The AKT inhibitor capivasertib has demonstrated clinical benefit in combination with the selective ER degrader fulvestrant in PIK3CA, PTEN and AKT-1 altered estrogen receptor positive breast cancer (ER+ BC). A genome-wide CRISPR screen was performed in PI3K-AKT pathway altered ER+ BC cells exploring modifiers of response to capivasertib which identified different resistance and sensitivity drivers. Loss of chromatin regulators including KDM5C and KAT6A increased sensitivity to capivasertib. Genetic knockout or pharmacological inhibition of KDM5C strongly enhanced the anti-proliferative effects of capivasertib monotherapy, as well as in combination with fulvestrant in treatment naïve and endocrine therapy or capivasertib resistant ER+ BC cell lines. RNA-seq and epigenetic profiling revealed that combining capivasertib with KDM5C KO had a modest effect on gene transcription, with some effect on cell cycle related genes and ER signalling. In contrast, combining capivasertib with fulvestrant enhanced the effects of fulvestrant on transcriptional output and promoter occupancy. Rather than influencing gene expression, loss of KDM5C combined with capivasertib increased cell stress, DNA damage, cell cycle arrest and cell death. Collectively the data suggests that chromatin regulators may have different functions following capivasertib treatment, with inhibition having potential to enhance sensitivity to capivasertib in PIK3CA, PTEN and AKT-1 altered ER+ BC cells.

Abstract KMT2D is a histone methyltransferase that regulates enhancer activation by catalyzing H3K4 mono/di-methylation, in part through a cooperative interaction with CBP/p300. Epigenetic regulation is commonly perturbed in cancer due to recurrent genetic alterations. In particular, KMT2D inactivating mutations are the most common genetic alterations in germinal center-derived B cell lymphoma, including ∼30% of diffuse large B cell lymphoma and up to 80% of follicular lymphoma. To identify novel synthetic lethal targets in KMT2D mutant lymphoma, we performed a genome-wide CRISPR knock-out screen in a panel of KMT2D-wt vs -null DLBCL cell lines and revealed the paralog histone methyltransferase KMT2C as a top hit in KMT2D-null cell lines. Interestingly, KMT2C is rarely mutated in KMT2D mutant lymphoma and its protein expression is well-conserved across DLBCL cell lines, suggesting KMT2C may compensate for the loss of KMT2D methyltransferase activity.To validate KMT2C dependency, we knocked out KMT2C in KMT2D proficient or mutant/deficient DLBCL cell lines as well as in KMT2D isogenic knockout models. KMT2C loss leads to G0/G1 cell cycle arrest and apoptotic induction in the KMT2D deficient but not in KMT2D proficient context. To gain mechanistic insights into the preferential sensitivity of KMT2D-null cell lines, we performed RNA-seq in cell lines with varying phenotypic response to KMT2C knockout. In line with the observed cell cycle arrest phenotype, we identified the DREAM (Dimerization partner, RB-like, E2F, and MuvB) complex targets as significantly downregulated upon KMT2C loss. The DREAM complex is a key regulator of cell cycle progression by repressing multiple genes particularly during cell cycle exit. Epigenetic profiling revealed that KMT2C binds to promoters of specific DREAM targets, suggesting a direct regulatory mechanism. Additionally, the protein DYRK1B is known to promote DREAM complex assembly at target promoters. We noticed KMT2C knockout upregulates DYRK1B protein expression only in KMT2D mutant cell lines. Further, overexpression of DYRK1B in KMT2D mutant lines induced anti-proliferative effects, suggesting dual mechanisms by which KMT2C regulates DREAM targets. These results provide mechanistic rationales into how KMT2C loss induces paralog lethality. Lastly, to identify potential combination partners with KMT2C knockout, we performed a combination screen using different small molecules for DLBCL treatment. We observed that inhibitors targeting CBP/p300 and chemotherapy agents like vincristine and doxorubicin combined with KMT2C loss drive deeper responses in KMT2D mutant models. Taken together, our data demonstrate that B cell lymphoma carrying KMT2D mutations are addicted to the residual KMT2C activity and suggest that targeting KMT2C as a monotherapy or in combination may benefit patients with KMT2D loss. Citation Format: Hsiangyu Hu, Neeraj K. Aryal, Tim Nieuwenhuis, Daniel Barrell, Ultan McDermott, Laura B. Prickett, Ming Tang, Derek Oien, Laura Pasqualucci, Anas Younes, Lisa Drew, Omid Tavana. Targeting KMT2C induces paralog synthetic lethality in KMT2D null DLBCL through DREAM targets regulation [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 7060.

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with a million new cases diagnosed globally each year and a 5-year survival rate of less than 20%. The lack of therapeutic options personalized to individual patients leads to high variation in survival. The combination of patient stratification with personalized treatment has the potential to improve outcomes; however, the variation in mutations found in NSCLC adenocarcinoma patients makes experimentally determining treatment combinations time-consuming and expensive. Here, we developed an interpretable mechanistic model to decipher complex signaling interplay and guide personalized therapy in NSCLC adenocarcinoma. This 'virtual tumor' model encompassed key tumor intrinsic oncogenic signaling pathways, for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Diverse genetic profiles were simulated for testing over 10,000 therapeutic strategies to identify optimal approaches to overcome resistance mechanisms specific to genetic profiles and p53 status. The virtual tumor model reproduced drug additivity screens, predicted radio-sensitizing genes validated in a CRISPR screen, and identified 53BP1 as a potential drug target that improved the therapeutic window during radiotherapy. A 19-gene signature derived from the virtual tumor framework stratified patients most likely to benefit from radiotherapy, which was validated using TCGA data. These results demonstrate the utility of virtual tumors to predict effective therapeutic combinations and present a computational resource for large-scale screening of personalized therapies to guide clinical decision-making in NSCLC patients.

ABSTRACT The 10x Genomics Gene Expression Flex protocol allows profiling of fixed or frozen material, greatly simplifying the logistics of sample collection, storage and transfer prior to single -cell sequencing. The method makes single-cell transcriptomics possible for existing fresh-frozen or FFPE tissue samples, but also facilitates the logistics of the sampling process, allowing instant preservation of samples. The technology relies on species-specific probes available for human and mouse. Nevertheless, processing of patient-derived (PDX) or cell line (CDX) xenografts, which contain mixed human and mouse cells, is currently not supported by this protocol due to the high degree of homology between the probe sets. Here we show that it is feasible to simultaneously profile populations containing both human and mouse cells by mixing the transcriptome probe sets of both species. Cellranger outputs a count table for each of the species allowing evaluation of the performance of the different probe sets. Cross-reactive probes are greatly outperformed by the specific probe hybridizations leading to a clear difference in the recovery of UMIs and unique genes per cell. Furthermore, we developed a pipeline that removes cross-reactive signal from the data and provides species-specific count tables for further downstream analysis. Hence, the 10x Genomics Gene Expression Flex protocol can be used to process xenograft samples without the need for separation of human and mouse cells by flow sorting and allows analysis of the human and mouse single-cell transcriptome from each sample. We anticipate it will be increasingly used for single-cell sequencing of cancer cell line and patient-derived xenografts, facilitating the preservation of the samples and allowing the interrogation of both the (human) xenograft and the (mouse) tumor microenvironment at single-cell resolution.

Breast cancer is one of the most common cancers worldwide, and around 80% of breast cancers are oestrogen receptor positive (ER+ BC). For patients with ER+ advanced BC with one or more PIK3CA/AKT1/PTEN tumor alterations, capivasertib (AKT inhibitor) in combination with fulvestrant (selective oestrogen receptor (ER) degrader) is a recommended treatment. An innate PI3K/AKT pathway inhibitor resistance can occur in patients with ER+ BC, limiting their efficacy. Therefore, novel therapeutic strategies are needed to increase sensitivity to treatments in patients with ER+ BC. With this aim, from CRISPR-based sensitization screens in ER+ BC cells, we have found that the effectiveness of the AKT inhibitor capivasertib is improved by targeting the epigenetic regulators, KDM5C (lysine demethylase 5C) or KAT6A (lysine acetyltransferase 6A). Knockout (KO) of KDM5C or KAT6A strongly enhances the response to capivasertib monotherapy and its treatment in combination with fulvestrant in PIK3CA-mut and PTEN-null ER+ BC cells, as well as re-sensitizes capivasertib-resistant ER+ BC cells. Importantly, the effects of the genetic KO were recapitulated with a pan-KDM5 inhibitor and a selective KAT6A/B inhibitor in combination with capivasertib in ER+BC cells, suggesting that combination therapies, consisting of capivasertib with KDM5 or KAT6A inhibitors, could be beneficial in patients. Moreover, transcriptomic analysis and chromatin profiling techniques show that both KDM5C and KAT6A KO downregulate expression and alter chromatic accessibility of a subset of ER-target genes and this phenotype is enhanced in combination with capivasertib, suggesting that targeting KDM5C or KAT6A sensitizes cells to AKTi through a novel epigenetic regulation of ER-signaling. Also, this study reports for the first time a landscape of epigenetic differences upon capivasertib monotherapy treatment and in combination with fulvestrant in ER+BC cells, revealing changes in chromatin accessibility as a consequence of treatment with AKTi and the ER degrader. This suggests that epigenetic remodelling can lead to resistance to capivasertib and fulvestrant therapy in cancer cells. In summary, this findings identified and validated KDM5C and KAT6A as novel epigenetic targets, the inhibition of which enhances efficacy of AKTi in PI3K pathway mutated ER+ breast cancer. Inhibitors of KDM5C and KAT6A may be potential combination partners for capivasertib in ER+ breast cancer. Valentina Cutano, Shanade Dunn, Lorna Hopcroft, Eleanor M. Wigmore, Lambert Montava Garriga, Sungmi Park-Chouinard, Wu Qing, Lee Hyung Joo, Cath Eberlein, Michele Chirichella, Daniel Barrell, Toby Gurran, Omid Tavana, Neeraj Aryal, Jerome T. Mettetal, Huayang Liu, Ho Man Chan, Susan E. Critchlow, Ultan McDermott, Simon T. Barry. The epigenetic regulators KDM5C and KAT6A influence AKT inhibitor response in ER positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3007.

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed globally each year, and a 5-year survival rate of less than 20%. A lack of therapeutic options personalized to individual patient genetics, and the targeted therapies that exist quickly succumbing to resistance, leads to high variation in survival. Patient stratification combined with greater personalisation of therapies have the potential to improve outcomes, however, the wide variation in mutations found in NSCLC adenocarcinoma patients mean that experimentally determining suitable treatment combinations is time-consuming and expensive. Here we present an in silico model encompassing tumour intrinsic key oncogenic signalling pathways, including EGFR, AKT, JAK/STAT and WNT for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Using this model, we simulate diverse genetic profiles and test over 10,000 therapeutic strategies to identify optimal strategies to overcome resistance mechanisms specific to genetic profiles and p53 status. Our in silico model reproduces drug additivity experiments, predicts radio-sensitising genes validated in a CRISPR screen and identifies 53BP1 as a potential drug target that improves the therapeutic window during radiotherapy, as well as potential to use ATM inhibitors to overcome p53 loss-of-function driven radiotherapy resistance. We further use the in silico model to identify a 19-gene signature to stratify patients most likely to benefit from radiotherapy and validated this using TCGA data. These results further demonstrate the utility of in silico mechanistic modelling and present a bespoke computational resource for large-scale screening of personalised therapies applied to NSCLC. ### Competing Interest Statement The authors have declared no competing interest.

In vivo expression of CASP8 and CFLAR (FLIP) modulate response to MEDI3039 in PDX models. A, Change in percent tumor growth from baseline was measured at day 18 in 18 CRC PDX models treated with a fixed dose of MEDI3039. B, Tumor plots of responder, nonresponder, and partial response models. x-axis, days; y-axis, tumor volume. Arrows indicate treatment with MEDI3039. Red line, MEDI039-treated mice; blue line, untreated mice. C, Tumor CASP8 mRNA expression in PDX nonresponders (red) and responders (blue) with MEDI3039 (each circle represents a tumor from a single mouse). D, Comparison of CASP8:CFLAR ratio in MEDI3039 responder (blue) and nonresponder (red) PDX models.

Several chemotherapeutic agents act by increasing DNA damage in cancer cells, triggering cell death. However, there is limited understanding of the extent and long-term consequences of collateral DNA damage in normal tissues. To investigate the impact of chemotherapy on mutation burdens and the cell population structure of normal tissue, we sequenced blood cell genomes from 23 individuals aged 3-80 years who were treated with a range of chemotherapy regimens. Substantial additional somatic mutation loads with characteristic mutational signatures were imposed by some chemotherapeutic agents, but the effects were dependent on the drug and blood cell types. Chemotherapy induced premature changes in the cell population structure of normal blood, similar to those caused by normal aging. The results show the long-term biological consequences of cytotoxic agents to which a substantial fraction of the population is exposed as part of disease management, raising mechanistic questions and highlighting opportunities for the mitigation of adverse effects.

MEDI3039 sensitivity is significantly affected by FLIP(L)–caspase-8 ratio. A, Percentage cell death at 24 hours post MEDI3039 10 pmol/L in a panel of colorectal cell lines. B, Scatter plot of summary correlation analysis of cell death protein expression and percent cell death induced in response to MEDI3039 across panel of colorectal cancer cell lines. C, CASP8/FLIP or CASP8/MCL1 ratio protein expression versus MEDI3039-induced cell death. Each circle indicates a cell line. MEDI3039 AUC for cell lines for different cell types split according to discretized CASP8/FLIP (D) or CASP8/MCL1 (E) ratio (mRNA expression).

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks standardisation. Conversely, deconvolution methods offer an indirect assessment with uncertain accuracy, underscoring the necessity for innovative approaches. SoftCTM is an open-source, multiorgan deep-learning (DL) model for the detection of tumour and non-tumour cells in H&E-stained slides, developed within the Overlapped Cell on Tissue Dataset for Histopathology (OCELOT) Challenge 2023. Here, using three large multicentre colorectal cancer (CRC) cohorts (N = 1,097 patients) with digital pathology and multi-omic data, we compare the utility and accuracy of TPE with SoftCTM versus CP and bioinformatic deconvolution methods (RNA expression, DNA methylation) for downstream molecular analysis, including CNV profiling. SoftCTM showed technical repeatability when applied twice on the same slide (r = 1.0) and excellent correlations in paired H&E slides (r > 0.9). TPEs profiled by SoftCTM correlated highly with RNA expression (r = 0.59) and DNA methylation (r = 0.40), while TPEs by CP showed a lower correlation with RNA expression (r = 0.41) and DNA methylation (r = 0.29). We show that CP and deconvolution methods respectively underestimate and overestimate tumour content compared to SoftCTM, resulting in 6-13% differing CNV calls. In summary, TPE with SoftCTM enables reproducibility, automation, and standardisation at single-cell resolution. SoftCTM estimates (M = 58.9%, SD +/- 16.3%) reconcile the overestimation by molecular data extrapolation (RNA expression: M = 79.2%, SD +/- 10.5, DNA methylation: M = 62.7%, SD +/- 11.8%) and underestimation by CP (M = 35.9%, SD +/- 13.1%), providing a more reliable middle ground. A fully integrated computational pathology solution could therefore be used to improve downstream molecular analyses for research and clinics. (c) 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Genome-wide CRISPR sgRNA libraries have emerged as transformative tools to systematically probe gene function. While these libraries have been iterated over time to be more efficient, their large size limits their use in some applications. Here, we benchmarked publicly available genome-wide single-targeting sgRNA libraries and evaluated dual targeting as a strategy for pooled CRISPR loss-of-function screens. We leveraged this data to design two minimal genome-wide human CRISPR-Cas9 libraries that are 50% smaller than other libraries and that preserve specificity and sensitivity, thus enabling broader deployment at scale.

Silencing of apoptotic pathway genes confers resistance to agonists of the death receptor pathway. Volcano plots of genes enriched or depleted for gRNA when rTRAIL-treated cells were compared with DMSO vehicle control cells following transduction with a genome-wide CRISPR/Cas9 library in MSTO-211H mesothelioma (A) and PC-9 lung adenocarcinoma (B) cells. Each dot represents gene-wise scores for MAGeCK gene level analysis with genes of interest highlighted. The dotted line indicates an FDR of 0.1. x-axis, log2 fold change of mean gRNA reads per genes comparing treatment to DMSO replicates. y-axis, −log10 FDR for genes based upon their negative (pink and red dots) and positive (blue dots) fold change and FDR calculated by the MAGeCK algorithm. C, Confirmation of deletion of target apoptotic genes following transfection with synthetic crRNA in MSTO-211H, NCI-H28, and H2804 cancer cells. D, Six-day viability assay in MSTO-211H cells following deletion of specific apoptotic genes and treatment with either rTRAIL or MEDI3039. y-axis: viability effect relative to control cells. E, Volcano plots as in A and B illustrating results of SAM CRISPR activatory screen in PC-9 cells treated with rTRAIL. F, MSTO-211H isogenic cell lines were screened versus the parental cell line with a concentration range of 60 compounds and viability measured at day 6. The AUC values for each isogenic cell line and the matched parental Cas9 line were subtracted to calculate a ΔAUC value, with high (positive) values indicating increased resistance to that compound in the isogenic lines, and low (negative) values increased sensitivity. x-axis: name of compounds screened. y-axis: ΔAUC values.

Sensitivity of cancer cell lines to MEDI3039. A panel of 758 cancer cell lines was treated for 6 days with a concentration range of the death receptor agonist MEDI3039 and viability measured as AUC. A, Frequency distribution plot illustrating bimodal distribution of MEDI3039 AUC values for solid (green) and hematopoietic (blue) cell lines. B, AUC values with SD error bars for each cell line (circles) in 19 tissue types and individually colored according to high or low CASP8 mRNA expression as defined by the bimodal mid-point. C, Plot of cell line expression of most significant CASP8 microarray probeset ranked on the basis of sensitivity to MEDI3039. D, A two-tailed t test was calculated for 30 tissue types using MEDI3039 AUC values and CASP8 expression, and the P value indicated (E) predictive 9 probeset (7 gene) predictive geneset identified by vSURF random forest analysis of microarray data. F, AUC plot demonstrating ability of 9 probeset predictive geneset to classify solid cell lines as “sensitive” or “resistant.” See Supplementary Fig. S2B for description of cancer type abbreviations used in B and D.

Drug combinations to overcome rTRAIL resistance in isogenic cell lines. A, Isogenic MSTO-211H cell lines were screened against 59 compounds in combination with a fixed dose of MEDI3039 (100 pmol/L). Viability was measured at day 6. For each combination, a ΔAUC was calculated by subtracting the observed from the expected AUC (based upon the activity of the MEDI3039 concentration as a single agent). Values >0.2 are indicative of synergy. x-axis: name of compounds screened. Bid-KO MSTO-211H or Bid-mutant Sup-T1 cells were treated with a concentration range of the IAP inhibitor AZD5582 (B) or LCL161 (C) for 6 days (blue line) or in combination with a fixed concentration of MEDI3039 (green, IC90 values of parental cell line). Indicated is the effect of the fixed concentration of MEDI3039 (red dotted) and the expected (additive) effect of the combination (gray dotted). x-axis: log10 scale concentration range. y-axis: relative viability effect. D, Clonogenic survival assays at day 14 in Cas9 versus crBID MSTO-211H and PC-9 cells treated with the indicated IAP inhibitors AZD and LCL161 as single agents or combined with MEDI3039. AZD, AZD5582. E, Annexin V/PI staining of Cas9 versus BID KO MSTO-211H and PC-9 cells following 24-hour treatment with MEDI3039 ±10 nmol/L AZD5582.

Background It is uncertain which biological features underpin the response of rectal cancer (RC) to radiotherapy. No biomarker is currently in clinical use to select patients for treatment modifications. Methods We identfied two cohorts of patients (total N = 249) with RC treated with neoadjuvant radiotherapy (45Gy/ 25) plus fl uoropyrimidine. This discovery set included 57 cases with pathological complete response (pCR) to chemoradiotherapy (23%). Pre-treatment cancer biopsies were assessed using transcriptome-wide mRNA expression and targeted DNA sequencing for copy number and driver mutations. Biological candidate and machine learning (ML) approaches were used to identify predictors of pCR to radiotherapy independent of tumour stage. Findings were assessed in 107 cases from an independent validation set (GSE87211). Findings Three gene expression sets showed signi fi cant independent associations with pCR: Fibroblast-TGF P Response Signature (F-TBRS) with radioresistance; and cytotoxic lymphocyte (CL) expression signature and consensus molecular subtype CMS1 with radiosensitivity. These associations were replicated in the validation cohort. In parallel, a gradient boosting machine model comprising the expression of 33 genes generated in the discovery cohort showed high performance in GSE87211 with 90% sensitivity, 86% speci fi city. Biological and ML signatures indicated similar mechanisms underlying radiation response, and showed better AUC and p-values than published transcriptomic signatures of radiation response in RC. Interpretation RCs responding completely to chemoradiotherapy (CRT) have biological characteristics of immune response and absence of immune inhibitory TGF P signalling. These tumours may be identi fi ed with a potential biomarker based on a 33 gene expression signature. This could help select patients likely to respond to treatment with a primary radiotherapy approach as for anal cancer. Conversely, those with predicted radio resistance may be candidates for clinical trials evaluating addition of immune-oncology agents and stromal TGF P signalling inhibition. Funding The Strati fi cation in Colorectal Cancer Consortium (S:CORT) was funded by the Medical Research Council and Cancer Research UK (MR/M016587/1).

Supplementary Table 5 shows combinations with broad activity across tumor types

Supplementary Table 9 shows the top 50 combinations (all cancer types) using different % response scoring thresholds

Supplementary Figure 1: Additional overview of screen and quality control. Supplementary Figure 2: Combination benefit in combination-cell line pairs. Supplementary Figure 3: Biomarkers of single agent and combination activity. Supplementary Figure 4: Combination activity of venetoclax plus AZD5991 in AML cell lines. Supplementary Figure 5: Combination activity by cancer type for AZD5991 + AZ3202. Supplementary Figure 6: Venetoclax + selumetinib and AZD5991 + selumetinib activity by cancer type. Supplementary Figure 7: Venetoclax + selumetinib and AZD5991 + selumetinib activity by cancer type. Supplementary Figure 8: Effect of MEK1/2 plus BCL2 inhibition on cell viability in AML cells. Supplementary Figure 9: Effect of MEK1/2 plus MCL1 inhibition on cell viability in AML cells. Supplementary Figure 10: Cancer type selectivity of venetoclax + AZD2811. Supplementary Figure 11: Effect of Aurora kinase B or pan Aurora kinase plus BCL2 inhibition on cell viability in DLBCL cell lines. Supplementary Figure 12: network analysis of AZD2811 plus venetoclax biomarkers and targets, and cancer type selectivity of AZD5991 + AZD5363. Supplementary Figure 13: Further analysis of capivasertib (AZD5363) plus AZD5991 in endometrial cell lines. Supplementary Figure 14: Effect of pan AKT inhibition plus MCL1 inhibition on cell viability in endometrial cell lines. Supplementary Figure 15: Further analysis of capivasertib (AZD5363) plus AZD5991 and other inhibitors in endometrial cell lines. Supplementary Figure 16: Effect of MCL1 genetic knockdown plus pan AKT inhibition on cell viability in endometrial cancer cell lines.

AZD2811 plus venetoclax combination in DLBCL. A, Combo Emax versus HSA in 25 B-cell NHL cell lines including 11 DLBCL cell lines. Cell lines with high combination activity (combo Emax > 0.5 and HSA > 0.1) are in red. B, Growth inhibition and HSA excess matrices in DLBCL cell line WSUDLCL2. C, Western blot analysis for cleaved PARP in WSUDLCL2 cells treated with AZD2811 or venetoclax alone or in combination. D, Matrix plots indicating combination activity (measured by growth inhibition) in WSUDLCL2 cells pretreated with pan caspase inhibitor Q-VD-OPH and exposed to AZD2811 combined with venetoclax for 72 hours. Matrix values represent cell viability normalized to day 0 on the scale of 0 to 200 (value < 100 = percentage of growth inhibition, value > 100 = cell death). E, Tumor growth in WSUDLCL2 xenografts treated with AZD2811 or venetoclax alone or in combination for 46 days (n = 6 per group, * 0.05 < P < 0.01, ** 0.01 < P < 0.001). Data are plotted as mean tumor volume ± SEM. PO, orally; QD, every day; QW, every week.