Cancer Cell-Specific Major Histocompatibility Complex II Expression as a Determinant of the Immune Infiltrate Organization and Function in the NSCLC Tumor Microenvironment

Dual molecule targeting HDAC6 leads to intratumoral CD4+ cytotoxic lymphocytes recruitment through MHC-II upregulation on lung cancer cells

BACKGROUND

Despite the current therapeutic treatments including surgery, chemotherapy, radiotherapy and more recently immunotherapy, the mortality rate of lung cancer stays high. Regarding lung cancer, epigenetic modifications altering cell cycle, angiogenesis and programmed cancer cell death are therapeutic targets to combine with immunotherapy to improve treatment success. In a recent study, we uncovered that a molecule called QAPHA ((E)-3-(5-((2-cyanoquinolin-4-yl)(methyl)amino)-2-methoxyphenyl)-N-hydroxyacrylamide) has a dual function as both a tubulin polymerization and HDAC inhibitors. Here, we investigate the impact of this novel dual inhibitor on the immune response to lung cancer.

METHODS

To elucidate the mechanism of action of QAPHA, we conducted a chemical proteomics analysis. Using an in vivo mouse model of lung cancer (TC-1 tumor cells), we assessed the effects of QAPHA on tumor regression. Tumor infiltrating immune cells were characterized by flow cytometry.

RESULTS

In this study, we first showed that QAPHA effectively inhibited histone deacetylase 6, leading to upregulation of HSP90, cytochrome C and caspases, as revealed by proteomic analysis. We confirmed that QAPHA induces immunogenic cell death (ICD) by expressing calreticulin at cell surface in vitro and demonstrated its efficacy as a vaccine in vivo. Remarkably, even at a low concentration (0.5 mg/kg), QAPHA achieved complete tumor regression in approximately 60% of mice treated intratumorally, establishing a long-lasting anticancer immune response. Additionally, QAPHA treatment promoted the infiltration of M1-polarized macrophages in treated mice, indicating the induction of a pro-inflammatory environment within the tumor. Very interestingly, our findings also revealed that QAPHA upregulated major histocompatibility complex class II (MHC-II) expression on TC-1 tumor cells both in vitro and in vivo, facilitating the recruitment of cytotoxic CD4+T cells (CD4+CTL) expressing CD4+, NKG2D+, CRTAM+, and Perforin+. Finally, we showed that tumor regression strongly correlates to MHC-II expression level on tumor cell and CD4+ CTL infiltrate.

CONCLUSION

Collectively, our findings shed light on the discovery of a new multitarget inhibitor able to induce ICD and MHC-II upregulation in TC-1 tumor cell. These two processes participate in enhancing a specific CD4+ cytotoxic T cell-mediated antitumor response in vivo in our model of lung cancer. This breakthrough suggests the potential of QAPHA as a promising agent for cancer treatment.

DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices

A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.

A Population of Tumor-Infiltrating CD4+ T Cells Co-Expressing CD38 and CD39 Is Associated with Checkpoint Inhibitor Resistance

PURPOSE

We previously showed that elevated frequencies of peripheral blood CD3+CD4+CD127-GARP-CD38+CD39+ T cells were associated with checkpoint immunotherapy resistance in patients with metastatic melanoma. In the present study, we sought to further investigate this population of ectoenzyme-expressing T cells (Teee).

EXPERIMENTAL DESIGN

Teee derived from the peripheral blood of patients with metastatic melanoma were evaluated by bulk RNA-sequencing (RNA-seq) and flow cytometry. The presence of Teee in the tumor microenvironment was assessed using publically available single-cell RNA-seq datasets of melanoma, lung, and bladder cancers along with multispectral immunofluorescent imaging of melanoma patient formalin-fixed, paraffin-embedded specimens. Suppressive function of Teee was determined by an in vitro autologous suppression assay.

RESULTS

Teee had phenotypes associated with proliferation, apoptosis, exhaustion, and high expression of inhibitory molecules. Cells with a Teee gene signature were present in tumors of patients with melanoma, lung, and bladder cancers. CD4+ T cells co-expressing CD38 and CD39 in the tumor microenvironment were preferentially associated with Ki67- CD8+ T cells. Co-culture of patient Teee with autologous T cells resulted in decreased proliferation of target T cells. High baseline intratumoral frequencies of Teee were associated with checkpoint immunotherapy resistance and poor overall survival in patients with metastatic melanoma.

CONCLUSIONS

These results demonstrate that a novel population of CD4+ T cells co-expressing CD38 and CD39 is found both in the peripheral blood and tumor of patients with melanoma and is associated with checkpoint immunotherapy resistance.

DIMPLE: AN R PACKAGE TO QUANTIFY, VISUALIZE, AND MODEL SPATIAL CELLULAR INTERACTIONS FROM MULTIPLEX IMAGING WITH DISTANCE MATRICES

The tumor microenvironment (TME) is a complex ecosystem containing tumor cells, other surrounding cells, blood vessels, and extracellular matrix. Recent advances in multiplexed imaging technologies allow researchers to map several cellular markers in the TME at the single cell level while preserving their spatial locations. Evidence is mounting that cellular interactions in the TME can promote or inhibit tumor development and contribute to drug resistance. Current statistical approaches to quantify cell-cell interactions do not readily scale to the outputs of new imaging technologies which can distinguish many unique cell phenotypes in one image. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. In application of DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.

Durable responses to alectinib in murine models of EML4-ALK lung cancer requires adaptive immunity

Lung cancers bearing oncogenic EML4-ALK fusions respond to targeted tyrosine kinase inhibitors (TKIs; e.g., alectinib), with variation in the degree of shrinkage and duration of treatment (DOT). However, factors that control this response are not well understood. While the contribution of the immune system in mediating the response to immunotherapy has been extensively investigated, less is known regarding the contribution of immunity to TKI therapeutic responses. We previously demonstrated a positive association of a TKI-induced interferon gamma (IFNγ) transcriptional response with DOT in EGFR-mutant lung cancers. Herein, we used three murine models of EML4-ALK lung cancer to test the role for host immunity in the alectinib therapeutic response. The cell lines (EA1, EA2, EA3) were propagated orthotopically in the lungs of immunocompetent and immunodeficient mice and treated with alectinib. Tumor volumes were serially measured by μCT and immune cell content was measured by flow cytometry and multispectral immunofluorescence. Transcriptional responses to alectinib were assessed by RNAseq and secreted chemokines were measured by ELISA. All cell lines were similarly sensitive to alectinib in vitro and as orthotopic tumors in immunocompetent mice, exhibited durable shrinkage. However, in immunodeficient mice, all tumor models rapidly progressed on TKI therapy. In immunocompetent mice, EA2 tumors exhibited a complete response, whereas EA1 and EA3 tumors retained residual disease that rapidly progressed upon termination of TKI treatment. Prior to treatment, EA2 tumors had greater numbers of CD8+ T cells and fewer neutrophils compared to EA1 tumors. Also, RNAseq of cancer cells recovered from untreated tumors revealed elevated levels of CXCL9 and 10 in EA2 tumors, and higher levels of CXCL1 and 2 in EA1 tumors. Analysis of pre-treatment patient biopsies from ALK+ tumors revealed an association of neutrophil content with shorter time to progression. Combined, these data support a role for adaptive immunity in durability of TKI responses and demonstrate that the immune cell composition of the tumor microenvironment is predictive of response to alectinib therapy.

Multiplexed In Situ Spatial Protein Profiling in the Pursuit of Precision Immuno-Oncology for Patients with Breast Cancer

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of many solid tumors. In breast cancer (BC), immunotherapy is currently approved in combination with chemotherapy, albeit only in triple-negative breast cancer. Unfortunately, most patients only derive limited benefit from ICIs, progressing either upfront or after an initial response. Therapeutics must engage with a heterogeneous network of complex stromal-cancer interactions that can fail at imposing cancer immune control in multiple domains, such as in the genomic, epigenomic, transcriptomic, proteomic, and metabolomic domains. To overcome these types of heterogeneous resistance phenotypes, several combinatorial strategies are underway. Still, they can be predicted to be effective only in the subgroups of patients in which those specific resistance mechanisms are effectively in place. As single biomarker predictive performances are necessarily suboptimal at capturing the complexity of this articulate network, precision immune-oncology calls for multi-omics tumor microenvironment profiling in order to identify unique predictive patterns and to proactively tailor combinatorial treatments. Multiplexed single-cell spatially resolved tissue analysis, through precise epitope colocalization, allows one to infer cellular functional states in view of their spatial organization. In this review, we discuss-through the lens of the cancer-immunity cycle-selected, established, and emerging markers that may be evaluated in multiplexed spatial protein panels to help identify prognostic and predictive patterns in BC.

MIAMI: mutual information-based analysis of multiplex imaging data

MOTIVATION

Studying the interaction or co-expression of the proteins or markers in the tumor microenvironment of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness.

RESULTS

We present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies.

AVAILABILITY AND IMPLEMENTATION

The associated R package can be found here, https://github.com/sealx017/MIAMI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cellular engagement and interaction in the tumor microenvironment predict non-response to PD-1/PD-L1 inhibitors in metastatic non-small cell lung cancer

Immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 agents have improved the survival of patients with metastatic non-small cell lung cancer (mNSCLC). Tumor PD-L1 expression is an imperfect biomarker as it does not capture the complex interactions between constituents of the tumor microenvironment (TME). Using multiplex fluorescent immunohistochemistry (mfIHC), we modeled the TME to study the influence of cellular distribution and engagement on response to ICI in mNSCLC. We performed mfIHC on pretreatment tissue from patients with mNSCLC who received ICI. We used primary antibodies against CD3, CD8, CD163, PD-L1, pancytokeratin, and FOXP3; simple and complex phenotyping as well as spatial analyses was performed. We analyzed 68 distinct samples from 52 patients with mNSCLC. Patients were 39–79 years old (median 67); 44% were male and 75% had adenocarcinoma histology. The most used ICI was atezolizumab (48%). The percentage of PD-L1 positive epithelial tumor cells (EC), degree of cytotoxic T lymphocyte (CTL) engagement with EC, and degree of CTL engagement with helper T lymphocytes (HTL) were significantly lower in non-responders versus responders (p = 0.0163, p = 0.0026 and p = 0.0006, respectively). The combination of these 3 characteristics generated the best sensitivity and specificity to predict non-response to ICI and was also associated with shortened overall survival (p = 0.0271). The combination of low CTL engagement with EC and HTL along with low expression of EC PD-L1 represents a state of impaired endogenous immune reactivity. Together, they more precisely identified non-responders to ICI compared to PD-L1 alone and illustrate the importance of cellular interactions in the TME.

DenVar: density-based variation analysis of multiplex imaging data

Summary

Multiplex imaging platforms have become popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies.

Availability and implementation

The associated R package can be found here, https://github.com/sealx017/DenVar.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Pancancer Analyses Reveal Genomics and Clinical Characteristics of the SETDB1 in Human Tumors

Background. Malignant tumor is one of the most common diseases that seriously affect human health. The prior literature has reported the biological function and potential therapeutic targets of SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) as an oncogene. However, SETDB1 has rarely been analyzed from a pan-cancer perspective. Methods. Bioinformatics analysis tools and databases, including GeneCards, National Center for Biotechnology Information (NCBI), UniProt, Illustrator for Biological Sequences (IBS), Human Protein Atlas (HPA), GEPIA, TIMER2, Sangerbox 3.0, UALCAN, Kaplan-Meier (K-M) plotter, cBioPortal, Catalogue Of Somatic Mutations In Cancer (COSMIC), PhosphoSitePlus, TISIDB, STRING, and GeneMANIA, were utilized to clarify the biological functions and clinical significance of SETDB1 from a pan-cancer perspective. Results. In this study, the pan-cancer analysis demonstrated that SETDB1 showed significantly differential expression in most tumor tissues and paracancerous tissues, and SETDB1 expression was associated with clinicopathological features and clinical prognosis. We also found that SETDB1 mutations occurred in most tumors and were related to tumorigenesis. In addition, DNA methylation of SETDB1 primarily occurred at the cg10444928 site and was associated with prognosis in several human tumors. The predicted phosphorylation site of SETDB1 was Ser1006. We found that SETDB1 was significantly related to the specific tumor-infiltrating immune cell populations and expression of clinically targetable immune checkpoints and may be a promising immunotherapy target. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses also indicated that SETDB1 may function as crucial regulator in carcinogenesis of human cancers. Conclusions. SETDB1 is an important oncogene involved in tumorigenesis and tumor progression through different biological mechanisms. Furthermore, SETDB1 may be a potential therapeutic target for cancer treatment.