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2018-09-06| In-DepthTechnology

Targeting tumor heterogeneity with salvo of sequencing

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By Ajay Vitthal Patil

Here we describe key features of the ‘Single Cell Analysis’ approach, that are important for understanding cancer heterogeneity and treatment in clinical setting.

Martin Amis once said, “We’re about five Einsteins away from answering the mystery of where the universe came from”. This is probably the perfect analogy by which one can explain our understanding of cellular behavior in heterogeneous tumor microenvironment. In most cases, tumor is a large mass of heterogeneous cells. Heterogeneous, not just by their cell type, but also by their individual expression profiles. Although they belong to the same tumor, increasing mutation load produce ‘deviated populations of cells’ with variable expression of biomarkers and neoantigens on each cell. Hence, oncologists need fairly complex diagnostic tools to comprehend the complete situation and make a diagnostic or therapeutic choice.

Advances in sequencing technology

In last two decades exponential advances in; DNA sequencing technology, expression profiling assays and data analysis facilitated direct clinical applications of these robust platforms. Next generation sequencing (NGS) methods and processing units brought down the cost of whole genome sequencing from $100 million in 2001 to $1000 today (1). Development of cost effective and high magnitude assays using such platforms, now allow us to refine our search for culprit variants to the resolution of single cell. NGS exponentially accelerated the genomic data collection capabilities and hence its application in the personalized medicine is both inevitable and promising. This follows the careful scrutiny and evaluation of the platform for its use in addressing few prominent problems in cancer diagnostics.

Mutation testing and antigen expression analysis – Addressing the chicken and egg conflict

Finding unique targets for therapy is difficult. Direct antigen expression analysis using mass spectrometry and allied protein quantitation approaches is very laborious and sometimes not possible with limited sample quantity obtained from human biopsy samples. Alternatively, NGS data from matched tumor and normal samples, can be used to predict neoepitopes through computational workflows. This provides, in depth profile of tumor mutation burden in patients. It serves as a good biomarker for checkpoint inhibitor therapies. There are several reports of varying response rates to checkpoint therapies based on expression analysis of target proteins (e.g. PD1, PD-L1 inhibitors). Different intra and intertumoral mutation profiles in patients with comparable target antigen expression, are partly responsible for this. Personalized NGS analysis could serve as a tool to assay tumor heterogeneity through their mutation profiles.

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Single cell analysis – Fine-mapping the tumor heterogeneity

NGS based global mutation profiling for each patient alone cannot fully grasp the heterogeneity factor. Because heterogeneity is ingrained in the form of varying copy number and expression of neoantigens, even among neighboring cells of the same tumor. Hence, such fine-mapping should extend to the level of single cell in order to comprehend the true nature of any tumor. Also, in this scenario, ‘single cell RNA (scRNA)’ analysis can complement DNA findings by highlighting aberrantly active gene modules considering the known potential of individual cells to drive drug resistance and metastasis (2). The technology typically follows the workflow of; isolation of single cell and RNA, reverse transcription (RT), amplification, library generation and sequencing. These workflows are progressing with tough challenges like ‘avoiding technical and biological noise from the samples’ and ‘standardizing protocols for personalized medicine application’. But once ready, single cell analysis beholds the unique opportunity to contain the vast heterogeneity of tumor within concise digital data.

Actionable data – Finding right needle in the genomic haystack

In practical terms understanding these complexities of tumor means understanding the data derived from relevant platforms. The neoepitopes are rarely common between patients – only 4% of the predicted neoepitopes were shared between 2 and more patients (3). A recent study showed that, from the total of 911,548 unique predicted neoepitopes, only 24 were common in more than 5% of patients (2, 4). Additionally, in solid tumors they are also touted to be too sparse and diverse to target (4). Hence, somatic DNA mutations are usually computed from whole-exome (WES) or whole-genome sequencing (WGS) data from matched tumor-normal samples using computational tools for variant detection (2). Patient-specific NGS data can be used to predict HLA types with computational tools like Polysolver (5) and Optiptype (6). Machine learning algorithms such as NetMHCpan (7) trained on experimental data can be used to detect short mutation affected regions in key proteins that can bind with high affinity to the predicted HLA types.

These robust platforms and methodologies in cancer therapeutics truly represent an elegant example of technological anastomosis with organic cellular events. In coming decade we shall see catapult advances in all these tools of understanding heterogeneity, leading us to the ultimate marvel of cancer cure.

References
Shalek and Benson, Sci. Transl. Med., 2017.
Effremova et al., Front Immunol., 2018.
Angelova et al., Genome Biol., 2015.
Charoentong et al., Cell Rep., 2017.
Shukla et al., Nat Biotechnol., 2015.
Szolek et al., Bioinformatics., 2014.
Nielsen et al., Genome Med., 2016.

 

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