The identification of cancer drivers is a cornerstone to delivery of precision oncology. So far sequencing of renal cell cancer (RCC) has largely been confined to the clear cell subtype of RCC. In contrast, sequencing analyses of the less common forms of RCC, papillary RCC (pRCC) and chromophobe RCC (ChRCC), have so far been limited. We analysed whole genome sequencing data on 164 tumour-normal pairs from the Genomics England 100,000 Genomes Project, providing a comprehensive, high-resolution map of copy number alterations, structural variation, and key global genomic features, including mutational signatures, intra-tumour heterogeneity and analysis of extrachromosomal DNA formation. Our research establishes correlations between genomic alterations and histological diversification and the extent to which genetically-mediated immune escape contributes to the development of these RCC subtypes. Implications: We demonstrate the distinctive genetics which characterises pRCC and ChRCC and how this information has the potential to inform patient treatment and clinical trials.

Type 1 conventional dendritic cells (cDC1s) acquire and cross-present tumor antigens to prime CD8⁺ T cells. Whether this selects for specific neoantigens is unclear. DNGR-1 (CLEC9A), a cDC1 receptor for F-actin exposed on dead cells, promotes cross-presentation of cell-associated antigens. Here we show that DNGR-1-deficient mice develop chemically induced tumors more rapidly and at higher incidence, and these are more frequently rejected on transplantation into wild-type recipients. Whole-exome sequencing reveals enrichment of predicted neoantigens derived from mutated F-actin-binding proteins. Consistent with this observation, tethering model antigens to F-actin enhances DNGR-1-dependent cross-presentation. These results suggest that DNGR-1-mediated recognition of F-actin exposed by dead cancer cells favors priming of CD8⁺ T cells specific for cytoskeletal neoantigens, which can then drive immune escape of cancer cells lacking or reverting those mutations. Thus, neoantigen cross-presentation by cDC1 can determine the immune visibility of the tumor mutational landscape and sculpt cancer evolution by immunoediting. Here the authors show DNGR-1 expressed by cDC1s promotes CD8⁺ T cell priming to cytoskeletal neoantigens from dying tumor cells, thereby shaping cancer immune visibility and tumor evolution through immunoediting.

The Tracking Cancer Evolution Through Therapy (TRACERx) program represents the most comprehensive effort to characterize tumor evolution in real time. Through longitudinal, multiregion, and multiomic profiling of tumors—and particularly of non-small-cell lung cancer and clear cell renal cell carcinoma—TRACERx has illuminated the dynamic interplay between genetic, nongenetic, and (micro)environmental factors that drive cancer progression, immune evasion, and therapeutic resistance. A central insight from TRACERx has been that not all tumor evolution is genomic: Transcriptomic diversity, epigenetic alterations, RNA editing, and changes in cell–cell interactions also drive adaptation. Methodological innovations—including tumor-informed and ultrasensitive circulating tumor DNA assays, representative sequencing, and integrative immune–genomic analyses—have yielded biomarkers resistant to sampling bias and/or predictive of recurrence, metastasis, and treatment response. By demonstrating that intratumor heterogeneity is a key determinant of clinical outcome and revealing its molecular, transcriptional, and ecosystem-level drivers, TRACERx has established a framework for linking evolutionary dynamics to patient care. As both a scientific framework and a clinical paradigm, TRACERx demonstrates how adaptive, iterative research can refine evolutionary models, improve patient risk stratification, and inspire next-generation cancer evolution studies across malignancies.

Driver mutations in IDH1 and IDH2 are initiating events in the evolution of chondrosarcoma and several other cancer types. Here, we present evidence that mutant IDH1 is recurrently lost in metastatic central chondrosarcoma. This may reflect either relaxed positive selection for the mutant IDH1 locus, or negative selection for the hypermethylation phenotype later in tumor evolution. This finding highlights the challenge for therapeutic intervention by mutant IDH1 inhibitors in chondrosarcoma.

Classical models of cancer focus on tumour-intrinsic genetic aberrations and immune dynamics and often overlook how the metabolic environment of healthy tissues shapes tumour development and immune efficacy. Here, we propose that tissue-intrinsic metabolic intensity and waste-handling capacity act as an upstream gatekeeper of anti-tumour immunity, determining whether immune infiltration translates into effective immune function and safeguards the tissue from tumourigenesis. Across human cancers, tumours arising in high-metabolism tissues – like kidney, brain, and eye – tend to show high T cell infiltration but poor prognosis, suggesting pre-existing metabolic environments prior to malignant transformation may undermine immune function. This pattern is mirrored across species: large mammals with lower mass-specific metabolic rates (e.g., elephants, whales) accumulate fewer metabolic byproducts and show lower cancer incidence (Peto’s paradox), while long-lived small mammals like bats and naked mole-rats resist tumourigenesis via suppressed glycolysis or altered hypoxia responses leading to lower metabolic rates and/or byproduct accumulation. Through integrative synthesis spanning human single-cell expression data and cross-species comparisons, we outline a framework of “immunometabolic gatekeeping,” where tissues with high metabolic rate and poor waste clearance foster immune-exhausting niches even before transformation. This unifying framework reconciles multiple paradoxes in cancer biology: Peto’s paradox, T cell infiltration non-prognosticity, tissue tropisms, sex-based inequalities, and size-based tipping points (e.g., the 3 cm rule in ccRCC), and suggests new principles for identifying high-risk patients and metabolic-immune combination strategies for prevention and treatment. By shifting focus from tumour-intrinsic mutations to host-tissue metabolism, this work offers a novel, integrative lens on cancer vulnerability and immune failure.

Immune checkpoint inhibition (ICI) has revolutionised cancer care, but many patients do not mount anti-tumor activity and most develop autoimmune toxicity. Mechanisms and risk factors underlying ICI response and immune related adverse events (irAEs) are incompletely understood. Thus, patient stratification and targeted irAE treatments are significant unmet clinical needs. Here, we use high-throughput spectral cytometry with machine-learning based analytics to characterise longitudinal immune dynamics under ICI. 706 cryopreserved PBMC samples from 137 patients consented to the EXACT study (NCT05331066) were utilised. All patients received standard of care adjuvant or advanced ICI for skin or renal cancer. Best overall response was annotated per RECIST 1.1(Responders: CR, PR, SD > 6 months). Patients on adjuvant ICI were designated as no-relapse at > 6 months from ICI initiation. irAEs were graded per CTCAE v5 and grade ≥3 considered severe. PBMCs were stained with 3 antibody panels comprising 114 markers. Data was acquired on a Sony ID7000 spectral analyser. Systems-level characterisation of 23,906 discrete PBMC subsets per sample was performed using IMU Biosciences’ proprietary machine learning platform. Following data QC, feature selection was refined through titration, variance, and correlation filtering. Predictive PBMC signatures were derived at baseline(BL) and C2 using univariate feature selection with bootstrapping followed by stepwise logistic regression, then validated through 100 iterations of 80:20 cross validation. PBMC types associated with irAE onset(Dev), increasing severity(Inc), and resolution(Res) compared to non-irAE on treatment controls were determined (t-test in a linear mixed effects model). Using these cell types, we then repurposed the Slingshot pseudotime method to derive patient trajectories from BL to Dev, and progression to Inc and/or Res. Benefit(responder/no-relapse) prediction achieved AUCs (mean ± SD) of 0.814±0.11 (BL), and 0.85±0.10 (C2). Severe irAE prediction achieved AUCs of 0.84±0.08 (BL), and 0.82±0.13 (C2). Dev and Inc samples of severe irAEs showed significant enrichment of activated non-classical monocytes, CD4 T, CD8 T, gd T, and NK cells. Dev of non-severe irAEs was indistinguishable from controls. In pseudotime, we found a bifurcating trajectory from BL to severe Dev vs. non-severe Dev. A further bifurcating trajectory distinguished progression from BL to on-treatment, then Dev vs. BL to Dev, then Inc. Res represented a return towards on-treatment controls in both lineages. Here, we used high-content PBMC profiling to generate immune signatures predictive of ICI outcome with compelling accuracy. We additionally gain mechanistic insights into irAE development and progression to severity. Our findings highlight the transformative potential of machine learning-powered immune profiling to identify predictors and drivers of benefit and toxicity outcomes under ICI with clear implications for patient stratification and irAE management. Max Emmerich, Duncan McKenzie, Carla Castignani, Jack Bibby, Jennie Yang, Marija Miletic, Laura Marandino, Zayd Tippu, Jonathan Lim, Taja Barber, Stephanie Hepworth, Paul Rouse, Lilian Williams, Kim Edmonds, Justine Korteweg, Serena Vanzan, James Larkin, Tom Hayday, Adam Laing, Samra Turajlic. Comprehensive blood profiling for immunotherapy outcome prediction and longitudinal immune trajectory characterisation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A002.

B-1 cells are innate-like immune cells abundant in serosal cavities with antibodies enriched in bacterial recognition, yet their existence in humans has been controversial1, 2–3. The CD5+ B-1a subset expresses anti-inflammatory molecules including IL-10, PDL1 and CTLA4 and can be immunoregulatory4, 5–6. Unlike conventional B cells that are continuously replenished, B-1a cells are produced early in life and maintained through self-renewal7. Here we show that the transcription factors TCF1 and LEF1 are critical regulators of B-1a cells. LEF1 expression is highest in fetal and bone marrow B-1 progenitors, whereas the levels of TCF1 are higher in splenic and peritoneal B-1 cells than in B-1 progenitors. TCF1–LEF1 double deficient mice have reduced B-1a cells and defective B-1a cell maintenance. These transcription factors promote MYC-dependent metabolic pathways and induce a stem-like population upon activation, partly via IL-10 production. In the absence of TCF1 and LEF1, B-1 cells proliferate excessively and acquire an exhausted phenotype with reduced IL-10 and PDL1 expression. Furthermore, adoptive transfer of B-1 cells lacking TCF1 and LEF1 fails to suppress brain inflammation. These transcription factors are also expressed in human chronic lymphocytic leukaemia B cells and in a B-1-like population that is abundant in pleural fluid and circulation of some patients with pleural infection. Our findings define a TCF1–LEF1-driven transcriptional program that integrates stemness and regulatory function in B-1a cells. The transcription factors TCF1 and LEF1 promote self-renewal and regulatory functions in B-1a cells.