Predictive Immune Modeling of Solid Tumors

LaFranzo, Natalie A.; Flanagan, Kevin C.; Quintanilha, Danielle

doi:10.3791/60645

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 Gene expression and immune cell quantification were performed using Cofactor's ImmunoPrism platform. 22 The ImmunoPrism platform is an RNA-based approach to characterizing key immune signals with high sensitivity and specificity. As described previously in Schillebeeckx et al, the platform uses a targeted-capture sequencing and machine learning approach to generate 'health expression models' for accurately identifying percentages of immune cell populations in tumor tissue from RNA expression data.…”

Section: Rna Extraction and Predictive Immune Modeling Analysismentioning

confidence: 99%

CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

Schroeder

LaFranzo

LaFleur

et al. 2021

J Immunother Cancer

View full text Add to dashboard Cite

BackgroundDedifferentiated liposarcoma (DDLPS) is one of the most common soft tissue sarcoma subtypes and is devastating in the advanced/metastatic stage. Despite the observation of clinical responses to PD-1 inhibitors, little is known about the immune microenvironment in relation to patient prognosis.MethodsWe performed a retrospective study of 61 patients with DDLPS. We completed deep sequencing of the T-cell receptor (TCR) β-chain and RNA sequencing for predictive modeling, evaluating both immune markers and tumor escape genes. Hierarchical clustering and recursive partitioning were employed to elucidate relationships of cellular infiltrates within the tumor microenvironment, while an immune score for single markers was created as a predictive tool.ResultsAlthough many DDLPS samples had low TCR clonality, high TCR clonality combined with low T-cell fraction predicted lower 3-year overall survival (p=0.05). Higher levels of CD14+ monocytes (p=0.02) inversely correlated with 3-year recurrence-free survival (RFS), while CD4+ T-cell infiltration (p=0.05) was associated with a higher RFS. Genes associated with longer RFS included PD-1 (p=0.003), ICOS (p=0.006), BTLA (p=0.033), and CTLA4 (p=0.02). In a composite immune score, CD4+ T cells had the strongest positive predictive value, while CD14+ monocytes and M2 macrophages had the strongest negative predictive values.ConclusionsImmune cell infiltration predicts clinical outcome in DDLPS, with CD4+ cells associated with better outcomes; CD14+ cells and M2 macrophages are associated with worse outcomes. Future checkpoint inhibitor studies in DDLPS should incorporate immunosequencing and gene expression profiling techniques that can generate immune landscape profiles.

show abstract

Section: Rna Extraction and Predictive Immune Modeling Analysismentioning

confidence: 99%

CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

Schroeder

LaFranzo

LaFleur

et al. 2021

J Immunother Cancer

View full text Add to dashboard Cite

show abstract

“…Populations were confirmed by flow cytometry using the following markers: M1 macrophages are CD80+/CCR7+/CD206 low/CD209 low and M2 macrophages are CD206+/CD209+/CD80 low/CCR7 low. LaFranzo et al describes in detail the Cofactor Genomics predictive immune modeling workflow (16,20), which is summarized as follows:…”

Section: Cofactor Immunoprism ® Assaymentioning

confidence: 99%

Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

et al. 2021

View full text Add to dashboard Cite

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy worldwide, as patients are typically diagnosed at a late stage and eventually develop chemoresistant disease following front-line platinum-taxane based therapy. Only modest results have been achieved with PD-1 based immunotherapy in ovarian cancer patients, despite the fact that immunological responses are observed in EOC patients. Therefore, the goal of this present study was to identify novel immune response genes and cell subsets significantly associated with improved high grade serous ovarian cancer (HGSOC) patient prognosis. A transcriptomic-based immune modeling analysis was employed to determine levels of 8 immune cell subsets, 10 immune escape genes, and 22 co-inhibitory/co-stimulatory molecules in 26 HGSOC tumors. Multidimensional immune profiling analysis revealed CTLA-4, LAG-3, and Tregs as predictive for improved progression-free survival (PFS). Furthermore, the co-stimulatory receptor ICOS was also found to be significantly increased in patients with a longer PFS and positively correlated with levels of CTLA-4, PD-1, and infiltration of immune cell subsets. Both ICOS and LAG-3 were found to be significantly associated with improved overall survival in The Cancer Genome Atlas (TCGA) ovarian cancer cohort. Finally, PVRL2 was identified as the most highly expressed transcript in our analysis, with immunohistochemistry results confirming its overexpression in HGSOC samples compared to normal/benign. Results were corroborated by parallel analyses of TCGA data. Overall, this multidimensional immune modeling analysis uncovers important prognostic immune factors that improve our understanding of the unique immune microenvironment of ovarian cancer.

show abstract

Multidimensional biomarker predicts disease control in response to immunotherapy in recurrent or metastatic head and neck squamous-cell carcinoma

Flanagan,

Earls,

Schillebeeckx

et al. 2023

J Cancer Res Clin Oncol

View full text Add to dashboard Cite

Purpose Anti-PD-1 therapy provides clinical benefit in 40–50% of patients with relapsed and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC). Selection of anti- PD-1 therapy is typically based on patient PD-L1 immunohistochemistry (IHC) which has low specificity for predicting disease control. Therefore, there is a critical need for a clinical biomarker that will predict clinical benefit to anti-PD-1 treatment with high specificity. Methods Clinical treatment and outcomes data for 103 RM-HNSCC patients were paired with RNA-sequencing data from formalin-fixed patient samples. Using logistic regression methods, we developed a novel biomarker classifier based on expression patterns in the tumor immune microenvironment to predict disease control with monotherapy PD-1 inhibitors (pembrolizumab and nivolumab). The performance of the biomarker was internally validated using out-of-bag methods. Results The biomarker significantly predicted disease control (65% in predicted non-progressors vs. 17% in predicted progressors, p < 0.001) and was significantly correlated with overall survival (OS; p = 0.004). In addition, the biomarker outperformed PD-L1 IHC across numerous metrics including sensitivity (0.79 vs 0.64, respectively; p = 0.005) and specificity (0.70 vs 0.61, respectively; p = 0.009). Conclusion This novel assay uses tumor immune microenvironment expression data to predict disease control and OS with high sensitivity and specificity in patients with RM-HNSCC treated with anti-PD-1 monotherapy.

show abstract

Predictive Immune Modeling of Solid Tumors

Cited by 4 publications

References 16 publications

CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

Multidimensional biomarker predicts disease control in response to immunotherapy in recurrent or metastatic head and neck squamous-cell carcinoma

Contact Info

Product

Resources

About