Journal of Experimental & Clinical Cancer Research

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have shown great promise in treating bladder cancer and are now part of the standard treatment for advanced disease. However, many patients still fail to respond to treatment and at present many biomarkers are assessed but have yet shown only limited results. Therefore, with the advent of combination treatments and the increase of immune related adverse event, the search for reliable predictive biomarkers is paramount. Using a multiplexed enhanced FRET-FLIM based technique (QF-Pro) we quantified the interaction of PD-1/PD-L1, CTLA-4/CD80 and TIGIT/CD155 immune checkpoints in a pre-treatment TMA of 46 patients treated with atezolizumab. The association between higher PD-1/PD-L1 ICP interaction state and treatment efficacy was demonstrated in the male sample cohort, where it identified patients with better PFS. Conversely, patients exhibiting higher CTLA-4/CD80 engagement had a worse response to atezolizumab. Remarkably, the dual assessment of patients with high PD-1/PD-L1 and low CTLA-4/CD80 allowed to identify the best responders. These results indicate that the monitoring of patients immune profile in urothelial carcinoma might be critical in identifying patients who may benefit from combination therapy.

Abstract Background: Cytolytic activity (CYT), a widely used transcriptomic surrogate of anti-tumor immune cytotoxicity derived from GZMA (granzyme A) and PRF1 (Perforin 1) expression, is associated with clinical outcomes across cancers. MicroRNAs (miRNAs) are key post-transcriptional regulators of tumor immunity, yet their pan-cancer roles in modulating cytolytic activity remain incompletely understood. Objective: This study aimed to identify conserved miRNA regulators of tumor cytolytic activity and their downstream gene-mediated networks across diverse cancer types, while evaluating their clinical and therapeutic relevance. Methods: Matched miRNA and mRNA expression profiles from 9,288 primary tumors across 31 TCGA cancer types were analyzed. A multi-stage framework was applied: per-cancer Spearman correlations (|{rho}| >= 0.30, FDR < 0.05) identified recurrent CYT-associated miRNAs (at least 3 cancer types); these were integrated with TargetScan-predicted targets and subjected to pan-cancer and cross-cancer triple filtering (miRNA-gene and gene-CYT associations). All associations underwent tumor purity adjustment using Consensus Purity Estimate (CPE), with LUMP (Leukocytes Unmethylation for Purity) as sensitivity analysis. Candidates were further prioritized by random forest modeling with bootstrap stability, cancer-type-adjusted Cox regression, mediation analysis, immune cell deconvolution, k-means molecular subtyping, pathway enrichment, and DGIdb-based drug-target prioritization. Results: The analysis converged on 38 high-confidence miRNA-gene-CYT regulatory triplets involving 9 conserved miRNAs and 31 target genes after stringent purity adjustment and multi-layer validation. All nine miRNAs exhibited complete bootstrap stability. Mediation analysis confirmed significant gene-level mediation in 37 of 38 triplets (FDR < 0.01), with mediated proportions up to 94%. The final miRNA signature defined two distinct pan-cancer immune subtypes (immune-hot vs. immune-cold) with significantly different cytolytic activity and overall survival (OS) (HR = 0.754, FDR = 1.12 x 10^-4). The network was enriched for T-cell activation and lymphocyte differentiation pathways and highlighted multiple druggable targets, including CTLA4 and CD274 (PD-L1), nominating 124 candidate compounds. Conclusions: In conclusion, this tumor purity-adjusted pan-cancer study defines a compact, reproducible, and clinically relevant miRNA network that regulates cytolytic activity across diverse malignancies. By linking miRNA biology to immune subtyping and actionable therapeutic targets, the present work provides a valuable foundation for advancing precision immuno-oncology.

Myeloproliferative neoplasms (MPN) are chronic hematologic malignancies characterized by clonal myeloid expansion, inflammation, oxidative stress, and progressive bone marrow (BM) remodeling that may culminate in fibrosis and secondary acute leukemia. Here, we evaluated the therapeutic efficacy and the underlying mechanisms of melatonin (MT) and liposomal melatonin (nano-MT) in preclinical MPN models. MT selectively inhibited clonogenic growth of patient-derived peripheral blood mononuclear cells and induced pluripotent stem cell-derived CD34 hematopoietic stem and progenitor cells in comparison to healthy controls. This effect was associated with increased apoptosis, reduced reactive oxygen species (ROS), and decreased glucose uptake, independently of MT receptor signaling. Transcriptomic profiling of primary MPN CD34 cells revealed suppression of MYC targets, G2M checkpoint signaling, ROS, and glycolysis pathways. In co-culture models, MT reduced stromal -smooth muscle actin and phosphorylated SMAD2/3, indicating inhibition of TGF-{beta}-driven mesenchymal stromal cell-to-myofibroblast formation. In tamoxifen-inducible SclCreER;JAK2V617F mice, nano-MT achieved efficient spleen and BM targeting. Therapeutically, nano-MT reduced erythrocytosis, myeloid progenitor expansion, and BM IL-1{beta} levels. Longitudinal micro-computed tomography and histological analyses demonstrated normalization of BM architecture, reduced osteosclerotic remodeling and splenomegaly, decreased reticulin deposition and megakaryocyte numbers. In a dose-escalation study, nano-MT restored erythrocyte, hematocrit, and platelet counts and normalized megakaryocyte-erythroid progenitors. Combination treatment with ruxolitinib further reduced leukocytosis, neutrophilia, and monocytosis. Collectively, these findings demonstrate that (nano-)MT attenuates MPN and BM remodeling by targeting metabolic, inflammatory, and fibrotic pathways. This study provides the first evidence for a therapeutic benefit of nano-MT in MPN and establishes a rationale for further translational evaluation.

BCL2L13 is a mitochondrial BCL2 family protein linked to mitophagy and ceramide metabolism, but its role in NSCLC metastatic plasticity remains unclear. Human lung cancer Tissue Microarray and matched patient specimens showed subtype and site dependent BCL2L13 expression, with higher cytoplasmic granular staining in primary NSCLC and reduced, heterogeneous staining in lymph node metastases, most evident in adenocarcinoma and squamous cell carcinoma. Because Epithelial mesenchymal transition and anoikis resistance are central requirements for metastatic dissemination, this primary to node attenuation provided the rationale to test BCL2L13 knockdown and overexpression in metastasis relevant NSCLC models. In A549 and LLC cell lines. TGF beta 1 induced coordinated mitophagy and EMT with mitochondrial enrichment of BCL2L13. BCL2L13 knockdown impaired TGF beta 1 and carbonyl cyanide m chlorophenyl hydrazone associated mitophagy, reducing LC3 beta mitochondria colocalization, TOMM20, LAMP1 overlap and mitochondrial LC3 II, p62, TOMM20 turnover; BNIP3 and NIX redistribution did not compensate. BCL2L13 loss enhanced EMT marker switching and migration, whereas overexpression partially opposed these changes. During detachment, BCL2L13 knockdown reduced anoikis associated apoptosis despite preserved mitochondrial recruitment of BAX, BAK, BNIP3,NIX, altered BID processing, non parallel caspase activity and shifted FAK phosphorylation. Pharmacological autophagy modulation did not reverse this anoikis phenotype. Lipidomics identified adhesion state dependent ceramide synthases CerS2, CerS6 linked sphingolipid remodeling: BCL2L13 knockdown increased C24 linked sphingolipid species in attached cells but reduced C16, C24 ceramide related profiles during anoikis. These findings identify BCL2L13 downregulation as a metastasis associated mitochondrial-lipid state that limits mitophagic quality control while favoring EMT and detachment survival in NSCLC adenocarcinoma.

Disseminated cancer cells (DCC) in non-metastatic carcinoma patient bone marrow (BM) are predictive of metastasis. Those detected by epithelial cytokeratin or EpCAM expression have poorly-characterized transcription profiles due to their extreme rarity: 1~2 cells per two million BM cells in every third non-metastatic patient. We here characterize the transcriptomes of DCCs. Single-cell RNA-sequencing (scRNA-seq) of 864 EpCAM-positive cells (from 1,151 cancer patients) in BM or lymph nodes (LN) revealed plasma, immune, myeloid, erythroid progenitor cells and two candidate DCC populations, termed M0-DCC and M1-DCC. M0-DCC, mostly from non-metastatic M0-stage patients, displayed the highest known adult stemness scores, and were transcriptomically reminiscent of human cleavage-stage, preimplantation embryos. M1-DCC represented cancer cells undergoing the epithelial-mesenchymal transition (EMT), corresponding to later, implanting and gastrulating embryos. Detection of early-embryo-like DCC categorised patients at highest risk for metastatic progression. Furthermore, high M0-DCC scores predicted the metastatic potential of human cell lines from the Cancer Cell Line Encyclopedia. M0-DCC gene expression profiles can be reversibly induced from M1-DCC-like cells in vitro. The close correspondence between gene expression profiles in immediate early embryonic development and metastatic founder cell candidates provides strong evidence that the onset of cancer and metastasis recruits mechanisms employed in fertilization.

Purpose/Objective There is paucity of data reporting outcomes of breast cancers with initial internal mammary nodal involvement and no visceral metastases, treated with curative hypofractionated radiotherapy . We report the outcomes from a tertiary centre alongside spatial patterns of recurrences in the above group Material/Methods For this retrospective cross-sectional study, consecutive patients contoured as per the ESTRO 2013 guidelines, treated between 2016-2022 were eligible if their diagnostic imaging demonstrated involvement of the internal mammary nodes. Radiotherapy (40 Gy/15#/3 weeks) was delivered to the residual breast / thoracic wall, SCF region corresponding to the ESTRO lymph node level 4 and internal mammary chain nodes. Residual IMN/ level 4 nodes received a boost of 10Gy/5#. Spatial mapping of sites of recurrence at the local site and three nodal sites (axilla, SCF and IMN) was performed using deformable image registration. Sites of recurrence at the local site and three nodal levels were contoured separately. Volumetric intersection of the recurrent gross tumour volume (GTV_recurrence) with treated clinical target volume (CTV) was calculated. Actuarial overall (OS), disease free survival (DFS) & cumulative incidence of local (LR), regional (RR) and loco-regional recurrence(LRR) were calculated using Kaplan Meier method. Univariate comparison of outcomes with or without residual disease was performed using the log rank test. Results The median age of the 61 eligible women was 49 years. 77% received neoadjuvant chemotherapy and the rest adjuvant chemotherapy. 82% patients had a mastectomy. Axillary lymph node dissection was done in 96.7%. Boosts to residual IMN and SCF nodes were delivered to 21(34.4%) and 2 (3.3%) respectively. Median follow up was 3.6 years. Out of the 61 patients, 42 patients were disease free with an estimated 3 year disease free survival of 75% (95% CI 64, 88%). Spatial mapping of locoregional recurrence was possible in all but 1 patient with local (only) recurrence who was lost to follow-up after mammogram only. Among the patients with loco regional recurrence 1 had recurrence in local site + SCF +axilla, 3 had recurrence in the SCF+axilla, 2 in the SCF+IMN and 1 in the axilla+SCF+IMN. Only one patient had isolated axillary recurrence or isolated SCF recurrence. There were no IMN only recurrences. Among the 8 patients with nodal recurrence, a total of 27 individual GTV_recurrence were identified in the axilla(n=11), SCF(n=11) and IMN (n=5). IMN recurrences showed complete or partial overlap with CTV. SCF recurrences were a mix with predominantly in-field recurrences while axillary recurrences occurred outside the treated volume.Four (6.6%) patients had Grade 2 lymphoedema as documented late side effect. Conclusion Aggressive treatment of IMN disease with adjuvant radiation is effective with good locoregional control. Systemic recurrences are common and may benefit from intensification strategies.

Keloids are benign fibroproliferative disorders majorly characterized by excessive extracellular matrix deposition, with recurrence rates exceeding 80% following conventional therapy. Although epigenetic dysregulation has been implicated in keloid pathogenesis, whether genome-wide DNA methylation actively drives pathological cellular reprogramming, and whether this state is therapeutically reversible, remains unclear. We performed genome-wide DNA methylation profiling on keloid tissues, matched primary keloid fibroblasts, and normal controls. Our analysis revealed a shared DNA hypermethylation pattern between keloid tissues and fibroblasts, which was validated by three independent public cohorts. By integrating DNA methylome and transcriptome, we demonstrated that DNA methylation-regulated genes were enriched in osteochondrogenesis-related pathways, such as cartilage and bone development pathways. Furthermore, pharmacologic inhibition of DNA hypermethylation by DNA demethylating agent decitabine reduced the expression of osteochondrogenic markers and inhibited collagen deposition and keloid growth in primary keloid fibroblasts and patient-derived xenograft (PDX) model, offering a potential therapeutic strategy of keloid.

Introduction: Gliomas integrate into neural circuits and heighten neuronal excitability, engaging in bidirectional communication whereby neuronal activity promotes tumor growth and proliferation. Aging reshapes the brain microenvironment through extracellular matrix changes, altered secretory factors, and immune dysfunction, creating conditions permissive to tumorigenesis and limiting immunotherapy efficacy in glioblastoma. However, its effect on neuronal excitability and signaling in glioblastoma remains poorly understood. Methods: We developed a novel classification system for glioblastoma by leveraging three classes of DNA methylation-based aging biomarkers: chronological, biological, and mitotic clocks. This approach stratified tumors into accelerated and decelerated epigenetic aging subtypes, which we then characterized at the molecular, functional, and clinical levels using multimodal analyses. Guided by these profiles, we evaluated the in vitro effects of the FDA-approved agents levetiracetam and riluzole, alone and in combination with temozolomide, on U87MG and A172 cell lines. Specifically, we assessed changes in cell viability, apoptosis, and the expression of marker genes related to stemness, neuronal hyperexcitability, and immunosuppression. Results: Tumors with decelerated epigenetic aging showed expression modules and CpG hypomethylation associated with neuronal activity and stemness, and carried significantly worse prognosis. Single-cell and spatial multi-omics analyses revealed enrichment for neurons and malignant neural stem-like cells in these tumors. They also displayed enhanced intercellular communication, driven predominantly by glutamate signaling across the malignant, neuronal, and immune compartments of the tumor microenvironment. In vitro pharmacological inhibition of glutamatergic signaling with levetiracetam and riluzole reduced cell viability, induced apoptosis, and suppressed expression of stemness, neuronal hyperexcitability, and immunosuppression markers. Both agents potentiated the cytotoxic and apoptotic effects of temozolomide, supporting glutamatergic inhibition as a strategy for improving chemosensitivity. Conclusion: By establishing a framework for decoding glioblastoma heterogeneity through epigenetic aging, we identified the glutamatergic pathway as a clinically actionable vulnerability. Our findings suggest that combining anti-glutamatergic therapies with temozolomide exerts synergistic antitumor effects while mitigating adverse chemotherapy-induced phenotypes, such as increased stemness, neuronal hyperexcitability, and immunosuppression, thereby laying the groundwork for novel therapeutic strategies.

Background: Biochemical recurrence (BCR) occurs in 20-40% of men after radical prostatectomy. Existing postoperative recurrence risk tools based on PSA and pathology are clinically useful but show only moderate and variable discrimination, highlighting the need for biomarkers that improve risk stratification and consequent treatment decisions. We hypothesized that the prostate microenvironment, including both the tumor and non-cancerous adjacent tissue, may contain prognostic features associated with adverse postoperative PSA outcomes. Methods: We assembled a cohort of matched tumor-adjacent benign and tumor prostate tissue from 243 men across three institutions to establish a discovery cohort (n=123; 43 postoperative PSA events, 35%) and validation cohort (n=120; 46 events, 38%). For primary binary analyses, a postoperative PSA event included BCR, defined as two consecutive postoperative PSA values >=0.2 ng/mL, or PSA persistence. We performed RNA sequencing of matched tumor-adjacent benign and tumor tissues, quantified immune signatures, and developed an integrated model combining the adjacent-tissue B-cell signature, preoperative PSA, and radical prostatectomy Gleason score (BRIGADE). CAPRA-S-adjusted Cox analyses excluding recurrence-time-0 cases evaluated time to BCR, and CD19 multiplex immunofluorescence provided tissue-level confirmation (n=10). Results: In prostatectomy specimens, tumors from patients without a postoperative PSA event were enriched for B-cell transcriptional programs, whereas tumors from event-positive patients showed elevated proliferation signatures. B-cell-related transcriptional programs were correlated between tumor and adjacent tissue. Tumor-adjacent benign B-cell scores were higher in no-event cases and discriminated postoperative PSA-event status in PCBN discovery (AUC 0.63) and BM validation (AUC 0.81) cohorts, outperforming numerous other immune-related signatures. In CAPRA-S-adjusted Cox sensitivity analyses excluding recurrence-time-0 cases, higher adjacent-tissue B-cell activity was associated with reduced recurrence risk in PCBN (HR 0.42, 95% CI 0.19-0.94; BH-adjusted p=0.035) and BM (HR 0.54, 95% CI 0.30-0.95; BH-adjusted p=0.034). Tissue-based validation showed that CD19+ B-cell density in adjacent benign tissue was higher in no-event than event-positive patients (median 0.1145 vs 0.0471; p=0.008). BRIGADE achieved an AUC of 0.68 in cross-validation and 0.83 in independent validation, compared to AUCs of 0.54-0.63 and 0.44-0.78 for the tested clinical predictors, respectively. At the fixed classification threshold, the validation-cohort odds ratio for BRIGADE was 2.75. The adjacent B-cell score remained associated with lower odds of a postoperative PSA event after adjustment for PSA and Gleason score. Conclusions: B-cell infiltration in tumor-adjacent benign prostate tissue may complement existing clinicopathologic models for stratifying adverse postoperative PSA outcomes and subsequent BCR after radical prostatectomy. The transcriptomic signal was recapitulated by CD19-based tissue staining, supporting further development of a pathology-based assay.

Purpose: Clinical response to immune checkpoint blockade (ICB) remains variable. We asked whether immune-cell populations in the tumor microenvironment (TME) are associated with benefit across treatments and tumor types. Experimental Design: We analyzed pretreatment bulk tumor RNA-seq from ICB cohorts and TCGA. Gene-level effects associated with ICB response or TCGA survival were projected onto Human Primary Cell Atlas profiles of 157 cell types. Cox and mixed-effects models accounted for cancer type, cohort, and therapy, as appropriate. After {gamma}{delta} T cells emerged as a leading population, we adjusted their associations for eight CD8 estimators and evaluated them using TRUST4-based TRG/TRD reconstruction and single-cell RNA-seq. Results: {gamma}{delta} T-cell programs were among the signatures consistently associated with ICB response and favorable TCGA survival. Across ICB cohorts, {gamma}{delta} T-cell abundance was associated with response (n=1,356; OR, 1.38; 95% CI, 1.23-1.56) and overall survival (n=1,074; HR, 0.82; 95% CI, 0.76-0.88), with associations persisting after CD8 adjustment. ICB-response-associated cell-type profiles were strongly concordant with chemotherapy response (r=0.92) and moderately concordant with radiation response (r=0.58); targeted and hormone therapy analyses were underpowered. TRUST4 reconstruction and single-cell RNA-seq provided orthogonal support for the {gamma}{delta} signal. Conclusions: Pretreatment {gamma}{delta} T-cell abundance was associated with favorable ICB outcomes and survival across cancers, while related cell-type programs extended to selected non-immunotherapy response settings. Although associative and context dependent, these findings support prospective evaluation of {gamma}{delta} T-cell abundance as a candidate tumor-immune biomarker.

Background: Spatial organization is increasingly recognized as a key determinant of tumor-immune interactions in head and neck squamous cell carcinoma (HNSCC). The GSE300147 Xenium spatial transcriptomic resource generated by McCord and colleagues established a framework for mapping spatially coordinated T-cell states in HNSCC. However, how tumor-enriched epithelial immune states relate to metabolic, redox, and stress-adaptive transcript programs remains incompletely defined. Methods: A secondary, data-driven reanalysis of GSE300147 was performed, focusing on 17 confirmed HNSCC Xenium sections after exclusion of a non-HNSCC ameloblastoma specimen. A total of 1,148,244 cells were analyzed, including 558,867 EpCAM+ tumor-enriched epithelial cells. Tumor-enriched epithelial cells were classified into Hot, Intermediate, and Cold states using a Composite Hotness framework integrating T-cell inflammatory signature score, checkpoint-associated signaling, CD274 expression, IFN/antigen-presentation signature score (IFN/AP), and tumor-immune proximity. Six metabolic ecosystem states, neighborhood profiling, spatial permutation testing, and an integrated Immune-Metabolic-Redox Ecosystem Score (IMRES) were then applied. Results: Immune activation was spatially heterogeneous across HNSCC sections. Immune-hot tumor-enriched epithelial regions showed not only inflammatory, checkpoint-associated, and antigen-presentation signature scores, but also coordinated metabolic, oxidative-redox, and stress-response transcript programs. IMRES, derived from available immune, metabolic, redox, and stress-response transcript components represented in the Xenium panel, increased progressively from Cold to Intermediate to Hot tumor-enriched epithelial states and was associated with NFE2L2, GDF15, HLA-DRA, CD274, KEAP1, and MDM2. Integrating IMRES with Composite Hotness identified a distinct Hot+IMREShigh ecosystem comprising 106,874 tumor-enriched epithelial cells. This state showed the strongest immune-active and stress-adaptive features and was positioned closer to immune populations than expected by random assignment. An alternative rank-based robustness analysis reproduced the IMRES-associated ecosystem axis and correlated with the original module-based score (Spearman r = 0.597). Conclusions: This secondary reanalysis extends the original spatial T-cell framework by defining a complementary tumor-centered immune-metabolic-redox ecosystem in HNSCC. IMRES provides a transcript-derived framework for identifying Hot+IMREShigh neighborhoods where immune activation, checkpoint signaling, metabolic remodeling, and stress adaptation converge, providing a hypothesis-generating framework for studying immune resistance and therapeutic vulnerability.

Prognostic models in oncology are developed one cancer at a time, from that cancer's own labelled outcomes, and fail where prognostic information is scarcest. Rare cancers account for roughly a fifth of diagnoses and most paediatric malignancies, yet seldom supply enough events for a reliable time-to-event model. We therefore asked whether a representation learned without outcome labels can supply what those cohorts cannot. A Transformer encoder was pretrained by masked field-value modelling on 9425135 tumour records from the SEER 17 registries, diagnosed in 2000 to 2023. Only diagnosis-time fields passing a fail-closed coding-verification gate were admitted, and each record was emitted as an era-specific and a harmonised view, keeping two decades of recoding auditable. The encoder was then frozen and read by a linear Cox head for overall survival. Nine rare cancers were removed from the pretraining corpus entirely, each requiring an independent pretraining run. On a sealed test partition, all nine exceeded an architecture-identical random frozen encoder in Harrell concordance by +0.0034 to +0.0368, every lower confidence limit above zero. At 256 labelled patients, all 67 cancers favoured the pretrained representation over budget-matched Cox regression, median difference +0.0283. The advantage was bounded: given the entire training set, Cox regression was favoured in seven of nine rare cancers. The encoder did not outperform a field-frequency baseline on its own objective, so upstream reconstruction did not predict downstream transfer. Outcome-agnostic registry pretraining carries prognostic signal into cancers it has never seen, and is most useful where labels are fewest, without establishing clinical utility.

In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we tested whether systematic exploration of AI generated hypotheses could uncover clinically meaningful signals in extensively studied data. We deployed AutoDiscovery, a newly launched large language model (LLM) framework designed to search for hypotheses based on surprisal and systematically interrogate complex datasets, on The Cancer Genome Atlas breast cancer cohort. The system did not identify clinically meaningful novel findings without human input. However, a seeded warm-start run with minimal text input from an oncologist revealed multiple interesting and surprising hypotheses. Among these was that a robust immune signature was present across all subtypes of invasive lobular carcinoma (ILC) that exceeded invasive ductal carcinoma (IDC). This observation was independently validated in independent cohorts and confirmed by high-sensitivity multi-immunofluorescence tumor tissue analyses. These results suggest immunotherapy approaches should be tested in ILC including early stage ER+HER2- ILC; these patients are currently excluded from large neoadjuvant immunotherapy trials. They further demonstrate that surprisal-based hypothesis generation frameworks can extract previously unappreciated patterns from deeply interrogated cancer datasets and imply that disease domain experts working with LLMs can derive more meaningful insights from complex data than either could achieve alone.

The role of UBE2N in myeloid cell-mediated immune suppression in cancer remains undefined. Here, we examined the function of UBE2N in myeloid cell-mediated tumor progression using a temporally inducible myeloid-specific knockout model (LysMCreERUbe2nfl/fl). Temporally induced deletion of Ube2n in myeloid cells (Ube2nMyeKO) significantly hindered growth of YUMM1.7 melanoma. This was accompanied by reduced myeloid cell burden within the tumor microenvironment. We observed altered abundance of PD-1, PD-L1, and SPP1 in the Ube2nMyeKO tumor microenvironment at the tissue level. In vitro analysis showed that knock-in expression of a catalytically deficient UBE2NC87S mutant in bone marrow-derived macrophages (BMDMs) markedly decreased expression of Spp1. We observed decreased SPP1 secretion in Ube2nMyeKO BMDM-conditioned media (CM). Treatment with Ube2nMyeKO BMDM-CM decreased co-expression of PD-1, TIM-3, and LAG-3 on chronically stimulated T cells. Antibody-mediated neutralization of SPP1 in Ube2nWT BMDM-CM decreased PD-1 expression on CD8+ T cells. Together, these findings suggest a role for myeloid UBE2N in YUMM1.7 progression.

Background: Immunochemotherapy (ICT) is considered standard in regards to care for small-cell lung cancer (SCLC) in extensive stages, yet reliable biomarkers for treatment response remain elusive. While previous univariate analyses suggest specific peripheral lymphocyte subsets correlate with survival, the systemic immune response involves complex, multivariate interactions that require advanced analytical approaches. Methods: This paper analysed high-dimensional flow cytometry data from 32 patients with stage IV SCLC treated with carboplatin, etoposide, and atezolizumab. Peripheral blood was analysed at baseline (V0) and longitudinally during treatment. To identify potential early predictive biomarkers and mitigate sample attrition in later cycles, we focused on baseline and measurements after two cycles of ICT (V1). We employed a rigorous machine learning framework utilising nested cross-validation, bootstrapping, and permutation-based statistical testing to evaluate eleven different regression and survival models. Results: Under model-appropriate metrics, regressors did not generalise (R2 <0); conversely, censoring-aware Random Survival Forests (RSF) successfully extracted robust prognostic signatures. Baseline immune profiles (V0) achieved a concordance index (C-index) of 0.66 (p= 0.015), while dynamic changes from V0 to V1 ({triangleup}V) achieved a C-index of 0.65 (p= 0.022). Crucially, absolute values measured after two cycles of ICT (V1) yielded no significant signal (p= 0.445). Feature importance analysis confirmed the prognostic value of Th17 normalisation and identified Naive Regulatory T cells and Memory B cells as candidate components. Conclusion: Machine learning validation confirms a predictive signal in the peripheral immune profile of SCLC patients. Early dynamic shifts in the balance between regulatory and effector immune arms are associated with prognosis, contrasting with the lack of signal in absolute counts after two cycles of ICT. These findings establish a proof of concept for multivariate liquid biopsy immune profiling, warranting confirmation in larger cohorts and highlighting the necessity of integrating systemic and tumour-intrinsic data.

Motivation: Cell-free DNA methylation provides a minimally invasive signal for early cancer detection and tissue-of-origin prediction. Most methods represent methylation measurements as independent fixed-window features and therefore do not explicitly model relationships among genomic regions. Results: We developed PANGEM (Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome), a graph-learning framework that represents genomic bins as nodes and integrates CpG context, genomic proximity, and sample-specific methylation similarity in the graph topology. Across five repeated stratified train-test splits, PANGEM achieved the highest mean performance among evaluated methods, with an AUROC/AUPR of 0.997/1.000 for binary cancer detection and macro-AUROC/AUPR of 0.977/0.870 for multiclass tissue-of-origin prediction. In the independent INSPIRE cohort, 72 of 78 cancer cases (92.3%) exceeded the binary classification threshold, and PANGEM correctly classified 9 of 17 head and neck cancer cases (52.9%), the highest accuracy among evaluated methods. Subnetwork analysis further identified recurrent, graph-connected methylation patterns, including a 111-DMR subnetwork with increased methylation in cancer samples.

High-grade serous ovarian carcinoma (HGSC) responds poorly to immune checkpoint blockade, partly due to a macrophage-dominated immunosuppressive microenvironment. We integrated single-cell spatial proteomics and spatial transcriptomics across 50 HGSC tumors and applied SPACEstat to resolve higher-order immune communities and their transcriptional programs. We identified six immune community types, with macrophage-dominated Myelonets representing the predominant spatial pattern of immune organisation. In chemotherapy-exposed tumors, Myelonets showed coordinated lipid metabolism-immunosuppression and inflammation-MHC-II macrophage transcriptional programs, with SPP1, C1Q, VEGF, MMPs, and CCL18 linked to immunosuppressive states and fibroblasts emerging as key mediators of macrophage communication. Chemotherapy contracted large Myelonets while increasing CD8+ T-cell organization into Lymphonets. Persistent macrophage dominance within Myelonets was associated with adverse outcomes among patients who achieved a complete response to treatment. Together, we identify Myelonets as clinically relevant, multicellular immunoregulatory niches sustained by spatiotemporally coordinated macrophage programs and stromal crosstalk.

Liver metastases (LM) remain a major cause of death from different cancer types, in particular malignancies of the gastrointestinal tract. Liver metastases predict a poor response to immunotherapy due, among others, to the immunotolerant microenvironment (ME) of the liver and loss of local and systemic cytotoxic T cells. Thus, strategies that can reprogram the immune ME of the liver and restore cytotoxic T cell reactivity are being sought. We previously reported that estrogen signaling blockade impedes the growth of LM by reducing MDSC accumulation and monocyte/macrophage polarization. The aim of this study was to elucidate the underlying mechanism(s) and assess whether estrogen signaling in the myeloid lineage was driving the immunotolerant ME of LM. To this end, we generated mice with conditional myeloid cell-specific deletions of estrogen receptors (ER) or ER{beta} and analyzed in these mice the effect of ER loss on the liver immune ME and the outgrowth of LM. In mice with ER, but not with ER{beta} deletion, we observed a marked reduction in the growth of murine colon carcinoma MC-38 liver metastases as compared to their respective controls. Flow cytometry and immunohistochemistry revealed a decrease in macrophages that were polarized to the pro-tumorigenic M2-like phenotype and a concomitant increase in activated CD8+ T and NK cells relative to controls. Bulk RNAseq analysis performed on hepatic immune cells infiltrating the liver revealed changes in the expression of key cytokines/chemokines mediating immune cell recruitment, activation and polarization, including Ccl5 (upregulated) and Csf1 (downregulated). Taken together, the data suggest that ER signaling in myeloid-derived cells programs the immune landscape and contributes to an immunosuppressive and metastases-growth permissive ME in the liver.

Bulk telomere length measured from tumour sequencing is routinely interpreted as a property of the cancer cells. However, a tumour specimen is a mixture, and the patient who supplies it has a telomere length of their own. Here I re-analyse published pan-cancer telomere estimates and ask how much of a tumour's telomere length is patient-specific. A calibration step comes first. Whole-genome and low-pass estimates recover the known cross-sectional attrition of leukocyte telomeres with age, at 26.6 bp per year in blood normals, whereas whole-exome estimates do not. After adjustment for cancer type, sequencing centre and sex, the exome slope is minus 0.6 bp per year. In 684 blood-normal aliquots sequenced by both assays, the whole-genome estimate declines at 38.9 bp per year, whereas the exome estimate from the same DNA shows no detectable decline. The difference between assays is 41.5 bp per year, with P = 3 x 10^-10. Because exome data constitute 78.6% of the original resource, downstream analyses use only whole-genome and low-pass libraries. Within those data, tumour telomere length tracks the patient's matched-normal telomere length. The Spearman correlation is 0.395 in TCGA, with positive associations in 22 of 23 cancer types. This finding replicates in PCAWG using a different telomere estimator, with a correlation of 0.472 and positive associations in all 24 histologies examined. Adjustment for cancer type, sequencing centre and library type leaves a regression coefficient of 0.385. The association is also stable after adjustment for age, sex, tumour purity, leukocyte fraction, ploidy, sequencing coverage and continental ancestry, with coefficients ranging from 0.406 to 0.429. Pure normal-cell admixture is rejected as the sole explanation. Under a two-compartment mixture model, the coefficient for host telomere length is expected to equal 1 and the host-by-purity interaction to equal minus 1. These restrictions are jointly rejected with P = 0.001. Tumour purity, leukocyte fraction and age each explain only about 1 to 3% of within-cohort variance and do not alter the cross-cancer ranking. By contrast, the between-cohort coefficient is not directly interpretable. Its apparent near one-to-one relationship with tissue-associated telomere length depends strongly on which tissue supplies the matched-normal reference and on the statistical spread of that predictor, falling to 0.44 when organ-matched solid tissue is used. Bulk tumour telomere length is therefore a composite phenotype containing a replicated patient-specific component. Telomere biomarker studies should include matched-normal telomere length as a covariate rather than treating tumour telomere length as exclusively tumour-intrinsic.

Background: Prostate cancer enzalutamide treatment is approved at a standard dose of 160 mg daily. Concerns for real-world patients -- older and more fragile than those enrolled in clinical trials -- have prompted consideration of initiating treatment with lower doses, but the long-term efficacy of this approach remains unknown. We evaluate the long-term survival and longevity in patients treated with standard versus upfront low-dose enzalutamide. Methods: Retrospective analysis of 151 patients treated with enzalutamide (102 receiving 160 mg; 49 receiving [&le;]80 mg) between 2014--2021 at the Centre Hospitalier Universitaire de Martinique, with complete follow-up through end of life (98.7% completeness of follow-up). Primary outcomes were overall survival (OS), progression-free survival (PFS), and longevity (attained age). Results: Doses [&le;]80 mg were associated with longer median OS (36.3 vs. 20.7 months), improved restricted mean OS (difference of 0.7 years, p=0.05), and enhanced longevity (median 82.5 vs. 78.3 years, p=0.004). PSA response rate at 12 weeks was higher with lower-dose (71.4% vs. 48.8%, p=0.016). In multivariable models adjusted for prognostic factors, [&le;]40 mg compared with 160 mg was non-inferior regarding OS (HR=0.61, 95% CI 0.36--1.06), superior regarding PFS (HR=0.59, 95% CI 0.35--0.99), and superior regarding longevity (HR=0.48, 95% CI 0.28--0.84). Bone metastasis, poor performance status, PSA response, time to PSA nadir, and disease duration were independent predictors of outcomes. A post-hoc analysis revealed a strong association between dose and physician-prescribing profiles, ranging from "endorse-lowest-dose" to "never-deviate-from-full-dose". Conclusions: Lower doses of enzalutamide were non-inferior to full-dose. Dose-adapted strategies warrant further investigation.