Cellular and genomic disease signature of peripheral blood mononuclear cells in patients with malignant pleural mesothelioma

Background: Recent data on the incidence malignant pleural mesothelioma
(MPM) and the continued large-scale use of asbestos throughout the
developing world portends an epidemic of asbestos-related disease. MPM is
an aggressive and fatal cancer with few treatment options. Recent advances
in large scale genomic and high throughput cellular analyses now provide
the tools to more easily attain markers of disease status and potential
responsiveness to immunotherapeutics.
Materials and Methods: Here we present pre-treatment cellular and genomic
biomarker data on a cohort of chemotherapy-naïve MPM patients, and
demographically matched healthy donors (HD). MPM patients were enrolled in
a Phase 1b study utilizing CRS-207, a live, attenuated Listeria monocytogenes
strain engineered to express the tumor-associated antigen, mesothelin. Four
different multi-color flow cytometry panels were used to provide resolution on
major immune cell populations of T cells, γδ T cells, B Cells, dendritic cells,
monocytes, and natural killer cells. Together, these panels provided deeper
resolution on 39 distinct subpopulations of major immune cell subsets. RNA
from these cells was used to perform multiplex gene expression analysis on
770 genes using the Nanostring nCounter PanCancer Pathway Panel.
Results: FACS analysis yielded numerous subpopulations with statistically
significant differences between MPM patients and healthy controls. Differences
in immune populations were analyzed by median and significant findings
included populations of CD4+ T cells, CD8+ T cells, B cells, classical
monocytes, and monocytic myeloid derived suppressor cells*. Class
comparison and hierarchical clustering of gene expression data revealed
genomic markers that were significantly expressed in MPM compared to
healthy controls. Immune subset deconvolution of gene expression data
provided similar findings as FACS analysis and corroborated this disease
signature across experimental platforms.
Conclusions: Understanding a patient’s biological disease signature can
aide in diagnosis, as well as in making informed choices about therapies
amidst the complex and broadening immunotherapeutic landscape. Until
recently, existing biomarker data in MPM has been limited to a small number
of serological markers and limited immune analysis. Here, we present the
first comprehensive report of a MPM disease signature from the cellular and
genomic perspectives. Correlation of patient baseline disease signatures
with treatment outcome may yield biomarkers predictive of treatment
efficacy. Predictive signatures are being investigated in the on-going Phase
1b study of CRS-207 and chemotherapy, as well as in the Phase 2 study of
CRS-207 with pembrolizumab in MPM patients who failed prior treatment.
*Inclusion of additional subjects confirmed the significance of all immune cell subsets
except for MDSCs.