The 17th Annual Frontiers in Cancer Science (FCS) conference (2025) highlighted the convergence of multiomics, computational biology, and ancestry-specific genomics to advance proactive cancer care. Key insights included the role of epigenetic plasticity in maintaining tumor-propagating states and the identification of metabolic vulnerabilities, such as the WNK1-mTORC1 axis in leukemia and MAF-driven glutamine metabolism in myeloma. The meeting underscored the systemic nature of cancer, detailing how "cancer-educated" neutrophils prime premetastatic niches and how spatial exclusion mechanisms hinder immunotherapy. Breakthroughs in therapeutic engineering were showcased, including CD7-directed chimeric antigen receptor T cells and irreversible KRASG12C inhibitors. A critical focus remained on precision oncology for diverse populations, advocating for ancestry-aware datasets and long-read sequencing to address genomic disparities in Asian cohorts. Furthermore, the integration of artificial intelligence-driven "fragmentomics" and machine learning offers new pathways for early detection and tracking disease lethality. Collectively, FCS 2025 demonstrated that the future of oncology lies in integrating high-resolution disease models with robust data science to transition from reactive treatment to personalized, interceptive management.

Extranodal NK/T-cell lymphoma (ENKTL) is an aggressive Epstein-Barr virus (EBV)-associated malignancy with a heterogeneous tumor microenvironment, yet macrophage heterogeneity and tumor-macrophage crosstalk remain poorly defined. Here, we integrate spatial transcriptomic and proteomic profiling with single-cell spatial molecular imaging of ENKTL samples and identify two subgroups defined by distinct macrophage programs. Subgroup 1 is enriched for inflammatory macrophages exhibiting IFN-α/γ responses and immune-regulatory molecules including IDO1 and CD274, whereas Subgroup 2 is immune-quiescent and macrophage-sparse, with its macrophage compartment skewed towards STAB1 macrophages with scavenging features. Notably, an NF-κB-activated and EBV-associated tumor subset is specifically enriched in Subgroup 1, displaying concurrent immunostimulatory and immunoregulatory features that parallel the co-enriched myeloid states and showing reproducible sample-level associations with these myeloid states across datasets. Spatial neighborhood analysis further delineates an inflammation niche where NF-κB tumor cells physically co-localize with these inflammatory macrophages, accompanied by enhanced tumor-myeloid and myeloid-myeloid signaling, implicating a process of tumor-associated myeloid recruitment followed by CCL- and IL1-mediated myeloid self-reinforcement. Critically, higher abundance of this niche correlates with improved survival across independent cohorts, revealing contrasting spatial tumor-immune architectures with prognostic relevance. Together, our study provides a spatially resolved framework for understanding ENKTL biology and guiding immunotherapeutic strategies.

KRAS inhibitors (KRASi) have emerged as promising new cancer therapeutics for KRAS-mutant cancers; however, resistance remains a potential clinical challenge. Here, we show that reactivation of ERK is a hallmark of KRASi-resistant colorectal cancers (CRCs) and further demonstrate that enhancer remodeling rewires cholesterol biosynthesis through the mevalonate (MVA) pathway to confer this resistance. Mechanistically, enhancer remodeling activates MVA pathway, which facilitates the trafficking of KRAS to the membrane and sustains the MAPK signaling despite KRAS inhibition. Pharmacological inhibition of the MVA pathway with statins effectively blocks KRAS localization to the cell membrane, overcoming KRASi resistance in CRC. Together, these findings identify epigenetic-metabolic coupling of cholesterol biosynthesis as a mechanism of KRASi resistance and highlight targetable metabolic vulnerability in KRAS-mutant CRC.

Abstract Background: Personalized cancer vaccines hold great promise by eliciting tumor-specific immune responses [1-3]. A key challenge is identifying the right targets — immunogenic protein sequences, or epitopes, presented on tumor cells. While computational pipelines can predict epitope candidates from tumor sequencing, experimental validation is costly and slow. Leveraging literature and database knowledge could bridge this gap by enabling evidence-driven selection of high-confidence targets, but is constrained by fragmented information across journals and immunology databases [4-5]. We introduce EpitopeMiner, which integrates sequence-based candidate screening with evidence-driven knowledge retrieval for epitope prioritization. Methods: A total of 25,966 tumor-specific epitopes were predicted from whole-genome sequencing of tumor-PBMC pairs from nine patients (including lung, sarcoma, NKTL, DLBCL) using a standard workflow: HLA typing (OptiType), variant calling (Strelka with wANNOVAR), MHC binding prediction (NetMHCpan) and RNA-supported protein-altering filtering. EpitopeMiner combines OpenAI’s Large Language Model (LLM) with an in-house Retrieval Augmented Generation (RAG) database comprising (a) 78,461 full-text research articles from PMC, PLOS One, and Europe PMC and, (b) ∼2.6 million unique epitopes from IEDB, dbPepNeo, SystemMHC, TANTIGEN, and caAtlas database. EpitopeMiner includes: (i) a screening module that processes an epitope list, detecting exact or ≥ 7 amino acid partial matches from the in-house database, and (ii) a reporting module that analyses each top-ranked hits, defined by highest sequence similarity and evidence density, to generate an LLM response covering 28 immunology keywords with citations. Results: Among the 25,966 epitopes predicted from the nine patients, EpitopeMiner found 7 exact matches, and 17.6% had ≥ 6 partial matches; mean processing time per epitope was 0.97 seconds. In benchmarking with 3 lung cancer driver-gene epitopes (KITDFGRAK, ITDFGRAKL, TDFGRAKLL), EpitopeMiner outperformed ChatGPT and Gemini, returning the highest amount of relevant immunological information — summarized as (total responses, % with evidence) — (18, 100%), (8, 100%), and (2, 100%) respectively, compared to ChatGPT’s (10, 70%), (4, 50%), (6, 33%) and Gemini’s (7, 0%), (1, 0%), (1, 0%). In addition, EpitopeMiner retrieved ≥10 partial matches for each epitope, whereas ChatGPT retrieved total of 3 and Gemini none. Conclusion: We built EpitopeMiner, a computational framework for sustainable literature and database curation. In a 9-patient dataset, EpitopeMiner retrieved experimentally and clinically validated epitope evidence at a scale and speed infeasible with manual analysis. EpitopeMiner outperformed general-purpose LLMs with cited responses, achieving 100% evidence coverage on benchmarks, reducing hallucinations and improving reliability. Citation Format: Agamjyot Singh Chadha, Isaac Jiasheng Cheong, Marcia Zhang, Wei Kit Tan, Wei Lin Tang, Jing Quan Lim, Solomonraj Wilson, Choon Kiat Ong, Bernett Lee, Chwee Ming Lim, Olaf Rotzschke, Mai Chan Lau. EpitopeMiner: Scalable knowledge mining for evidence-driven personalized cancer vaccine design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6698.

Extranodal natural killer (NK)/T-cell lymphoma (ENKTCL) is one of the most aggressive non-Hodgkin lymphomas characterized by NK-cell or T-cell origins, a geographic prevalence in Asian and South American populations, and Epstein-Barr virus (EBV) infection. ENKTCL used to be a fatal disease upon treatment with anthracycline-containing chemotherapies. However, the treatment paradigms for ENKTCL have evolved; consequently, patients have achieved significantly improved clinical outcomes. Today, combined-modality therapies co-targeting genomic instability and metabolic vulnerability comprise the standard of care for early stage ENKTCL, whereas therapies that include targeting of immune and metabolic dysregulation are the backbone of treatment for advanced-stage ENKTCL. New agents targeting surface antigens, oncogenic signaling pathways, and EBV are under investigation and have demonstrated promising responses. Hematopoietic stem cell transplantation is beneficial when used in therapy-responsive, high-risk patients and ENKTCL-associated hemophagocytic lymphohistiocytosis, but it is not recommended for early stage ENKTCL. Although nearly 80% of patients with newly diagnosed ENKTCL achieve long-term survival, treatment of relapsed/refractory ENKTCL is critical. Because it is now understood that EBV infection has a major role in disease progression, a deeper understanding of EBV-associated oncogenesis and alterations in the tumor microenvironment foster the development of novel therapeutic strategies, such as cellular therapies and vaccines targeting EBV. Future multicenter collaborations worldwide will be needed to further optimize mechanism-based treatment in the era of precision medicine, paving a curative pathway for this once fatal disease.

Personalized cancer vaccines represent a transformative approach in immunotherapy, leveraging tumor-specific antigens, such as neoantigens, to stimulate durable and targeted immune responses. However, identifying immunogenic neoantigen epitopes through conventional approaches - relying on in silico predictions followed by experimental validation - remains a significant challenge due to limitations of computational tools and scattering of experiment data. To address these challenges, we introduce EpitopeMiner, a domain-specific large language model (LLM) enhanced with a Retrieval-Augmented Generation (RAG) framework, specifically designed to identify immunogenic epitopes predicted by existing computational tools. EpitopeMiner utilizes a custom database of MHC-I-associated epitope-related literature to provide domain-specific knowledge, enhancing the precision and relevance of LLM-generated responses. EpitopeMiner offers three key features: First, the ability to identify epitopes with similar sequences and potentially similar immunogenic effects, which is especially valuable for neoantigens that are patient-specific and rarely found in public datasets. Second, it supports multiple epitope searches with structured outputs, enhancing scalability. Third, it provides original text chunks and paper identifiers, significantly simplifying validation and further exploration of the retrieved knowledge. Applying EpitopeMiner to well-characterized MHC class I epitopes demonstrates its ability to consistently retrieve relevant papers, efficiently providing targeted insights on T-cell response and immunogenicity, outperforming a commercial AI-powered literature tool. Notably, when applied to lymphoma patient-derived neoantigens, EpitopeMiner successfully retrieves immune response information for a few similar epitopes, despite operating with a relatively smaller database compared to the benchmark tool. All in all, EpitopeMiner bridges the gap between computational prediction and experimental validation, providing a scalable solution for extracting knowledge from public data, fostering cross-study synergies, and accelerating the development of personalized cancer vaccines. ### Competing Interest Statement The authors have declared no competing interest.

Given that most PCNSL cases are diagnosed at a late age, they may be unsuitable for consolidation therapy with autologous stem cell transplantation (HDC-ASCT). We thus examine the outcomes of PCNSL in a multiethnic Asian population treated with HD-MTX-based chemotherapy and consolidation WBRT, so as to benchmark current treatment outcomes. We conducted a retrospective study involving patients (n = 129) diagnosed with PCNSL at the National Cancer Centre Singapore from 2000 to 2019. The median follow-up duration was 47.0 months. Survival analyses were performed using the Kaplan-Meier method and Cox proportional models. The cohort consisted of 76 male and 53 female patients with a median age of 60 years. 85 patients received HD-MTX-based induction therapy as per DeAngelis, while 44 were treated with the Shah protocol. In univariate analyses, male sex, ECOG scores ≥ 1, non-germinal center subtype, use of DeAngelis protocol, and methotrexate dose < 3 g/m2 were significantly correlated with worse PFS and OS. In a multivariate model, the Shah protocol conferred significantly improved outcomes as compared with the DeAngelis protocol for PFS (HR 0.43, 95

7001 Background: Natural killer T-cell lymphoma (NKTCL), characterized by extranodal involvement, challenges the efficacy of the Ann-Arbor staging system (AASS) in its precise prognostic stratification. A revised staging system is warranted for precise prognosis prediction in the modern chemotherapy era. Methods: A training cohort of patients with newly diagnosed NKTCL was assessed to revise the AASS in the context of the modern chemotherapy era. The results were validated in an independent international cohort that received asparaginase-based chemotherapy. Results: Our analysis of 2017 newly diagnosed NKTCL patients from 19 centers across two countries highlights the limitations of the AASS, which demonstrates an uneven patient distribution and insufficient differentiation of outcomes, particularly between stages III and IV. We proposed a revised staging system in which AASS stage I patients with nasal-type disease only were classified as stage I, whereas those with local invasion or limited non-nasal-type disease were reclassified as stage II. AASS stage II patients with regional lymph node involvement were assigned to stage III. Additionally, patients with distant lymph node involvement or extensive skin/subcutaneous soft tissue involvement were classified as stage IVA, those with extensive visceral organ invasion were classified as stage IVB, and those with bone marrow infiltration or hemophagocytic lymphohistiocytosis were classified as stage IVC. This proposal better distinguishes clinical outcomes across different stages, achieves a more equitable distribution of patients, and demonstrates multiple advancements over the AASS. Conclusions: The revised staging system is promising for the staging of NKTCL patients with different prognoses and could be useful for decisions regarding the treatment strategy and future clinical trial designs.

Background: The prognostic impact of specific genomic changes in mantle cell lymphoma (MCL) is not well characterized beyond altered TP53, which is recognized as a high-risk marker and commonly assessed at diagnosis, and the “proliferation signature” developed using gene expression in fresh frozen tissues (Rosenwald et al, Cancer Cell 2003). To bridge this knowledge gap, we applied comprehensive tumor sequencing to investigate how genomic abnormalities affect prognosis in patients (pts) with MCL with and without TP53 alterations. Methods: The Atlas of Blood Cancer Genomes project is an international collaborative effort including 25 sites for collecting and sequencing all blood cancers (Love et al, ASH 2021). We recruited MCL pts with detailed clinical data and subjected their tumors to whole exome, whole transcriptome, and targeted sequencing. TP53-aberrant cases (i.e., mutation or deletion) were identified from sequencing and clinical pathology reports. Progression-free survival (PFS) and overall survival (OS) were measured using the Kaplan-Meier method, with statistical comparisons by the log-rank test. Risk for a gene signature was defined as the coefficient from the respective Cox proportional hazard model. Results: RNA and DNA sequencing were performed successfully for 252 pts with MCL. Clinical features and treatment regimens were consistent with prior disease descriptions and have been reported previously (Koff et al, ASH 2022). In the entire cohort, median PFS was 38 months, and median OS was not reached. As expected, TP53 abnormalities were associated with inferior OS compared to TP53-wildtype (WT; 5-year OS 48% and 79%, respectively, p<0.001). A novel gene expression signature (“immune signature”) was determined by identifying genes that displayed prognostic ability independent of the previously described MCL proliferation signature (Rosenwald et al, Cancer Cell 2003; Scott et al, J Clinical Oncology 2017). This signature was distinct from the proliferation signature both in terms of included genes and ability to discriminate between risk groups; the correlation plot between the immune and proliferation signature scores showed a low R2 value of 0.01. The immune signature appears to derive from differences in tumor microenvironment (TME) CD8+ T and T follicular helper cells: pts designated as high-risk by the immune signature demonstrated lower proportions of these TME subsets as assessed by CIBERSORT (p=0.001). For the cohort with tumors sequenced prior to treatment (n=208), 5-year OS was 45% for pts with a high-risk immune score (bottom quartile), 80% for pts with intermediate-risk immune score (middle 2 quartiles), and 91% for low-risk immune score (top quartile, p <0.001), with improved discrimination compared to the proliferation signature (56%, 79%, and 80% for high-, intermediate-, and low-risk proliferation scores, respectively; p=0.03). The immune signature also risk-stratified outcomes in MCL subgroups with and without TP53 alteration. For pts with TP53-WT (n=74), low-risk immune score predicted 5-year OS of 86%, while high-risk immune score was associated with 5-year OS of 38% (p<0.001), compared to 72% vs 82% for low-risk and high-risk proliferation scores (p=0.16). For pts with aberrant TP53 (n=43), low-risk immune score had 5-year OS of 77%, and high-risk immune score had 5-year OS of 9% (p<0.001), compared to 35% vs 52% for low-risk and high-risk proliferation scores (p=0.28). Application of the immune signature also further stratified pts deemed high-risk by the proliferation score (n=52): within this group, low-risk immune score associated with 5-year OS of 76%, and high-risk immune score predicted 5-year OS of 29% (p<0.001). Similar stratification was also observed when the immune signature was applied to pts with low- and intermediate-risk proliferation scores (n=156; 5-year OS of 88% vs 57% for low-risk vs high-risk immune score respectively, p<0.001). Conclusions: In this largest-ever study of MCL's genomic landscape, we identify a novel gene expression signature that stratifies risk within and across existing prognostic groups, including TP53-altered cases. Our findings support development of the immune signature as a tool that can be used in routine clinical practice to improve risk stratification of all MCL patients at diagnosis. Additional study is warranted to define therapeutic implications of differential TME T cell subset composition in MCL.

Anaplastic large-cell lymphoma (ALCL) accounts for 15% of all peripheral T-cell lymphomas globally and can be further divided into subcategories, of which patients with ALK-negative ALCL have dismal prognosis and overall survival. We established a patient-derived xenograft (PDX) and in vitro model (designated PTCL-S1) of TP63-rearranged ALK-negative ALCL from the primary tumour site of a 55-year old Chinese woman. Whole genome sequencing of the patient's tumour identified various mutations including AKT1 and NOTCH1, as well as the TP63-TBL1XR1 gene fusion. RNA sequencing followed by Sanger sequencing confirmed the gene rearrangement in original tumour, PDX and PTCL-S1 cell line. Immunohistochemistry profiling of the PDX model and cell-line were consistent with the patient's primary tumour sample (CD3 + /CD30 + /CD79a-). Cytotoxic agents (doxorubicin, etoposide and gemcitabine) commonly used in ALCL treatment exhibited potent anti-proliferative activity in the cell-line. In conclusion, the established PTCL-S1 cell line can be a useful tool for further investigation of the understanding of TP63-rearranged ALK-negative ALCL.

Anti-PD-1 immunotherapy has demonstrated significant antitumor efficacy in relapsed or refractory NK/T-cell lymphoma (R/R NKTL), but resistance remains a substantial challenge. In this study, we evaluate DNA methyltransferase (DNMT) inhibitors combined with anti-PD-1 mAb in 21 patients with R/R NKTL for whom prior immunotherapy failed. This combination therapy achieved an objective response rate of 66.7% (14/21), with a complete response rate of 47.6% (10/21) and a 2-year overall survival rate of 50.2%. Preclinical models revealed that anti-PD-1 resistance was linked to the absence of CD8+ T-cell infiltration and suppressed IFN pathways. DNMT inhibitors reversed these effects, restoring CD8+ T-cell activities and tumor sensitivity to PD-1 blockade. Mechanistically, DNMT inhibitors triggered DNA demethylation of endogenous retroviral elements, activating viral mimicry via upregulated endogenous nucleic acids and type I IFN signaling. These findings underscore DNMT inhibitors' role in overcoming PD-1 resistance and support their combination with anti-PD-1 as a promising strategy for R/R NKTL. SIGNIFICANCE:Resistance to anti-PD-1 immunotherapy remains a substantial challenge in R/R NKTL. In this study, we reported that combining DNMT inhibitors with anti-PD-1 mAb achieves a high complete response rate of 47.6% in immunotherapy-R/R NKTL patients. Mechanistically, DNMT inhibitors potentiate anti-PD-1 efficacy by triggering viral mimicry, remodeling the immune microenvironment and augmenting antitumor immunity.

Supplementary Table S1 Table S1 Prior ICB treatment information of patients. Supplementary Table S2 Baseline characteristics of patients. Supplementary Table S3 Primers for DNA methylation analysis. Supplementary Table S4 Primers for RT-qPCR.

Supplementary methods. Supplementary Figure 1. Phenotyping of B-cells in non-malignant tissues. A, Quantitation of marker positivity across ten tonsil and two reactive lymph node samples (rLN). Analysis is spatially resolved between the GC and extra-GC zones. B, Spatial map of cellular coordinates based on cell segmentation of images in Figure 1B. Marker-positivity is indicated, and a total proportion of positive and negative cells is depicted as a pie chart. These maps were used to derive sub-population phenotypes depicted in Figure 1C. Scale bar is 100μm. C, Proliferation analysis (i.e., Ki67-positivity) among sub-populations in five tonsil samples. Median with interquartile range, whiskers denote 10th and 90th percentile. Supplementary figure 2. Example pseudo-colored mfIHC images for MYC, BCL2, BCL6 cases in DLBCL. Images of a range of mean fluorescent intensities are shown with equal scaling for reference. Supplementary figure 3. Global distribution of MYC, BCL2 and BCL6 sub-populations within DLBCL cohorts. Heat-maps displaying the percentage extent of individual markers and each sub-population within the DLBCL NUH, CMMC, SGH and MDA cohorts. Hierarchical k-means clustering of patients according to sub-population extent is applied. Positivity shading for single markers ranges between 0-100% positivity, whereas shading for sub-populations reflects 0-50% positivity and remains fully saturated until 100%. IPI Risk Group - International Prognostic Index Risk Group, FISH - fluorescence in situ hybridization. Supplementary figure 4. Intra-tumor heterogeneity of sub-populations. A, Correlation of sub-population extent quantification between two biopsies of the same patient for which at least two tissue microarray (TMA) biopsies are available. Correlation is shown separately for lymph node and extranodal biopsies. Spearman rho is indicated for each correlation. Axes are in exponential and equivalent in all panels. B, Sub-population percentage extent quantification across multiple TMA cores (columns) of the same patient (rows). Pie charts are ordered according to decreasing cell numbers evaluated per core. All patients from the NUH cohort with at least five cores are evaluated. A heterogenous cluster is highlighted by the red box. Supplementary figure 5. Spatial heterogeneity of sub-population interactions. A, Conceptual schematic of pair correlation function (PCF) plots depicting a clustered distribution (left, green) and a random distribution (right, grey). Representative counterpart spatial maps are above each plot. B, PCF analysis for sub-populations to investigate spatial clustering (top). Mean results for two independent cohorts (shading is cohort standard deviation). An example tissue microarray core is shown as physical distance reference for spatial analyses (bottom left). Absolute number of neighboring cells expected within a given radius (data from 3500 randomly selected cells across all images, mean with standard deviation) (bottom right). C, Actual spatial map of sub-populations of an example DLBCL case (top). Extent of all sub-populations within the sample is shown on the left. Simulated, hypothetical random distribution of cells for the same case (middle). PCF analysis for the shown sample and its matched simulated random distribution (bottom). Scale bars in B and C are 100µm. D, Mean deviations from expected neighbor abundance (Δ%) summarizing cell-cell interactions between sub-populations for the sample shown in (C). E, Sub-population interaction matrices from spatially distinct biopsies (cores in tissue microarray) for example DLBCL patients. Biopsies of stable, spatially homogenous, sub-population interaction profiles are grouped (top), whereas biopsies of a differing, heterogenous, interaction profile are grouped separately (bottom). Supplementary figure 6. Global deviations from expected spatial neighbor abundance (Δ%). Hierarchical clustering (minimum variance method) of measured Δ% for all cases in the SGH and MDA cohorts. Extents of sub-populations are indicated for reference (top). For the MDA cohort, multiple biopsies (n = 1-3) from the same patient were included in the analysis to determine spatial interaction similarity across spatially distinct regions (bottom). Supplementary figure 7. Correlation of predicted MYC, BCL2 and BCL6 sub-population percentage extent based on single oncogene positivity and observed percentage extent in DLBCL cohorts. Spearman rho, axes are equivalent in all panels. Supplementary figure 8. Variance of M+2+6- percentage extent in the context of positivity calling across a 15% cut-off. A, M+2+6- scoring variance across multiple pathological imaging fields. All whole-tissue DLBCL sections from University of Palermo (UP), and samples from the NUH TMA with at least four fields scored per patient and a mean M+2+6- score above 5% are shown. Mean with SD. Ordinates between 50-100% are compressed for clarity. Dashed line denotes M+2+6- 15% positivity. B, Stability of M+2+6- case positivity calling across scoring increasing number of imaging fields. All cases from panel A with at least five fields scored in this study are shown. Only one case is called M+2+6- Low (<15%) at the first image scored, and subsequently called M+2+6- High (≥15%) after two or more fields scored. Supplementary figure 9. Mapping of mRNA expression data into percentage extent data. A, Cumulative histogram of MYC, BCL2 and BCL6 protein percentage extent positivity in DLBCL cohorts (data transformed from Figure 4A) (top). B, Aggregated single oncogene cumulative distribution of MYC, BCL2 and BCL6 protein percentage extent positivity across all measured protein cohorts and its smoothed empirical cumulative distribution function (eCDF).C, Distribution of inferred single oncogene percentage extent in GEP cohorts. (see Supplementary table 6 for all values). Supplementary figure 10. Analysis of the GOYA clinical trial. A, Correlation of MYC mRNA with quantitative IHC score. Linear regression (left) and Wilcoxon rank sum test (right). B, Analysis as in (A) for BCL2. C, Kaplan-Meier curves for PFS and OS for patients stratified across the 15% M+2+6- metric (GEP-derived). Multivariate Cox proportional hazards model is available in Supplementary table 9. PFS - progression free survival, OS - overall survival. Supplementary figure 11. Proliferative advantage of cyclin D2 (CCND2) overexpressing B-cells. Representative FACS plots documenting to the expansion over time of the cyclin D2 positive GC B-cell population in cyclin D2 overexpressing GC B-cells (CCND2-Lyt2) and non-cyclin D2 overexpressing GC B-cells (Empty vector Lyt2). All GC B-cells co-overexpress BCL2, BCL6, MYC and GFP. Supplementary table 3. Non-parametric correlation of sub-population percentage extent with clinicopathological features. Supplementary table 4. Pooled univariate analysis for MYC, BCL2 and BCL6 single oncogene and sub-populations percentage extents as a continuous variable at 5% increments as predictors for overall survival (OS) in mfIHC cohorts of DLBCL (Cox proportional hazards model). Supplementary table 5. Univariate analysis of clinicopathological features as a predictor of overall survival (OS) after first-line R-CHOP treatment in the NUH, SGH and MDA cohorts of DLBCL (Cox proportional hazards model). Supplementary table 7. Pooled univariate analysis for sub-population metrics as a continuous variable at 5% increments as predictors for overall survival (OS) in GEP DLBCL cohorts (Cox proportional hazards model). Supplementary table 8. Multivariate analysis of continuous M+2+6- metric at 5% increments as a predictor of overall survival (OS) in cohorts with gene-expression data (Cox proportional hazards model). Supplementary table 9. Univariate and multivariate analysis of continuous M+2+6- metric as a continuous variable at 5% increments as predictor of progression-free survival (PFS) and overall survival (OS) in the GOYA trial cohort (Cox proportional hazards model). Supplementary table 10. Multivariate analysis of M+2+6- metric dichotomized at 15% as a predictor of overall survival (OS) in cohorts with gene-expression data (Cox proportional hazards model). Supplementary table 15. Clinicopathologic characteristics of DLBCL patients evaluated by multiplexed fluorescent immunohistochemistry (mfIHC) in this study. Supplementary table 16. Manual multiplexed fluorescent immunohistochemistry (mfIHC) staining protocol performed on the NUH and CMMC cohort TMA. Supplementary table 17. Automated multiplexed fluorescent immunohistochemistry (mfIHC) staining protocol performed on the SGH, MDA and BCA cohort TMA.

Introduction Hemophagocytic lymphohistiocytosis (HLH) is a severe inflammatory condition characterized by overt immune activation and cytokine storm. Without timely intervention, HLH rapidly progresses to multiple organ failure and almost certainly lead to death. HLH can be broadly categorized into primary and secondary HLH. While primary HLH (pHLH) is caused by genetic predisposition, secondary HLH (sHLH) can be triggered by a variety of factors, including infection, cancer and autoimmunity. Here we use single-cell expression and TCR profiling technologies to identify the trigger for aberrant activation of CD8+ cells in sHLH. Methods Patients were diagnosed with NKTL according to the 2008 World Health Organization classification. Ten whole blood samples profiled for this study were from three patients with NKTCL and concomitant HLH, four patients with NKTCL without HLH and three healthy individuals without any hematological malignancies. All subjects in this study provided written informed consent. Single cell 5' gene expression libraries, BCR and TCR libraries were prepared using the 10X Genomics Chromium Next GEM Single Cell 5′v2 Library & Gel Bead Kit and V(D)J Enrichment Kits according to the manufacturer's protocol. Results Peripheral blood mononuclear cells (PBMCs) from sHLH patients, NKTL patients without sHLH, and healthy controls (HCs) were analyzed. sHLH patients displayed significantly elevated levels of activated CD8+ T cells and an inverted CD8:CD4 ratio, indicative of heightened proliferation and activation. Single-cell RNA sequencing identified unique subsets of CD8+ T cells in sHLH patients, particularly CD38+Ki67+ effector memory T cells (Act-TEM), which were nearly absent in NKTL and HCs. These cells exhibited markers of exhaustion (PD-1, TIGIT, and EOMES) but retained active proliferation and cytotoxic capacity, diverging from canonical T-cell developmental trajectories. Gene set enrichment analysis highlighted their enrichment in interferon response and cell proliferation pathways, contrasting with the NFκB activation pathways observed in typical effector memory T cells (TEM). T-cell receptor (TCR) sequencing also revealed no significant oligoclonal expansion in sHLH patients, suggesting a non-TCR-mediated activation mechanism. Naïve CD8+ T cells in sHLH patients produced granzyme B, further supporting non-canonical activation. Ex vivo cytokine treatments identified IL-15 as a key driver of this phenotype, capable of inducing granzyme B production and promoting the differentiation of CD8+ T cells into Act-TEM. Elevated IL-15 levels in plasma and monocytes, along with increased CCR5 expression on CD8+ T cells, corroborate the role of IL-15 in bystander activation too. Conclusion We report IL-15 mediated bystanding activation of CD8+ T-cells in sHLH. This highlights IL-15 as a potential immune-modulating target to soothe the cytokine storm in patients with sHLH.

PURPOSE:Despite initially responding to first-line treatment, many patients with non-Hodgkin's lymphoma (NHL) eventually relapse or are refractory. These patients are empirically subjected to salvage therapies that may not be efficacious. We had previously presented feasibility evidence of an ex vivo functional precision medicine (FPM) platform, quadratic phenotypic optimization platform (QPOP), being potentially useful in identifying alternative therapeutic options for patients with relapsed/refractory (R/R)-NHL. We now present an analysis of the completed prospective study, with clinical concordance and benefit of QPOP in predicting treatment responses of patients with R/R-NHL. MATERIALS AND METHODS:One hundred seventeen patients with R/R B-cell NHL (B-NHL) or natural killer or T-cell NHL (NK/T-NHL) were recruited for QPOP testing. Isolated tumor cells were incubated for 48 hours with drug combinations determined by an orthogonal array composite design. QPOP reports with patient-specific optimal drug therapies were generated. Patients were given off-label treatments according to the clinician's decision. Patient characteristics and responses to treatments after QPOP testing were recorded. RESULTS:Within 126 QPOP cases, 105 (52 B-NHL and 53 NK/T-NHL) were evaluable. QPOP-suggested drug responses were concordant with actual patient outcome, with an overall test accuracy of 74.5%. An overall response rate of 59% was achieved in those prescribed off-label QPOP-guided combinations. In all, 59.3% of QPOP-guided patients had improved response durations compared with their previous treatment. Compared with those who received salvage therapy, QPOP-guided patients also had longer progression-free survival 2 years after treatment. CONCLUSION:There is increasing evidence that genetic factors are not sole determinants of patient drug response and FPM approaches may improve cancer treatment guidance. This study further confirms that advancements in ex vivo combinatorial drug screening platforms like QPOP could complement existing genomic methods in identifying effective treatment options for patients with cancer, especially in R/R cases.

Supplementary Figure S1. Tumor-infiltrating CD8+ cell was positive correlation to efficacy of the combined DNMT inhibition and anti-PD-1 mAb therapy. Supplementary Figure S2. No significant reduction in body weight of EL4 tumor-bearing mice with anti-PD-1 treatment. Supplementary Figure S3. Gating strategies for Figure 2D-F. Supplementary Figure S4. PD-L1 and PD-1 expression in pre-clinical models. Supplementary Figure S5. Immune related pathways were downregulated in EL4 P3 tumor. Supplementary Figure S6. No significant reduction in body weight of EL4 P3 tumor-bearing mice under different treatment. Supplementary Figure S7. DNMT inhibitors enhance CD8+ T cell activated and cytotoxicity in vivo. Supplementary Figure S8. DNMT inhibitors treatment did not affect PD-L1 expression in pre-clinical models. Supplementary Figure S9. DNMT inhibitors induce MHC-I and ISGs expression in EL4 cells. Supplementary Figure S10. IFNAR1 blockade attenuated the antitumor immunity induced by the combination of DAC and α-PD-1. Supplementary Figure S11. STING knockdown did not impair DNMT inhibitors induced CD8+ T cells activity. Supplementary Figure S12. DNMT inhibitors induces ERVs expression in human NKTL cells.

ABSTRACT:This study aimed to assess the efficacy and safety of combining cemiplimab, an anti-programmed cell death protein 1 (PD-1) antibody, with isatuximab, an anti-CD38 antibody, in relapsed or refractory extranodal natural killer/T-cell lymphoma (R/R ENKTL). The hypothesis was that CD38 blockade could enhance the antitumor activity of PD-1 inhibitors. Eligible patients received cemiplimab (250 mg on days 1 and 15) and isatuximab (10 mg/kg on days 2 and 16) IV every 4 weeks for 6 cycles. Responders then received cemiplimab (350 mg) and isatuximab (10 mg/kg) every 3 weeks for up to 24 months. The primary end point was the complete response (CR) rate based on the best response. Of 37 patients enrolled, the CR rate was 51% (19/37), exceeding the primary end point of 40%, and the objective response rate was 65% (24/37). After a median follow-up of 30.2 months (95% confidence interval [CI], 25.6-34.8 months), the median progression-free survival was 9.5 months (95% CI, 1.4-17.6 months), whereas the median overall survival had not yet been reached. Patients achieving CR received a median of 28 cycles (range, 4-33 cycles), and the median duration of response for responders (n = 24) was 29.4 months (95% CI, 15.4-43.4 months). Structural variations disrupting the 3'-untranslated region of PD-L1 and high programmed death ligand 1 (PD-L1) expression were observed in responders. Most adverse events were mild (grade 1-2), with grade ≥3 events (32%) and no treatment-related deaths. The combination of isatuximab and cemiplimab demonstrated sustained antitumor activity and a manageable safety profile in R/R ENKTL. This phase 2 trial is registered at www.clinicaltrials.gov as number NCT04763616.

Epstein-Barr virus (EBV) is a significant epigenetic driver in the development of epithelial-origin nasopharyngeal carcinoma (NPC) and gastric cancer (GC), which together represent 80% of EBV-associated malignancies. Despite its known association, the specific mechanisms, particularly those involving EBV-induced histone modifications, remain poorly understood. Through integrative analyses of single-cell and bulk transcriptome data from epithelial tumor tissues and EBV-infected cells, we identified KDM5B as a critical histone-modifying factor consistently upregulated following EBV infection. We demonstrated that EBV stimulates KDM5B expression via interactions of its latent gene EBNA1 with transcription factor CEBPB and through direct binding of its lytic gene BZLF1 to Zta-response elements on the KDM5B promoter. Functional assays revealed that KDM5B acts as an oncogene, correlating with poor survival outcomes in EBV-associated epithelial cancers. Mechanistically, KDM5B inhibited the tumor suppressor gene PLK2 through histone demethylation, thereby activating the PI3K/AKT/mTOR signaling pathway and promoting malignant progression. Furthermore, treatment with the KDM5B inhibitor AS-8351 markedly attenuated this signaling activity and exhibited strong anti-tumor effect in both in vitro and in vivo patient-derived xenograft models from EBV-associated tumors. Together, these findings provide novel insights into how EBV hijacks KDM5B to mediate histone demethylation of PLK2, facilitating tumor progression through the PI3K/AKT/mTOR pathway in epithelial cancers, highlighting promising therapeutic strategies targeting epigenetic alterations in EBV-associated cancers.

Nodal follicular helper T-cell (T FH) lymphoma of the angioimmunoblastic (AITL) subtype has a dismal prognosis. Using whole-exome sequencing (n = 124), transcriptomic (n = 78), and methylation (n = 40) analysis, we identified recurrent mutations in known epigenetic drivers (TET2, DNMT3A, IDH2 R172 ) and novel ones (TET3, KMT2D). TET2, IDH2 R172 , DNMT3A co-mutated AITLs had poor prognosis (p < 0.0001). Genes regulating T-cell receptor (TCR) signaling (CD28, PLCG1, VAV1, FYN) or activation (RHOA G17V ) or regulators of the PI3K-pathway (PIK(3)C members, PTEN, PHLPP1, PHLPP2) were mutated. CD28 mutation/fusion was associated with poor prognosis (p = 0.02). WES of purified, neoplastic T-cell (CD3+PD1+) demonstrated high concordance with whole tumor biopsies and validated the presence of TET2 and DNMT3A in tumor and non-lymphoid cells, but other mutations (CD28, RHOA G17V , IDH2 R172 , PLCG1) in neoplastic cells. Integrated DNA-methylation and mRNA expression analysis revealed epigenetic alterations in genes regulating TCR, cytokines, PI3K-signaling, and apoptosis. RNA-seq analysis identified fusion transcripts regulating TCR-activation (8%), revealed a restricted TCR-repertoire (α = 87%, β = 72%), and showed the presence of Epstein-Barr virus transcriptome (73%). GEP demonstrated the association of B-cells or dendritic cells in the tumor milieu with prognosis (p < 0.01). RNA-seq and WES analysis of 12 AITL-patient-derived-xenografts (PDX) showed that bi-allelic TET2 and DNMT3A mutations or sub-clonal mutations (PLCG1, PHLPP2) propagated in sequential passages, and gene signatures related to T FH and T CM (central-memory) were well-maintained through passages. Gene expression signatures associated with late PDX passages (3rd-5th) were enriched with proliferation and metabolic reprogramming-related genes and predicted prognosis in an independent AITL series. Low PHLPP2 mRNA expression predicted poor prognosis (p = 0.05) and engineered PHLPP2 or TET2 loss in CD4+ T-cells showed enhanced PI(3)K activation, thus uncovering a therapeutic target for clinical trials.