Abstract Diffuse large B-cell lymphomas (DLBCLs) with a dark zone (DZ)-like transcriptional profile correlate with poor outcomes to rituximab-based chemoimmunotherapy, but the mechanisms underlying this resistance remain unclear. We hypothesize that DZ-like DLBCLs retain immune-evasion properties of the physiological germinal center (GC) DZ, contributing to treatment resistance. We investigated the molecular basis of spatial T-cell exclusion in GCs and its relevance to immune resistance in DZ-like lymphomas. Digital Spatial Profiling (DSP) and spatial transcriptomics were performed on DZ and light zone (LZ) regions of 10 tonsil GCs to define DZ gene signatures. DZ transcriptional programs in normal DZ and malignant B cells were enriched for cell cycle checkpoints, DNA damage response, ATR activation, and chromatin compaction pathways, correlating with reduced T-cell infiltration. Given that activation-induced cytidine deaminase (AID) is a known driver of the DZ program, we evaluated its role in T-cell exclusion. We observed persistent DZ signatures in AID-deficient and WT murine GCs, with no associated T-cell infiltration. While AID-high B cells enriched for DZ features, AID-low cells overlapped with ATR activation within the DZ program, suggesting ATR-dependent T-cell exclusion independent of AID mutagenesis. ATR inhibition in DZ-like DLBCL cell lines reversed the DZ spatial signature and increased T-cell attraction in microfluidic co-culture systems. In vivo, ATR inhibition in immunized mice significantly increased overall GC T-cell infiltration, particularly CD8+ T cells within the DZ. Finally, in PDX models, ATR inhibition significantly improved CAR-19-mediated cytotoxicity, with the most pronounced effect in DZ-like DLBCL clones, which otherwise exhibited resistance to CAR-T cell killing. These findings support ATR inhibitors as potential adjuncts to chemoimmunotherapy, immune checkpoint blockade, or CAR-T cell therapy in lymphomas, particularly in lymphomas characterized by immune exclusion. This abstract is included in the 18-ICML Abstract Book, https://doi.org/10.1002/hon.70094_172 Citation Format: Valeria Cancila, Giorgio Bertolazzi, Allison S. Y. Chan, Giovanni Medico, Giulia Bastianello, Gaia Morello, Daniel Paysan, Clemence Lai, Hong Liang, Girija Shenoy, Patrick W. Jaynes, Giovanna Schiavoni, Fabrizio Mattei, Silvia Piconese, Maria V. Revuelta, Francesco Noto, Luca Businaro, Adele De Ninno, Ilenia Cammarata, Fabio Pagni, Saradha Venkatachalapathy, Sabina Sangaletti, Arianna Di Napoli, Giada Cicio, Davide Vacca, Silvia Lonardi, Luisa Lorenzi, Andrés J. M. Ferreri, Beatrice Belmonte, Min Liu, Manikandan Lakshmanan, Michelle S. N. Ong, Zhang Biyan, Tingyi See, Kong-Peng Lam, Gabriele Varano, Mario P. Colombo, Silvio Bicciato, Giorgio Inghirami, Leandro Cerchietti, Maurilio Ponzoni, Roberta Zappasodi, Evelyn Metzger, Joe Beechem, Fabio Facchetti, Marco Foiani, Stefano Casola, Anand D. Jeyasekharan, Claudio Tripodo. Aggressive B-cell lymphomas retain ATR-dependent determinants of T-cell exclusion from the Germinal Center Dark Zone [abstract]. In: Proceedings of Frontiers in Cancer Science 2025; 2025 Nov 5-7; Singapore. Philadelphia (PA): AACR; Cancer Res 2026;86(13_Suppl):Abstract nr LT02.

Acute myeloid leukemia (AML) is a hematopoietic malignancy caused by abnormal proliferation and differentiation of blasts. PRMT5, a methyltransferase that catalyzes symmetric dimethylation of arginine (SDMA) residues, has been implicated in cancer stem cell homeostasis and shown to be a potential therapeutic target in AML. However, given the toxicity of complete PRMT5 inhibition, there is a need to identify effective synergistic therapies. Through a targeted screen of compounds that inhibit key nodes of PRMT5-regulated pathways, we identified a synthetic lethality between inhibition of PRMT5 and LSD1, a lysine demethylase known to affect AML blast differentiation. The two inhibitors broadly reshape the transcriptome of targeted cells and synergize to promote AML differentiation and eventually growth inhibition and apoptosis, in a p53-dependent manner. To leverage this synthetic lethal interaction, we generated new dual compounds to inhibit both enzymes and recapitulated the effects of the drug combination. Our results uncover an unexpected convergence of PRMT5- and LSD1-regulated targets, paving the way for new therapeutic opportunities.

PURPOSE:Claudin 18.2 (CLDN18.2), a tight junction protein normally expressed in gastric epithelium and rendered accessible during malignant transformation, has emerged as a key therapeutic target. Here, we seek to characterize the immune microenvironment of CLDN18.2-expressing gastric cancer and identify immune features associated with CLDN18.2 expression status. PATIENTS AND METHODS:Tumor immune features were profiled in 103 patients with HER2-neg/low advanced GC treated with first-line immune checkpoint inhibitor (ICI) using multiplex immunohistochemistry (7,423,483 cells across 2,315 regions of interest [ROIs]). Findings were validated in five published whole-transcriptome sequencing/microarray cohorts (1,521 samples), including the CheckMate 649 phase 3 trial. Spatial architecture was assessed by cellular neighborhood analysis. Digital spatial profiling (DSP) was performed on 480 ROIs from a CLDN18.2-annotated tissue microarray (75 patients). RESULTS:CLDN18.2high tumors showed reproducible enrichment of humoral pathways, with higher CD20+ B-cell density and enrichment of B-cell/plasma-cell signatures. CLDN18.2 status alone was not associated with survival on first-line ICI therapy; in CLDN18.2low tumors, B-cell enrichment was associated with ICI benefit, unlike CLDN18.2high tumors. Spatial analyses indicated that CLDN18.2high tumors preferentially harbored tumor-infiltrating B-cells, rather than tertiary lymphoid structure/lymphoid aggregate-like neighborhoods, and this tumor-compartment localization was associated with diminished ICI benefit. DSP confirmed that CD20+ B-cell enrichment was most pronounced within tumor regions, alongside compartment-specific activation of humoral and extra-follicular response programs and interferon-associated and remodeling signatures. CONCLUSIONS:CLDN18.2high GC exhibits a humoral-enriched microenvironment with increased tumor-infiltrating B-cells and spatial immune organization associated with reduced ICI benefit. Spatial and humoral-state biomarkers may inform ongoing CLDN18.2-directed combination trials.

Abstract Intratumoral heterogeneity in the spatial arrangement of phenotypically distinct tumor subpopulations is increasingly recognized, yet whether spatial topology independently determines clinical outcomes and reflects distinct biological programs remains unclear. Using diffuse large B-cell lymphoma (DLBCL) as a model, we reported (AACR 2025) that the spatial distribution of MYC+BCL2+BCL6- double expressor (DE) cells — quantified by point process modeling of multiplex immunohistochemistry (mIHC) across 476 patients in four independent cohorts — independently predicts survival after chemoimmunotherapy, and identified a transcriptional signature of dispersed DE cells associated with inferior outcomes in gene expression cohorts (4,594 patients). The mechanistic basis for why genotypically identical MYC+BCL2+ cells adopt distinct spatial configurations with divergent clinical impact is unknown. Given that MYC-driven ribosome biogenesis (RiBi) normally triggers the impaired RiBi checkpoint (IRBC), wherein the RPL5/RPL11/5S rRNA complex binds and inhibits MDM2 to stabilize p53, we investigated whether this prognostic spatial signal reflects differential IRBC engagement. Using single-cell RNA-sequencing in de novo (n=17) and relapsed/refractory (R/R; n=99) settings and digital spatial profiling (GeoMx DSP-WTA, n=64), we found that although cells with dispersed and clustered signatures both upregulated RPL5 under MYC-driven ribosomal stress, dispersed DE cells selectively evaded IRBC engagement: they exhibited elevated MDM2 expression, reduced p53 stabilization, and diminished p53 transcriptional activity, conserved across de novo and R/R contexts and independent of TP53 mutation status. We postulate that NF-κB pathway activation in dispersed DE cells transcriptionally drives MDM2 overexpression sufficient to overwhelm RPL5/RPL11-mediated inhibition, establishing a feed-forward loop: sustained MDM2 activity inactivates p53, which normally suppresses NF-κB, thereby promoting PTGES3-mediated prostaglandin E2 (PGE2) synthesis. Cell-cell communication analysis confirmed enriched PGE2 signaling in dispersed versus clustered configurations across both disease settings. Critically, hyperplex spatial proteomics (PhenoCycler, n=152) revealed that dispersed DE cells, despite significantly greater spatial proximity to T cells than clustered counterparts, orchestrated a profoundly immunosuppressive niche characterized by regulatory T cell enrichment and terminal CD8+ T cell exhaustion — a spatial immune phenotype consistent with tumor-derived PGE2-mediated suppression of anti-tumor effector responses and conserved across de novo and R/R disease. These findings establish IRBC avoidance as a spatially determined mechanism of non-mutational p53 inactivation in MYC-driven lymphoma and identify the p53-NF-κB-PGE2-T cell exhaustion axis as a potential therapeutic vulnerability in spatially dispersed double expressor DLBCL. Citation Format: Shruti Sridhar, Charmaine Ong, Chartsiam Tipogamut, Qiang Pan Hammarström, Kasthuri Kannan, David W. Scott, Xubin Li, Michael R. Green, Claudio Tripodo, Anand D. Jeyasekharan. Spatially dispersed MYC-BCL2 co-expressing cells confer poor survival in DLBCL through functional p53 loss and PGE2-mediated immune evasion [abstract]. In: Proceedings of the Fifth AACR International Meeting on Advances in Malignant Lymphoma: From Discovery to Clinical Impact; 2026 Jun 24-27; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2026;7(3_Suppl):Abstract nr A051.

Ribosomes, once considered homogenous, exhibit dynamic compositional heterogeneity driven by differential ribosomal protein (RP) gene expression, modulating translational control. The exact cellular contributions to ribosomal heterogeneity in human tissues and their biological or clinical significance remain largely unknown. This study addresses these by mapping the expression of 76 cytoplasmic RP genes across 161 cell types from 15 human tissues using single-cell RNA sequencing from the Human Cell Atlas. We reveal extensive tissue- and cell-type-specific RP expression patterns, with RPL23, RPS20, RPS17 and RPL27A showing variability across most tissues. The testis cells exhibited the greatest variability and the largest number of variable RP genes, with distinct RP signatures distinguishing germ and somatic lineages; these signatures form temporally coordinated expression modules throughout spermatogenesis. In the context of disease, a comparative analysis of male infertility patients revealed widespread RP gene dysregulation in testicular cell types, highlighting the importance of proper RP composition for reproductive health. Furthermore, given the link between RP gene mutations and inherited anaemia syndromes, we investigated RP gene expression during erythropoiesis. We observed disrupted RP gene expression in Diamond-Blackfan Anaemia patients, contrasting with stable patterns in normal erythropoiesis. Our findings underscore the underappreciated cellular specificity and dynamic regulation of RP gene expression, strongly implicating ribosome compositional heterogeneity as fundamental to both cellular identity and disease pathogenesis. ### Competing Interest Statement The authors have declared no competing interest. Council of Scientific and Industrial Research, 09/084(0774)/2020-EMR-I Indian Institute of Technology Madras, https://ror.org/03v0r5n49, SB/25-26/0034/BT/IITM/008752, BIO/18-19/304/ALUM/KARH Excelra Knowledge Solutions Private Limited, CR/22-23/0026/BT/EXCE/008752 National Medical Research Council, https://ror.org/04x3cxs03, MOH-000715-00

The germinal center (GC) dark zone (DZ) and light zone represent distinct anatomical regions in lymphoid tissue where B cell proliferation, immunoglobulin diversification, and selection are coordinated. Diffuse large B cell lymphomas (DLBCLs) with DZ-like gene expression profiles exhibit poor outcomes, though the reasons are unclear and are not directly related to proliferation. Physiological DZs exhibit an exclusion of T cells, prompting exploration of whether T cell paucity contributes to DZ-like DLBCL. We used spatial transcriptomic approaches to achieve higher resolution of T cell spatial heterogeneity in the GC and to derive potential pathways that underlie T cell exclusion. We showed that T cell exclusion from the DZ was linked to DNA damage response (DDR) and chromatin compaction molecular features characterizing the spatial DZ signature, and that these programs were independent of activation-induced cytidine deaminase (AID) activity. As ATR is a key regulator of DDR, we tested its role in the T cell inhibitory DZ transcriptional imprint. ATR inhibition reversed not only the DZ transcriptional signature, but also DZ T cell exclusion in DZ-like DLBCL in vitro microfluidic models and in in vivo samples of murine lymphoid tissue. These findings highlight that ATR activity underpins a physiological scenario of immune silencing. ATR inhibition may reverse the immune-silent state and enhance T cell-based immunotherapy in aggressive lymphomas with GC DZ-like characteristics.

This commentary explores the concept and utility of studying oncogene co-expression at single-cell resolution and its clinical and biological implications. We emphasize the importance of scalable methods, mathematically driven quantification models, and artificial intelligence integration to enhance the clinical utility of this approach.

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.

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.

The 16th annual Frontiers in Cancer Science conference convened leading experts to discuss the latest developments in cancer research. Key research themes included mechanisms of treatment resistance and innovative strategies to target resistant cancer cells, metabolic plasticity and its therapeutic vulnerabilities, modulation of the tumor microenvironment to enhance therapeutic efficacy, recent advances in immunotherapies and engineered immune cells, and strategies to overcome tumor immune evasion. The conference also highlighted the development of advanced spatial transcriptomic technologies as a powerful tool to decipher tumor heterogeneity and identify novel therapeutic targets. Additionally, the transformative potential of artificial intelligence and machine learning was explored in optimizing therapy selection, refining prognostication models, and improving patient outcomes, with a focus on advancing personalized, cost-effective cancer care. These collective insights underscore the rapid progress in the field and the potential for translating these discoveries into effective clinical interventions, marking a significant step toward addressing the complexities of cancer biology and enhancing patient care worldwide.

Next-generation sequencing (NGS) is increasingly utilized in oncological practice; however, only a minority of patients benefit from targeted therapy. Developing drug response prediction (DRP) models is important for the "untargetable" majority. Prior DRP models typically use whole-transcriptome and whole-exome sequencing data, which are clinically unavailable. We aim to develop a DRP model toward the repurposing of chemotherapy, requiring only information from clinical-grade NGS (cNGS) panels of restricted gene sets. Data sparsity and limited patient drug response information make this challenging. We firstly show that existing DRPs perform equally with whole-exome versus cNGS (∼300 genes) data. Drug IDentifier (DruID) is then described, a DRP model for restricted gene sets using transfer learning, variant annotations, domain-invariant representation learning, and multi-task learning. DruID outperformed state-of-the-art DRP methods on pan-cancer data and showed robust response classification on two real-world clinical datasets, representing a step toward a clinically applicable DRP tool.

Chimeric antigen receptor T-cell (CAR-T)-mediated therapies have shown promising clinical benefit in patients with refractory or relapsing (R/R) diffuse large B-cell lymphoma (DLBCL). However, CAR-T treatment presents challenges such as lack of drug accessibility, financial barriers, variable physician preference or experience, and risk assessment based on patient-specific characteristics. This article thus aims to provide an overview of the CAR-T landscape for R/R DLBCL in Asia, with a focus on identifying barriers to access, from the perspective of Asian and international lymphoma experts. Presently, existing clinical data indicate that CAR-T therapy is a potentially curative strategy for R/R DLBCL in addition to stem cell transplantation, provided the patient’s disease profile and treatment history have been thoroughly considered. However, longer-term follow-up data from large-scale studies are needed to confirm curative potential and define optimal sequencing of CAR-T in the context of novel emerging treatments, such as bi-specific antibodies, in the management of R/R DLBCL. Consequently, further research into CAR-T would benefit from collaboration between institutions. Furthermore, there is a wide disparity in CAR-T accessibility across regions due to complicated logistics and cost, which represent a significant barrier to patients in Asia. Hence, there is a need to increase representation and engagement across different stakeholders such as policymakers, payers, and the industry to arrive at a consensus on patient selection, establish clear guidelines, and develop strategies to lower CAR-T costs. Ultimately, data can support a multi-stakeholder approach when devising strategies to make CAR-T feasible and sustainable for patients.