before-seen “intersection of biology and technology” (“Steve Jobs” by Walter Isaacson, 2011). The international Human Genome Project was a key landmark or rather, a precursor of this revolution. What started out as an extremely expensive venture has now made genome sequencing affordable enough for routine clinical application (almost!). The cost of sequencing has dropped precipitously, from $3 billion in the late nineties to approximately $1000 for a single genome today. This rate of advancement in sequencing technologies has truly defied Moore’s law.

Newer technologies and their application to biomedical research meant more and more data generated everyday. Making sense out of these data required additional technologies, which in turn, drove systematic evolution of a specialized field – computational or quantitative biology. This discipline uses techniques in physics, mathematics, computer sciences and related branches to decipher riddles in biology. Today, closely related interdisciplinary branches such as bioinformatics, systems biology, and network pharmacology have emerged. These varied branches are driving progress by analyzing and interpreting the tremendous amounts of data generated in the omics world.

Projects in academia and in industry are becoming increasingly collaborative in nature. Successfully translating these research findings into the clinic is critical to providing more effective treatment options for many diseases. These developments are poised to make personalized medicine or individualized medicine a reality.

Science, Medicine, Sequencing, Biology, Cancer
Sciberomics - Snapshots of Science and Life (Image by author)

Sciberomics and Science Outreach

In light of the interdisciplinary research and its application to humans, science communication assumes a vital role. Not only does it inform curious minds, but it also serves as an antidote to ignorance and misinformation. It spreads public awareness about science and facilitates dialog between peers. Science outreach is critical to driving public opinion, which can, directly and indirectly influence policy and funding. Add to that the availability of innumerable platforms for communication, and one would have to agree that there has been no better time for science writing.

All this has prompted me to join the world of active blogging. I am really excited to launch my new blog, and to use this platform to communicate science. How did I decide on a name for the blog? Well, I have to confess I am guilty of neologizing. I wanted the name to reflect the fact that this blog will communicate science, in cyberspace. Though I intend to cover all areas of science, I realize that I may end up being partial to the omics field. Taking all these factors into consideration, the newly minted word Sciberomics seems like a good fit as a name.

At Sciberomics, I will discuss recent developments in biology and medicine, focusing on how they affect human life. Blog posts will include studies that are hot off the press, areas that are mired in controversies and topics that are hotly debated. Active discussion and feedback from readers, in the form of comments are welcome and will provide flavor to the blog. The aim of Sciberomics is outreach to peers and non-scientific audience alike.

So, here goes Sciberomics – Snapshots of Science and Life. Welcome!