MSACL 2023 Abstract

Introduction:
The development of immune checkpoint inhibitors (ICIs) has made significant strides for the treatment of a variety of refractory cancer types. The use of ICIs is a promising strategy for the treatment of triple negative breast cancer (TNBC), for which treatment options are limited. Clinical trials involving the treatment of TNBC patients with ICIs combined with chemotherapy have provided a growing body of evidence for patient response, yet the results related to response to treatment are conflicting. Furthermore, across several clinical trials, a small subset of patients showed durable responses to combinatory treatment, while others acquired resistance, and many did not benefit from the addition of ICIs to their chemotherapy regimen. The most widely used predictive biomarker for patient response to ICIs is the tumor expression of PD-L1, which serves to suppress the immune system through its interaction with PD-1, a checkpoint receptor expressed on T-cells. PD-L1 expression has failed, however, to reliably differentiate between responders and non-responders to ICI treatment in patients with TNBC. Further, the assays for determining PD-L1 expression have also not been standardized in the clinic. Treatment with ICIs is accompanied by severe, life-long side effects, and it is therefore crucial to correctly identify patients who will benefit from treatment. To better understand the key factors that regulate patient response to ICI treatment, a growing area of research has focused on metabolism and the tumor microenvironment (TME). There is mounting evidence that tumors can evade immune attack by not only interacting with immunosuppressive checkpoint proteins such as PD-1, but by also metabolically suppressing the TME and hindering T-cell function. It is therefore important to consider the metabolic interplay occurring in the TME and its role in determining patient response and acquired resistance to ICI treatment.

Objective:
The primary objective is to apply desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to characterize the metabolic profiles of sensitive and resistant TNBC tissues treated with ICI treatment and chemotherapy. Furthermore, we aim to delineate between the metabolic profiles of regions of viable tumor and of the TME such as stromal tissue. This will serve as a first step towards understanding the metabolic interplay between tumor cells and the TME, as well as identifying potential predictive biomarkers of response.

Methods:
The Zhang laboratory at Baylor College of Medicine has established a series of murine TNBC models and isogenic derivates with acquired resistance to ICI treatment. Sensitive and resistant murine TNBC tissues collected both before and after ICI and chemotherapy treatment, were obtained from the Zhang laboratory and stored at -80°C until analysis. The tissues were sectioned at a thickness of 12µm. The TNBC tissue sections were analyzed using a Thermo Orbitrap Q-Exactive HF mass spectrometer suited with a DESI-MS imaging interface. DESI-MSI was performed in both the negative and positive ion modes at a spatial resolution of 200µm. Following DESI-MSI, the tissue sections were stained with hematoxylin and eosin (H&E) and distinct histological regions were annotated by a pathologist. Mass spectra correlating to regions of viable tumor were extracted and statistical analyses performed using significance analyses of microarrays (SAM).

Results:
The DESI-MSI method was optimized in both ion polarities for the detection of lipid species directly from TNBC tissue sections. In the negative ion mode, a wide variety of lipid species were detected including cardiolipins, ceramides, fatty acids, phosphatidylserines, phosphatidylinositols and phosphatidylethanolamines. In the positive ion mode, lipid species including triacylglycerols, diacylglycerols and phosphatidylcholines were detected. The mass spectra obtained in both ion polarities presented trends that were characteristic of sensitive and resistant TNBC tissues. We are currently performing statistical analyses using SAM to determine specific lipid species which are significantly altered in tissues with resistance to treatment.

Conclusions:
DESI-MSI provides spatial lipid profiling of murine TNBC tissues with both sensitivity and resistance to combination ICI and chemotherapy treatment. It further provides delineation between the lipid profiles of tumor regions and regions of the TME, providing insights into metabolic interplay between these regions.