Loss of the PD-1 receptor promotes the development of T cell non-Hodgkin lymphomas by modulating oncogenic signalling pathways, and blocking these pathways reduces tumourigenesis. PD-1 functions as an inhibitory receptor in the immune system and is a target of cancer immunotherapy. Jürgen Ruland and colleagues now show that PD-1 also functions as a tumour suppressor that is often lost in human T cell lymphomas. Experimentally, loss of PD-1 promotes the development of T cell non-Hodgkin lymphomas by modulating oncogenic signalling pathways. Blocking these pathways reduces tumorigenesis. These findings may have implications for T cell lymphoma therapies. T cell non-Hodgkin lymphomas are a heterogeneous group of highly aggressive malignancies with poor clinical outcomes1. T cell lymphomas originate from peripheral T cells and are frequently characterized by genetic gain-of-function variants in T cell receptor (TCR) signalling molecules1,2,3,4. Although these oncogenic alterations are thought to drive TCR pathways to induce chronic proliferation and cell survival programmes, it remains unclear whether T cells contain tumour suppressors that can counteract these events. Here we show that the acute enforcement of oncogenic TCR signalling in lymphocytes in a mouse model of human T cell lymphoma drives the strong expansion of these cells in vivo. However, this response is short-lived and robustly counteracted by cell-intrinsic mechanisms. A subsequent genome-wide in vivo screen using T cell-specific transposon mutagenesis identified PDCD1, which encodes the inhibitory receptor programmed death-1 (PD-1), as a master gene that suppresses oncogenic T cell signalling. Mono- and bi-allelic deletions of PDCD1 are also recurrently observed in human T cell lymphomas with frequencies that can exceed 30%, indicating high clinical relevance. Mechanistically, the activity of PD-1 enhances levels of the tumour suppressor PTEN and attenuates signalling by the kinases AKT and PKC in pre-malignant cells. By contrast, a homo- or heterozygous deletion of PD-1 allows unrestricted T cell growth after an oncogenic insult and leads to the rapid development of highly aggressive lymphomas in vivo that are readily transplantable to recipients. Thus, the inhibitory PD-1 receptor is a potent haploinsufficient tumour suppressor in T cell lymphomas that is frequently altered in human disease. These findings extend the known physiological functions of PD-1 beyond the prevention of immunopathology after antigen-induced T cell activation, and have implications for T cell lymphoma therapies and for current strategies that target PD-1 in the broader context of immuno-oncology.

Achieving durable clinical responses to immune checkpoint inhibitors remains a challenge. Here, we demonstrate that immunotherapy with anti–CTLA-4 and its combination with anti–PD-1 rely on tumor cell–intrinsic activation of the cytosolic RNA receptor RIG-I. Mechanistically, tumor cell–intrinsic RIG-I signaling induced caspase-3–mediated tumor cell death, cross-presentation of tumor-associated antigen by CD103⁺ dendritic cells, subsequent expansion of tumor antigen–specific CD8⁺ T cells, and their accumulation within the tumor tissue. Consistently, therapeutic targeting of RIG-I with 5′– triphosphorylated RNA in both tumor and nonmalignant host cells potently augmented the efficacy of CTLA-4 checkpoint blockade in several preclinical cancer models. In humans, transcriptome analysis of primary melanoma samples revealed a strong association between high expression of DDX58 (the gene encoding RIG-I), T cell receptor and antigen presentation pathway activity, and prolonged overall survival. Moreover, in patients with melanoma treated with anti–CTLA-4 checkpoint blockade, high DDX58 RIG-I transcriptional activity significantly associated with durable clinical responses. Our data thus identify activation of RIG-I signaling in tumors and their microenvironment as a crucial component for checkpoint inhibitor–mediated immunotherapy of cancer.

PURPOSE: Rapidly expanding medical literature challenges oncologists seeking targeted cancer therapies. General-purpose large language models (LLMs) lack domain-specific knowledge, limiting their clinical utility. This study introduces the LLM system Medical Evidence Retrieval and Data Integration for Tailored Healthcare (MEREDITH), designed to support treatment recommendations in precision oncology. Built on Google's Gemini Pro LLM, MEREDITH uses retrieval-augmented generation and chain of thought . METHODS: We evaluated MEREDITH on 10 publicly available fictional oncology cases with iterative feedback from a molecular tumor board (MTB) at a major German cancer center. Initially limited to PubMed -indexed literature (draft system), MEREDITH was enhanced to incorporate clinical studies on drug response within the specific tumor type, trial databases, drug approval status, and oncologic guidelines. The MTB provided a benchmark with manually curated treatment recommendations and assessed the clinical relevance of LLM-generated options (qualitative assessment). We measured semantic cosine similarity between LLM suggestions and clinician responses (quantitative assessment). RESULTS: MEREDITH identified a broader range of treatment options (median 4) compared with MTB experts (median 2). These options included therapies on the basis of preclinical data and combination treatments, expanding the treatment possibilities for consideration by the MTB. This broader approach was achieved by incorporating a curated medical data set that contextualized molecular targetability. Mirroring the approach MTB experts use to evaluate MTB cases improved the LLM's ability to generate relevant suggestions. This is supported by high concordance between LLM suggestions and expert recommendations (94.7% for the enhanced system) and a significant increase in semantic similarity from the draft to the enhanced system (from 0.71 to 0.76, P = .01). CONCLUSION: Expert feedback and domain-specific data augment LLM performance. Future research should investigate responsible LLM integration into real-world clinical workflows.

Anti-viral immunity continuously declines over time after SARS-CoV-2 infection. Here, we characterize the dynamics of anti-viral immunity during long-term follow-up and after BNT162b2 mRNA-vaccination in convalescents after asymptomatic or mild SARS-CoV-2 infection. Virus-specific and virus-neutralizing antibody titers rapidly declined in convalescents over 9 months after infection, whereas virus-specific cytokine-producing polyfunctional T cells persisted, among which IL-2-producing T cells correlated with virus-neutralizing antibody titers. Among convalescents, 5% of individuals failed to mount long-lasting immunity after infection and showed a delayed response to vaccination compared to 1% of naïve vaccinees, but successfully responded to prime/boost vaccination. During the follow-up period, 8% of convalescents showed a selective increase in virus-neutralizing antibody titers without accompanying increased frequencies of circulating SARS-CoV-2-specific T cells. The same convalescents, however, responded to vaccination with simultaneous increase in antibody and T cell immunity revealing the strength of mRNA-vaccination to increase virus-specific immunity in convalescents.

Breast cancer is a disease of genomic alterations, of which the panorama of somatic mutations and how these relate to subtypes and therapy response is incompletely understood. Within SCAN‐B ( ClinicalTrials.gov : NCT02306096), a prospective study elucidating the transcriptomic profiles for thousands of breast cancers, we developed a RNA‐seq pipeline for detection of SNVs/indels and profiled a real‐world cohort of 3,217 breast tumors. We describe the mutational landscape of primary breast cancer viewed through the transcriptome of a large population‐based cohort and relate it to patient survival. We demonstrate that RNA‐seq can be used to call mutations in genes such as PIK3CA , TP53 , and ERBB2 , as well as the status of molecular pathways and mutational burden, and identify potentially druggable mutations in 86.8% of tumors. To make this rich dataset available for the research community, we developed an open source web application, the SCAN‐B MutationExplorer ( http://oncogenomics.bmc.lu.se/MutationExplorer ). These results add another dimension to the use of RNA‐seq as a clinical tool, where both gene expression‐ and mutation‐based biomarkers can be interrogated in real‐time within 1 week of tumor sampling.