Human endogenous retroviruses (HERVs) represent 8% of the human genome. HERV products may represent tumor antigens relevant for cancer immunotherapy. We developed a bioinformatic approach to identify shared CD8 ⁺ T cell epitopes derived from cancer-associated HERVs in solid tumors. Six candidates among the most commonly shared HLA-A2 epitopes with evidence of translation were selected for immunological evaluation. In vitro priming assays confirmed the immunogenicity of these epitopes, which induced high-avidity CD8 ⁺ T cell clones. These T cells specifically recognize and kill HLA-A2 ⁺ tumor cells presenting HERV epitopes on HLA molecules, as demonstrated by mass spectrometry. Furthermore, epitope-specific CD8 ⁺ T cells were identified by dextramer staining among tumor-infiltrating lymphocytes from HLA-A2 ⁺ patients with breast cancer. Last, we showed that HERV-specific T cells lyse patient-derived organoids. These shared virus-like epitopes are of major interest for the development of cancer vaccines or T cell–based immunotherapies, especially in tumors with low/intermediate mutational burden.

Human Endogenous Retroviruses (HERVs) are accounting for 8% of the human genome. These sequences are remnants from ancient germline infections by exogenous retroviruses. After million years of evolution and multiple integrations, HERVs have acquired many damages rendering them defective. At steady state, HERVs are mostly localized in the heterochromatin and silenced by methylation. Multiple conditions have been described to induce their reactivation, including auto-immune diseases and cancers. HERVs re-expression leads to RNA (simple and double-stranded) and DNA production (by reverse transcription), modulating the innate immune response. Some studies also argue for a role of HERVs in shaping the evolution of innate immunity, notably in the development of the interferon response. However, their exact role in the innate immune response, particularly in cancer, remains to be defined. In this review, we see how HERVs could be key-players in mounting an antitumor immune response. After a brief introduction on HERVs characteristics and biology, we review the different mechanisms by which HERVs can interact with the immune system, with a focus on the innate response. We then discuss the potential impact of HERVs expression on the innate immune response in cancer.

Acute leukaemias are life-threatening haematological cancers characterised by the infiltration of transformed immature haematopoietic cells in the blood and bone marrow. Prompt and accurate diagnosis of the three main acute leukaemia subtypes (ie acute lymphocytic leukaemia [ALL], acute myeloid leukaemia [AML], and acute promyelocytic leukaemia [APL]) is of utmost importance to guide initial treatment and prevent early mortality but requires cytological expertise that is not always available. We aimed to benchmark different machine-learning strategies using a custom variable selection algorithm to propose an extreme gradient boosting model to predict leukaemia subtypes on the basis of routine laboratory parameters. This multicentre model development and validation study was conducted with data from six independent French university hospital databases. Patients aged 18 years or older diagnosed with AML, APL, or ALL in any one of these six hospital databases between March 1, 2012, and Dec 31, 2021, were recruited. 22 routine parameters were collected at the time of initial disease evaluation; variables with more than 25% of missing values in two datasets were not used for model training, leading to the final inclusion of 19 parameters. The performances of the final model were evaluated on internal testing and external validation sets with area under the receiver operating characteristic curves (AUCs), and clinically relevant cutoffs were chosen to guide clinical decision making. The final tool, Artificial Intelligence Prediction of Acute Leukemia (AI-PAL), was developed from this model. 1410 patients diagnosed with AML, APL, or ALL were included. Data quality control showed few missing values for each cohort, with the exception of uric acid and lactate dehydrogenase for the cohort from Hôpital Cochin. 679 patients from Hôpital Lyon Sud and Centre Hospitalier Universitaire de Clermont-Ferrand were split into the training (n=477) and internal testing (n=202) sets. 731 patients from the four other cohorts were used for external validation. Overall AUCs across all validation cohorts were 0·97 (95% CI 0·95-0·99) for APL, 0·90 (0·83-0·97) for ALL, and 0·89 (0·82-0·95) for AML. Cutoffs were then established on the overall cohort of 1410 patients to guide clinical decisions. Confident cutoffs showed two (0·14%) wrong predictions for ALL, four (0·28%) wrong predictions for APL, and three (0·21%) wrong predictions for AML. Use of the overall cutoff greatly reduced the number of missing predictions; diagnosis was proposed for 1375 (97·5%) of 1410 patients for each category, with only a slight increase in wrong predictions. The final model evaluation across both the internal testing and external validation sets showed accuracy of 99·5% for ALL diagnosis, 98·8% for AML diagnosis, and 99·7% for APL diagnosis in the confident model and accuracy of 87·9% for ALL diagnosis, 86·3% for AML diagnosis, and 96·1% for APL diagnosis in the overall model. AI-PAL allowed for accurate diagnosis of the three main acute leukaemia subtypes. Based on ten simple laboratory parameters, its broad availability could help guide initial therapies in a context where cytological expertise is lacking, such as in low-income countries. None.

Human endogenous retroviruses (HERVs) represent 8% of the human genome. The expression of HERVs and their immune impact have not been extensively studied in Acute Myeloid Leukemia (AML). In this study, we used a reference of 14 968 HERV functional units to provide a thorough analysis of HERV expression in normal and AML bone marrow cells. We show that the HERV retrotranscriptome accurately characterizes normal and leukemic cell subpopulations, including leukemia stem cells, in line with different epigenetic profiles. We then show that HERV expression delineates AML subtypes with different prognoses. We finally propose a method to select and prioritize CD8⁺ T cell epitopes derived from AML‐specific HERVs and we show that lymphocytes infiltrating patient bone marrow at diagnosis contain naturally occurring CD8⁺ T cells against these HERV epitopes. We also provide in vitro data supporting the functionality of HERV‐specific CD8⁺ T‐cells against AML cells. These results show that HERVs represent an important source of genetic information that can help enhancing disease stratification or biomarker identification and an important reservoir of alternative tumor‐specific T cell epitopes relevant for cancer immunotherapy.

Class I Human Leukocyte Antigen (HLA) evolutionary divergence (HED) is a metric which reflects immunopeptidome diversity and has been associated with immune checkpoint inhibitor responses in solid tumors. Its impact and interest in allogeneic hematopoietic stem cell transplantation (HCT) have not yet been thoroughly studied. This study analyzed the clinical and immune impact of class I and II HED in 492 acute myeloid leukemia (AML) recipients undergoing HCT. The overall cohort was divided into a training (n=338) and a testing (n=132) set. Univariate cox screening found a positive impact of a high class I HED and a negative impact of a high class II HED on both disease-free (DFS) and overall survival (OS). These results were combined in a unique marker, class I/class II HED ratio, and assessed in the testing cohort. The final multivariate cox model confirmed the positive impact of a high versus low class I/class II HED ratio on both DFS (Hazard Ratio (HR) 0.41 [95% CI 0.2-0.83]; p=0.01) and OS (HR 0.34 [0.19-0.59]; p<0.001), independently of HLA matching and other HCT parameters. No significant association was found between the ratio and graft-versus-host disease (GvHD) nor with neutrophil and platelet recovery. A high class I HED was associated with a tendency for an increase in NK, CD8 T-cell, and B cell recovery at 12 months. These results introduce HED as an original and independent prognosis marker reflecting immunopeptidome diversity and alloreactivity after HCT.