Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.

Dual Roles for Ikaros in Regulation of Macrophage Chromatin State and Inflammatory Gene Expression.

Macrophage activation by bacterial LPS leads to induction of a complex inflammatory gene program dependent on numerous transcription factor families. The transcription factor Ikaros has been shown to play a critical role in lymphoid cell development and differentiation; however, its function in myeloid cells and innate immune responses is less appreciated. Using comprehensive genomic analysis of Ikaros-dependent transcription, DNA binding, and chromatin accessibility, we describe unexpected dual repressor and activator functions for Ikaros in the LPS response of murine macrophages. Consistent with the described function of Ikaros as transcriptional repressor, Ikzf1-/- macrophages showed enhanced induction for select responses. In contrast, we observed a dramatic defect in expression of many delayed LPS response genes, and chromatin immunoprecipitation sequencing analyses support a key role for Ikaros in sustained NF-κB chromatin binding. Decreased Ikaros expression in Ikzf1+/- mice and human cells dampens these Ikaros-enhanced inflammatory responses, highlighting the importance of quantitative control of Ikaros protein level for its activator function. In the absence of Ikaros, a constitutively open chromatin state was coincident with dysregulation of LPS-induced chromatin remodeling, gene expression, and cytokine responses. Together, our data suggest a central role for Ikaros in coordinating the complex macrophage transcriptional program in response to pathogen challenge.

Origins and Timing of Emerging Lesions in Advanced Renal Cell Carcinoma.

Renal cell carcinoma (RCC) with venous tumor thrombus (VTT) arising from the primary tumor occurs in approximately 10% of cases and is thought to represent more advanced disease. The intravascular nature of VTT suggests that it may serve as a source for hematogenous metastases. RCC with VTT and distant metastasis provides unique opportunities to examine the origins and emergence timing of these distinct tumor lesions, and to identify molecular correlates with disease state. We performed multi-region exome and RNA-sequencing analysis of 16 patients with RCC with VTT, with eight patients also having sequenced metastasis, to identify genomic alterations, biological pathways, and evolutionary processes contributing to VTT and metastasis, and to ask whether metastasis arises directly from or independent of VTT. No specific genomic alterations were associated with VTT. Hallmark copy-number alterations (deletions of 14q, 8p, and 4q) were associated with metastasis and disease recurrence, and secondary driver alterations tended to accumulate in metastatic lineages. Mismatch repair mutational signatures co-occurred across most tumors, suggesting a role for intracellular DNA damage in RCC. Robust phylogenetic timing analysis indicated that metastasis typically emerged before VTT, rather than deriving from it, with the earliest metastases predicted to emerge years before diagnosis. As a result, VTT in metastatic cases frequently derived from a metastatic lineage. Relative to the primary tumor, VTT upregulated immediate-early genes and transcriptional targets of the TNFα/NF-κB pathway, whereas metastases upregulated MTOR and transcriptional targets downstream of mTORC1 activation. IMPLICATIONS: These results suggest that VTT and metastasis formation occur independently, VTT presence alone does not necessarily imply more advanced disease with inevitably poor prognosis.

SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays.

Hit selection from high-throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings have contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework to integrate these methods. We describe selection by iterative pathway group and network analysis looping (SIGNAL), an integrated, iterative approach that uses both pathway and network methods to optimize gene prioritization. SIGNAL is accessible as a rapid user-friendly web-based application (https://signal.niaid.nih.gov). A record of this paper's transparent peer review is included in the Supplemental information.

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues.

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly with many downstream assays such as next generation sequencing. Automated tissue dissection is a new methodology that automates and improves tumor enrichment in these common, low tumor content tissues by decreasing the user-dependent imprecision of traditional macro-dissection and time, cost, and expertise limitations of laser capture microdissection by using digital image annotation overlay onto unstained slides. Here, digital hematoxylin and eosin (H&E) annotations are used to target small tumor areas using a blade that is 250 µm2 in diameter in unstained formalin fixed paraffin embedded (FFPE) or fresh frozen sections up to 20 µm in thickness for automated tumor enrichment prior to nucleic acid extraction and whole exome sequencing (WES). Automated dissection can harvest annotated regions in low tumor content tissues from single or multiple sections for nucleic acid extraction. It also allows for capture of extensive pre- and post-harvest collection metrics while improving accuracy, reproducibility, and increasing throughput with utilization of fewer slides. The described protocol enables digital annotation with automated dissection on animal and/or human FFPE or fresh frozen tissues with low tumor content and could also be used for any region of interest enrichment to boost adequacy for downstream sequencing applications in clinical or research workflows.

Indication-specific tumor evolution and its impact on neoantigen targeting and biomarkers for individualized cancer immunotherapies.

BACKGROUND: Individualized neoantigen-specific immunotherapy (iNeST) requires robustly expressed clonal neoantigens for efficacy, but tumor mutational heterogeneity, loss of neoantigen expression, and variable tissue sampling present challenges. It is assumed that clonal neoantigens are preferred targets for immunotherapy, but the distributions of clonal neoantigens are not well characterized across cancer types. METHODS: We combined multiregion sequencing (MR-seq) analysis of five untreated, synchronously sampled metastatic solid tumors with re-analysis of published MR-seq data from 103 patients in order to characterize their globally clonal neoantigen content and factors that would impact neoantigen targeting. RESULTS: Branching evolution in colorectal cancer and renal cell carcinoma led to fewer clonal neoantigens and to clade-specific neoantigens (those shared across a subset of tumor regions but not fully clonal), with the latter not being readily distinguishable in single tumor samples. In colorectal, renal, and bladder cancer, most tumors had few globally clonal neoantigens. Prioritizing mutations with higher purity-adjusted and ploidy-adjusted variant allele frequency enriched for globally clonal neoantigens (those found in all tumor regions), whereas estimated cancer cell fraction derived from clustering-based tools, surprisingly, did not. Neoantigen quality was associated with loss of neoantigen expression in the bladder cancer case, and HLA-allele loss was observed in the renal and non-small cell lung cancer cases. CONCLUSIONS: We show that tumor type, multilesion sampling, neoantigen expression, and HLA allele retention are important factors for iNeST targeting and patient selection, and may also be important factors to consider in the development of biomarker strategies.

A small sustained increase in NOD1 abundance promotes ligand-independent inflammatory and oncogene transcriptional responses.

Small, genetically determined differences in transcription [expression quantitative trait loci (eQTLs)] are implicated in complex diseases through unknown molecular mechanisms. Here, we showed that a small, persistent increase in the abundance of the innate pathogen sensor NOD1 precipitated large changes in the transcriptional state of monocytes. A ~1.2- to 1.3-fold increase in NOD1 protein abundance resulting from loss of regulation by the microRNA cluster miR-15b/16 lowered the threshold for ligand-induced activation of the transcription factor NF-κB and the MAPK p38. An additional sustained increase in NOD1 abundance to 1.5-fold over basal amounts bypassed this low ligand concentration requirement, resulting in robust ligand-independent induction of proinflammatory genes and oncogenes. These findings reveal that tight regulation of NOD1 abundance prevents this sensor from exceeding a physiological switching checkpoint that promotes persistent inflammation and oncogene expression. Furthermore, our data provide insight into how a quantitatively small change in protein abundance can produce marked changes in cell state that can serve as the initiator of disease.

Mutation position is an important determinant for predicting cancer neoantigens.

Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.

Comprehensive RNAi-based screening of human and mouse TLR pathways identifies species-specific preferences in signaling protein use.

Toll-like receptors (TLRs) are a major class of pattern recognition receptors, which mediate the responses of innate immune cells to microbial stimuli. To systematically determine the roles of proteins in canonical TLR signaling pathways, we conducted an RNA interference (RNAi)-based screen in human and mouse macrophages. We observed a pattern of conserved signaling module dependencies across species, but found notable species-specific requirements at the level of individual proteins. Among these, we identified unexpected differences in the involvement of members of the interleukin-1 receptor-associated kinase (IRAK) family between the human and mouse TLR pathways. Whereas TLR signaling in mouse macrophages depended primarily on IRAK4 and IRAK2, with little or no role for IRAK1, TLR signaling and proinflammatory cytokine production in human macrophages depended on IRAK1, with knockdown of IRAK4 or IRAK2 having less of an effect. Consistent with species-specific roles for these kinases, IRAK4 orthologs failed to rescue signaling in IRAK4-deficient macrophages from the other species, and only mouse macrophages required the kinase activity of IRAK4 to mediate TLR responses. The identification of a critical role for IRAK1 in TLR signaling in humans could potentially explain the association of IRAK1 with several autoimmune diseases. Furthermore, this study demonstrated how systematic screening can be used to identify important characteristics of innate immune responses across species, which could optimize therapeutic targeting to manipulate human TLR-dependent outputs.