In brief

Using single-cell RNA sequencing and machine learning, the team created a platform for predicting tyrosine kinase inhibitor treatment responses of chronic myeloid leukaemia patients based on the expression profiles of their leukemic stem cells and natural killer cells.

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The root of cancer drug resistance

5 Sep 2023

Unique gene signatures associated with drug-resistant leukaemia paves the way for improved clinical strategies to help these patients.

Cancer remains a leading cause of death worldwide, but clinical outcomes for some cancer patients have dramatically improved recently. As an example, targeted therapies called tyrosine kinase inhibitors (TKI) can significantly boost survival rates for patients with chronic myeloid leukaemia (CML).

However, this is only good news for some. Frustratingly, about 40 percent of CML patients develop primary resistance to TKIs, rendering this therapeutic mode ineffective.

“In the absence of reliable biomarkers, patients undergo a ‘wait and watch’ approach to see whether they are optimally responding to the treatment,” explained Shyam Prabhakar, Associate Director, Spatial and Single Cell Systems at A*STAR’s Genome Institute of Singapore (GIS).

During this uncertain period, patients may end up switching treatment protocols, which delays them from receiving the best possible intervention for their condition.

Prabhakar and colleagues collaborated with researchers from Duke-NUS Medical School, Singapore, Singapore General Hospital and Cancer Science Institute of Singapore to uncover the factors that contribute to TKI resistance as a means of improving CML patient outcomes. The researchers built a comprehensive atlas of the molecular features of CML in the hopes of identifying specific biomarkers associated with TKI sensitivity or resistance.

The team used a technique called single-cell RNA sequencing (scRNA-seq) to study individual cells in the bone marrow of CML patients collected before they received TKI treatment. They identified eight gene expression features in bone marrow cells that correlated with either a positive response to TKIs or extreme resistance leading to a more advanced stage of the disease called blast crisis (BC).

Next, the researchers turned to machine learning algorithms to help guide predictions on whether a patient is likely to respond to TKI. They found that certain gene expression profiles in leukemic stem cells and natural killer cells can predict the response to a TKI called imatinib with over 80 percent accuracy. This enabled the researchers to predict which patients who would eventually progress to BC without any false positives.

Prabhakar said that their single-cell atlas could put an end to the risky ‘wait and watch’ clinical approach currently used to tackle CML treatment: “Accurately identifying patients who are likely to respond sub-optimally to first-line TKI treatment can provide clinicians with valuable insights, enabling them to administer more optimal second-line treatments from the outset.”

Ongoing research efforts focus on identifying the origin of TKI-resistant gene expression signatures and validating these biomarkers in larger patient cohorts to drive the discovery of potential therapeutic targets and improved patient outcomes.

The A*STAR-affiliated researchers contributing to this research are from the Genome Institute of Singapore (GIS).

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Krishnan, V., Schmidt, F., Nawaz, Z., Venkatesh, P.N., Lee, K.L., et al. A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia. Blood 141, 2738–2755 (2023). | article

About the Researcher

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Shyam Prabhakar

Associate Director, Spatial and Single Cell Systems and Senior Group Leader, Systems Biology and Data Analytics

Genome Institute of Singapore (GIS)
Shyam Prabhakar obtained a BTech in Electronics Engineering from IIT Madras and a PhD in Applied Physics from Stanford University. He was sole recipient of the 2001 American Physical Society PhD thesis award for Beam Physics. Following postdoctoral fellowships in Mathematics at Stanford and Genomics at the Lawrence Berkeley National Laboratory, he joined the Genome Institute of Singapore (GIS). He heads the Singapore Single Cell Network and the GIS Spatial and Single Cell Genomics Platform (S2GP), an open facility for all researchers in Singapore. He co-leads the Genetic Diversity Network within the international Human Cell Atlas (HCA) single cell consortium; HCA Asia; the Asian Epigenome Network; and A*STAR’s AI and Analytics (AI3) Horizontal Programme. He is currently Associate Director, Spatial and Single Cell Systems and Senior Group Leader, Systems Biology and Data Analytics at GIS.

This article was made for A*STAR Research by Wildtype Media Group