Biology subjects

Riemer, A. B.

Publications and source records attributed to Riemer, A. B..

8 recordsLinked to original sources

bioRxiv2026

Mutanome-guided immunopeptidomics of blood plasma for neoepitope detection in solid tumors is constrained by cfDNA variant calling sensitivity and MS detection limits

Introduction: Neoepitopes form the basis of tumor-specific immune responses. Tissue biopsy, the primary source for neoepitope detection, is limited and invasive. Therefore, we aimed to identify neoepitopes by mutanome-guided immunopeptidomics from plasma of cancer patients. Methods: Mass spectrometry (MS) data analysis of HLA ligands from plasma (n = 4) was guided by patient-specific mutanomes of cell-free DNA (cfDNA) from plasma or tumor genomic DNA (tgDNA) from tissue. Matched tumor tissue and healthy donor plasma served as controls. Neoepitopes were validated with synthetic peptides, and immunogenicity was assessed using IFN-gamma ELISpot and intracellular cytokine staining. Results: Wild-type immunopeptidomes from tissue and plasma overlapped by 58%, with 91% of plasma HLA ligands rediscovered in tissue. 13 out of 15 tumor-associated HLA ligands detected in plasma were rediscovered in the matching tissue. However, no neoepitopes in plasma were identified by immunopeptidomics guided by cfDNA mutanomes, likely reflecting the limited overlap between cfDNA and tgDNA mutanomes (15%). Using the tgDNA mutanome as a complementary reference, two neoepitopes were detected in one patient's plasma, albeit at the MS detection limit. Both neoepitopes were also discovered in tissue, along with three tissue-exclusive neoepitopes. Two tissue-exclusive neoepitopes induced antigen-specific T cell responses in healthy donor PBMCs. Conclusion: In summary, plasma immunopeptidomics enables profiling of HLA ligands from wild-type proteins, including TAAs. In principle, neoepitope detection from plasma at the peptide level is feasible, but tissue remains the gold standard for variant calling and neoepitope identification. Improved detection methods may enable minimally invasive approaches in the future.

immunology↗

bioRxiv2026

A targetable dependency on nonsense-mediated decay for proteostasis and immune control in small cell lung cancer

Small cell lung cancer (SCLC) is among the deadliest cancers with excessive somatic alterations, expectedly resulting in immunogenic epitopes. However, patient benefit from immunotherapy is limited. We found abundant frameshift mutations in SCLC, regarded as highly immunogenic, counterbalanced by a hyperactive nonsense-mediated decay (NMD) pathway, responsible for frameshift-mRNA degradation. NMD activity correlated with tumor mutational burden (TMB) across cancers, suggesting that SCLC depends on NMD for cellular homeostasis and immune evasion. NMD inhibition impaired SCLC proliferation and induced ER stress-dependent apoptosis in a TMB-dependent manner, thereby enabling pharmacological inhibition in vivo which effectively controlled tumor growth. By integrating genome and transcriptome sequencing with MHC-I immunopeptidomics and functional in vitro and in vivo assays, we identified that NMD inhibition boosted neoantigen expression and presentation by tumor cells and increased T cell recognition, thus enhancing overall tumor immunogenicity and further improving immunotherapy efficacy in vivo. Our work shows that SCLC - as a TMBhigh cancer - relies on NMD for survival and immune escape, uncovering a novel TMB-dependent tractable vulnerability for this devastating disease.

cancer biology↗

bioRxiv2025

Integrative spatial and multi-omic profiling in bladder cancer links L1 retrotransposition to extrachromosomal DNA, genomic instability, and viral mimicry response

Bladder cancer is one of the most frequent cancers and shows high recurrence rates. Despite recent advances, key knowledge gaps remain in understanding the molecular mechanisms of disease progression, which would support the development of early detection methods and effective personalized treatments. We apply integrated multi-omics and spatial analyses in a cohort of 49 bladder cancer patients to comprehensively profile genetic, epigenetic, transcriptomic, and spatial features of bladder cancer, alongside cell-free DNA blood analysis. Combining low-pass whole-genome cell-free DNA sequencing, Oxford Nanopore long-read tumor DNA sequencing, RNA-sequencing, and spatial transcriptomics, we provide insights into molecular alterations driving bladder cancer. We show frequent somatic LINE-1 (L1) insertions, with up to more than 500 insertions per tumor. We find that L1 insertions are active and occur early in bladder cancer development. We link aberrant somatic L1 insertion in bladder cancer with downstream genomic rearrangements and chromosomal instability, with an excess of structural variants and extrachromosomal DNA (ecDNA) in patients with particularly high L1 counts. By detecting ecDNA within tissue architecture using spatial transcriptomics, we identify the localization of ecDNA to distinct spatial clusters with differential expression of APOBEC3B and immune response pathways. These results, combined with replication timing analysis and gene set enrichment analysis (GSEA), offer evidence for the previously hypothesized viral mimicry response to L1 retrotransposition, mediated via APOBEC3B-editing, the cGAS-STING pathway, and RIG-I and MDA5 responses.

cancer biology↗

bioRxiv2025

Upstream open reading frame translation enhances immunogenic peptide presentation in mitotically arrested cancer cells

Mitosis is a critical phase of the cell cycle and a vulnerable point where cancer cells can be effectively disrupted, leading to cell death and inhibition of tumor growth. However, challenges such as drug resistance remain significant in clinical applications. During mitosis, mRNA translation is generally downregulated, while non-canonical translation of specific transcripts proceeds. Here, we demonstrate that mitotic cancer cells redistribute ribosomes toward the 5 untranslated region (5 UTR) and the start of the coding sequence (CDS), enhancing the translation of thousands of upstream open reading frames (uORFs) and upstream overlapping open reading frames (uoORFs). This mitotic induction of uORF/uoORF enriches the presentation of immunopeptides at the surface of cancer cells following treatment with mitotic inhibitors. Functional assays indicate the potential of such neoepitopes to provoke cancer-cell killing by T cells. Altogether, our findings highlight the therapeutic potential of targeting uORF/uoORF-derived neoepitopes in combination with mitotic inhibitors to enhance immune recognition and tumor cell elimination.

cancer biology↗

bioRxiv2023

optiPRM: A targeted immunopeptidomics LC-MS workflow with ultra-high sensitivity for the detection of mutation-derived tumor neoepitopes from limited input material

Personalized cancer immunotherapies such as vaccines and T cell receptor (TCR)-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen (HLA) class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. For their detection by liquid chromatography mass spectrometry (LC-MS), we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft (PDX) from as little as 2.5x106 cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, we here present optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, which allowed for the identification of actionable neoepitopes from sample sizes usually available in the clinic.

immunology↗

bioRxiv2021

Light contamination in stable isotope-labelled internal peptide standards is frequent and a potential source of false discovery and quantitation error in proteomics

In mass spectrometry-based proteomics, heavy internal standards are used to validate target peptide detections and to calibrate peptide quantification. Here we report light contamination present in heavy labelled synthetic peptides of high isotopic enrichment. Application of such peptides as assay-internal standards potentially compromises the detection and quantification especially of low abundant cellular peptides. Therefore, it is important to adopt guidelines to prevent false-positive identifications of endogenous light peptides as well as errors in their quantification from biological samples.

biochemistry↗

bioRxiv2019

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e-16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.\n\nSynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

immunology↗

bioRxiv2019

The shared neoantigen landscape of MSI cancers reflects immunoediting during tumor evolution

The immune system can recognize and attack cancer cells, especially those with a high load of mutation-induced neoantigens. Such neoantigens are particularly abundant in DNA mismatch repair (MMR)-deficient, microsatellite-unstable (MSI) cancers. MMR deficiency leads to insertion/deletion (indel) mutations at coding microsatellites (cMS) and to neoantigen-inducing translational frameshifts. The abundance of mutational neoantigens renders MSI cancers sensitive to immune checkpoint blockade. However, the neoantigen landscape of MMR-deficient cancers has not yet been systematically mapped. In the present study, we used a novel tool to monitor neoantigen-inducing indel mutations in MSI colorectal and endometrial cancer. Our results show that MSI cancers share several highly immunogenic neoantigens that result from specific, recurrent indel mutation events. Notably, the frequency of such indel mutations was negatively correlated to the predicted immunogenicity of the resulting neoantigens. These observations suggest continuous immunoediting of emerging MMR-deficient cells during tumor evolution.\n\nOne sentence summaryQuantitative indel mutation analysis reveals evidence of immune selection in mismatch repair-deficient cancers

cancer biology↗