Ludwig Cancer Research

Cancer cachexia is a complex metabolic syndrome characterized by involuntary weight loss, skeletal muscle atrophy, adipose tissue remodeling, anorexia, and systemic inflammation. Affecting up to 80% of patients with advanced cancer, it is associated with poor prognosis and lacks effective therapies. This review focuses on metabolic reprogramming occurring between organs, integrating recent mechanistic insights into cross talk between central and peripheral tissues. Key mediators, including CCL2, IL-6, LIF, and GDF-15, are discussed in the context of neuroimmune and metabolic pathways that drive tissue wasting. Particular attention is given to the CCR2/CCL2 axis, which mediates immune cell infiltration into the brain, liver, and tumors, contributing to anorexia, muscle loss, and hepatic dysfunction. Understanding these integrated interorgan pathways highlights potential therapeutic targets to preserve metabolic homeostasis, improve functional outcomes, and extend survival in patients with cancer-associated cachexia.

Chromosomal instability (CIN), a pervasive feature of esophageal adenocarcinoma (EAC), drives tumor aggressiveness and metastasis. CIN stimulates the cGAS–STING pathway, typically linked to antitumor immunity. However, despite the high CIN burden in EAC, the cGAS–STING pathway remains largely intact. To address this paradox, we interrogated multiple esophageal cancer models, finding myeloid-attracting chemokines—with CXCL8 as a prominent hit—as conserved CIN-driven targets in EAC. Using multiplexed immunofluorescence microscopy, we quantified ongoing CIN in human EAC tumors by measuring cGAS-positive micronuclei, validated by whole-genome sequencing. Coupling in situ CIN detection with single-nucleus RNA sequencing and multiplex immunophenotyping of human EAC, we link CIN to tumor-intrinsic innate immune activation, CXCL8 expression, and myeloid cell–mediated immunosuppression. In patients with EAC, CINhigh, myeloid-dominated tumors correlate with poor outcomes and aberrant cGAS–STING signaling. These insights explain the counterintuitive maintenance of cGAS–STING and highlight the disruption of the CIN–cGAS–inflammation axis as a potential therapeutic strategy in EAC.

Glioblastoma is characterized by profound immune dysregulation at both intratumoral and systemic levels. Whether the peripheral immune landscape reflects tumor-related clinical and radiological features and carries prognostic relevance remains unclear. We investigated whether systemic immune cell subsets and circulating cytokines at diagnosis are associated with disease characteristics and survival in newly diagnosed glioblastoma. Peripheral blood mononuclear cells (PBMCs) and plasma were collected preoperatively from patients treated between 2019 and 2024. Immune profiling was performed by flow cytometry, and circulating cytokines were assessed using a multiplex CD8/NK cytokine assay. Sixty-six patients with newly diagnosed IDH-wildtype glioblastoma were included. Median overall survival (OS) was 14.4 months, and median progression-free survival (PFS) was 8.5 months. Classical monocyte and myeloid-derived suppressor cell (MDSC) frequencies correlated with contrast-enhancing tumor volume. Higher classical monocyte frequencies were associated with shorter OS, whereas higher frequencies of immature natural killer (NK) cells were independently associated with longer OS. Cytotoxic cytokines inversely correlated with suppressive myeloid subsets and positively with immature NK cells, supporting a coordinated systemic immune polarization. Corticosteroid use at sampling did not significantly affect key immune subpopulations. Integrated peripheral immune and cytokine profiling identifies a coordinated systemic immune axis linking tumor burden, myeloid expansion, and survival in newly diagnosed glioblastoma. Immature NK cell frequency is independently associated with improved survival, whereas classical monocytes and MDSCs reflect adverse immune polarization. These findings support the development of accessible peripheral immune biomarkers for risk stratification and translational strategies.

The rapid expansion of precision oncology has led to a marked increase in the number of identified oncodriver genes and associated variants. This surge has elevated the frequency of mutations with unknown functional impact, highlighting the growing need for molecular modeling tools such as Swiss-PO to support variant interpretation for both clinical and research contexts. Swiss-PO was expanded and redesigned to integrate large-scale oncogenomic data with structure-based and sequence-based analytical tools. The platform combines data of oncodriver genes, multiple sequence alignment (MSA) for conservation analysis across orthologs and gene families, experimental and predicted three-dimensional (3D) structures prediction visualization to assess impacts of mutations. Additional modules include a BRAF kinase mutation classification model and a dedicated webpage for ligands targeting proteins, and unified integration with external databases to enable multidimensional analyses. The updated Swiss-PO platform integrates data for nearly 1,500 oncodriver genes, encompassing more than 3 million mutations and post-translational modification annotations, over 26,000 experimental and predicted 3D protein structures, more than 4,000 MSAs , and information on over 200,000 protein ligands. In addition, the platform includes a BRAF kinase mutation class predictor to support therapeutic decision-making, establishing Swiss-PO as one of the most comprehensive publicly available resources for precision oncology. With these enhancements, Swiss-PO strengthens its role as a powerful and versatile resource for oncologists, bioinformaticians, and molecular biologists engaged in the interpretation of cancer-associated mutations and the advancement of precision medicine. The website is available at Swiss-PO.

Innate lymphoid cells type 2 (ILC2s) are key regulators of tissue homeostasis and inflammation. In cancer, ILC2s can exhibit pro-tumoral functions by increasing the myeloid derived suppressor cells (MDSC)/T-cell ratio. Nevertheless, the upstream ILC2 triggers remain poorly defined. Here, we identify nerve growth factor (NGF) as the driver of ILC2 pro-tumoral functions in patients with bladder cancer. We show that ILC2s express the NGF receptor TrkA and respond to NGF by secreting type-2 cytokines. In the tumor microenvironment, NGF-producing mast cells accumulate and activate ILC2s to induce regulatory T cells (Tregs), ultimately fostering tumor growth. In patients, NGF levels inversely correlate with survival in ILC2-rich tumors, underscoring the clinical significance of this axis. In vivo administration of a selective TrkA inhibitor improves survival in orthotopic tumor-bearing female mice and sensitizes them to immune checkpoint blockade (ICB). Overall, we identify NGF as an ILC2 activator that shapes pro-tumoral ILC2 functions. The blockade of TrkA+ ILC2s might represent a targetable strategy to improve survival, particularly in ICB-resistant patients.

Abstract Inactivation of VHL is a truncal alteration in clear cell renal cell carcinoma, but additional events are required for oncogenesis, most commonly PBRM1 inactivation. To better understand this co-operation, we used an oncogenic cell-tagging strategy to analyze the earliest transcriptional and cellular consequences of Vhl and/or Pbrm1 inactivation in the renal tubular epithelium, in vivo, at single-cell resolution. Pbrm1 inactivation did not globally alter HIF-dependent transcription or increase early tubular proliferation induced by Vhl inactivation. Instead, it had independent effects on epithelial organization. Combined genetic and morphological analyses suggested that Pbrm1 inactivation allows cells to sustain Vhl /HIF-dependent proliferation by disrupting tubular architectures that ordinarily restrain this proliferation, resulting in extra-tubular cell accumulation, multilayered epithelia, and tumor formation. Our findings frame a new model for the VHL - PBRM1 interaction that explains loss of epithelial homeostasis through an interaction between discrete effects that drive proliferation and remove structural tissue restraints on that proliferation. Statement of Significance VHL and PBRM1 are frequently co-inactivated in ccRCC. Combining transcriptomic and histological analyses of Vhl and/or Pbrm1 -inactivated renal cells in vivo, this study highlights independent effects on transcription and epithelial organization that converge to promote sustained proliferation and dysplasia. The work illuminates how tissue homeostasis is disrupted by oncogenic co-operation.

B cell maturation antigen (BCMA) is the primary target for chimeric antigen receptor (CAR)-T cell therapies and other immunotherapies in multiple myeloma (MM). Frequent relapses and the need for sequential treatments underscore the importance of alternative targets. We previously developed a dual-antigen-specific CAR recognizing both BCMA and transmembrane activator and CAML interactor (TACI). Here, we efficiently expressed this CAR in patient-derived marrow-infiltrating lymphocytes (MILs) to investigate MM cell killing via both endogenous T cell receptor (TCR) specificities and the CAR. While the patients' MIL TCRs did not recognize or kill autologous malignant plasma cells (PCs), the CAR consistently mediated efficient PC killing even at low or undetectable antigen levels by flow cytometry. Quantification of BCMA and TACI molecules on target cells by direct stochastic optical reconstruction microscopy (dSTORM) revealed that both BCMA and TACI were significantly underestimated by flow cytometry. Analysis by dSTORM allowed us to delineate the dual-antigen sensitivity of the CAR, indicating that CAR-MILs were polyfunctional and killed targets at very low antigen densities. Our findings support the further exploration of TACI as target in MM and underline the value of super-resolution microscopy in assessing antigen density thresholds for CAR-T cells.

Stem cell populations employ cell-intrinsic and niche-mediated mechanisms to preserve long-term self-renewal and regenerative potential. We propose that tumor-initiating cells (TICs) hijack this developmental circuitry to sustain their growth and establish an early immunosuppressive microenvironment during malignant progression. Recent work implicates CXCR4+ tissue-resident macrophages (TRMs) in the mammary gland as a central orchestrator of this process. Conserved CXCL12/CXCR4-AKT-β-catenin signaling links CXCR4+ TRM support of normal mammary stem cells to TIC maintenance, underscoring how developmental niches are exploited in malignancy. During tumorigenesis, aberrant CXCL12 production from tumor-associated fibroblasts promotes the expansion of CXCR4+ TRMs, while CXCR4 signaling enhances ALDH1A2-dependent retinoic acid production, which induces regulatory T cells and thereby suppresses anti-tumor immunity at the earliest stages of tumor development. From this perspective, early immune evasion is not only a hallmark of cancer but also a therapeutic window. Targeting TRMs early in cancer development could delay, or even prevent, malignant initiation. More broadly, we propose that TIC-niche crosstalk constitutes a tractable vulnerability, and that incorporating TRM-directed interventions alongside conventional and immune-based therapies may shift the balance toward durable tumor control.

Despite decades of study, large parts of the mammalian metabolome remain unexplored1. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks in human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains challenging2–4. Biochemical language models have transformed the interpretation of DNA, RNA and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here we present an approach that leverages chemical language models5–7 to anticipate the existence of previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns from the structures of known metabolites to anticipate the existence of previously unrecognized metabolites. Integration of DeepMet with mass spectrometry-based metabolomics data facilitates metabolite discovery. We harness DeepMet to reveal several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to advance the mapping of the mammalian metabolome.