Neoantigen identification: Technological advances and challenges.

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

CD4+ T cell activation distinguishes response to anti-PD-L1+anti-CTLA4 therapy from anti-PD-L1 monotherapy.

Cancer patients often receive a combination of antibodies targeting programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen-4 (CTLA4). We conducted a window-of-opportunity study in head and neck squamous cell carcinoma (HNSCC) to examine the contribution of anti-CTLA4 to anti-PD-L1 therapy. Single-cell profiling of on- versus pre-treatment biopsies identified T cell expansion as an early response marker. In tumors, anti-PD-L1 triggered the expansion of mostly CD8+ T cells, whereas combination therapy expanded both CD4+ and CD8+ T cells. Such CD4+ T cells exhibited an activated T helper 1 (Th1) phenotype. CD4+ and CD8+ T cells co-localized with and were surrounded by dendritic cells expressing T cell homing factors or antibody-producing plasma cells. T cell receptor tracing suggests that anti-CTLA4, but not anti-PD-L1, triggers the trafficking of CD4+ naive/central-memory T cells from tumor-draining lymph nodes (tdLNs), via blood, to the tumor wherein T cells acquire a Th1 phenotype. Thus, CD4+ T cell activation and recruitment from tdLNs are hallmarks of early response to anti-PD-L1 plus anti-CTLA4 in HNSCC.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer.

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer.

CD8+ T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8+ T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8+ T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8+ T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific 'self-like' features. MSI CRC CD8+ T cells showed tumor-specific activation reminiscent of canonical 'T cell hot' tumors, whereas the MSS CRC CD8+ T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of 'pseudo-T cell hot' tumors. Consequently, MSI and MSS CRC CD8+ T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8+ T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8+ T cells to build a bulk tumor transcriptome classification for CRC patients. This "Immune Subtype Classification" (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8+ T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Implementation of Regulation (EU) No 536/2014 as a Non-commercial Sponsor: An Internal Survey and a Descriptive Analysis of Timelines.

INTRODUCTION: To safeguard the safety and interests of subjects participating in clinical trials, their conduct is heavily regulated. The EU Clinical Trials Regulation (CTR) 536/2014 brings fundamental changes that will require sponsors to adapt their current way of working. One major change is the significant shortening of the permitted reply timelines to requests for information (RFI), which may require adaptations to established processes within an organisation. This study aimed to assess these reply timelines at the European Organisation for Research and Treatment of Cancer (EORTC), a non-commercial sponsor. Additionally, it aimed to investigate how the impact of the different CTR requirements is perceived by the organisation's staff. METHODS: A retrospective analysis was performed to assess the length of reply to grounds of non-acceptance (GNA) timelines. Questionnaires were circulated to internal staff to assess their views on the impact of important changes the CTR introduces on the organisation's processes. RESULTS: The average reply time to comments from regulators was 27.5 days, which is longer than the 12-day time limit required by CTR, which indicates that the organisation's processes require re-optimization to allow efficient activation of trials compliant with the new legislation. The majority of the staff that completed the questionnaire assessed the impact the CTR would have on the organisation to be positive. Finally, there was a large consensus about the changes related to the submission timelines, transition period and the user management of the Clinical Trial Information System (CTIS) having a very important impact on the organisation as a whole. Participants referred to the streamlined process of a clinical trial in different countries as foreseen in the CTR, as an aspect that would benefit the organisation. DISCUSSION: For all retrospectively studied timelines, the average timelines to reply combined for competent authorities (CA) and ethics committees (EC) were longer than the 12 days allowed under the CTR. EORTC will have to adapt internal processes to meet the time limit imposed by the CTR without compromising its scientific integrity. The questionnaire respondents had the required expertise to provide an opinion on the CTR's impact on the organisation. There was a large consensus about the changes relating to the submission timelines having a very important impact on the organisation. This observation is in line with the results of the retrospective part of this study. CONCLUSION: Based on the results of the retrospective and prospective parts of the study, it is clear that the shorter reply timelines are the main factor that will affect the organisation. EORTC has spent significant resources in adapting its processes to comply with the CTR's new requirements. Experience with the first studies under the new regulation can be utilized to implement further process adaptations.

Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer.

The consensus molecular subtype (CMS) classification of colorectal cancer is based on bulk transcriptomics. The underlying epithelial cell diversity remains unclear. We analyzed 373,058 single-cell transcriptomes from 63 patients, focusing on 49,155 epithelial cells. We identified a pervasive genetic and transcriptomic dichotomy of malignant cells, based on distinct gene expression, DNA copy number and gene regulatory network. We recapitulated these subtypes in bulk transcriptomes from 3,614 patients. The two intrinsic subtypes, iCMS2 and iCMS3, refine CMS. iCMS3 comprises microsatellite unstable (MSI-H) cancers and one-third of microsatellite-stable (MSS) tumors. iCMS3 MSS cancers are transcriptomically more similar to MSI-H cancers than to other MSS cancers. CMS4 cancers had either iCMS2 or iCMS3 epithelium; the latter had the worst prognosis. We defined the intrinsic epithelial axis of colorectal cancer and propose a refined 'IMF' classification with five subtypes, combining intrinsic epithelial subtype (I), microsatellite instability status (M) and fibrosis (F).

A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling.

The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer (n = 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4+ T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate the first panoramic view on the shared complexity of stromal cells in different cancers.

Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer.

Immunotherapy for metastatic colorectal cancer is effective only for mismatch repair-deficient tumors with high microsatellite instability that demonstrate immune infiltration, suggesting that tumor cells can determine their immune microenvironment. To understand this cross-talk, we analyzed the transcriptome of 91,103 unsorted single cells from 23 Korean and 6 Belgian patients. Cancer cells displayed transcriptional features reminiscent of normal differentiation programs, and genetic alterations that apparently fostered immunosuppressive microenvironments directed by regulatory T cells, myofibroblasts and myeloid cells. Intercellular network reconstruction supported the association between cancer cell signatures and specific stromal or immune cell populations. Our collective view of the cellular landscape and intercellular interactions in colorectal cancer provide mechanistic information for the design of efficient immuno-oncology treatment strategies.

Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources.

Cells constantly adapt to environmental fluctuations. These physiological changes require time and therefore cause a lag phase during which the cells do not function optimally. Interestingly, past exposure to an environmental condition can shorten the time needed to adapt when the condition re-occurs, even in daughter cells that never directly encountered the initial condition. Here, we use the molecular toolbox of Saccharomyces cerevisiae to systematically unravel the molecular mechanism underlying such history-dependent behavior in transitions between glucose and maltose. In contrast to previous hypotheses, the behavior does not depend on persistence of proteins involved in metabolism of a specific sugar. Instead, presence of glucose induces a gradual decline in the cells' ability to activate respiration, which is needed to metabolize alternative carbon sources. These results reveal how trans-generational transitions in central carbon metabolism generate history-dependent behavior in yeast, and provide a mechanistic framework for similar phenomena in other cell types.

The Antifungal Plant Defensin HsAFP1 Is a Phosphatidic Acid-Interacting Peptide Inducing Membrane Permeabilization.

HsAFP1, a plant defensin isolated from coral bells (Heuchera sanguinea), is characterized by broad-spectrum antifungal activity. Previous studies indicated that HsAFP1 binds to specific fungal membrane components, which had hitherto not been identified, and induces mitochondrial dysfunction and cell membrane permeabilization. In this study, we show that HsAFP1 reversibly interacts with the membrane phospholipid phosphatidic acid (PA), which is a precursor for the biosynthesis of other phospholipids, and to a lesser extent with various phosphatidyl inositol phosphates (PtdInsP's). Moreover, via reverse ELISA assays we identified two basic amino acids in HsAFP1, namely histidine at position 32 and arginine at position 52, as well as the phosphate group in PA as important features enabling this interaction. Using a HsAFP1 variant, lacking both amino acids (HsAFP1[H32A][R52A]), we showed that, as compared to the native peptide, the ability of this variant to bind to PA and PtdInsP's is reduced (≥74%) and the antifungal activity of the variant is reduced (≥2-fold), highlighting the link between PA/PtdInsP binding and antifungal activity. Using fluorescently labelled HsAFP1 in confocal microscopy and flow cytometry assays, we showed that HsAFP1 accumulates at the cell surface of yeast cells with intact membranes, most notably at the buds and septa. The resulting HsAFP1-induced membrane permeabilization is likely to occur after HsAFP1's internalization. These data provide novel mechanistic insights in the mode of action of the HsAFP1 plant defensin.

Yeast as a model for the identification of novel survival-promoting compounds applicable to treat degenerative diseases.

Programmed cell death (PCD) plays an important role in development and normal metabolic functioning of organisms. Excessive cell death is the cause of many degenerative diseases, like neurodegenerative disorders and Wilson's disease, for which current therapies remain insufficient. Current therapies are mainly focused on decreasing the disease symptoms following cell death, rather than blocking the cell death process itself. The latter can be obtained by either decreasing the presence of the toxic trigger (like protein aggregation in case of many commonly known neurodegenerative diseases) or by blocking death-inducing signaling cascade(s). Given the high conservation in PCD processes between yeast and mammalian cells, in this review, we will focus on yeast as a model organism to study PCD-related diseases as well as on its use for drug discovery purposes. More specifically, we will provide a comprehensive overview of new compounds, which were identified in yeast-based drug screens, that either decrease the amount of toxic trigger or inhibit PCD signaling cascades under PCD-inducing conditions.

Identification of survival-promoting OSIP108 peptide variants and their internalization in human cells.

The plant-derived decapeptide OSIP108 increases tolerance of yeast and human cells to apoptosis-inducing agents, such as copper and cisplatin. We performed a whole amino acid scan of OSIP108 and conducted structure-activity relationship studies on the induction of cisplatin tolerance (CT) in yeast. The use of cisplatin as apoptosis-inducing trigger in this study should be considered as a tool to better understand the survival-promoting nature of OSIP108 and not for purposes related to anti-cancer treatment. We found that charged residues (Arg, His, Lys, Glu or Asp) or a Pro on positions 4-7 improved OSIP108 activity by 10% or more. The variant OSIP108[G7P] induced the most pronounced tolerance to toxic concentrations of copper and cisplatin in yeast and/or HepG2 cells. Both OSIP108 and OSIP108[G7P] were shown to internalize equally into HeLa cells, but at a higher rate than the inactive OSIP108[E10A], suggesting that the peptides can internalize into cells and that OSIP108 activity is dependent on subsequent intracellular interactions. In conclusion, our studies demonstrated that tolerance/survival-promoting properties of OSIP108 can be significantly improved by single amino acid substitutions, and that these properties are dependent on (an) intracellular target(s), yet to be determined.