The immunopeptidome landscape associated with T cell infiltration, inflammation and immune editing in lung cancer

Immunopeptidomics-guided discovery and characterization of neoantigens for personalized cancer immunotherapy

Neoantigens have emerged as ideal targets for personalized cancer immunotherapy. We depict the pan-cancer peptide atlas by comprehensively collecting immunopeptidomics from 531 samples across 14 cancer and 29 normal tissues, and identify 389,165 canonical and 70,270 noncanonical peptides. We reveal that noncanonical peptides exhibit comparable presentation levels as canonical peptides across cancer types. Tumor-specific peptides exhibit significantly distinct biochemical characteristics compared with those observed in normal tissues. We further propose an immunopeptidomic-guided machine learning-based neoantigen screening pipeline (MaNeo) to prioritize neo-peptides as immunotherapy targets. Benchmark analysis reveals MaNeo results in the accurate identification of shared and tumor-specific canonical and noncanonical neo-peptides. Last, we use MaNeo to detect and validate three neo-peptides in cancer cell lines, which can effectively induce increased proliferation of active T cells and T cell responses to kill cancer cells but not damage healthy cells. The pan-cancer peptide atlas and proposed MaNeo pipeline hold great promise for the discovery of canonical and noncanonical neoantigens for cancer immunotherapies.

Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer

Melanoma and non-small cell lung cancer (NSCLC) display exceptionally high mutational burdens. Hence, immune targeting in these cancers has primarily focused on tumor antigens (TAs) predicted to derive from nonsynonymous mutations. Using comprehensive proteogenomic analyses, we identified 589 TAs in cutaneous melanoma (n = 505) and NSCLC (n = 90). Of these, only 1% were derived from mutated sequences, which was explained by a low RNA expression of most nonsynonymous mutations and their localization outside genomic regions proficient for major histocompatibility complex (MHC) class I-associated peptide generation. By contrast, 99% of TAs originated from unmutated genomic sequences specific to cancer (aberrantly expressed tumor-specific antigens (aeTSAs), n = 220), overexpressed in cancer (tumor-associated antigens (TAAs), n = 165) or specific to the cell lineage of origin (lineage-specific antigens (LSAs), n = 198). Expression of aeTSAs was epigenetically regulated, and most were encoded by noncanonical genomic sequences. aeTSAs were shared among tumor samples, were immunogenic and could contribute to the response to immune checkpoint blockade observed in previous studies, supporting their immune targeting across cancers.

Cascade-amplified Oxidative Stress via Bandgap-Tuned KBiO3 Perovskite for Cancer Therapy

Sonodynamic therapy (SDT) is a promising cancer treatment due to its ability to utilize ultrasound (US) to activate sonosensitizers, generating reactive oxygen species (ROS) for tumor suppression. High-valence bismuth, known for its unique photoacoustic properties and biocompatibility, has shown great potential when combined with SDT. However, conventional sonosensitizers with large bandgaps and electron-hole recombination have limited SDT's effectiveness. Herein, a bismuth-based piezoelectric sonosensitizer is developed, DSPE-PEG 2000-modified KBiO3 (KBP), which features a reduced bandgap (1.9 eV). This facilitates electron transfer and depletes glutathione in the tumor microenvironment. Under the US, the piezoelectric sonosensitizer KBP generates a significant amount of ROS, leading to cancer cell pyroptosis via the ROS-NLRP3-Caspase-1-GSDMD pathway. Both in vitro and in vivo experiments demonstrated that the piezoelectric SDT treatment can effectively inhibit tumor growth. This research offers a novel approach to cancer treatment by leveraging the advantages of piezoelectric SDT, demonstrating promising clinical potential for tumor inhibition.

Defining lung adenocarcinoma subtypes with glucocorticoid-related genes and constructing a prognostic index for immunotherapy guidance

Background

Several studies have shown that glucocorticoid-related genes (GCGs) play a crucial role in cancer. However, the mechanism of GCGs in lung adenocarcinoma (LUAD) is not fully understood. This study aimed to identify distinct subtypes of LUAD by integrating GCGs and to develop prognostic models for precise prognosis prediction and immunotherapy guidance.

Methods

In this study, sample data of LUAD were collected from The Cancer Genome Atlas (TCGA) database, and unsupervised clustering was used to identify LUAD subtypes with different GCGs characteristics. Survival-related genes were screened by differential expression analysis and protein-protein interaction (PPI) network analysis. After that, the least absolute shrinkage and selection operator (LASSO) combined with Cox regression analysis was used to establish the prognosis model. Differences in the immune microenvironment of different risk groups were analyzed, and Tumor Immune Dysfunction and Exclusion (TIDE) was used to predict the response of patients to immunotherapy. Finally, the CellMiner database was used to predict potential drugs.

Results

Two subtypes of LUAD were identified, namely cluster 1 (high survival rate) and cluster 2 (low survival rate). A prognostic model was constructed based on 9 characteristic genes, including CLCA1, CYP17A1, GRIA2, IGFBP1, IGF2BP1, NTSR1, RPE65, VGF, and WNT16, and the prognosis of LUAD patients was positively predicted. There were differences in the immune microenvironment of different risk LUAD patients, and high-risk LUAD patients may benefit less from immunotherapy. BGB-283 was a candidate for LUAD targeting VGF.

Conclusions

Our study elucidates the impact of GCGs on LUAD prognosis and immune responses, offering insights for prognostic forecasting and immunotherapeutic strategies for LUAD patients.

Investigative needle core biopsies support multimodal deep-data generation in glioblastoma

Glioblastoma (GBM) is an aggressive primary brain cancer with few effective therapies. Stereotactic needle biopsies are routinely used for diagnosis; however, the feasibility and utility of investigative biopsies to monitor treatment response remains ill-defined. Here, we demonstrate the depth of data generation possible from routine stereotactic needle core biopsies and perform highly resolved multi-omics analyses, including single-cell RNA sequencing, spatial transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics on standard biopsy tissue obtained intra-operatively. We also examine biopsies taken from different locations and provide a framework for measuring spatial and genomic heterogeneity. Finally, we investigate the utility of stereotactic biopsies as a method for generating patient-derived xenograft (PDX) models. Multimodal dataset integration highlights spatially mapped immune cell-associated metabolic pathways and validates inferred cell-cell ligand-receptor interactions. In conclusion, investigative biopsies provide data-rich insight into disease processes and may be useful in evaluating treatment responses.

[Public Database-based Study to Explore the Expression and Role of DDB1 in Lung Adenocarcinoma]

BACKGROUND

Lung adenocarcinoma (LUAD) is the predominant subtype of non-small cell lung cancer (NSCLC). Damage-specific DNA binding protein 1 (DDB1), as a core protein of the CUL4-DDB1 ubiquitin ligase complex, is involved in the regulation of DNA damage repair, epigenetic modification, and cell cycle checkpoint activation. While the involvement of DDB1 in tumour progression through DNA repair and RNA transcriptional regulation has been reported, its expression and role in LUAD remain to be elucidated. This study aims to investigate the expression and role of DDB1 in LUAD.

METHODS

The expression, clinicopathological features and prognosis of DDB1 in LUAD were analysed using databases such as UALCAN, Kaplan-Meier Plotter and GEPIA; The interaction network and enriched functional pathways were constructed by GeneMANIA and Metascape; the correlation between DDB1 and immune cells by combining with TISIDB infiltration was evaluated, and the clustering results of cell subtypes and the expression of DDB1 in different immune cell subpopulations were analysed by single-cell sequencing; finally, tissue microarrays were used to further verify the expression and prognostic value of DDB1 in LUAD.

RESULTS

The mRNA and protein expression of DDB1 in LUAD tissues were significantly higher than those in normal tissues (P<0.01), and the high expression correlated with later clinical stage (P<0.001), lymph node metastasis (P<0.001) and poor prognosis (P<0.001). Functional enrichment showed that DDB1 was involved in DNA repair and RNA transcriptional regulation, and TISIDB evaluation revealed that DDB1 was negatively correlated with the expression level of immune cells, suggesting the potential regulation of the immune microenvironment. Single cell analysis showed that DDB1 was mainly expressed in T cells, alveolar macrophages and dendritic cells. Tissue microarrays confirmed that overall survival was shorter in the DDB1 high expression group (P<0.001), and Cox multifactorial analysis showed that DDB1 was an independent predictor of LUAD prognosis.

CONCLUSIONS

DDB1 is highly expressed in LUAD, which is associated with poor prognosis, and is closely related to tumor immune cell infiltration, and is involved in tumourigenesis and development through DNA repair and RNA transcriptional regulation. DDB1 can be used as a potential prognostic marker and therapeutic target for LUAD.

Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer

Background

Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. Neoadjuvant therapy (NAT), administered prior to surgery, is integral to breast cancer treatment strategies. It aims to downsize tumors, optimize surgical outcomes, and evaluate tumor responsiveness to treatment. However, accurately predicting NAT efficacy remains challenging due to the disease's complexity and the diverse responses across different molecular subtypes.

Methods

In this study, we harnessed multimodal data, including proteomic, genomic, MRI imaging, and clinical information, sourced from multiple cohorts such as I-SPY2, TCGA-BRCA, GSE161529, and METABRIC. Post data preprocessing, Lasso regression was utilized for feature extraction and selection. Five machine learning algorithms were employed to construct diagnostic models, with pathological complete response (pCR) as the predictive endpoint.

Results

Our results revealed that the multi-omics Ridge regression model achieved the optimal performance in predicting pCR, with an AUC of 0.917. Through unsupervised clustering using the R package MOVICS and nine clustering algorithms, we identified four distinct multimodal breast cancer subtypes associated with NAT. These subtypes exhibited significant differences in proteomic profiles, hallmark cancer gene sets, pathway activities, tumor immune microenvironments, transcription factor activities, and clinical characteristics. For instance, CS1 subtype, predominantly ER-positive, had a low pCR rate and poor response to chemotherapy drugs, while CS4 subtype, characterized by high immune infiltration, showed a better response to immunotherapy. At the single-cell level, we detected significant heterogeneity in the tumor microenvironment among the four subtypes. Malignant cells in different subtypes displayed distinct copy number variations, differentiation levels, and evolutionary trajectories. Cell-cell communication analysis further highlighted differential interaction patterns among the subtypes, with implications for tumor progression and treatment response.

Conclusion

Our multimodal diagnostic model and subtype analysis provide novel insights into predicting NAT efficacy in breast cancer. These findings hold promise for guiding personalized treatment strategies. Future research should focus on experimental validation, in-depth exploration of the underlying mechanisms, and extension of these methods to other cancers and treatment modalities.

Deleterious KOs in the HLA Class I Antigen Processing and Presentation Machinery Induce Distinct Changes in the Immunopeptidome

The human leukocyte antigen (HLA) processing and presentation machinery (APPM) is altered in various diseases and in response to drug treatments. Defects in the machinery may change presentation levels or alter the repertoire of presented peptides, globally or in an HLA allele-restricted manner, with direct implications for adaptive immunity. In this study, we investigated the immunopeptidome landscape across a panel of isogenic HAP1 cell line clones, each with a KO of a single gene encoding a key protein in the APPM, including B2M, TAP1, TAP2, TAPBP, IRF2, PDIA3, ERAP1, GANAB, SPPL3, CANX, and CALR. We applied immunopeptidomics and proteomics to assess the successful gene KOs on the protein level, understand how these proteins participate in antigen presentation, and contextualize protein expression and antigen presentation. We validated the absence of the KO proteins in the respective samples and found that knocking-out an APPM component leads to the loss of peptide subsets that are normally presented on the control wildtype cells. We assessed the immunopeptidomes qualitatively and quantitatively, considering factors like peptide diversity, peptide length distribution, and binding affinity to the endogenously expressed HLA alleles in HAP1 cells. We demonstrated prominent HLA allele-restricted alterations in several KO conditions. The absence of CALR, CANX, and TAP1 led to significant changes in HLA allele-specific presentation levels. Overall, this work represents the first systematic analysis of how the absence of individual APPM components, knocked out in a single cell line under controlled conditions, affects the immunopeptidome. This approach could facilitate the creation of predictive tools capable of prioritizing HLA-bound peptides likely to be presented when presentation defects occur, such as in cancer and viral infections.

Significance of LRFN4 in prognosis and tumor microenvironment of lung adenocarcinoma

Background

LRFN4 is expressed in various tumors and leukemia cell lines. This study aims to explore the effect of LRFN4 in lung adenocarcinoma (LUAD).

Methods

The data on LUAD patients were collected from the Cancer Genome Atlas and Gene Expression Omnibus database. The expression of LRFN4 in LUAD and LUAD cell lines was analyzed via differential gene analysis, qRT-PCR assay, and Western blotting assay. The correlation of LRFN4 expression with the onset of LUAD were calculated using Pearson correlation analysis. The transcription factors correlated with LRFN4 expression were screened by differential expression analysis and Pearson correlation analysis. Moreover, the effect of LRFN4 on the immune landscape of LUAD was analyzed using CIBERSORT algorithm. The GDSC and CTRP databases were used to analyze the drug sensitivity of hub gene.

Results

LRFN4 was highly expressed in LUAD patients and cells, and LRFN4 expression was correlated with metastasis, pathological stages, and age of LUAD patients. The transcription factors E2F1 and E2F3 could regulate LRFN4 expression by binding upstream of LRFN4. The 8 immune cell infiltration levels were differential between LRFN4 high and LRFN4 low patients. The ESTIMATEScore and ImmuneScore levels were decreased, the TumorPurity level was elevated, and 6 immune checkpoint expressions were increased in LRFN4high patients. Moreover, LRFN4high patients had inferior prognosis. The mutation rate of TP53, TTN, and MUC6 and the level of TMB were increased in LRFN4 high patients. The expressions of TCF3, E2F1, E2F3, and LRFN4 were correlated with the IC50 of multiple drugs.

Conclusion

LRFN4 may serve as a novel prognostic biomarker for LUAD, shows specific overexpression in LUAD and may be a potential therapeutic target for LUAD patients.

Spatial transcriptome analysis of lung squamous cell carcinoma arising from interstitial pneumonia provides insights into tumor heterogeneity

Interstitial pneumonia (IP) is a refractory disease that causes severe inflammation and fibrosis in the interstitium of the lungs, often resulting in the development of lung cancer (LC) during treatment. Previous studies have demonstrated that the prognosis of LC complicated by IP is inferior to that of LC without IP. It is therefore of the utmost importance to gain a deeper understanding of the heterogeneity of such tumors. In the present study, we conducted spatial transcriptome analysis of squamous cell carcinoma arising from IP. The results suggested involvement of the glucocorticoid receptor pathway in treatment resistance. Immunostaining of squamous cell carcinoma specimens from patients with IP demonstrated that the tumors expressed NR3C1 to varying degrees. Furthermore, higher NR3C1 expression levels were associated with a significantly increased risk of recurrence. Our results point to a novel subtype of lung squamous cell carcinoma. Further analysis of the molecular mechanisms associated with this subtype may facilitate the development of novel diagnostic criteria and therapeutic approaches.

Hypoxia promotes tumor immune evasion by suppressing MHC-I expression and antigen presentation

Hypoxia is a common feature of solid tumors that has previously been linked to resistance to radiotherapy and chemotherapy, and more recently to immunotherapy. In particular, hypoxic tumors exclude T cells and inhibit their activity, suggesting that tumor cells acquire a mechanism to evade T-cell recognition and killing. Our analysis of hypoxic tumors indicates that hypoxia downregulates the expression of MHC class I and its bound peptides (i.e., the immunopeptidome). Hypoxia decreases MHC-I expression in an oxygen-dependent manner, via activation of autophagy through the PERK arm of the unfolded protein response. Using an immunopeptidomics-based LC-MS approach, we find a significant reduction of presented antigens under hypoxia. Inhibition of autophagy under hypoxia enhances antigen presentation. In experimental tumors, reducing mitochondrial metabolism through a respiratory complex-I inhibitor increases tumor oxygenation, as well as MHC-I levels and the immunopeptidome. These data explain the molecular basis of tumor immune evasion in hypoxic conditions, and have implications for future therapeutic interventions targeting hypoxia-induced alterations in antigen presentation.

Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy

Understanding how intratumoral immune populations coordinate antitumor responses after therapy can guide treatment prioritization. We systematically analyzed an established immunotherapy, donor lymphocyte infusion (DLI), by assessing 348,905 single-cell transcriptomes from 74 longitudinal bone marrow samples of 25 patients with relapsed leukemia; a subset was evaluated by both protein- and transcriptome-based spatial analysis. In acute myeloid leukemia (AML) DLI responders, we identified clonally expanded ZNF683+ CD8+ cytotoxic T lymphocytes with in vitro specificity for patient-matched AML. These cells originated primarily from the DLI product and appeared to coordinate antitumor immune responses through interaction with diverse immune cell types within the marrow microenvironment. Nonresponders lacked this cross-talk and had cytotoxic T lymphocytes with elevated TIGIT expression. Our study identifies recipient bone marrow microenvironment differences as a determinant of an effective antileukemia response and opens opportunities to modulate cellular therapy.

Convergent inducers and effectors of T cell paralysis in the tumour microenvironment

Tumorigenesis embodies the formation of a heterotypic tumour microenvironment (TME) that, among its many functions, enables the evasion of T cell-mediated immune responses. Remarkably, most TME cell types, including cancer cells, fibroblasts, myeloid cells, vascular endothelial cells and pericytes, can be stimulated to deploy immunoregulatory programmes. These programmes involve regulatory inducers (signals-in) and functional effectors (signals-out) that impair CD8+ and CD4+ T cell activity through cytokines, growth factors, immune checkpoints and metabolites. Some signals target specific cell types, whereas others, such as transforming growth factor-β (TGFβ) and prostaglandin E2 (PGE2), exert broad, pleiotropic effects; as signals-in, they trigger immunosuppressive programmes in most TME cell types, and as signals-out, they directly inhibit T cells and also modulate other cells to reinforce immunosuppression. This functional diversity and redundancy pose a challenge for therapeutic targeting of the immune-evasive TME. Fundamentally, the commonality of regulatory programmes aimed at abrogating T cell activity, along with paracrine signalling between cells of the TME, suggests that many normal cell types are hard-wired with latent functions that can be triggered to prevent inappropriate immune attack. This intrinsic capability is evidently co-opted throughout the TME, enabling tumours to evade immune destruction.

The 2024 Report on the Human Proteome from the HUPO Human Proteome Project

The Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify at least one isoform of every protein-coding gene and (2) to make proteomics an integral part of multiomics studies of human health and disease. The past year has seen major transitions for the HPP. neXtProt was retired as the official HPP knowledge base, UniProtKB became the reference proteome knowledge base, and Ensembl-GENCODE provides the reference protein target list. A function evidence FE1-5 scoring system has been developed for functional annotation of proteins, parallel to the PE1-5 UniProtKB/neXtProt scheme for evidence of protein expression. This report includes updates from neXtProt (version 2023-09) and UniProtKB release 2024_04, with protein expression detected (PE1) for 18138 of the 19411 GENCODE protein-coding genes (93%). The number of non-PE1 proteins ("missing proteins") is now 1273. The transition to GENCODE is a net reduction of 367 proteins (19,411 PE1-5 instead of 19,778 PE1-4 last year in neXtProt). We include reports from the Biology and Disease-driven HPP, the Human Protein Atlas, and the HPP Grand Challenge Project. We expect the new Functional Evidence FE1-5 scheme to energize the Grand Challenge Project for functional annotation of human proteins throughout the global proteomics community, including π-HuB in China.

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Profiling Fibroblast Growth Factor Receptor 3 Expression Based on the Immune Microenvironment in Upper Tract Urothelial Carcinoma

BACKGROUND

Although several studies have shown favorable outcomes in upper tract urothelial carcinoma (UTUC) with fibroblast growth factor receptor 3 (FGFR3) mutations and/or expression, the relationship between immune cell markers and FGFR3 expression remains unknown.

OBJECTIVE

To clarify the FGFR3-based immune microenvironment and investigate biomarkers to predict the treatment response to pembrolizumab (Pem) in patients with UTUC.

DESIGN, SETTING, AND PARTICIPANTS

We conducted immunohistochemical staining in 214 patients with UTUC. The expression levels of FGFR3, CD4, CD8, CD68, CD163, CD204, and programmed cell death ligand 1 (PD-L1) were examined.

INTERVENTION

All UTUC patients underwent radical nephroureterectomy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We assessed the relationship between these immune markers and patient prognosis.

RESULTS AND LIMITATIONS

A total of 109 (50.9%) patients showed high FGFR3 expressions and a favorable prognosis compared with the remaining patients. Among the six immune markers, CD8 high expression was an independent favorable factor, whereas CD204 expression was an independent prognostic factor for cancer death. From the FGFR3-based immune clustering, three immune clusters were identified. Cluster A showed low FGFR3 with tumor-associated macrophage-rich components (CD204+) followed by a poor prognosis due to a poor response to Pem. Cluster B showed low FGFR3 with an immune hot component (CD8+), followed by the most favorable prognosis owing to a good response to Pem. Cluster C showed high FGFR3 expression but an immune cold component, followed by a favorable prognosis due to the high FGFR3 expression, but a poor response was confirmed with Pem.

CONCLUSIONS

Although most patients exhibit a poor response to Pem, individuals with low FGFR3 expression and immune hot status may benefit clinically from Pem treatment.

PATIENT SUMMARY

We conducted immunohistochemical staining to evaluate fibroblast growth factor receptor 3 (FGFR3)-related immune microenvironment by evaluating the expressions of CD4, CD8, CD68, CD163, CD204, and PD-L1 in 214 upper tract urothelial carcinoma patients. We identified three distinct immune clusters based on FGFR3 expressions and found that patients with a low FGFR3 expression but immune hot status received the maximum benefit from an immune checkpoint inhibitor.

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

RNA splicing junction landscape reveals abundant tumor-specific transcripts in human cancer

RNA splicing is a critical process governing gene expression and transcriptomic diversity. Despite its importance, a detailed examination of transcript variation at the splicing junction level remains scarce. Here, we perform a thorough analysis of RNA splicing junctions in 34,775 samples across multiple sample types. We identified 29,051 tumor-specific transcripts (TSTs) in pan-cancer, with a majority of these TSTs being unannotated. Our findings show that TSTs are positively correlated with tumor stemness and linked to unfavorable outcomes in cancer patients. Additionally, TSTs display mutual exclusivity with somatic mutations and are overrepresented in transposable-element-derived transcripts possessing oncogenic functions. Importantly, TSTs can generate putative neoantigens for immunotherapy. Moreover, TSTs can be detected in blood extracellular vesicles from cancer patients. Our results shed light on the intricacies of RNA splicing and offer promising avenues for cancer diagnosis and therapy.

A post-translational cysteine-to-serine conversion in human and mouse insulin generates a diabetogenic neoepitope

Type 1 diabetes (T1D) affects a genetically susceptible population that develops autoreactive T cells attacking insulin-producing pancreatic β cells. Increasingly, neoantigens are recognized as critical drivers of this autoimmune response. Here, we report a novel insulin neoepitope generated via post-translational cysteine-to-serine conversion (C>S) in human patients, which is also seen in the autoimmune-prone non-obese diabetic (NOD) mice. This modification is driven by oxidative stress within the microenvironment of pancreatic β cells and is further amplified by T1D-relevant inflammatory cytokines, enhancing neoantigen formation in both pancreatic β cells and dendritic cells. We discover that C>S-modified insulin is specifically recognized by CD4 + T cells in human T1D patients and NOD mice. In humans with established T1D, HLA-DQ8-restricted, C>S-specific CD4 + T cells exhibit an activated memory phenotype and lack regulatory signatures. In NOD mice, these neoepitope-specific T cells can orchestrate islet infiltration and promote diabetes progression. Collectively, these data advance a concept that microenvironment-driven and context-dependent post-translational modifications (PTMs) can generate neoantigens that contribute to organ-specific autoimmunity.

In situ endoscopic photodynamic therapy combined with immature DC vaccination induces a robust T cell response against peritoneal carcinomatosis

BACKGROUND

Immunogenic cell death (ICD) and ferroptosis have recently emerged as key factors in the anticancer immune response. Among the treatments able to induce ICD and the associated release of danger signals is photodynamic therapy (PDT). Ferroptosis for its part results from lipid peroxidation and is induced by CD8+ T cells to kill nearby cancer cells on IFN-γ production. We aimed to combine the two concepts, that is, to evaluate whether the strong pro-oxidant effects of PDT may promote ferroptosis and antigen release and to develop a procedure for in situ PDT to prepare the soil for highly endocytotic immature dendritic cell (iDC) adoptive transfer. This approach was implemented for managing peritoneal carcinomatosis, a lesion often associated with poor outcomes.

METHODS

We used three-dimensional (3D) heterotypic spheroids made of cancer cells, exposed them to a white light-activated OR141 photosensitizer (PS), and subsequently complexified them by adding iDC and naive lymphocytes. We next used a model of mouse peritoneal carcinomatosis and administered PDT using laparoscopy to locally induce photoactivation using the endoscope light. The immune response following adoptive transfer of iDC was tracked both in vivo and ex vivo using isolated immune cells from in situ vaccinated mice.

RESULTS

Cancer cells undergoing PDT-induced cell death significantly increased ICD markers and the infiltration of iDCs in spheroids, relying on ferroptosis. These actions induced the sequential activation of CD8+ and CD4+ T cells as revealed by a significant spheroid 3D structure deterioration and, remarkably, were not recapitulated by conventional ferroptosis inducer RSL3. Using LED light from an endoscope for in situ photoactivation of PS enabled us to apply the vaccination modality in mice with peritoneal tumors. Consecutive intraperitoneal injection of iDCs resulted in delayed tumor growth, increased survival rates, and prevented tumor relapse on rechallenge. CD8+ T cell response was supported by depletion experiments, nodal detection of early activated T cells, and ex vivo T cell-induced cytotoxicity toward spheroids.

CONCLUSIONS

The combination of in situ PDT locally delivered by an endoscope light and iDC administration induces a durable memory immune response against peritoneal carcinomatosis thereby opening new perspectives for the treatment of a life-threatening condition.

ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

Causal role of immune cells in lung cancer subtypes: Mendelian randomization study

Lung cancer, characterized by its high incidence and mortality rates, is a challenging malignancy to treat. Immunotherapy has emerged as a crucial treatment modality, yet its effectiveness varies significantly among patients due to the diverse immune microenvironment involved. Our study aims to analyze the similarities and differences in immune cell profiles across different subtypes of lung cancer. We employed a comprehensive two-sample Mendelian randomization analysis to establish causal connections between immune cells and lung cancer. We examined differential expression of 731 immune cell types and compared their profiles among various lung cancer subtypes. Our analysis revealed that 47 immune cell types exhibited differential expression in lung cancer, with 15 showing a protective effect and 32 having a tumor-promoting effect. Notably, we observed greater similarities in immune cell profile between squamous carcinoma and adenocarcinoma subtypes, while small cell lung cancerHHHH displayed less overlap with the other two types. Specifically, CD4+ naive T cells showed differential expression across all three lung cancer subtypes, whereas three other immune cell types exhibited differential expression exclusively in adenocarcinoma and squamous cell carcinoma. Our findings substantiate a causal link between immune cell dynamics and lung cancer progression. Moreover, our identification of distinct immune cell composition among histological subtypes of lung cancer may serve as a valuable reference for further investigation into immunotherapeutic strategies.

Exploring the causal relationship between the immune cell-inflammatory factor axis and lung cancer: a Mendelian randomization study

Background

Lung cancer is a major health burden globally and smoking is a well-known risk factor. It has been observed that chronic inflammation contributes to lung cancer progression, with immune cells and inflammatory cytokines implicated in tumor development. Clarifying the causal links between these immune components and lung cancer could enhance prevention and therapy.

Methods

We performed Mendelian randomization (MR) to explore causal connections between immune cells, inflammatory markers, and lung cancer risk, using genetic variants as instruments. Data from GWAS on these variables underpinned our MR analyses.

Results

Our findings indicated an inverse association between some immune cells and lung cancer risk, implying that more immune cells might be protective. NK T cells (CD16-CD56) and myeloid cells (HLA DR+ on CD33dim HLA DR+ CD11b+) had an inverse correlation with lung cancer risk. Furthermore, a direct relationship was observed between inflammatory cytokines and these immune cells. In contrast, IL-18 was inversely associated with lung cancer, while IL-13 showed a direct correlation.

Conclusion

The study underscores the role of immune and inflammatory factors in lung cancer. These insights could lead to new therapeutic strategies for combating lung cancer.

Unveiling major histocompatibility complex-mediated pan-cancer immune features by integrated single-cell and bulk RNA sequencing

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, yet persistent challenges such as low response rate and significant heterogeneity necessitate attention. The pivotal role of the major histocompatibility complex (MHC) in ICI efficacy, its intricate impacts and potentials as a prognostic marker, warrants comprehensive exploration. This study integrates single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, and spatial transcriptomic analyses to unveil pan-cancer immune characteristics governed by the MHC transcriptional feature (MHC.sig). Developed through scRNA-seq analysis of 663,760 cells across diverse cohorts and validated in 30 solid cancer types, the MHC.sig demonstrates a robust correlation between immune-related genes and infiltrating immune cells, highlighting its potential as a universal pan-cancer marker for anti-tumor immunity. Screening the MHC.sig for therapeutic targets using CRISPR data identifies potential genes for immune therapy synergy and validates its predictive efficacy for ICIs responsiveness across diverse datasets and cancer types. Finally, analysis of cellular communication patterns reveals interactions between C1QC+macrophages and malignant cells, providing insights into potential therapeutic agents and their sensitivity characteristics. This comprehensive analysis positions the MHC.sig as a promising marker for predicting immune therapy outcomes and guiding combinatorial therapeutic strategies.

Immunopeptidomics in the cancer immunotherapy era

Cancer is the primary cause of death worldwide, and conventional treatments are painful, complicated, and have negative effects on healthy cells. However, cancer immunotherapy has emerged as a promising alternative. Principle of cancer immunotherapy is the re-activation of T-cell to combat the tumor that presents the peptide antigen on major histocompatibility complex (MHC). Those peptide antigens are identified with the set of omics technology, proteomics, genomics, and bioinformatics, which referred to immunopeptidomics. Indeed, immunopeptidomics can identify the neoantigens that are very useful for cancer immunotherapies. This review explored the use of immunopeptidomics for various immunotherapies, i.e., peptide-based vaccines, immune checkpoint inhibitors, oncolytic viruses, and chimeric antigen receptor T-cell. We also discussed how the diversity of neoantigens allows for the discovery of novel antigenic peptides while post-translationally modified peptides diversify the overall peptides binding to MHC or so-called MHC ligandome. The development of immunopeptidomics is keeping up-to-date and very active, particularly for clinical application. Immunopeptidomics is expected to be fast, accurate and reliable for the application for cancer immunotherapies.

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Gene and protein sequence features augment HLA class I ligand predictions

The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

Immune checkpoint blockade resistance in lung cancer: emerging mechanisms and therapeutic opportunities

Immune checkpoint blockade (ICB) therapy works by inhibiting suppressive checkpoints that become upregulated after T cell activation, like PD-1/PD-L1 and CTLA-4. While the initial FDA approvals of ICB have revolutionized cancer therapies and fueled a burgeoning immuno-oncology field, more recent clinical development of new agents has been slow. Here, focusing on lung cancer, we review the latest research uncovering tumor cell intrinsic and extrinsic ICB resistance mechanisms as major hurdles to treatment efficacy and clinical progress. These include genomic and non-genomic tumor cell alterations, along with host and microenvironmental factors like the microbiome, metabolite accumulation, and hypoxia. Together, these factors can cooperate to promote immunosuppression and ICB resistance. Opportunities to prevent resistance are constantly evolving in this rapidly expanding field, with the goal of moving toward personalized immunotherapeutic regimens.

Immunogenicity of Non-Mutated Ovarian Cancer-Specific Antigens

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

Predicting the mechanism of action of YQYYJD prescription in the treatment of non-small cell lung cancer using transcriptomics analysis

ETHNOPHARMACOLOGICAL RELEVANCE

The efficacy of the herbal formula Yiqi Yangyin Jiedu (YQYYJD) in the treatment of advanced lung cancer has been reported in clinical trials. However, the key anti-lung cancer herbs and molecular mechanisms underlying its inhibition of lung cancer are not well-understood.

AIM OF THE STUDY

To identify the key anti-lung cancer herbs in the YQYYJD formula and investigate their therapeutic effect and potential mechanism of action in non-small cell lung cancer (NSCLC) using transcriptomics and bioinformatics techniques.

MATERIALS AND METHODS

A mouse Lewis lung carcinoma (LLC) subcutaneous inhibitory tumor model was established with 6 mice in each group. Mice were treated with the YQYYJD split formula: Yiqi Formula (YQ), Yangyin Formula (YY), and Ruanjian Jiedu Formula (RJJD) for 14 days. The tumor volume and mouse weight were recorded, and the status of tumor occurrence was further observed by taking photos. The tumor was stained with hematoxylin-eosin to observe its histopathological changes. Immunohistochemistry was used to detect the expression of the proliferation marker Ki67 and the apoptotic marker Caspase-3 in tumor tissues. Flow cytometry was used to detect the number of CD4+ and CD8+ T cells and cytokines interleukin-2 (IL-2) and interferon-gamma (IFN-γ) in the spleen and tumor tissues. The differential genes of key drugs against tumors were obtained by transcriptome sequencing of tumors. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed on differential genes to obtain pathways and biological processes where targets were aggregated. TIMER2.0 and TISIDB databases were used to evaluate the impact of drugs on immune cell infiltration and immune-related genes. The binding activity of the key targets and compounds was verified by molecular docking.

RESULTS

YQ, YY, and RJJD inhibited the growth of subcutaneous transplanted tumors in LLC mice to varying degrees and achieved antitumor effects by inhibiting the expression of tumor cell proliferation, apoptosis, and metastasis-related proteins. Among the three disassembled prescriptions, YQ better inhibited the growth of subcutaneous transplanted tumors in LLC mice, significantly promoted tumor necrosis, significantly increased the expression of Caspase-3 protein in tumor tissue, and significantly decreased the expression of Ki-67 (P < 0.05), thereby increasing the infiltration of CD8+ T cells. YQ significantly increased the expression of CD4+ and CD8+ T cells in tumor and splenic tissues of tumor-bearing mice and up-regulated the expression of IL-2 and IFN-γ. Transcriptome sequencing and bioinformatics results showed that after YQ intervention, differentially expressed genes were enriched in more than one tumor-related pathway and multiple immune regulation-related biological functions. There were 12 key immune-related target genes.

CONCLUSION

YQ was the key disassembled prescription of YQYYJD, exerting significant antitumor effects and immune regulation effects on NSCLC. It may have relieved T cell exhaustion and regulated the immune microenvironment to exert antitumor effects by changing lung cancer-related targets, pathways, and biological processes.

Current Landscape of Cancer Immunotherapy: Harnessing the Immune Arsenal to Overcome Immune Evasion

Cancer immune evasion represents a leading hallmark of cancer, posing a significant obstacle to the development of successful anticancer therapies. However, the landscape of cancer treatment has significantly evolved, transitioning into the era of immunotherapy from conventional methods such as surgical resection, radiotherapy, chemotherapy, and targeted drug therapy. Immunotherapy has emerged as a pivotal component in cancer treatment, harnessing the body's immune system to combat cancer and offering improved prognostic outcomes for numerous patients. The remarkable success of immunotherapy has spurred significant efforts to enhance the clinical efficacy of existing agents and strategies. Several immunotherapeutic approaches have received approval for targeted cancer treatments, while others are currently in preclinical and clinical trials. This review explores recent progress in unraveling the mechanisms of cancer immune evasion and evaluates the clinical effectiveness of diverse immunotherapy strategies, including cancer vaccines, adoptive cell therapy, and antibody-based treatments. It encompasses both established treatments and those currently under investigation, providing a comprehensive overview of efforts to combat cancer through immunological approaches. Additionally, the article emphasizes the current developments, limitations, and challenges in cancer immunotherapy. Furthermore, by integrating analyses of cancer immunotherapy resistance mechanisms and exploring combination strategies and personalized approaches, it offers valuable insights crucial for the development of novel anticancer immunotherapeutic strategies.

Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy

With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor-specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the "dark matter" of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine-learning-supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides

Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.

Incongruity between T cell receptor recognition of breast cancer hotspot mutations ESR1 Y537S and D538G following exogenous peptide loading versus endogenous antigen processing

T cell receptor engineered T cell (TCR T) therapies have shown recent efficacy against certain types of solid metastatic cancers. However, to extend TCR T therapies to treat more patients across additional cancer types, new TCRs recognizing cancer-specific antigen targets are needed. Driver mutations in AKT1, ESR1, PIK3CA, and TP53 are common in patients with metastatic breast cancer (MBC) and if immunogenic could serve as ideal tumor-specific targets for TCR T therapy to treat this disease. Through IFN-γ ELISpot screening of in vitro expanded neopeptide-stimulated T cell lines from healthy donors and MBC patients, we identified reactivity towards 11 of 13 of the mutations. To identify neopeptide-specific TCRs, we then performed single-cell RNA sequencing of one of the T cell lines following neopeptide stimulation. Here, we identified an ESR1 Y537S specific T cell clone, clonotype 16, and an ESR1 Y537S/D538G dual-specific T cell clone, clonotype 21, which were HLA-B*40:02 and HLA-C*01:02 restricted, respectively. TCR Ts expressing these TCRs recognized and killed target cells pulsed with ESR1 neopeptides with minimal activity against ESR1 WT peptide. However, these TCRs failed to recognize target cells expressing endogenous mutant ESR1. To investigate the basis of this lack of recognition we performed immunopeptidomics analysis of a mutant-overexpressing lymphoblastoid cell line and found that the ESR1 Y537S neopeptide was not endogenously processed, despite binding to HLA-B*40:02 when exogenously pulsed onto the target cell. These results indicate that stimulation of T cells that likely derive from the naïve repertoire with pulsed minimal peptides may lead to the expansion of clones that recognize non-processed peptides, and highlights the importance of using methods that selectively expand T cells with specificity for antigens that are efficiently processed and presented.

Multiplexed high-throughput immune cell imaging in patients with high-risk triple negative early breast cancer: Analysis from the International Breast Cancer Study Group (IBCSG) Trial 22-00

BACKGROUND

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) subtype, with dismal prognosis and limited option in advanced settings, yet stromal tumor infiltrating lymphocytes (sTILs) in this subtype has a predictive role.

PATIENTS AND METHODS

The International Breast Cancer Study Group (IBCSG) Trial 22-00 is a randomized phase III clinical trial testing the efficacy of low-dose metronomic oral Cyclophosphamide-Methotrexate (CM) maintenance following standard adjuvant chemotherapy treatment for early-stage hormone receptor-negative breast cancer patients. A case-cohort sampling was used. We characterized immune cells infiltrates in patients with TNBC by 6 plex immunofluorescence (IF) staining for CD4, FOXP3, CD3, cytokeratine and CD8 RESULTS: We confirmed that high immune CD3+ T cells as well as stromal and intra-epithelial Tregs (CD4+Foxp3+ T cells) infiltrates were associated with a better Distant Recurrence-Free Interval (DRFI), especially in LN+ patient, regardless of the treatment. More importantly, we showed that the spatial distribution of immune cells at baseline is crucial, as CM maintenance was detrimental for T cells excluded LN+ TNBC patients.

CONCLUSIONS

immune spatial classification on immune cells infiltrates seems crucial and could help patients' selection in clinical trial and greatly improve responses to specific therapies.

Challenges in developing personalized neoantigen cancer vaccines

The recent success of cancer immunotherapies has highlighted the benefit of harnessing the immune system for cancer treatment. Vaccines have a long history of promoting immunity to pathogens and, consequently, vaccines targeting cancer neoantigens have been championed as a tool to direct and amplify immune responses against tumours while sparing healthy tissue. In recent years, extensive preclinical research and more than one hundred clinical trials have tested different strategies of neoantigen discovery and vaccine formulations. However, despite the enthusiasm for neoantigen vaccines, proof of unequivocal efficacy has remained beyond reach for the majority of clinical trials. In this Review, we focus on the key obstacles pertaining to vaccine design and tumour environment that remain to be overcome in order to unleash the true potential of neoantigen vaccines in cancer therapy.

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy

Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.

The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics

The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.

Breast cancer immunopeptidomes contain numerous shared tumor antigens

Hormone receptor-positive breast cancer (HR+) is immunologically cold and has not benefited from advances in immunotherapy. In contrast, subsets of triple-negative breast cancer (TNBC) display high leukocytic infiltration and respond to checkpoint blockade. CD8+ T cells, the main effectors of anticancer responses, recognize MHC I-associated peptides (MAPs). Our work aimed to characterize the repertoire of MAPs presented by HR+ and TNBC tumors. Using mass spectrometry, we identified 57,094 unique MAPs in 26 primary breast cancer samples. MAP source genes highly overlapped between both subtypes. We identified 25 tumor-specific antigens (TSAs) mainly deriving from aberrantly expressed regions. TSAs were most frequently identified in TNBC samples and were more shared among The Cancer Genome Atlas (TCGA) database TNBC than HR+ samples. In the TNBC cohort, the predicted number of TSAs positively correlated with leukocytic infiltration and overall survival, supporting their immunogenicity in vivo. We detected 49 tumor-associated antigens (TAAs), some of which derived from cancer-associated fibroblasts. Functional expansion of specific T cell assays confirmed the in vitro immunogenicity of several TSAs and TAAs. Our study identified attractive targets for cancer immunotherapy in both breast cancer subtypes. The higher prevalence of TSAs in TNBC tumors provides a rationale for their responsiveness to checkpoint blockade.

Immunopeptidomics in the Era of Single-Cell Proteomics

Immunopeptidomics, as the analysis of antigen peptides being presented to the immune system via major histocompatibility complexes (MHC), is being seen as an imperative tool for identifying epitopes for vaccine development to treat cancer and viral and bacterial infections as well as parasites. The field has made tremendous strides over the last 25 years but currently still faces challenges in sensitivity and throughput for widespread applications in personalized medicine and large vaccine development studies. Cutting-edge technological advancements in sample preparation, liquid chromatography as well as mass spectrometry, and data analysis, however, are currently transforming the field. This perspective showcases how the advent of single-cell proteomics has accelerated this transformation of immunopeptidomics in recent years and will pave the way for even more sensitive and higher-throughput immunopeptidomics analyses.

TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for the direct identification and characterization of naturally presented tumor-associated peptides, a key prerequisite for the development of T cell-based immunotherapies. This study reports on the implementation of ion mobility separation-based time-of-flight (TOFIMS) MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. Applying TOFIMS-based immunopeptidomics, a novel extensive benignTOFIMS dataset was generated from 94 primary benign samples of solid tissue and hematological origin, which enabled the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides, the refinement of previously described tumor antigens, as well as the identification of frequently presented self antigens and not yet described tumor antigens comprising low abundant mutation-derived neoepitopes that might serve as targets for future cancer immunotherapy development.

Recent advances in immunopeptidomic-based tumor neoantigen discovery

The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.

Sepsis and acute respiratory failure in patients with cancer: how can we improve care and outcomes even further?

PURPOSE OF REVIEW

Care and outcomes of critically ill patients with cancer have improved over the past decade. This selective review will discuss recent updates in sepsis and acute respiratory failure among patients with cancer, with particular focus on important opportunities to improve outcomes further through attention to phenotyping, predictive analytics, and improved outcome measures.

RECENT FINDINGS

The prevalence of cancer diagnoses in intensive care units (ICUs) is nontrivial and increasing. Sepsis and acute respiratory failure remain the most common critical illness syndromes affecting these patients, although other complications are also frequent. Recent research in oncologic sepsis has described outcome variation - including ICU, hospital, and 28-day mortality - across different types of cancer (e.g., solid vs. hematologic malignancies) and different sepsis definitions (e.g., Sepsis-3 vs. prior definitions). Research in acute respiratory failure in oncology patients has highlighted continued uncertainty in the value of diagnostic bronchoscopy for some patients and in the optimal respiratory support strategy. For both of these syndromes, specific challenges include multifactorial heterogeneity (e.g. in etiology and/or underlying cancer), delayed recognition of clinical deterioration, and complex outcomes measurement.

SUMMARY

Improving outcomes in oncologic critical care requires attention to the heterogeneity of cancer diagnoses, timely recognition and management of critical illness, and defining appropriate ICU outcomes.

Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery

The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.