New genetic technology for gene expression analysis of cancer cells

Thought LeadersDr. Rong LuAssistant Professor at the Keck School of MedicineUniversity of Southern CaliforniaIn this interview, News-Medical speaks to Dr. Rong Lu ​​​about her recent research into the development of a new genetic technology for gene expression analysis of cancer cells.

Please can you introduce yourself, tell us what inspired your recent research into gene expression signatures, cancer progression, and chemotherapy resistance?

My name is Rong Lu. I am an assistant professor in the Department of Stem Cell Biology and Regenerative Medicine at the Keck School of Medicine of the University of Southern California. My lab focuses on understanding the heterogeneity of hematopoietic stem cells in blood regeneration during bone marrow transplantation.

Many patients receiving bone marrow transplantation suffer from leukemia and other types of cancer. Unfortunately, many of these patients experience relapse, as their therapeutic regimens cannot remove all cancer cells. Cancer cells exhibit tremendous heterogeneity in their treatment responses. There is an urgent need to identify the subset of cancer cells escaping treatment in order to improve the therapeutic efficacy, which is what inspired this project.

In talking with Dr. Akil Merchant, a physician-scientist, I learned about an encouraging success in medical oncology: combination chemotherapy for acute lymphoblastic leukemia (ALL). Consisting of a short-term high-intensity therapy followed by multiple cycles of low-dose maintenance therapy, this complex regimen was derived from decades of clinical research.

However, it remained unclear how the intensive and maintenance therapies synergize to create an effective treatment for ALL. This provided us with a great model to understand the impact of cancer cell heterogeneity on the overall treatment outcome.

While several technologies exist to study cancer cell heterogeneity, it has been challenging to relate the molecular differences among individual cancer cells to their variable therapeutic responses. Moreover, most studies analyze cancer cells after treatment. This means that only treatment-resistant cells are studied, and these cells have already undergone many molecular changes both during and after treatment.

Research shows that even within a single cancer patient, there is a vast diversity of individual tumor cells. Why is this, and what problems does this have for research into cancer cell behaviors?

Cancer cells carry multiple genetic and epigenetic alterations, which can continuously and stochastically trigger additional molecular changes, a process known as “cancer cell evolution.” The cancer cell evolution produces tremendous genetic and epigenetic variations across individual cancer cells. The distinct molecular characteristics of individual cancer cells drive their heterogeneous behaviors in both disease progression and treatment response.

The vast cellular diversity requires us to study cancer cells at the single-cell level. However, most existing technologies analyze cancer cells at the population level and cannot detect the small subsets of cancer cells responsible for metastasis and/or treatment resistance.

Recent studies have used naturally occurring genetic mutations as markers to track small subsets of cancer cells. However, these mutations are rare and difficult to detect, which limits the number of cancer cells that can be studied. In addition, different mutations often occur at different times, precluding direct comparison.

In your work, you used a new genetic technology. How did you develop this experimental system?

We developed a genetic technology to simultaneously interrogate gene expression and cellular proliferation and migration at the single-cell level. We track each cell and its progeny using a unique synthetic "genetic barcode". These barcodes allow us to relate the gene expression signatures of individual cells with their corresponding growth and migration characteristics. We have also developed a bioinformatics algorithm to identify genes expressed in correlation with a particular cellular behavior.

This technology can relate the distinct treatment responses of individual cancer cells to their molecular profiles prior to treatment, and compare the gene expression characteristics of cancer cells that are sensitive to the treatment with those that are resistant. By revealing the molecular status of individual cancer cells at the moment of exposure to treatment, our technology provides a new approach to identifying genes underlying treatment resistance. We hope that our new technology will ultimately inform efforts to extend the success in treating ALL to other types of cancer.

cancer cellsImage Credit: Lightspring/Shutterstock.com

What did you investigate using this experimental system?

We studied the growth, metastasis, and chemotherapy resistance of primary human B-cell acute lymphoblastic leukemia (B-ALL) in a xenografted mouse model using this experimental system.

We found that individual B-ALL cells responded differently to chemotherapies, and their different responses correlate with the expression of a few genes. In addition, we found an unexpected yet common form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and is driven by cells with distinct gene expression.

How did gene expression signatures differ between cancer cells?

Our study shows that individual cancer cells expressed their genes in distinct ways, and we could not find any subset of cells that share common global gene expression profiles. However, when we grouped the cells based on their particular behaviors, such as migration and chemotherapy response, we were able to identify specific genes that were expressed in correlation with these behaviors.

In your work, you demonstrated that cancer cells with distinct signatures tend to grow in different organs. What sort of challenges does this finding present?

This finding suggests that cancer metastasis is driven by a small subset of cancer cells that are undetectable using conventional population-level studies. These cells are therefore difficult to identify and study. On the other hand, this finding also suggests that we may be able to target and treat these cells based on their distinct gene expression signatures in the future.

How does your research affect our view of current leukemia models?

Our findings from this study affect the view of current leukemia models from several perspectives. First, as a “liquid cancer,” leukemia has been thought to spread throughout the body uniformly. Here, we discovered an unexpected yet common form of leukemia expansion that is spatially confined to the bone marrow at single anatomical sites. These spatially confined leukemia clones expand aggressively without circulating. Our finding uncovers a potential bias in clinical diagnosis as current bone marrow biopsies only sample from one or two anatomical sites to evaluate leukemia progression.

Secondly, we provide the first experimental evidence of how combination therapy succeeds in targeting different cancer cells in ALL. Combination therapy was developed through decades of methodical clinical trials. Our findings provide the first mechanistic explanation for the therapy’s success and can help improve the treatment of other types of cancer by offering a new strategy to identify and characterize treatment-resistant cells.

Lastly, our findings highlight a critical sampling problem for the xenograft mouse model that is regularly used as the final assay before clinical trials in humans. We showed that less than 1% of primary leukemia cells are actually tested in the xenograft model. Moreover, the sampling problem is even worse when patient samples are collected after relapse, which is commonly used in therapeutic development. This indicates that we may need to better distinguish the data from patient samples at different disease stages.

How can you see your research influencing the future treatment of leukemia?

Our research revealed new gene targets for future treatments of leukemia. Certainly, more work remains to be done to translate our findings to clinical applications.

Moreover, we demonstrated a new technology to identify and characterize treatment-resistant cells through deciphering intratumoral heterogeneity. This technology can help improve treatments for other types of leukemia and cancers. Overall, I could see that our work may lead to future targeted therapy for leukemia and other cancers, and help advance precision medicine.

Image Credit: Zerbor/Shutterstock.com

Does your new technology have more global applications?

Yes. The new technology demonstrated in this study can be easily extended to studies of other diseases and biological processes. It can identify genes responsible for cellular proliferation, migration, and treatment response in vitro and in vivo. For example, it can be used to test new treatments in drug development, particularly in predicting the outcome of combination therapies.

What are the next steps for you and your research into the genetic signatures of cancer cells?

Based on the gene expression signatures that we have identified, we are now further investigating the role of these genes in leukemia progression across different disease stages. We are also using this new technology to study leukemia genesis.

In particular, we are interested in understanding how leukemia is developed from a heterogeneous cell population and why some people carrying leukemia-associated mutations remain healthy while others develop diseases. In addition to developing cancer treatment, we also hope to improve the early detection and prevention of this fatal disease.

Where can readers find more information?

About Professor Rong Lu

Dr. Rong Lu is the Richard N. Merkin Assistant Professor of Stem Cell Biology and Regenerative Medicine, Biomedical Engineering, Medicine, and Gerontology at USC, and a Leukemia & Lymphoma Society Scholar.Professor Rong Lu

Dr. Lu has been an active researcher in the stem cell field since 2003, working on embryonic stem cell and hematopoietic stem cell models. In 2007, She received Ph.D. in molecular biology under the guidance of Dr. Ihor R. Lemischka at Princeton University. After that, she developed expertise in cell biology during her postdoctoral training under the mentorship of Dr. Irving L. Weissman at Stanford University.

In 2014, Dr. Lu set up an independent research laboratory at the University of Southern California. In 2018, she became a Richard N. Merkin Assistant Professor. In 2019, she became a Leukemia & Lymphoma Society Scholar. In 2020, she received an NIH/NHLBI Emerging Investigator Award (R35).

Dr. Rong Lu’s lab studies stem cell coordination, regulation, and malfunction from a single cell perspective. Her research integrates a broad range of disciplines, including molecular biology, cell biology, systems biology, bioengineering, and bioinformatics.

Danielle Ellis

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Danielle Ellis

Danielle graduated with a 2:1 in Biological Sciences with Professional Training Year from Cardiff University. During her Professional Training Year, Danielle worked with registered charity the Frozen Ark Project, creating and promoting various forms of content within their brand guidelines. Danielle has a great appreciation and passion for science communication and enjoys reading non-fiction and fiction in her spare time. Her other interests include doing yoga, collecting vinyl, and visiting museums.

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