Abstract 895: Tumor-wide neoantigen-specific T-cells infiltrating mutant IDH1 low-grade gliomas and persisting in peripheral blood allow for personalized TCR-based immunotherapies

BACKGROUND: The low mutational burden and immunologically “cold” microenvironment of mutant IDH1 low-grade gliomas (LGG) are considerable challenges facing immunotherapy against these tumor types. However, we hypothesize that LGG-targeting T-cells may exist at low frequency and with limited regional infiltration within the tumor. Multi-region tumor sampling coupled with high-throughput T-cell receptor (TCR) profiling across the LGG landscape detected neoantigen-specific T-cells that persisted in peripheral blood. TCR-engineered T-cells transduced with these TCRs demonstrated neoantigen-specific immunogenicity.

METHODS: Maximal-anatomical sampling of at least 10 distinct tumor regions were collected at the initial resection for three WHO Grade II diffuse astrocytoma patients for exome-based prediction of clonally and subclonally expressed neoantigens, RNAseq analysis of regional immune cell composition, and TCR beta deep sequencing. We used these predictions to generate a barcoded library of patient-specific peptide-HLA multimers loaded with predicted neoepitopes. With this library, neoantigen-specific CD8+ T-cells were captured and isolated from patient peripheral blood. Single cell TCR sequencing allowed us to identify the neoantigen-reactive TCR clonotypes which were transduced subsequently into Jurkat76 cell lines for functional validation.

RESULTS: We screened patient-derived peripheral blood drawn two years after initial resection in 3 mutant IDH1 LGG patients and detected a total of 20 TCR clonotypes recognizing neoepitopes derived from truncal, tumor-wide mutations in CNTNAP1 (n=8), TP53 (n=3), and MRPL46 (n=2) as well as subclonal mutations in PRMT5 (n=1) and ZDHHC5 (n=6). Multi-sampling RNAseq analysis indicated varying degrees of interpatient and intratumoral immune infiltration as well as distally located populations of neoantigen-reactive T-cells within the tumor, suggesting widespread migration of neoantigen-specific T-cells across the glioma landscape. We proceeded with TCR functional analysis for one patient (P375) with 5 detected TCR clonotypes recognizing neoantigens derived from mutations in PRMT5, MRPL46, and TP53. Jurkat76 cells transduced with the mutant-PRMT5-specific TCR demonstrated a neoantigen-specific immune response when co-cultured with mutant-PRMT5 pulsed-antigen presenting cells expressing HLA-A*0201 (T2 cells).

CONCLUSION: Our study demonstrates the existence and persistence of neoantigen-targeting T-cells within the blood and tumor of mutant IDH1 LGG patients. We identified a TCR clonotype that successfully recognizes and induces an immune response against mutant-PRMT5. These findings suggest a feasible methodology to develop personalized T-cell-based immunotherapies for patients with mutant IDH1 LGGs.

Citation Format: Darwin W. Kwok, Michael Y. Zhang, Cliff Wang, Nicholas Stevers, Tyler Borrman, Zheng Pan, Benjamin Yuen, Songming Peng, Diana Nguyen, Michael Martin, Chibo Hong, Stephanie Hilz, Joanna Phillips, Anny Shai, Nancy Ann Oberheim Bush, Shawn Hervey-Jumper, Michael McDermott, Stefanie Mandl, Hideho Okada, Joseph Costello. Tumor-wide neoantigen-specific T-cells infiltrating mutant IDH1 low-grade gliomas and persisting in peripheral blood allow for personalized TCR-based immunotherapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 895.

Abstract 3125: TCR specificity prediction of circulating T cells and TILs in personalized adoptive neoTCR T cell therapy

Background: Adoptive T-cell therapies would benefit from accurate computational prediction of T cell receptor (TCR) specificity to its target antigen. Despite the diversity of complementarity determining region (CDR) among T cells, many sequence features of CDRs are conserved. Conserved CDR sequences allow for shared specificity, that is, recognition of the same antigen, potentially increasing the pool of candidate TCRs available for treating patients. Utilizing sequence similarity, enrichment of V-genes, CDR lengths, and evidence of clonal expansion, several computational algorithms have been developed to predict the specificity of TCRs. Using neoantigen-specific TCRs (neoTCRs) in our phase 1 clinical trial (NCT039703820), we investigated the shared specificity of TCRs in the context of personalized autologous T cell therapy for cancer patients.

Methods: Leveraging a high throughput TCR discovery and validation platform, neoantigen-specific T cells were isolated from patient peripheral blood mononuclear cells (PBMCs) and their neoTCR sequences were identified. RNA-seq was performed on tumor biopsies and TCR sequences derived from tumor-infiltrating lymphocytes (TILs) were extracted using MiXCR software. TCR specificity algorithms TCRdist3, GLIPH2, and TCRmatch were then used to identify specificity groups within and between neoTCR sequences derived from patient PBMCs, TCR sequences derived from TILs, and publicly available TCR sequences of known specificities.

Results: NeoTCRs identified during our phase 1 clinical trial and their known neoantigens provided an experimentally validated benchmark for testing accuracy of TCR specificity prediction. TCR specificity algorithms accurately clustered neoTCR β chains into groups of shared antigen specificity. TCR chain sequences detected from TILs with high similarity to TCR sequences of neoTCRs from PMBCs were found, suggesting shared antigen specificity between circulating T cells and those found in the tumor. In addition, TCR specificity algorithms identified a public TCR sequence known to recognize an HPV-derived epitope presented by HLA-A*02:01 with high similarity to a TCR sequence identified in a tumor biopsy of an HPV16+ HLA-A*02:01+ patient with head and neck cancer.

Conclusion: Neoantigens and their associated neoTCRs identified by our TCR discovery and validation platform can be used as a benchmark for TCR specificity prediction algorithms. TCR specificity algorithms provide insight into TCRs with predicted shared specificity in the blood and within the tumor. Accurate identification of TIL TCRs with shared specificity to neoTCRs could inform on tumor trafficking and aid in therapeutic product selection. Similarly, accurate specificity matching of TIL TCRs to public TCRs with known targets could aid in identification of efficacious targets within the tumor.

Citation Format: Tyler Borrman, Eric Stawiski, Zheng Pan, Chad Smith, Susan Foy, Stefanie J. Mandl. TCR specificity prediction of circulating T cells and TILs in personalized adoptive neoTCR T cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3125.

820 Machine learning significantly improves neoantigen-HLA predictions utilizing > 26,000 data points from the PACTImmuneTM Database

Background

PACT Pharma has developed a state-of-the-art approach to validate predicted neoepitopes (neoEs) and their cognate T cell receptors (neoTCRs) by capturing neoepitope-specific T cells from peripheral blood. This neoTCR discovery and validation process is being applied in clinical trial (NCT03970382) evaluating personalized neoTCR-T cell therapy to treat patients across eight solid tumor types. Extensive pre-, on- and post-treatment data related to this trial has been accumulated in the PACTImmune Database (PIDB) which represents a growing data asset for patient-specific tumor immunogenicity in solid tumors. Here we present a specific use case of applying machine learning (ML) to significantly improve neoE-HLA predictions and further model anticipated improvements of TCR capture as a direct consequence.

Methods

PACT has developed capabilities for high-throughput manufacturing of single polypeptide (comPACT protein) which consists of the predicted neoE peptide together with Beta-2-Microglobulin and the HLA heavy chain. comPACT molecules are considered successfully produced when protein yields reach concentrations >1uM. Data used for this study consisted of >26000 neoE-HLA predictions for 62 different HLA alleles. We applied ML to learn patterns that are predictive of neoE-HLAs that can be successfully produced as comPACTs, using scikit-learn and XGBoost. Data was first split into training and testing data. Models were trained on training data and model hyperparameters were tuned using 5-fold cross validation (5xCV). The performance of the models during 5xCV and on test data was measured using the area under the receiver operating characteristic curve (AUC). We additionally performed experimental prospective validation of the models. To do this, 603 neoE-HLAs (from 7 previously unseen cancer samples) were selected for comPACT production using netMHCpan4.1 and the newly trained models.

Results

The mean AUC for the 5xCV of the selected models ranged from 0.75 to 0.86 depending upon the HLA allele (SD <0.05 for every model). The AUC on the test data ranged from 0.75 to 0.92 (median = 0.85). Prospective validation resulted on average in a 22% higher success rate (range 11%–39%) using the new models as compared to the netMHCpan4.1 predictions. This is expected to result in increased capture of neoepitope-specific CD8+ T cells as the PIDB indicates that 3.2% of the successful comPACTs result in validated neoTCRs.

Conclusions

PIDB based ML predictions of neoE-HLAs led to a significant increase in TCR-capturing comPACT success rates. Because of this work, it is predicted both neoE-specific CD8+ T cell capture and actionable neoTCR options will increase per patient.

Genome-wide mapping of human DNA replication by optical replication mapping supports a stochastic model of eukaryotic replication

The heterogeneous nature of eukaryotic replication kinetics and the low efficiency of individual initiation sites make mapping the location and timing of replication initiation in human cells difficult. To address this challenge, we have developed optical replication mapping (ORM), a high-throughput single-molecule approach, and used it to map early-initiation events in human cells. The single-molecule nature of our data and a total of >2,500-fold coverage of the human genome on 27 million fibers averaging ∼300 kb in length allow us to identify initiation sites and their firing probability with high confidence. We find that the distribution of human replication initiation is consistent with inefficient, stochastic activation of heterogeneously distributed potential initiation complexes enriched in accessible chromatin. These observations are consistent with stochastic models of initiation-timing regulation and suggest that stochastic regulation of replication kinetics is a fundamental feature of eukaryotic replication, conserved from yeast to humans.

Liquid chromatin Hi-C characterizes compartment-dependent chromatin interaction dynamics

Nuclear compartmentalization of active and inactive chromatin is thought to occur through microphase separation mediated by interactions between loci of similar type. The nature and dynamics of these interactions are not known. We developed liquid chromatin Hi-C to map the stability of associations between loci. Before fixation and Hi-C, chromosomes are fragmented, which removes strong polymeric constraint, enabling detection of intrinsic locus-locus interaction stabilities. Compartmentalization is stable when fragments are larger than 10-25 kb. Fragmentation of chromatin into pieces smaller than 6 kb leads to gradual loss of genome organization. Lamin-associated domains are most stable, whereas interactions for speckle- and polycomb-associated loci are more dynamic. Cohesin-mediated loops dissolve after fragmentation. Liquid chromatin Hi-C provides a genome-wide view of chromosome interaction dynamics.

High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides

MOTIVATION

The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides.

RESULTS

Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides.

AVAILABILITY AND IMPLEMENTATION

Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A chromosomal connectome for psychiatric and metabolic risk variants in adult dopaminergic neurons

BACKGROUND

Midbrain dopaminergic neurons (MDN) represent 0.0005% of the brain's neuronal population and mediate cognition, food intake, and metabolism. MDN are also posited to underlay the neurobiological dysfunction of schizophrenia (SCZ), a severe neuropsychiatric disorder that is characterized by psychosis as well as multifactorial medical co-morbidities, including metabolic disease, contributing to markedly increased morbidity and mortality. Paradoxically, however, the genetic risk sequences of psychosis and traits associated with metabolic disease, such as body mass, show very limited overlap.

METHODS

We investigated the genomic interaction of SCZ with medical conditions and traits, including body mass index (BMI), by exploring the MDN's "spatial genome," including chromosomal contact landscapes as a critical layer of cell type-specific epigenomic regulation. Low-input Hi-C protocols were applied to 5-10 × 103 dopaminergic and other cell-specific nuclei collected by fluorescence-activated nuclei sorting from the adult human midbrain.

RESULTS

The Hi-C-reconstructed MDN spatial genome revealed 11 "Euclidean hot spots" of clustered chromatin domains harboring risk sequences for SCZ and elevated BMI. Inter- and intra-chromosomal contacts interconnecting SCZ and BMI risk sequences showed massive enrichment for brain-specific expression quantitative trait loci (eQTL), with gene ontologies, regulatory motifs and proteomic interactions related to adipogenesis and lipid regulation, dopaminergic neurogenesis and neuronal connectivity, and reward- and addiction-related pathways.

CONCLUSIONS

We uncovered shared nuclear topographies of cognitive and metabolic risk variants. More broadly, our PsychENCODE sponsored Hi-C study offers a novel genomic approach for the study of psychiatric and medical co-morbidities constrained by limited overlap of their respective genetic risk architectures on the linear genome.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Spatial genome exploration in the context of cognitive and neurological disease

The 'non-linear' genome, or the spatial proximity of non-contiguous sequences, emerges as an important regulatory layer for genome organization and function, including transcriptional regulation. Here, we review recent genome-scale chromosome conformation mappings ('Hi-C') in developing and adult human and mouse brain. Neural differentiation is associated with widespread remodeling of the chromosomal contact map, reflecting dynamic changes in cell-type-specific gene expression programs, with a massive (estimated 20-50%) net loss of chromosomal contacts that is specific for the neuronal lineage. Hi-C datasets provided an unexpected link between locus-specific abnormal expansion of repeat sequences positioned at the boundaries of self-associating topological chromatin domains, and monogenic neurodevelopmental and neurodegenerative disease. Furthermore, integrative cell-type-specific Hi-C and transcriptomic analysis uncovered an expanded genomic risk space for sequences conferring liability for schizophrenia and other cognitive disease. We predict that spatial genome exploration will deliver radically new insights into the brain nucleome in health and disease.

Neuron-specific signatures in the chromosomal connectome associated with schizophrenia risk

To explore the developmental reorganization of the three-dimensional genome of the brain in the context of neuropsychiatric disease, we monitored chromosomal conformations in differentiating neural progenitor cells. Neuronal and glial differentiation was associated with widespread developmental remodeling of the chromosomal contact map and included interactions anchored in common variant sequences that confer heritable risk for schizophrenia. We describe cell type-specific chromosomal connectomes composed of schizophrenia risk variants and their distal targets, which altogether show enrichment for genes that regulate neuronal connectivity and chromatin remodeling, and evidence for coordinated transcriptional regulation and proteomic interaction of the participating genes. Developmentally regulated chromosomal conformation changes at schizophrenia-relevant sequences disproportionally occurred in neurons, highlighting the existence of cell type-specific disease risk vulnerabilities in spatial genome organization.

Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes

T cell specificity emerges from a myriad of processes, ranging from the biological pathways that control T cell signaling to the structural and physical mechanisms that influence how TCRs bind peptides and MHC proteins. Of these processes, the binding specificity of the TCR is a key component. However, TCR specificity is enigmatic: TCRs are at once specific but also cross-reactive. Although long appreciated, this duality continues to puzzle immunologists and has implications for the development of TCR-based therapeutics. In this review, we discuss TCR specificity, emphasizing results that have emerged from structural and physical studies of TCR binding. We show how the TCR specificity/cross-reactivity duality can be rationalized from structural and biophysical principles. There is excellent agreement between predictions from these principles and classic predictions about the scope of TCR cross-reactivity. We demonstrate how these same principles can also explain amino acid preferences in immunogenic epitopes and highlight opportunities for structural considerations in predictive immunology.

ATLAS: A database linking binding affinities with structures for wild-type and mutant TCR-pMHC complexes

The ATLAS (Altered TCR Ligand Affinities and Structures) database (https://zlab.umassmed.edu/atlas/web/) is a manually curated repository containing the binding affinities for wild-type and mutant T cell receptors (TCRs) and their antigens, peptides presented by the major histocompatibility complex (pMHC). The database links experimentally measured binding affinities with the corresponding three dimensional (3D) structures for TCR-pMHC complexes. The user can browse and search affinities, structures, and experimental details for TCRs, peptides, and MHCs of interest. We expect this database to facilitate the development of next-generation protein design algorithms targeting TCR-pMHC interactions. ATLAS can be easily parsed using modeling software that builds protein structures for training and testing. As an example, we provide structural models for all mutant TCRs in ATLAS, built using the Rosetta program. Utilizing these structures, we report a correlation of 0.63 between experimentally measured changes in binding energies and our predicted changes. Proteins 2017; 85:908-916. © 2016 Wiley Periodicals, Inc.

Replication timing is regulated by the number of MCMs loaded at origins

Replication timing is a crucial aspect of genome regulation that is strongly correlated with chromatin structure, gene expression, DNA repair, and genome evolution. Replication timing is determined by the timing of replication origin firing, which involves activation of MCM helicase complexes loaded at replication origins. Nonetheless, how the timing of such origin firing is regulated remains mysterious. Here, we show that the number of MCMs loaded at origins regulates replication timing. We show for the first time in vivo that multiple MCMs are loaded at origins. Because early origins have more MCMs loaded, they are, on average, more likely to fire early in S phase. Our results provide a mechanistic explanation for the observed heterogeneity in origin firing and help to explain how defined replication timing profiles emerge from stochastic origin firing. These results establish a framework in which further mechanistic studies on replication timing, such as the strong effect of heterochromatin, can be pursued.

Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology

DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.