Layilin Anchors Regulatory T Cells in Skin

Regulatory T cells (Tregs) reside in nonlymphoid tissues where they carry out unique functions. The molecular mechanisms responsible for Treg accumulation and maintenance in these tissues are relatively unknown. Using an unbiased discovery approach, we identified LAYN (layilin), a C-type lectin-like receptor, to be preferentially and highly expressed on a subset of activated Tregs in healthy and diseased human skin. Expression of layilin on Tregs was induced by TCR-mediated activation in the presence of IL-2 or TGF-β. Mice with a conditional deletion of layilin in Tregs had reduced accumulation of these cells in tumors. However, these animals somewhat paradoxically had enhanced immune regulation in the tumor microenvironment, resulting in increased tumor growth. Mechanistically, layilin expression on Tregs had a minimal effect on their activation and suppressive capacity in vitro. However, expression of this molecule resulted in a cumulative anchoring effect on Treg dynamic motility in vivo. Taken together, our results suggest a model whereby layilin facilitates Treg adhesion in skin and, in doing so, limits their suppressive capacity. These findings uncover a unique mechanism whereby reduced Treg motility acts to limit immune regulation in nonlymphoid organs and may help guide strategies to exploit this phenomenon for therapeutic benefit.

Abstract 2244: Evaluation of large antibody panels in single-cell genomic immunophenotyping of fresh and preserved human leukocytes

Background: Multi-omic single cell studies are revolutionizing knowledge of the tumor immune microenvironment and positioned to detect rare cell subsets that correlate with response to immunotherapy. DNA-barcoded antibody panels enable the unambiguous classification of cell subsets via simultaneous measurement of surface proteins and transcriptome. However, single-cell sequencing performs best on fresh samples while multi-center clinical trials routinely obtain frozen specimens. Peripheral blood mononuclear cells (PBMCs) are commonly used tissues for charactering immune cell types, dynamics, and signaling in human studies. Our group has pioneered analyses of the tumor micro-environment in several cancer types, and also developed genomic capabilities with the resolution of flow cytometry over thousands of markers. We hypothesized that the development of advanced bioinformatics tools and large antibody panels (137 antibodies) can augment the analysis of frozen samples.

Methods: Single cell genomics on matched fresh and frozen human PBMCs was performed using the TotalSeq-C human universal cocktail (BioLegend) and the 10x Genomics Chromium single-cell platform. We created bioinformatics pipelines to compare the quality control metrics, isotype matched control performance, and cell subtype analyses for fresh and frozen samples. Advanced visualization and analysis tools were developed to allow interrogation of the data in a flow cytometry paradigm, including complex gating strategies to identify rarer cell subtypes.

Results: In terms of library quality, fresh and frozen cells had similar quality control parameters, although mean reads per cell were moderately lower for frozen vs. fresh tissue in both the RNA and antibody arms of the study. RNA and antibody data were integrated, clustered, and annotated for cell type and subtype. Cell type proportions were comparable across fresh and frozen samples. A panel of 7 antibody isotype controls demonstrated limited non-specific binding. Comparison of individual surface proteins across fresh and frozen samples revealed similar distributions in most cases, although signal was attenuated for a small number of epitopes in frozen samples. Our bioinformatics approaches allowed rare cell subtype identification with sensitivity and selectivity rivaling flow cytometry.

Conclusions: Multi-modal single cell genomics, including a 137 antibody panel, and novel bioinformatics analytics reveal that fresh and frozen PBMC samples are largely concordant at both the transcriptome and cell-surface protein levels. Our pilot study is being expanded to larger scale clinical settings and may help characterize important cell populations that are associated with disease status, pharmacodynamics, and therapeutic response.

Citation Format: Nathan O. Siemers, Jasmine Chen, Parminder Mankoo, Shazia Ilyas. Evaluation of large antibody panels in single-cell genomic immunophenotyping of fresh and preserved human leukocytes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2244.

Abstract 5550: Translational biomarkers for SAR439459, an anti-TGFβ antibody for cancer immunotherapy

TGFβ is a potent immunosuppressive cytokine, acting on multiple cell types of both the innate and adaptive immune systems. Emerging preclinical and clinical data implicate TGFβ in tumor immune evasion, poor prognosis and resistance to PD1/PDL1 directed checkpoint therapy. Thus, neutralization of TGFβ is suggested to relieve immunosuppression via mechanisms that are distinct yet complementary to checkpoint inhibitors such as PD1. SAR439459 is a potent human pan-neutralizing anti-TGFβ antibody that has entered first-in-human studies in advanced solid tumors (NCT03192345*).

I. Biomarkers for indication selection: A gene expression signature of TGFβ pathway activation was derived from analysis of TGFβ-stimulated versus naïve cancer cell lines. Queries of the signature against The Cancer Genome Atlas (TCGA) revealed that primary head and neck, ovarian, and colorectal cancers are enriched for activation.

II. Biomarkers for patient selection: An analysis of the patients enriched for activation revealed that mesenchymal tumors predominate. Machine learning methods were applied to panels of gene expression data for colorectal (CRC), ovarian serous and head and neck squamous cell carcinoma to derive a two-gene biomarker for selection of patients with mesenchymal tumors. Implementation in a logistic regression model achieves good for prediction in CRC, and similar performance in other indications. Patient cohorts are enriched over three-fold for the mesenchymal subtype and false-negative rates are acceptable. Implementation of the two-gene biomarker on a low complexity platform and its validation on an independent panel of samples is described.

III. Biomarkers for assessment of the tumor microenvironment: In partnership with NeoGenomics, the immune contexture of patient tumors was evaluated using MultiOmyx, a multiplex IHC assay, on CRC and melanoma. Multiplexing was conducted with 12 biomarkers (jointly describing 22 immune cell types) on single FFPE section from each tumor sample. Pilot studies included a range of inflammation to evaluate how well the analytics assess each tumor type and correlate to possible treatment effects. Statistical methods were developed to assess differences at the cell population level, including replicate concordance, volcano plots for analyses of variance, and correlation matrices. The MultiOmyx assay demonstrated excellent technical reproducibility and precision, a favorable dynamic range, inflammation status differences in select immune cells and regions of interest, and included both positive and negative correlations between cell populations.

* https://clinicaltrials.gov/ct2/show/NCT03192345

Citation Format: Joachim Theilhaber, Jean Cavallo, Stephen L. Madden, Charlene Manning, Sherry Cao, Parminder Mankoo, Robert Pomponio, Hongjing Qu, Natalia Malkova, Gary Shapiro, Christopher Winter, Dmitri Wiederschain, Michele Sanicola-Nadel, Fangxian Sun, Tun Tun Lin, Richard C. Gregory, Jack Pollard. Translational biomarkers for SAR439459, an anti-TGFβ antibody for cancer immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5550.

Integrated analyses of microRNAs demonstrate their widespread influence on gene expression in high-grade serous ovarian carcinoma

BACKGROUND

The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets.

METHODS AND RESULTS

Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 3'-untranslated region (with less frequency observed for coding sequence and 5'-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anti-correlated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability.

CONCLUSIONS

This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers.

MODBASE, a database of annotated comparative protein structure models and associated resources

MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).

Core signaling pathways in human pancreatic cancers revealed by global genomic analyses

There are currently few therapeutic options for patients with pancreatic cancer, and new insights into the pathogenesis of this lethal disease are urgently needed. Toward this end, we performed a comprehensive genetic analysis of 24 pancreatic cancers. We first determined the sequences of 23,219 transcripts, representing 20,661 protein-coding genes, in these samples. Then, we searched for homozygous deletions and amplifications in the tumor DNA by using microarrays containing probes for approximately 10(6) single-nucleotide polymorphisms. We found that pancreatic cancers contain an average of 63 genetic alterations, the majority of which are point mutations. These alterations defined a core set of 12 cellular signaling pathways and processes that were each genetically altered in 67 to 100% of the tumors. Analysis of these tumors' transcriptomes with next-generation sequencing-by-synthesis technologies provided independent evidence for the importance of these pathways and processes. Our data indicate that genetically altered core pathways and regulatory processes only become evident once the coding regions of the genome are analyzed in depth. Dysregulation of these core pathways and processes through mutation can explain the major features of pancreatic tumorigenesis.

An integrated genomic analysis of human glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common and lethal type of brain cancer. To identify the genetic alterations in GBMs, we sequenced 20,661 protein coding genes, determined the presence of amplifications and deletions using high-density oligonucleotide arrays, and performed gene expression analyses using next-generation sequencing technologies in 22 human tumor samples. This comprehensive analysis led to the discovery of a variety of genes that were not known to be altered in GBMs. Most notably, we found recurrent mutations in the active site of isocitrate dehydrogenase 1 (IDH1) in 12% of GBM patients. Mutations in IDH1 occurred in a large fraction of young patients and in most patients with secondary GBMs and were associated with an increase in overall survival. These studies demonstrate the value of unbiased genomic analyses in the characterization of human brain cancer and identify a potentially useful genetic alteration for the classification and targeted therapy of GBMs.