= Emerging. More than 5 years before clinical availability. (16.60%, 2024)
= Expected to be clinically available in 1 to 4 years. (37.02%, 2024)
= Clinically available now. (46.38%, 2024)
MSACL 2024 : Liebenberg

MSACL 2024 Abstract

Self-Classified Topic Area(s): Imaging > Precision Medicine

Podium Presentation in Steinbeck 3 on Wednesday at 13:30 (Chair: Robert Voyksner / Jessica Colón-Franco)

Alterations in Glutamate to Glutamine Ratios Detected by Direct Mass Spectrometry Techniques Allow Diagnosis and Molecular Subtyping of Breast Cancer

Keziah E. Liebenberg (1), Erin Craig (2), Meredith L. Spradlin (1), Michael F. Keating (1), Robert Tibshirani (2), Livia S. Eberlin (1)
(1) Baylor College of Medicine, Houston, TX, USA, (2) Stanford University, Stanford, CA, USA

Keziah Liebenberg (Presenter)
Baylor College of Medicine


Glutamine metabolism serves as a critical energy source for cancer cells, where it plays vital roles in the production of ATP and biosynthesis of nucleotides and amino acids. Glutaminolysis is upregulated in a variety of cancer types. Recent evidence suggests a correlation between molecular subtypes and glutaminolysis alterations in breast cancer. We therefore hypothesize that detection of glutamine and related metabolites by direct mass spectrometry techniques can provide the basis for diagnosing and subtyping breast cancer. Here we employed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and the MasSpec Pen to investigate alterations in the glutamine to glutamate ratios in different breast cancer subtypes. The results indicate that these methods have the potential to serve as valuable tools for clinical diagnosis and subtyping of breast cancer.

The primary objective of this study is the utilization of direct mass spectrometry techniques to identify significant alterations in glutaminolysis for the detection and subtyping of breast cancer directly from tissues.

A retrospective statistical analysis was conducted on data collected from breast cancer and normal tissues using DESI-MSI and the MasSpec Pen. For the DESI-MSI and MasSpec Pen studies, 122 and 159 breast tissue samples, respectively, were collected and analyzed with an orbitrap mass spectrometer. The log ratio of ion intensities of glutamate to glutamine was calculated for normal breast tissues as well as for the four major breast cancer molecular subtypes: luminal A, luminal B, human epidermal growth factor receptor 2 (HER-2), and triple negative breast cancer (TNBC). With regards to the data acquired with DESI-MSI, the log glutamate to glutamine ratios (GGRs) were calculated per-pixel (n=33,328) and averaged per-patient (n=122). A permutation test was conducted to determine statistical significance between the log GGR values of different classes. The mean log GGR values were permuted 100,000 times between two tissue types, where the difference in log GGR means was calculated following each iteration. The p-value was measured by determining the number of test statistics equal to or greater than the initial statistic prior to the permutations. The p-values were adjusted for multiple comparisons using the Benjamini-Hochberg method.

During glutaminolysis, glutamine is converted to glutamate by the enzyme glutaminase. A metric for the dysregulation of glutaminolysis can be provided by calculating the GGR, for which a higher GGR value correlates to an upregulation of glutaminolysis. In our study, statistical analysis of the DESI-MSI dataset revealed that the log GGR was significantly increased in breast cancer (mean = 0.85) compared to normal breast tissue (mean = -0.07; p-value < 1e-6). Similarly, the log GGR value was found to be significantly increased in breast cancer (mean = 0.47) compared to normal breast tissue (mean = -0.58; p-value 2e-6) in the MasSpec pen dataset. These results reveal that glutaminolysis is potentially upregulated in breast cancer compared to normal breast tissue. Interestingly, the log GGR values of each breast cancer subtype was significantly increased when compared to normal breast tissue in the DESI-MSI dataset. TNBC, the most aggressive breast cancer subtype, exhibited the greatest log GGR value (mean = 1.53; p-value <1e-6). Luminal A and luminal B, the least aggressive breast cancer subtypes, displayed the lowest log GGR values (luminal A: mean = 0.64; p-value = 8e-4 and luminal B: mean = 0.63; p-value = 0.0013). Based on their log GGR values, these two luminal subtypes could not be distinguished from one another, as they have a similar molecular profile, but could be collectively distinguished from normal breast tissue. The HER-2 subtype also displayed significant upregulation of glutaminolysis compared to normal breast tissue (log GGR mean = 1.03; p-value = 5e-5). Further statistical analyses have revealed that all breast cancer subtypes, excluding luminal A and luminal B, could be individually distinguished from one another based on their log GGR value. Preliminary immunofluorescence experiments have revealed a correlation between an increased glutaminase expression and breast cancer invasion, where invasive ductal carcinoma tissue revealed a greater expression of glutaminase compared to ductal carcinoma in situ. A simple logistic regression model is currently being developed to determine the utility of the log GGR value for direct diagnosis and subtyping of breast cancer tissue.

Glutaminolysis was found to be significantly dysregulated in breast cancer compared to normal breast tissue. Furthermore, the HER-2 and TNBC subtypes display the greatest dysregulation of glutaminolysis which correlates to literature supporting the relationship between increased glutaminolysis and cancer aggressiveness. This study has shown that the log GGR value has the potential to provide diagnostic and therapeutic information as it can identify breast cancer subtypes that could be vulnerable to treatment with glutaminase inhibitors. Collectively, these results show that DESI-MSI and the MasSpec Pen serve as powerful techniques to probe dysregulations in glutaminolysis in breast tissues and are potentially powerful for rapid clinical diagnosis and subtyping of breast cancer.

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