Project title: Dissecting the ecosystem of neuroendocrine tumors by single-cell genomics

Description

Tirosh will comprehensively characterize the cellular diversity of NETs through single-cell genomic technologies and perform extensive analyses to compare NET composition with healthy tissues and other cancer types to improve our understanding of tumor behavior.

What question will the researchers try to answer?

Can single-cell genomic analysis of small intestinal and lung NETs improve our understanding of intercellular interactions contributing to the development, growth, spread, and evolution of tumors?

Why is this important?

Understanding NETs at this level of analysis will provide new insights into the many different cell types that comprise a single NET and may ultimately contribute to uncovering new genes that may lead to improved treatment options that may overcome drug resistance.

What will researchers do?

Gene expression analysis will be carried out at the single-cell level, which will provide new insights into the cellular makeup of NET tumors.

How might this improve the treatment of NETs?

Charting the cellular diversity within NETs could advance the understanding and treatment of NETs through the identification of: NET cell-of-origin, mechanisms that enable metastasis, causes of drug resistance, and the potential for immunotherapies.

What is the next step?

The enormous amount of new data generated from this project would serve as a foundation for a broad range of studies by many different disciplines exploring causes and treatments of NETs.

Outcomes:

Each neuroendocrine tumor (NET) is a complex ecosystem, composed of diverse malignant, immune and stromal cells. Thus, charting the cellular diversity within NETs could advance our understanding and treatment of NETs in a variety of ways, including the identification of mechanisms that enable metastasis, causes of drug resistance, and the potential for immunotherapies. Yet, previous studies profiled NETs in bulk, thereby providing only an average measurement NET cells.

Our proposal is centered at comprehensively characterizing the cellular diversity of NETs through single cell genomic technologies. We applied single cell RNA-seq mostly to small intestinal NETs (siNETs), profiling thousands of cells in nine of those tumors. Due to the relative rarity of siNETS and other logistical complications, we managed to profile fresh tumor samples only in 3 cases but also profiled 6 frozen samples with a protocol that is specifically designed to enable analysis of frozen samples. The data we produced provides, to our knowledge, the largest single cell dataset of siNETs and will be useful for multiple groups that are trying to better understand these tumors.

From initial analysis, we could divide the siNETs into two groups that represent previously unrecognized subtypes, suggesting that they might respond differently to treatments. We are now further trying to understand the distinction between those subtypes. Our analysis of proliferation signatures suggest that across all of the siNET samples, the neuroendocrine cells are among the least proliferating cells in the tissue. This appears inconsistent with those being the neoplastic cells and raises questions about the mode of growth of NETs. We also found that in one of the siNET subtypes described above, B cells are highly proliferating. We identified a potential mechanism for the high B-cell proliferation, and we will continue to explore whether that might be linked to the pathogenesis of NETs.

Finally, we also profiled a few additional neuroendocrine tumors besides siNETs. One of these was particularly interesting – a mixed lung tumor with neuroendocrine and non-neuroendocrine cells. In this tumor we found that there are three main types of malignant cells – neuroendocrine cells, non-neuroendocrine cells and apparent progenitors that may give rise to both of the former populations. This suggests that NETs could have a hierarchy of progenitor and differentiated cells such that progenitors might represent a hidden, yet important, population of cells. This is reminiscent to the Cancer Stem Cell hypothesis and suggest that future treatments might need to specifically target such hidden stem cells as they are the ones that drive the growth of the tumor. Additional analysis of mixed tumors would be needed to evaluate how often such stem-like cells could be identified and what are their features.

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