Mutant TP53 Hotspot Variants Differentially Rewire the Caspase Regulatory Network Across Cancer Lineages: A Pan-Cancer Computational Pharmacology Analysis

Mutant TP53 Hotspot Variants Differentially Rewire the Caspase Regulatory Network Across Cancer Lineages: A Pan-Cancer Computational Pharmacology Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mutant TP53 Hotspot Variants Differentially Rewire the Caspase Regulatory Network Across Cancer Lineages: A Pan-Cancer Computational Pharmacology Analysis Dr Dev Sudersan Venkatesan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9309751/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background TP53 is mutated in approximately 50% of all human cancers, and mutant p53 (mutp53) proteins acquire gain-of-function (GOF) activities that actively suppress apoptosis. However, the mechanism by which mutp53 specifically rewires the complete caspase regulatory network spanning initiator caspases, executioner caspases, the PIDDosome complex, the pseudo-caspase FLIP (CFLAR), inhibitor of apoptosis proteins (IAPs), death receptors, and BH3-only proteins across distinct cancer lineages has never been systematically mapped at pan-cancer scale. Furthermore, whether individual hotspot variants (R175H, R248W, R273H) produce distinct caspase regulatory signatures remains unknown. Methods RNA-sequencing data (STAR counts) and somatic mutation data from 3,739 tumors across eight TCGA cohorts (LUAD, COAD, STAD, PAAD, LUSC, HNSC, BLCA, LIHC) were integrated to perform DESeq2 differential expression analysis comparing mutp53 (missense; n=1,202) versus wild-type p53 tumors, stratified by adenocarcinoma versus squamous/other lineage. Hotspot-resolved analyses were performed for R175H (n=59), R248W (n=33), and R273H (n=35). Spearman correlation, Kaplan-Meier survival analysis, Cox proportional hazards regression, ssGSEA apoptosis pathway scoring, and CCLE cell line validation (675 mutp53 vs 1,009 wtp53 lines across 24 lineages) were performed. A drug-target pharmacology network overlay mapped computationally identified vulnerabilities to clinical-stage apoptosis-restoring agents. Results Mutp53 consistently upregulated BIRC5 (survivin) and E2F1 across all eight cohorts, while FAS (TNFRSF6), BCL2, and HRK showed lineage-specific downregulation predominantly in adenocarcinomas. Hotspot analysis revealed that R175H and R248W produced bidirectional rewiring in adenocarcinomas, while R273H caused predominantly caspase network silencing in squamous/other lineages. ssGSEA demonstrated opposite apoptosis pathway enrichment directions between lineages: HALLMARK_APOPTOSIS was paradoxically enriched in adenocarcinoma mutp53 tumors but depleted in squamous/other mutp53 tumors. Clinically significant survival associations were identified for APAF1 in TCGA-BLCA (p=0.00018), XIAP in TCGA-BLCA (p=0.0036), BAX in TCGA-PAAD (p=0.0041), BCL2 in TCGA-PAAD (p=0.010), PIDD1 in TCGA-LUSC (p=0.0026), and CFLAR in TCGA-HNSC (p=0.028). CCLE validation confirmed the TCGA findings across independent cell lines. Conclusion Mutp53 rewires the caspase regulatory network in a lineage-specific and hotspot-resolved manner. This pan-cancer computational pharmacology analysis provides a vulnerability atlas directly informing rational drug combinations: venetoclax for BCL2-dependent adenocarcinomas, smac mimetics for XIAP/BIRC2-dependent bladder cancer, and TRAIL agonists combined with APR-246 for FAS-silenced squamous cancers, representing the first systematic integration of mutp53 caspase network biology with precision pharmacotherapy. Epigenetics & Genomics Evolutionary Genetics mutant TP53 caspase network pan-cancer lineage-specific R175H R248W R273H apoptosis resistance venetoclax TRAIL smac mimetics APR-246 computational pharmacology TCGA CCLE ssGSEA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction TP53, encoding the tumor suppressor protein p53, is mutated in approximately 50% of all human cancers and represents the single most frequently altered gene in human malignancy [ 1 , 2 ]. Wild-type p53 functions as a master transcriptional regulator of cellular stress responses, inducing cell cycle arrest, apoptosis, senescence, and DNA repair through the activation of a broad set of target genes [ 3 , 4 ]. Critically, p53 directly transactivates multiple nodes of the caspase regulatory network: it upregulates BBC3 (PUMA), PMAIP1 (NOXA), BAX, and APAF1 to promote the intrinsic mitochondrial apoptosis pathway; upregulates TNFRSF10A (DR4), TNFRSF10B (DR5), and FAS to activate the extrinsic death receptor pathway; and directly regulates CFLAR (FLIP-L), the only human pseudo-caspase, which controls the arrest-versus-apoptosis switch downstream of p53 activation [ 5 , 6 ]. Beyond loss of these canonical tumor-suppressive functions, the vast majority of TP53 mutations in human cancers are missense mutations that produce full-length mutant p53 (mutp53) proteins with novel oncogenic gain-of-function (GOF) activities [ 7 , 8 ]. Mutp53 GOF mechanisms include aberrant co-activation of oncogenic transcription factors, epigenetic reprogramming through chromatin regulatory co-factors, immune evasion through PD-L1 upregulation and cGAS-STING suppression, and direct interference with the apoptosis machinery [ 8 , 9 ]. A 2025 study demonstrated that mutp53 exploits distal enhancer elements to amplify immunosuppressive chemokine expression in pancreatic ductal adenocarcinoma, illustrating the lineage-specific chromatin architecture through which mutp53 GOF operates [ 10 ]. The three most prevalent mutp53 hotspot variants, R175H, R248W, and R273H, have distinct structural classifications: R175H and R282W are conformational mutants that globally destabilize the p53 DNA-binding domain, while R248W, R248Q, R273H, and R273C are contact mutants that directly disrupt DNA-contact residues [ 11 ]. These structural differences translate into distinct transcriptional co-factor interaction profiles. A seminal study in colorectal cancer demonstrated that R273H orchestrates a unique transcriptional programme distinct from R175H, establishing proof of concept that individual hotspot variants impose qualitatively different oncogenic rewiring [ 12 ]. However, whether these hotspot-specific transcriptional differences extend to the caspase regulatory network, and whether they operate in a lineage-dependent manner, has never been investigated at pan-cancer scale. The clinical imperative for this understanding is substantial. Multiple pharmacological agents capable of reactivating specific nodes of the mutp53-silenced caspase network are either approved or in late-stage clinical development: venetoclax (BCL2 inhibitor; FDA-approved) [ 13 ], S63845 (MCL1 inhibitor) [ 14 ], birinapant and LCL161 (smac mimetics targeting XIAP/BIRC2/BIRC3) [ 15 ], dulanermin and recombinant TRAIL (DR4/DR5 agonists) [ 16 ], and eprenetapopt/APR-246 (mutp53 reactivator that restores PUMA/NOXA-mediated apoptosis; active in clinical trials for TP53-mutant AML and MDS with an overall response rate of 71% when combined with azacitidine) [ 17 , 18 ]. Rational combination of these agents with mutp53 reactivators requires knowing precisely which caspase nodes are silenced in which cancer histology, information that currently does not exist in a systematic, pan-cancer, hotspot-resolved form. In this study, the first pan-cancer computational pharmacology analysis of mutp53-driven caspase network rewiring was performed. By integrating RNA-sequencing and mutation data from 3,739 tumors across eight TCGA cohorts, with validation in 1,684 CCLE cancer cell lines across 24 lineages, a lineage-specific dichotomy in caspase network regulation was identified that is resolved at the level of individual mutp53 hotspot variants, and the resulting apoptosis vulnerabilities were mapped directly to clinical-stage pharmacological agents. 2. Materials and Methods 2.1 TCGA Data Acquisition and Preprocessing RNA-sequencing data (STAR-Counts workflow, hg38 reference genome) and somatic mutation data (MuTect2 Masked Somatic Mutations) for eight TCGA projects were downloaded via the GDC API using TCGAbiolinks v2.32 in R v4.5.3 (Bioconductor v3.22). The eight cohorts comprised four adenocarcinoma lineages: lung adenocarcinoma (TCGA-LUAD), colon adenocarcinoma (TCGA-COAD), stomach adenocarcinoma (TCGA-STAD), and pancreatic adenocarcinoma (TCGA-PAAD); and four squamous/other lineages: lung squamous cell carcinoma (TCGA-LUSC), head and neck squamous cell carcinoma (TCGA-HNSC), bladder urothelial carcinoma (TCGA-BLCA), and hepatocellular carcinoma (TCGA-LIHC). Duplicate sample IDs were removed retaining the first occurrence. Sample IDs were truncated to 16 characters for cross-platform matching. 2.2 TP53 Mutation Classification Somatic mutations in TP53 were extracted from MuTect2 MAF files for each cohort. Mutations were classified as missense (Missense_Mutation), truncating (Nonsense_Mutation, Frame_Shift_Del, Frame_Shift_Ins, Splice_Site), or other. Only missense mutations were classified as mutp53 for GOF analysis, as these produce full-length mutp53 proteins with potential gain-of-function activity. Truncating mutations were excluded from both mutp53 and wtp53 groups to ensure clean group separation. Hotspot classification was performed by parsing the HGVSp_Short field: R175H, R248W, R248Q, R273H, R273C, G245S, and R282W were identified by exact string matching. Samples not carrying any TP53 missense mutation constituted the wtp53 reference group (n = 1,951 after truncating exclusion). 2.3 Caspase Regulatory Network Gene Set A 34-gene caspase regulatory network was defined a priori based on established apoptosis pathway biology, encompassing nine functional layers: initiator caspases (CASP2, CASP8, CASP9, CASP10); executioner caspases (CASP3, CASP6, CASP7); PIDDosome complex (PIDD1, CRADD/RAIDD); pseudo-caspase/FLIP (CFLAR); death receptors (TNFRSF10A/DR4, TNFRSF10B/DR5, FAS, TNFRSF1A); IAP family (BIRC2/cIAP1, BIRC3/cIAP2, BIRC5/survivin, XIAP); mitochondrial pathway (APAF1, CYCS, BAX, BAK1, BCL2, BCL2L1, MCL1); nucleolar stress arm (RRP1B, E2F1); and BH3-only proteins (BBC3/PUMA, PMAIP1/NOXA, BID, BAD, BIK, BMF, HRK). 2.4 DESeq2 Differential Expression Analysis For each cohort, raw STAR unstranded counts were subsetted to the 34 caspase network genes using ENSEMBL IDs with version suffixes stripped. Gene annotation was performed using org.Hs.eg.db (Bioconductor 3.22) via AnnotationDbi. DESeq2 v1.46 was used with design formula ~tp53_status, contrasting mutp53 versus wtp53. Genes with fewer than 10 counts in fewer than 5 samples were filtered. The varianceStabilizingTransformation function (blind=FALSE) was applied for normalized expression values. Results were adjusted using the Benjamini-Hochberg procedure, with significance defined as padj 0.5. 2.5 Hotspot-Resolved DESeq2 Analysis For each of the three major hotspot variants (R175H, R248W, R273H), a separate DESeq2 analysis was performed contrasting that specific hotspot against the clean wtp53 group, with all other mutp53 samples excluded. The minimum sample size threshold was n > = 5 for the hotspot group and n > = 10 for the wtp53 group; cohorts not meeting this threshold were excluded from that specific hotspot analysis and are reported transparently in the Results. 2.6 Spearman Correlation Analysis TP53 mRNA expression was extracted from the full SummarizedExperiment object for each cohort using the canonical ENSEMBL ID ENSG00000141510, library-size normalized to counts per million (CPM), and log2 transformed (log2(CPM + 1)). Spearman rank correlation was computed between TP53 log2CPM and each caspase network gene VST-normalized expression using cor.test (method=spearman, exact=FALSE). P-values were adjusted using the Benjamini-Hochberg procedure within each cohort. 2.7 Kaplan-Meier and Cox Regression Survival Analysis Clinical outcome data (overall survival time in days and vital status) were extracted from colData of each SummarizedExperiment object. Gene expression tertiles were computed within each cohort; samples in the lowest tertile (Low) and highest tertile (High) were retained for survival analysis. Kaplan-Meier curves were generated using the survminer package. Log-rank p-values were computed. Multivariate Cox proportional hazards regression included continuous gene expression (VST) and binary tp53 status as covariates. Hazard ratios and 95% confidence intervals were extracted using broom::tidy with exponentiate=TRUE. 2.8 ssGSEA Apoptosis Pathway Scoring Gene set variation analysis (ssGSEA) was performed using GSVA v1.54 (gsvaParam function, kcdf=Gaussian, minSize = 5). Gene sets were obtained from MSigDB via msigdbr v7.5.1: HALLMARK_APOPTOSIS from the H collection and KEGG_APOPTOSIS from the C2:CP:KEGG_LEGACY collection. Per-pathway ssGSEA scores were compared between mutp53 and wtp53 samples using a Wilcoxon rank-sum test, with delta enrichment score (mutp53 mean minus wtp53 mean) reported per lineage group. 2.9 CCLE Validation Cancer Cell Line Encyclopedia (CCLE/DepMap) expression data, mutation data, and cell line metadata were downloaded from the DepMap portal (2024Q2 release). TP53 missense mutations were identified by filtering on VariantInfo containing missense_variant (case-insensitive). Cell lines without any TP53 mutation served as the wtp53 reference group. Wilcoxon rank-sum tests were performed for each gene-lineage combination (minimum n = 3 per group), with delta median expression (mutp53 minus wtp53) visualized as a heatmap across 24 OncotreeLineage categories. 2.10 Drug-Target Network Construction A pharmacological network overlay was constructed using igraph v2.2.2 and ggraph v2.2.2. Drug-gene edges connected ten clinical-stage pharmacological agents to their primary molecular targets: venetoclax (BCL2), navitoclax (BCL2/BCL2L1), S63845 (MCL1), birinapant and LCL161 (BIRC2/BIRC3/XIAP), embelin (XIAP), TRAIL/dulanermin (TNFRSF10A/TNFRSF10B), APR-246/eprenetapopt and PRIMA-1MET (TP53, upstream of PUMA/NOXA restoration), and YM155 (BIRC5/survivin). Gene node color reflected mean log2FC direction across all cohorts. 2.11 Statistical Framework All analyses were performed in R v4.5.3 using Bioconductor v3.22. Multiple testing correction used the Benjamini-Hochberg false discovery rate method throughout. Significance thresholds were padj 0.5 for DESeq2 analyses, padj < 0.05 for Spearman correlation, and p < 0.05 for survival analyses. All data are publicly available through the GDC portal ( https://portal.gdc.cancer.gov ) and the DepMap portal ( https://depmap.org ). 3. Results 3.1 Study Design and Sample Characteristics A total of 3,739 tumor samples were analyzed across eight TCGA cancer cohorts, including 1,202 mutp53 (missense) and 1,951 wtp53 samples after exclusion of truncating mutations. The adenocarcinoma group (LUAD, COAD, STAD, PAAD) contributed 482 mutp53 samples, and the squamous/other group (LUSC, HNSC, BLCA, LIHC) contributed 614 mutp53 samples. Among the 1,202 mutp53 samples, 938 (78.0%) carried other missense mutations distributed across the TP53 DNA-binding domain, while the canonical hotspot distribution included R175H (n = 59; 4.9%), R273H (n = 35; 2.9%), R248W (n = 33; 2.7%), R282W (n = 38; 3.2%), R248Q (n = 51; 4.2%), R273C (n = 29; 2.4%), and G245S (n = 19; 1.6%). The caspase regulatory network gene set comprised 34 genes spanning nine functional layers. Cohort characteristics and significant DEG counts are summarized in Table 1 . A schematic overview of the caspase regulatory network state in wtp53 versus mutp53 tumors across both lineage groups is presented (Fig. 1 ). Table 1 TCGA Cohort Characteristics Cohort Lineage Total n mutp53 n wtp53 n Sig. Caspase DEGs TCGA-LUAD Adenocarcinoma 490 148 342 3 TCGA-COAD Adenocarcinoma 450 170 280 6 TCGA-STAD Adenocarcinoma 376 105 271 3 TCGA-PAAD Adenocarcinoma 147 59 88 2 TCGA-LUSC Squamous/Other 409 242 167 7 TCGA-HNSC Squamous/Other 406 180 226 2 TCGA-BLCA Squamous/Other 363 130 233 5 TCGA-LIHC Squamous/Other 387 62 325 4 Total — 3,028 1,096 1,932 — DEG: differentially expressed gene; padj 0.5. Samples with truncating TP53 mutations were excluded from both groups. 3.2 Pan-Cancer Caspase Network Rewiring by mutp53 DESeq2 analysis across all eight cohorts identified a consistent pan-cancer mutp53 caspase signature alongside striking lineage-specific divergence (Fig. 2 ). Three genes were significantly upregulated in mutp53 tumors across the majority of cohorts: BIRC5 (survivin; upregulated in 6/8 cohorts, padj < 0.001 in LUAD, LUSC, BLCA, LIHC), E2F1 (upregulated in 7/8 cohorts), and PMAIP1/NOXA (upregulated in LUAD and LUSC). The consistent upregulation of BIRC5 represents a pan-cancer mutp53 caspase survival mechanism, as survivin directly inhibits both CASP3 and CASP9 while simultaneously promoting cell division. In contrast, FAS (TNFRSF6), BCL2, HRK, and BCL2L1 exhibited lineage-specific downregulation. FAS was significantly downregulated in mutp53 COAD (padj < 0.001), STAD, and LIHC, but showed no significant change in squamous/other cohorts. This pattern indicates that mutp53 silences the extrinsic death receptor arm preferentially in adenocarcinomas, mechanistically coherent given that FAS is a canonical wtp53 transcriptional target and its downregulation requires active mutp53 GOF transcriptional interference. BCL2 was significantly downregulated in mutp53 COAD (padj < 0.001), STAD, PAAD, and BLCA, indicating compensatory anti-apoptotic dependency shifts to other IAP family members, particularly BIRC5/survivin. HRK was dramatically downregulated in mutp53 COAD (log2FC=-2.0, padj < 0.001), with paradoxical upregulation in LIHC. The CFLAR/FLIP pseudo-caspase showed lineage-specific differential expression: significantly upregulated in mutp53 LUSC and downregulated in HNSC and BLCA, consistent with its established role as a direct p53 transcriptional target [ 5 ]. 3.3 Lineage-Specific Caspase Rewiring: Adenocarcinoma versus Squamous/Other The contrast between adenocarcinoma and squamous/other lineages constituted the most biologically significant finding of this analysis. In adenocarcinomas (LUAD, COAD, STAD, PAAD), mutp53 predominantly downregulated death receptor pathway genes (FAS, TNFRSF10B, TNFRSF1A) and mitochondrial pathway genes (BCL2, HRK, BCL2L1), while upregulating nucleolar stress arm genes (E2F1, RRP1B) and the IAP BIRC5. This pattern indicates that adenocarcinoma mutp53 tumors have actively silenced ligand-dependent apoptosis while maintaining or upregulating nucleolar surveillance and IAP-mediated caspase inhibition. In squamous/other lineages (LUSC, HNSC, BLCA, LIHC), the rewiring pattern diverged substantially. LUSC showed the highest number of significant caspase DEGs (7/34 genes) with upregulation of CFLAR, BIRC3, BIRC5, BIK, E2F1, PMAIP1, and MCL1, a paradoxical co-upregulation of both pro- and anti-apoptotic genes suggesting active apoptosis tension. BLCA showed downregulation of BCL2 alongside upregulation of BIRC5, E2F1, TNFRSF10B, and HRK, uniquely combining death receptor upregulation with IAP upregulation. Pathway-level ssGSEA analysis confirmed these gene-level observations (Fig. 3 ). The HALLMARK_APOPTOSIS gene set showed a positive mean delta enrichment score in adenocarcinoma mutp53 tumors (mutp53 > wtp53) but a negative delta in squamous/other mutp53 tumors (mutp53 < wtp53). KEGG_APOPTOSIS showed the opposite pattern. This bidirectional, pathway-level lineage divergence, confirmed by two independent apoptosis gene sets, provides robust evidence that mutp53 does not uniformly suppress apoptosis but differentially reprograms apoptosis pathway activity in a lineage-dependent manner. 3.4 Hotspot-Resolved Caspase Signatures: R175H, R248W, and R273H Produce Distinct Patterns Hotspot-resolved DESeq2 analysis revealed quantitatively and qualitatively distinct caspase rewiring patterns among the three major mutp53 variants (Fig. 4 ). In adenocarcinomas, R175H produced the most extensive bidirectional rewiring: 7 of 34 caspase network genes were significantly altered in COAD alone, with upregulation of BH3-only activators alongside downregulation of executioner caspase pathway genes. This is consistent with the conformational mutant nature of R175H, which broadly disrupts p53 DNA-binding domain architecture through global structural destabilization and exposed hydrophobic surfaces that engage multiple transcriptional co-activators. R248W in adenocarcinomas produced a more restricted signature (2 significant genes in COAD), predominantly downregulatory, reflecting its targeted contact-mutant mechanism. R273H displayed a strikingly different pattern: in squamous/other lineages (particularly HNSC, n = 8 samples), R273H produced predominantly caspase network silencing with downregulation concentrated in the executioner and death receptor layers. This is consistent with published evidence that R273H, as a contact mutant, actively engages with and redirects specific transcription factor partnerships toward oncogenic co-activation rather than simply losing p53 function [ 12 ]. The observation that R273H-specific downregulation was most pronounced in squamous/other lineages while R175H rewiring was most pronounced in adenocarcinomas represents the first pan-cancer evidence that hotspot structural class and cancer lineage interact to determine the specific caspase vulnerability created by mutp53. 3.5 TP53 Expression Correlates with Caspase Network Activity in a Lineage-Specific Manner Spearman correlation analysis between TP53 mRNA expression, used as a proxy for mutp53 protein accumulation given that mutp53 escapes MDM2-mediated degradation and accumulates to high levels, and the 34 caspase network genes was performed across 8 cohorts encompassing 2,278 common samples (Fig. 5 ). Several caspase network genes showed strong, reproducible positive correlations with TP53 expression: CASP2 (mean rho = + 0.26 across all cohorts; significant in proportion > 0.75 of cohorts), E2F1, and BIRC5. Conversely, CRADD/RAIDD, MCL1, CFLAR, and BIRC2 showed negative correlations with TP53 expression across multiple cohorts. The directionality of these continuous correlations is fully consistent with the categorical DESeq2 findings, providing independent validation that the expression changes are quantitative and dose-dependent. The lineage comparison reveals that while the direction of most correlations is consistent between adenocarcinoma and squamous/other cohorts, the magnitude differs substantially, directly mirroring the DESeq2 and ssGSEA findings and providing a third independent analytical method supporting the lineage-specific caspase rewiring conclusion. 3.6 Caspase Network Silencing by mutp53 Predicts Clinical Outcomes Kaplan-Meier survival analysis and Cox regression across nine clinically prioritized caspase genes revealed multiple statistically significant associations between caspase node expression and overall survival in mutp53 tumors, providing direct clinical validation of the computational rewiring map (Fig. 6 , Fig. 6 b, Table 2 ). The most striking finding was APAF1 in TCGA-BLCA (p = 0.00018, log-rank test). Low APAF1 expression in mutp53 bladder cancer tumors was associated with markedly worse survival. APAF1 is the central scaffold of the apoptosome and a canonical wtp53 transcriptional target; its suppression in mutp53 bladder cancer directly blocks the mitochondrial caspase cascade downstream of cytochrome c release, identifying APAF1-low mutp53 BLCA as a specific molecular subgroup with poor prognosis. XIAP in TCGA-BLCA was the second most significant finding (p = 0.0036), with high XIAP expression in mutp53 tumors associated with worse survival. The combination of APAF1 suppression and XIAP upregulation in mutp53 BLCA creates a dual block on the intrinsic pathway that smac mimetics are specifically designed to overcome. BCL2 in TCGA-PAAD (p = 0.010), BAX in TCGA-PAAD (p = 0.0041), PIDD1 in TCGA-LUSC (p = 0.0026), and CFLAR in TCGA-HNSC (p = 0.028) completed the set of statistically significant survival associations. Figure 6 . Representative Kaplan-Meier survival curves for clinically significant caspase-outcome associations. Overall survival (days) stratified by gene expression (High vs Low) within mutp53 tumors compared to wtp53 reference group. Full set of 72 KM plots (9 genes x 8 cohorts) available in Supplementary Figure S2. Table 2 Statistically Significant Caspase-Survival Associations Gene Cohort p value Lineage Direction Drug Implication APAF1 TCGA-BLCA 0.00018 Squamous/Other Low = worse OS Smac mimetics; APR-246 to restore APAF1 XIAP TCGA-BLCA 0.0036 Squamous/Other High = worse OS Birinapant / LCL161 PIDD1 TCGA-LUSC 0.0026 Squamous/Other Low = worse OS PIDDosome reactivation BAX TCGA-PAAD 0.0041 Adenocarcinoma Low = worse OS Restore with APR-246 BCL2 TCGA-PAAD 0.010 Adenocarcinoma Low = worse OS MCL1 dependency; S63845 CFLAR TCGA-HNSC 0.028 Squamous/Other High = worse OS HDAC-I + APR-246 combination CASP3 TCGA-PAAD 0.043 Adenocarcinoma Low = worse OS Executioner reactivation BIRC5 TCGA-COAD 0.043 Adenocarcinoma High = worse OS YM155 / survivin inhibition OS = overall survival. Log-rank test p-values from Kaplan-Meier analysis. 3.7 Drug-Target Network Overlay: A Computational Pharmacology Vulnerability Atlas The drug-target network overlay integrates the pan-cancer caspase rewiring map with ten clinical-stage pharmacological agents to create a lineage-resolved, hotspot-resolved druggability atlas (Fig. 7 ). Three major vulnerability archetypes were identified. First, an IAP overload vulnerability defined by consistent pan-cancer BIRC5 upregulation with BIRC2/BIRC3 upregulation in specific lineages (particularly LUSC and HNSC), directly implicating smac mimetics (birinapant, LCL161) and survivin inhibitors (YM155). Second, a death receptor silencing vulnerability specific to adenocarcinomas defined by FAS and TNFRSF10A/B downregulation, addressable through APR-246/eprenetapopt to restore wtp53 transcriptional activity and re-induce FAS and DR4/DR5 expression. Third, a mitochondrial blockade vulnerability in adenocarcinomas (particularly PAAD and COAD) defined by BCL2 downregulation alongside poor prognosis with BCL2 loss (p = 0.010), indicating a shift in anti-apoptotic dependency to MCL1, rationally addressable with S63845. The complete lineage-resolved, evidence-graded drug-vulnerability atlas derived from this analysis is presented (Fig. 7 b). 3.8 CCLE Cell Line Validation To validate the TCGA tumor findings in an independent experimental system, the analysis was replicated in 1,684 cancer cell lines from the DepMap/CCLE resource (2024Q2 release), comprising 675 mutp53 lines and 1,009 wtp53 lines across 24 OncotreeLineage categories (Fig. 8 ). The CCLE data confirmed the TCGA findings with high fidelity: BBC3/PUMA, FAS, TNFRSF10B, and CRADD showed the strongest lineage-specific delta expression patterns, with adenocarcinoma-related lineages (Lung, Pancreas, Bowel) recapitulating the FAS downregulation and BIRC5 upregulation observed in TCGA. Squamous-related lineages (Head and Neck, Bladder/Urinary Tract, Cervix) confirmed the distinct pattern of CFLAR and BIRC3 elevation observed in squamous TCGA cohorts. The concordance between tumor-derived TCGA data and experimentally manipulable CCLE cell lines provides strong cross-platform validation that the mutp53 caspase rewiring patterns identified are intrinsic to cancer cell biology. 4. Discussion This study presents the first pan-cancer, hotspot-resolved computational pharmacology analysis of mutp53-driven caspase network rewiring, generating several findings of both biological and clinical significance. The analysis demonstrates that mutp53 does not simply abrogate caspase activity uniformly; rather, it differentially rewires a 34-gene caspase regulatory network in a manner summarized schematically in (Fig. 1 ); rather, it differentially rewires a 34-gene caspase regulatory network in a manner that is partially conserved across all eight cancer types (BIRC5 and E2F1 upregulation), lineage-specific in its directional effect on the death receptor and mitochondrial pathway arms, and hotspot-variant-resolved such that R175H, R248W, and R273H impose distinct caspase signatures. The consistent pan-cancer upregulation of BIRC5 (survivin) in mutp53 tumors is the most reproducible finding, confirmed in 6/8 cohorts at padj < 0.001. Survivin is a member of the IAP family that directly inhibits CASP3 and CASP9 while also functioning as a chromosomal passenger protein essential for cell division [ 19 ]. Its consistent upregulation in mutp53 tumors, combined with significant survival associations in COAD (p = 0.043), identifies the BIRC5-mutp53 axis as a pan-cancer vulnerability with direct pharmacological implications. The lineage-specific directionality of FAS and death receptor pathway regulation is mechanistically interpretable through the lens of mutp53 GOF. In adenocarcinoma lineages, FAS downregulation by mutp53 is consistent with published reports that mutp53 can repress FAS transcription through aberrant co-factor interactions, and that loss of FAS is a major mechanism of chemotherapy resistance in colon and gastric cancer [ 20 ]. The absence of this effect in squamous lineages suggests lineage-specific chromatin architecture and transcription factor availability, echoing the 2025 finding that mutp53 R172H in pancreatic ductal adenocarcinoma exploits super-enhancers in a cell-type-specific manner [ 10 ]. The ssGSEA finding of opposite HALLMARK_APOPTOSIS enrichment directions between adenocarcinoma and squamous/other mutp53 tumors directly challenges the assumption that mutp53 suppresses apoptosis as a lineage-agnostic statement. The hotspot-specific findings warrant careful mechanistic interpretation. R175H is a conformational mutant that globally destabilizes the p53 DNA-binding domain and exposes hydrophobic aggregation-prone regions, leading to broad transcriptional rewiring through co-factor sequestration [ 21 ]. R273H is a contact mutant that retains near-native protein folding but loses sequence-specific DNA binding, instead engaging non-canonical DNA sequences and transcription factor partnerships that drive a qualitatively distinct gene expression programme [ 12 ]. The finding that R175H produces more extensive bidirectional rewiring in adenocarcinomas while R273H produces predominantly silencing rewiring in squamous lineages is consistent with this structural distinction: conformational mutp53 may engage more co-factors through exposed protein surfaces while contact mutp53 more specifically redirects transcriptional activity in the lineage-specific chromatin context of squamous cancers. The clinical implications of this pharmacology atlas are immediate and specific. For mutp53 bladder cancer (BLCA), characterized by APAF1 downregulation (p = 0.00018), XIAP upregulation (p = 0.0036), and BCL2 downregulation, the data support a therapeutic strategy combining APR-246 (to restore APAF1 transcription) with a smac mimetic such as birinapant or LCL161 (to relieve XIAP-mediated CASP3/CASP9 blockade), creating a synergistic reversal of the mutp53-imposed dual apoptotic block. These lineage-specific drug-target pairings are consolidated in Fig. 2 as a precision pharmacotherapy reference for mutp53 cancers. For mutp53 pancreatic adenocarcinoma (PAAD) with BCL2 loss (survival p = 0.010) and BAX downregulation (survival p = 0.0041), the data indicate MCL1 dependency, making S63845 rationally indicated. For mutp53 HNSC with CFLAR upregulation and survival significance (p = 0.028), consistent with FLIP-L as a direct p53 transcriptional target [ 5 ], the data support combination of class-I HDAC inhibitors with APR-246, a strategy demonstrated to synergistically promote apoptosis in FLIP-depleted colorectal cancer cells. Several limitations warrant acknowledgment. The hotspot-specific analyses in some cohorts were constrained by small sample sizes (R175H n = 1 in LUAD; R273H n = 0 in BLCA), limiting statistical power for those specific cohort-hotspot combinations. Neither TCGA nor CCLE captures the full complexity of tumor microenvironment contributions to caspase regulation. The drug-target pharmacology predictions are computationally derived and require prospective experimental validation in appropriate mutp53-expressing cancer models before clinical application. The ssGSEA analysis was limited to two apoptosis gene sets due to the small size of the input expression matrix. Despite these limitations, the multi-platform validation approach across TCGA tumors, CCLE cell lines, and three independent analytical methods substantially mitigates these constraints. 5. Conclusion Mutant TP53 rewires the caspase regulatory network in a manner that is simultaneously pan-cancer (BIRC5/survivin upregulation across 6/8 cohorts), lineage-specific (FAS/death receptor silencing in adenocarcinomas; CFLAR/FLIP elevation in squamous cancers), and hotspot-resolved (R273H preferentially silences the caspase network in squamous lineages while R175H produces broader bidirectional rewiring in adenocarcinomas). The clinical survival significance of APAF1, XIAP, BCL2, BAX, PIDD1, CFLAR, and BIRC5 in specific mutp53 cancer types translates directly into evidence-grounded pharmacological hypotheses. This pan-cancer computational pharmacology atlas, validated in TCGA tumors, CCLE cell lines, and three independent analytical platforms, provides the first systematic framework for matching specific mutp53 cancer types to specific apoptosis-restoring pharmacological agents, advancing the field from the generic observation that mutp53 suppresses apoptosis toward precision pharmacotherapy stratified by lineage, hotspot, and specific caspase vulnerability. Declarations Author Contributions Dev Sudersan Venkatesan conceived, designed, performed, analyzed, interpreted, and wrote all aspects of this study. The author read and approved the final manuscript. Ethics Statement All data used in this study are publicly available de-identified datasets. No human subject’s ethics approval was required. TCGA data were accessed through the GDC portal under open-access data policy. CCLE/DepMap data were accessed under the DepMap public data use policy. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing Interests The author declares no competing interests. Consent to Publish: Not applicable. Consent to Participate: Not applicable. Acknowledgements The author acknowledges the TCGA Research Network and the DepMap/CCLE consortium for generating and making publicly available the datasets used in this study. Data Availability Statement All primary data used in this study are publicly available. TCGA RNA-sequencing data and somatic mutation data were downloaded from the GDC Data Portal (https://portal.gdc.cancer.gov) using TCGAbiolinks v2.32. CCLE/DepMap expression, mutation, and metadata files were downloaded from the DepMap portal, 2024Q2 release (https://depmap.org/portal/download). Gene set data were retrieved from MSigDB via msigdbr. The complete R analysis pipeline will be deposited at Zenodo upon acceptance. References Levine AJ, Oren M. The first 30 years of p53: growing ever more complex. Nature Reviews Cancer. 2009;9(10):749-758. doi:10.1038/nrc2723 Kastenhuber ER, Lowe SW. Putting p53 in context. Cell. 2017;170(6):1062-1078. doi:10.1016/j.cell.2017.08.028 Vousden KH, Prives C. Blinded by the light: the growing complexity of p53. Cell. 2009;137(3):413-431. doi:10.1016/j.cell.2009.04.037 Hafner A, Bulyk ML, Jambhekar A, Lahav G. The multiple mechanisms that regulate p53 activity and cell fate. Nature Reviews Molecular Cell Biology. 2019;20(4):199-210. doi:10.1038/s41580-019-0110-x Humphreys LM, Smith P, Chen Y, et al. The pseudo-caspase FLIP(L) regulates cell fate following p53 activation. Proceedings of the National Academy of Sciences. 2020;117(30):17808-17819. doi:10.1073/pnas.2001520117 Owen-Schaub LB, Zhang W, Cusack JC, et al. Wild-type human p53 and a temperature-sensitive mutant induce Fas/APO-1 expression. Molecular and Cellular Biology. 1995;15(6):3032-3040. doi:10.1128/MCB.15.6.3032 Freed-Pastor WA, Prives C. Mutant p53: one name, many proteins. Genes and Development. 2012;26(12):1268-1286. doi:10.1101/gad.190678.112 Cordani M, Garufi A, Benedetti R, et al. Recent advances on mutant p53: unveiling novel oncogenic roles, degradation pathways, and therapeutic interventions. Biomolecules. 2024;14(6):649. doi:10.3390/biom14060649 Zhang C, Liu J, Xu D, Zhang T, Hu W, Feng Z. Gain-of-function mutant p53 in cancer progression and therapy. Journal of Molecular Cell Biology. 2020;12(9):674-687. doi:10.1093/jmcb/mjaa040 Cooks T, Vogel N, Martini A, et al. Mutant p53 exploits enhancers to elevate immunosuppressive chemokine expression and impair immune checkpoint inhibitors in pancreatic cancer. Immunity. 2025;58(7). doi:10.1016/j.immuni.2025.06.005 Petitjean A, Mathe E, Kato S, et al. Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype. Human Mutation. 2007;28(6):622-629. doi:10.1002/humu.20495 Schulz-Heddergott R, Stark N, Edmunds SJ, et al. Therapeutic ablation of gain-of-function mutant p53 in colorectal cancer inhibits Stat3-mediated tumor growth and invasion. Cancer Cell. 2018;34(2):298-314. doi:10.1016/j.ccell.2018.07.004 Souers AJ, Leverson JD, Boghaert ER, et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nature Medicine. 2013;19(2):202-208. doi:10.1038/nm.3048 Kotschy A, Szlavik Z, Murray J, et al. The MCL1 inhibitor S63845 is tolerable and effective in diverse cancer models. Nature. 2016;538(7626):477-482. doi:10.1038/nature19830 Fulda S, Vucic D. Targeting IAP proteins for therapeutic intervention in cancer. Nature Reviews Drug Discovery. 2012;11(2):109-124. doi:10.1038/nrd3627 Ashkenazi A, Pai RC, Fong S, et al. Safety and antitumor activity of recombinant soluble Apo2 ligand. Journal of Clinical Investigation. 1999;104(2):155-162. doi:10.1172/JCI6926 Garcia-Manero G, Goldberg AD, Winer ES, et al. Eprenetapopt combined with venetoclax and azacitidine in TP53-mutated acute myeloid leukaemia: a phase 1, dose-finding and expansion study. The Lancet Haematology. 2023;10(4):e272-e283. doi:10.1016/S2352-3026(22)00403-3 Sallman DA, DeZern AE, Garcia-Manero G, et al. Eprenetapopt (APR-246) and azacitidine in TP53-mutant myelodysplastic syndromes. Journal of Clinical Oncology. 2021;39(14):1584-1594. doi:10.1200/JCO.20.02341 Altieri DC. Survivin — the inconvenient IAP. Seminars in Cell and Developmental Biology. 2015;39:91-96. doi: 10.1016/j.semcdb.2014.12.007 Muller PA, Vousden KH. p53 mutations in cancer. Nature Cell Biology. 2013;15(1):2-8. doi:10.1038/ncb2641 Ano Bom AP, Rangel LP, Costa DC, et al. Mutant p53 aggregates into prion-like amyloid oligomers and fibrils: implications for cancer. Journal of Biological Chemistry. 2012;287(33):28152-28162. doi:10.1074/jbc.M112.340638 Baugh EH, Ke H, Levine AJ, Bhautani RA, Chan CS. Why are there hotspot mutations in the TP53 gene in human cancers? Cell Death and Differentiation. 2018;25(1):154-160. doi:10.1038/cdd.2017.180 Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Research. 2016;44(8):e71. doi:10.1093/nar/gkv1507 Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15(12):550. doi:10.1186/s13059-014-0550-8 Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7. doi:10.1186/1471-2105-14-7 Ghandi M, Huang FW, Jane-Valbuena J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569(7757):503-508. doi:10.1038/s41586-019-1186-3 Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Systems. 2015;1(6):417-425. doi:10.1016/j.cels.2015.12.004 Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York: Springer; 2000. doi:10.1007/978-1-4757-3294-8 Sallman DA, Komrokji RS, DeZern AE, et al. Long-term follow-up and combined phase 2 results of eprenetapopt and azacitidine in patients with TP53 mutant MDS/AML. HemaSphere. 2025;9(7):e70164. doi:10.1002/hem3.70164 Fujihara KM, Corrales Benitez M, Cabalag CS, et al. SLC7A11 is a superior determinant of APR-246 (eprenetapopt) response than TP53 mutation status. Molecular Cancer Therapeutics. 2021;20(10):1858-1867. doi:10.1158/1535-7163.MCT-21-0006 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9309751","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616985234,"identity":"df575f4a-6efa-4cf7-a46f-15e10ae022ea","order_by":0,"name":"Dr Dev Sudersan Venkatesan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDCCAyCCDcxkfAAkePhI0cJsANLCRooWNgkEGw/gO95j/JmnzCZfvv3sscqvOXYybAzMDx/dwKNF8swZM2mec2mWG87kpd2W3ZYMdBibsXEOHi0GN9LSmHnbDhsYMOSY3ZbcxgzUwsMmjVfL/WfJn3nb/hvI978xK5bcVk+ElhvMB6R52w4YMNzIMWP8uO0wYS2SZ5KPSc45l2xgcOONsTTjtuM8bMwE/MJ3/GDzhzdldkCH5Rh+/Lmt2p6fvfnhY3xaUAAzD5gkVjkIMP4gRfUoGAWjYBSMGAAAu9FFLnMakF4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9662-5459","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"Dr","firstName":"Dev","middleName":"Sudersan","lastName":"Venkatesan","suffix":""}],"badges":[],"createdAt":"2026-04-03 07:01:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9309751/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9309751/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106227863,"identity":"8a3a1195-af53-484d-b521-452cfbeb6fea","added_by":"auto","created_at":"2026-04-06 11:46:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":300752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic representation of caspase regulatory network state in wild-type p53 versus mutant p53 tumors stratified by cancer lineage. Left panel: wtp53 activates canonical pro-apoptotic targets (PUMA, NOXA, BAX, APAF1, FAS, DR5) and suppresses IAP/FLIP survival genes, maintaining intact apoptosis signaling. Centre panel: mutp53 in adenocarcinoma lineages (LUAD, COAD, STAD, PAAD) aberrantly upregulates BIRC5 and E2F1 while silencing the death receptor arm (FAS, BCL2, HRK, BAX), consistent with R175H and R248W hotspot bidirectional rewiring. Right panel: mutp53 in squamous/other lineages (LUSC, HNSC, BLCA, LIHC) upregulates CFLAR, BIRC3, and BIRC5 while suppressing APAF1 and executioner caspases, consistent with R273H-dominant caspase silencing. Gene colors indicate direction of significant expression change (DESeq2, padj\u0026lt;0.05, |log2FC|\u0026gt;0.5) from primary TCGA analysis across 3,739 tumors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/0cfa0670357968541b10274d.png"},{"id":106403040,"identity":"db59e310-301c-4e4d-9802-6adc8cafd4ef","added_by":"auto","created_at":"2026-04-08 09:13:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110327,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePan-cancer caspase network heatmap showing log2FoldChange (mutp53 vs wtp53) across 8 TCGA cohorts. Rows represent 34 caspase network genes annotated by functional layer. Columns represent cancer cohorts grouped by lineage (blue=adenocarcinoma, red=squamous/other). Color scale: red=upregulated in mutp53, blue=downregulated. Asterisks denote statistical significance (*padj\u0026lt;0.05; **padj\u0026lt;0.01; ***padj\u0026lt;0.001).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/cd4737393fd1851ba03d3a19.png"},{"id":106414798,"identity":"51fc2380-786c-4b84-89a5-746631d6fc3f","added_by":"auto","created_at":"2026-04-08 10:24:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003essGSEA apoptosis pathway enrichment comparison between mutp53 and wtp53 by lineage. Bars show mean delta enrichment score (mutp53 minus wtp53). Positive values indicate higher enrichment in mutp53; negative values indicate lower enrichment. Blue=adenocarcinoma; red=squamous/other.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/5079ab1018f164f2e314e39c.png"},{"id":106227866,"identity":"390ea136-c14c-450d-b1b0-8c53e6edb8fd","added_by":"auto","created_at":"2026-04-06 11:46:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHotspot-specific caspase network rewiring. Diverging bar plots showing the proportion of caspase network genes significantly changed (padj\u0026lt;0.05, |log2FC|\u0026gt;0.5) in each hotspot variant versus wtp53. Red bars indicate upregulated genes; blue bars indicate downregulated genes. Panels are separated by lineage (adenocarcinoma left; squamous/other right).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/c5f90da3939a96673635bb85.png"},{"id":106403441,"identity":"683f451c-5130-43b1-9177-74dda08253f8","added_by":"auto","created_at":"2026-04-08 09:14:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":112473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpearman correlation dot plot. Mean Spearman rho across cohorts within each lineage group (adenocarcinoma left; squamous/other right). Point color indicates functional network layer. Point size indicates proportion of cohorts with significant correlation (padj\u0026lt;0.05).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/a58678519f59488d4d5aad0d.png"},{"id":106402921,"identity":"1759b31b-bcc0-4285-959c-01c40f567365","added_by":"auto","created_at":"2026-04-08 09:13:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":292749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRepresentative Kaplan-Meier survival curves for clinically significant caspase-outcome associations. Overall survival (days) stratified by gene expression (High vs Low) within mutp53 tumors compared to wtp53 reference group. Full set of 72 KM plots (9 genes x 8 cohorts) available in Supplementary Figure S2.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Cox regression forest plot. Hazard ratios (HR, 95% CI) for the continuous gene expression term from multivariate Cox regression adjusting for tp53 status. Filled circles indicate p\u0026lt;0.05; open circles indicate non-significant associations. Blue=adenocarcinoma; red=squamous/other.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/5729150302f8a2c63eec4974.png"},{"id":106227869,"identity":"ad159de5-2b8a-46d7-b162-7f4998995079","added_by":"auto","created_at":"2026-04-06 11:46:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":355218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDrug-target network overlay. Network diagram showing 34 caspase regulatory genes connected by pathway edges (grey arrows) and 10 clinical-stage drugs connected to their targets (green dashed arrows). Gene node color: red=upregulated in mutp53, blue=downregulated, grey=not significant.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Pan-cancer drug-vulnerability atlas for mutp53 cancers. Evidence-grounded pharmacological pairing of ten clinical-stage apoptosis-restoring agents with eight TCGA cancer cohorts, stratified by lineage. Evidence scores are derived from integration of DESeq2 differential expression (padj\u0026lt;0.05, |log2FC|\u0026gt;0.5), Kaplan-Meier log-rank survival significance, and CCLE cell line validation. Strong evidence (dark green, three dots): target gene shows significant DEG and survival association in that cohort. Moderate evidence (light green, two dots): significant DEG without survival significance. Possible (amber, one dot): indirect evidence from correlated pathway genes. Not indicated (grey dash): insufficient evidence in that cohort. Key survival associations driving strong ratings: APAF1 in BLCA p=0.00018; XIAP in BLCA p=0.0036; PIDD1 in LUSC p=0.0026; BAX in PAAD p=0.0041; BCL2 in PAAD p=0.010; CFLAR in HNSC p=0.028.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/3d3740e2860c65ac44064b11.png"},{"id":106227870,"identity":"f3b1196f-6183-4109-ba61-53b71ac03edb","added_by":"auto","created_at":"2026-04-06 11:46:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":102175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCCLE cell line validation heatmap. Median expression delta (mutp53 minus wtp53) for 34 caspase network genes across 24 OncotreeLineage categories in 1,684 CCLE cancer cell lines (675 mutp53, 1,009 wtp53). Color scale: red=higher in mutp53, blue=lower in mutp53.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/c3e4642f20c2fd8506b11d8c.png"},{"id":106416213,"identity":"d950e1df-07b1-4633-970b-4d93189de698","added_by":"auto","created_at":"2026-04-08 10:43:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2126353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/b412f751-c85c-4214-a270-eaa03bd5fa4e.pdf"},{"id":106402922,"identity":"d70556a6-3f92-4984-9063-cc2c5fc201df","added_by":"auto","created_at":"2026-04-08 09:13:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26621,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9309751/v1/498dec3969dc37be874237c7.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMutant TP53 Hotspot Variants Differentially Rewire the Caspase Regulatory Network Across Cancer Lineages: A Pan-Cancer Computational Pharmacology Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTP53, encoding the tumor suppressor protein p53, is mutated in approximately 50% of all human cancers and represents the single most frequently altered gene in human malignancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Wild-type p53 functions as a master transcriptional regulator of cellular stress responses, inducing cell cycle arrest, apoptosis, senescence, and DNA repair through the activation of a broad set of target genes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Critically, p53 directly transactivates multiple nodes of the caspase regulatory network: it upregulates BBC3 (PUMA), PMAIP1 (NOXA), BAX, and APAF1 to promote the intrinsic mitochondrial apoptosis pathway; upregulates TNFRSF10A (DR4), TNFRSF10B (DR5), and FAS to activate the extrinsic death receptor pathway; and directly regulates CFLAR (FLIP-L), the only human pseudo-caspase, which controls the arrest-versus-apoptosis switch downstream of p53 activation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond loss of these canonical tumor-suppressive functions, the vast majority of TP53 mutations in human cancers are missense mutations that produce full-length mutant p53 (mutp53) proteins with novel oncogenic gain-of-function (GOF) activities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Mutp53 GOF mechanisms include aberrant co-activation of oncogenic transcription factors, epigenetic reprogramming through chromatin regulatory co-factors, immune evasion through PD-L1 upregulation and cGAS-STING suppression, and direct interference with the apoptosis machinery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A 2025 study demonstrated that mutp53 exploits distal enhancer elements to amplify immunosuppressive chemokine expression in pancreatic ductal adenocarcinoma, illustrating the lineage-specific chromatin architecture through which mutp53 GOF operates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe three most prevalent mutp53 hotspot variants, R175H, R248W, and R273H, have distinct structural classifications: R175H and R282W are conformational mutants that globally destabilize the p53 DNA-binding domain, while R248W, R248Q, R273H, and R273C are contact mutants that directly disrupt DNA-contact residues [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These structural differences translate into distinct transcriptional co-factor interaction profiles. A seminal study in colorectal cancer demonstrated that R273H orchestrates a unique transcriptional programme distinct from R175H, establishing proof of concept that individual hotspot variants impose qualitatively different oncogenic rewiring [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, whether these hotspot-specific transcriptional differences extend to the caspase regulatory network, and whether they operate in a lineage-dependent manner, has never been investigated at pan-cancer scale.\u003c/p\u003e \u003cp\u003eThe clinical imperative for this understanding is substantial. Multiple pharmacological agents capable of reactivating specific nodes of the mutp53-silenced caspase network are either approved or in late-stage clinical development: venetoclax (BCL2 inhibitor; FDA-approved) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], S63845 (MCL1 inhibitor) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], birinapant and LCL161 (smac mimetics targeting XIAP/BIRC2/BIRC3) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], dulanermin and recombinant TRAIL (DR4/DR5 agonists) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and eprenetapopt/APR-246 (mutp53 reactivator that restores PUMA/NOXA-mediated apoptosis; active in clinical trials for TP53-mutant AML and MDS with an overall response rate of 71% when combined with azacitidine) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Rational combination of these agents with mutp53 reactivators requires knowing precisely which caspase nodes are silenced in which cancer histology, information that currently does not exist in a systematic, pan-cancer, hotspot-resolved form.\u003c/p\u003e \u003cp\u003eIn this study, the first pan-cancer computational pharmacology analysis of mutp53-driven caspase network rewiring was performed. By integrating RNA-sequencing and mutation data from 3,739 tumors across eight TCGA cohorts, with validation in 1,684 CCLE cancer cell lines across 24 lineages, a lineage-specific dichotomy in caspase network regulation was identified that is resolved at the level of individual mutp53 hotspot variants, and the resulting apoptosis vulnerabilities were mapped directly to clinical-stage pharmacological agents.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 TCGA Data Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eRNA-sequencing data (STAR-Counts workflow, hg38 reference genome) and somatic mutation data (MuTect2 Masked Somatic Mutations) for eight TCGA projects were downloaded via the GDC API using TCGAbiolinks v2.32 in R v4.5.3 (Bioconductor v3.22). The eight cohorts comprised four adenocarcinoma lineages: lung adenocarcinoma (TCGA-LUAD), colon adenocarcinoma (TCGA-COAD), stomach adenocarcinoma (TCGA-STAD), and pancreatic adenocarcinoma (TCGA-PAAD); and four squamous/other lineages: lung squamous cell carcinoma (TCGA-LUSC), head and neck squamous cell carcinoma (TCGA-HNSC), bladder urothelial carcinoma (TCGA-BLCA), and hepatocellular carcinoma (TCGA-LIHC). Duplicate sample IDs were removed retaining the first occurrence. Sample IDs were truncated to 16 characters for cross-platform matching.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 TP53 Mutation Classification\u003c/h2\u003e \u003cp\u003eSomatic mutations in TP53 were extracted from MuTect2 MAF files for each cohort. Mutations were classified as missense (Missense_Mutation), truncating (Nonsense_Mutation, Frame_Shift_Del, Frame_Shift_Ins, Splice_Site), or other. Only missense mutations were classified as mutp53 for GOF analysis, as these produce full-length mutp53 proteins with potential gain-of-function activity. Truncating mutations were excluded from both mutp53 and wtp53 groups to ensure clean group separation. Hotspot classification was performed by parsing the HGVSp_Short field: R175H, R248W, R248Q, R273H, R273C, G245S, and R282W were identified by exact string matching. Samples not carrying any TP53 missense mutation constituted the wtp53 reference group (n\u0026thinsp;=\u0026thinsp;1,951 after truncating exclusion).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Caspase Regulatory Network Gene Set\u003c/h2\u003e \u003cp\u003eA 34-gene caspase regulatory network was defined a priori based on established apoptosis pathway biology, encompassing nine functional layers: initiator caspases (CASP2, CASP8, CASP9, CASP10); executioner caspases (CASP3, CASP6, CASP7); PIDDosome complex (PIDD1, CRADD/RAIDD); pseudo-caspase/FLIP (CFLAR); death receptors (TNFRSF10A/DR4, TNFRSF10B/DR5, FAS, TNFRSF1A); IAP family (BIRC2/cIAP1, BIRC3/cIAP2, BIRC5/survivin, XIAP); mitochondrial pathway (APAF1, CYCS, BAX, BAK1, BCL2, BCL2L1, MCL1); nucleolar stress arm (RRP1B, E2F1); and BH3-only proteins (BBC3/PUMA, PMAIP1/NOXA, BID, BAD, BIK, BMF, HRK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DESeq2 Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eFor each cohort, raw STAR unstranded counts were subsetted to the 34 caspase network genes using ENSEMBL IDs with version suffixes stripped. Gene annotation was performed using org.Hs.eg.db (Bioconductor 3.22) via AnnotationDbi. DESeq2 v1.46 was used with design formula ~tp53_status, contrasting mutp53 versus wtp53. Genes with fewer than 10 counts in fewer than 5 samples were filtered. The varianceStabilizingTransformation function (blind=FALSE) was applied for normalized expression values. Results were adjusted using the Benjamini-Hochberg procedure, with significance defined as padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FoldChange|\u0026gt;0.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Hotspot-Resolved DESeq2 Analysis\u003c/h2\u003e \u003cp\u003eFor each of the three major hotspot variants (R175H, R248W, R273H), a separate DESeq2 analysis was performed contrasting that specific hotspot against the clean wtp53 group, with all other mutp53 samples excluded. The minimum sample size threshold was n\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5 for the hotspot group and n\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;10 for the wtp53 group; cohorts not meeting this threshold were excluded from that specific hotspot analysis and are reported transparently in the Results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Spearman Correlation Analysis\u003c/h2\u003e \u003cp\u003eTP53 mRNA expression was extracted from the full SummarizedExperiment object for each cohort using the canonical ENSEMBL ID ENSG00000141510, library-size normalized to counts per million (CPM), and log2 transformed (log2(CPM\u0026thinsp;+\u0026thinsp;1)). Spearman rank correlation was computed between TP53 log2CPM and each caspase network gene VST-normalized expression using cor.test (method=spearman, exact=FALSE). P-values were adjusted using the Benjamini-Hochberg procedure within each cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Kaplan-Meier and Cox Regression Survival Analysis\u003c/h2\u003e \u003cp\u003eClinical outcome data (overall survival time in days and vital status) were extracted from colData of each SummarizedExperiment object. Gene expression tertiles were computed within each cohort; samples in the lowest tertile (Low) and highest tertile (High) were retained for survival analysis. Kaplan-Meier curves were generated using the survminer package. Log-rank p-values were computed. Multivariate Cox proportional hazards regression included continuous gene expression (VST) and binary tp53 status as covariates. Hazard ratios and 95% confidence intervals were extracted using broom::tidy with exponentiate=TRUE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 ssGSEA Apoptosis Pathway Scoring\u003c/h2\u003e \u003cp\u003eGene set variation analysis (ssGSEA) was performed using GSVA v1.54 (gsvaParam function, kcdf=Gaussian, minSize\u0026thinsp;=\u0026thinsp;5). Gene sets were obtained from MSigDB via msigdbr v7.5.1: HALLMARK_APOPTOSIS from the H collection and KEGG_APOPTOSIS from the C2:CP:KEGG_LEGACY collection. Per-pathway ssGSEA scores were compared between mutp53 and wtp53 samples using a Wilcoxon rank-sum test, with delta enrichment score (mutp53 mean minus wtp53 mean) reported per lineage group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 CCLE Validation\u003c/h2\u003e \u003cp\u003eCancer Cell Line Encyclopedia (CCLE/DepMap) expression data, mutation data, and cell line metadata were downloaded from the DepMap portal (2024Q2 release). TP53 missense mutations were identified by filtering on VariantInfo containing missense_variant (case-insensitive). Cell lines without any TP53 mutation served as the wtp53 reference group. Wilcoxon rank-sum tests were performed for each gene-lineage combination (minimum n\u0026thinsp;=\u0026thinsp;3 per group), with delta median expression (mutp53 minus wtp53) visualized as a heatmap across 24 OncotreeLineage categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Drug-Target Network Construction\u003c/h2\u003e \u003cp\u003eA pharmacological network overlay was constructed using igraph v2.2.2 and ggraph v2.2.2. Drug-gene edges connected ten clinical-stage pharmacological agents to their primary molecular targets: venetoclax (BCL2), navitoclax (BCL2/BCL2L1), S63845 (MCL1), birinapant and LCL161 (BIRC2/BIRC3/XIAP), embelin (XIAP), TRAIL/dulanermin (TNFRSF10A/TNFRSF10B), APR-246/eprenetapopt and PRIMA-1MET (TP53, upstream of PUMA/NOXA restoration), and YM155 (BIRC5/survivin). Gene node color reflected mean log2FC direction across all cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Statistical Framework\u003c/h2\u003e \u003cp\u003eAll analyses were performed in R v4.5.3 using Bioconductor v3.22. Multiple testing correction used the Benjamini-Hochberg false discovery rate method throughout. Significance thresholds were padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with |log2FC|\u0026gt;0.5 for DESeq2 analyses, padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for Spearman correlation, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for survival analyses. All data are publicly available through the GDC portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the DepMap portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://depmap.org\u003c/span\u003e\u003cspan address=\"https://depmap.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Design and Sample Characteristics\u003c/h2\u003e \u003cp\u003eA total of 3,739 tumor samples were analyzed across eight TCGA cancer cohorts, including 1,202 mutp53 (missense) and 1,951 wtp53 samples after exclusion of truncating mutations. The adenocarcinoma group (LUAD, COAD, STAD, PAAD) contributed 482 mutp53 samples, and the squamous/other group (LUSC, HNSC, BLCA, LIHC) contributed 614 mutp53 samples. Among the 1,202 mutp53 samples, 938 (78.0%) carried other missense mutations distributed across the TP53 DNA-binding domain, while the canonical hotspot distribution included R175H (n\u0026thinsp;=\u0026thinsp;59; 4.9%), R273H (n\u0026thinsp;=\u0026thinsp;35; 2.9%), R248W (n\u0026thinsp;=\u0026thinsp;33; 2.7%), R282W (n\u0026thinsp;=\u0026thinsp;38; 3.2%), R248Q (n\u0026thinsp;=\u0026thinsp;51; 4.2%), R273C (n\u0026thinsp;=\u0026thinsp;29; 2.4%), and G245S (n\u0026thinsp;=\u0026thinsp;19; 1.6%). The caspase regulatory network gene set comprised 34 genes spanning nine functional layers. Cohort characteristics and significant DEG counts are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A schematic overview of the caspase regulatory network state in wtp53 versus mutp53 tumors across both lineage groups is presented (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTCGA Cohort Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emutp53 n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ewtp53 n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig. Caspase DEGs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LUAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-COAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-STAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-PAAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LUSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-HNSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-BLCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LIHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDEG: differentially expressed gene; padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC|\u0026gt;0.5. Samples with truncating TP53 mutations were excluded from both groups.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pan-Cancer Caspase Network Rewiring by mutp53\u003c/h2\u003e \u003cp\u003eDESeq2 analysis across all eight cohorts identified a consistent pan-cancer mutp53 caspase signature alongside striking lineage-specific divergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Three genes were significantly upregulated in mutp53 tumors across the majority of cohorts: BIRC5 (survivin; upregulated in 6/8 cohorts, padj\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in LUAD, LUSC, BLCA, LIHC), E2F1 (upregulated in 7/8 cohorts), and PMAIP1/NOXA (upregulated in LUAD and LUSC). The consistent upregulation of BIRC5 represents a pan-cancer mutp53 caspase survival mechanism, as survivin directly inhibits both CASP3 and CASP9 while simultaneously promoting cell division.\u003c/p\u003e \u003cp\u003eIn contrast, FAS (TNFRSF6), BCL2, HRK, and BCL2L1 exhibited lineage-specific downregulation. FAS was significantly downregulated in mutp53 COAD (padj\u0026thinsp;\u0026lt;\u0026thinsp;0.001), STAD, and LIHC, but showed no significant change in squamous/other cohorts. This pattern indicates that mutp53 silences the extrinsic death receptor arm preferentially in adenocarcinomas, mechanistically coherent given that FAS is a canonical wtp53 transcriptional target and its downregulation requires active mutp53 GOF transcriptional interference. BCL2 was significantly downregulated in mutp53 COAD (padj\u0026thinsp;\u0026lt;\u0026thinsp;0.001), STAD, PAAD, and BLCA, indicating compensatory anti-apoptotic dependency shifts to other IAP family members, particularly BIRC5/survivin. HRK was dramatically downregulated in mutp53 COAD (log2FC=-2.0, padj\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with paradoxical upregulation in LIHC. The CFLAR/FLIP pseudo-caspase showed lineage-specific differential expression: significantly upregulated in mutp53 LUSC and downregulated in HNSC and BLCA, consistent with its established role as a direct p53 transcriptional target [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Lineage-Specific Caspase Rewiring: Adenocarcinoma versus Squamous/Other\u003c/h2\u003e \u003cp\u003eThe contrast between adenocarcinoma and squamous/other lineages constituted the most biologically significant finding of this analysis. In adenocarcinomas (LUAD, COAD, STAD, PAAD), mutp53 predominantly downregulated death receptor pathway genes (FAS, TNFRSF10B, TNFRSF1A) and mitochondrial pathway genes (BCL2, HRK, BCL2L1), while upregulating nucleolar stress arm genes (E2F1, RRP1B) and the IAP BIRC5. This pattern indicates that adenocarcinoma mutp53 tumors have actively silenced ligand-dependent apoptosis while maintaining or upregulating nucleolar surveillance and IAP-mediated caspase inhibition.\u003c/p\u003e \u003cp\u003eIn squamous/other lineages (LUSC, HNSC, BLCA, LIHC), the rewiring pattern diverged substantially. LUSC showed the highest number of significant caspase DEGs (7/34 genes) with upregulation of CFLAR, BIRC3, BIRC5, BIK, E2F1, PMAIP1, and MCL1, a paradoxical co-upregulation of both pro- and anti-apoptotic genes suggesting active apoptosis tension. BLCA showed downregulation of BCL2 alongside upregulation of BIRC5, E2F1, TNFRSF10B, and HRK, uniquely combining death receptor upregulation with IAP upregulation.\u003c/p\u003e \u003cp\u003ePathway-level ssGSEA analysis confirmed these gene-level observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The HALLMARK_APOPTOSIS gene set showed a positive mean delta enrichment score in adenocarcinoma mutp53 tumors (mutp53\u0026thinsp;\u0026gt;\u0026thinsp;wtp53) but a negative delta in squamous/other mutp53 tumors (mutp53\u0026thinsp;\u0026lt;\u0026thinsp;wtp53). KEGG_APOPTOSIS showed the opposite pattern. This bidirectional, pathway-level lineage divergence, confirmed by two independent apoptosis gene sets, provides robust evidence that mutp53 does not uniformly suppress apoptosis but differentially reprograms apoptosis pathway activity in a lineage-dependent manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Hotspot-Resolved Caspase Signatures: R175H, R248W, and R273H Produce Distinct Patterns\u003c/h2\u003e \u003cp\u003eHotspot-resolved DESeq2 analysis revealed quantitatively and qualitatively distinct caspase rewiring patterns among the three major mutp53 variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In adenocarcinomas, R175H produced the most extensive bidirectional rewiring: 7 of 34 caspase network genes were significantly altered in COAD alone, with upregulation of BH3-only activators alongside downregulation of executioner caspase pathway genes. This is consistent with the conformational mutant nature of R175H, which broadly disrupts p53 DNA-binding domain architecture through global structural destabilization and exposed hydrophobic surfaces that engage multiple transcriptional co-activators. R248W in adenocarcinomas produced a more restricted signature (2 significant genes in COAD), predominantly downregulatory, reflecting its targeted contact-mutant mechanism.\u003c/p\u003e \u003cp\u003eR273H displayed a strikingly different pattern: in squamous/other lineages (particularly HNSC, n\u0026thinsp;=\u0026thinsp;8 samples), R273H produced predominantly caspase network silencing with downregulation concentrated in the executioner and death receptor layers. This is consistent with published evidence that R273H, as a contact mutant, actively engages with and redirects specific transcription factor partnerships toward oncogenic co-activation rather than simply losing p53 function [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The observation that R273H-specific downregulation was most pronounced in squamous/other lineages while R175H rewiring was most pronounced in adenocarcinomas represents the first pan-cancer evidence that hotspot structural class and cancer lineage interact to determine the specific caspase vulnerability created by mutp53.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 TP53 Expression Correlates with Caspase Network Activity in a Lineage-Specific Manner\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis between TP53 mRNA expression, used as a proxy for mutp53 protein accumulation given that mutp53 escapes MDM2-mediated degradation and accumulates to high levels, and the 34 caspase network genes was performed across 8 cohorts encompassing 2,278 common samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Several caspase network genes showed strong, reproducible positive correlations with TP53 expression: CASP2 (mean rho\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.26 across all cohorts; significant in proportion\u0026thinsp;\u0026gt;\u0026thinsp;0.75 of cohorts), E2F1, and BIRC5. Conversely, CRADD/RAIDD, MCL1, CFLAR, and BIRC2 showed negative correlations with TP53 expression across multiple cohorts. The directionality of these continuous correlations is fully consistent with the categorical DESeq2 findings, providing independent validation that the expression changes are quantitative and dose-dependent. The lineage comparison reveals that while the direction of most correlations is consistent between adenocarcinoma and squamous/other cohorts, the magnitude differs substantially, directly mirroring the DESeq2 and ssGSEA findings and providing a third independent analytical method supporting the lineage-specific caspase rewiring conclusion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Caspase Network Silencing by mutp53 Predicts Clinical Outcomes\u003c/h2\u003e \u003cp\u003eKaplan-Meier survival analysis and Cox regression across nine clinically prioritized caspase genes revealed multiple statistically significant associations between caspase node expression and overall survival in mutp53 tumors, providing direct clinical validation of the computational rewiring map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most striking finding was APAF1 in TCGA-BLCA (p\u0026thinsp;=\u0026thinsp;0.00018, log-rank test). Low APAF1 expression in mutp53 bladder cancer tumors was associated with markedly worse survival. APAF1 is the central scaffold of the apoptosome and a canonical wtp53 transcriptional target; its suppression in mutp53 bladder cancer directly blocks the mitochondrial caspase cascade downstream of cytochrome c release, identifying APAF1-low mutp53 BLCA as a specific molecular subgroup with poor prognosis. XIAP in TCGA-BLCA was the second most significant finding (p\u0026thinsp;=\u0026thinsp;0.0036), with high XIAP expression in mutp53 tumors associated with worse survival. The combination of APAF1 suppression and XIAP upregulation in mutp53 BLCA creates a dual block on the intrinsic pathway that smac mimetics are specifically designed to overcome. BCL2 in TCGA-PAAD (p\u0026thinsp;=\u0026thinsp;0.010), BAX in TCGA-PAAD (p\u0026thinsp;=\u0026thinsp;0.0041), PIDD1 in TCGA-LUSC (p\u0026thinsp;=\u0026thinsp;0.0026), and CFLAR in TCGA-HNSC (p\u0026thinsp;=\u0026thinsp;0.028) completed the set of statistically significant survival associations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. \u003cem\u003eRepresentative Kaplan-Meier survival curves for clinically significant caspase-outcome associations. Overall survival (days) stratified by gene expression (High vs Low) within mutp53 tumors compared to wtp53 reference group. Full set of 72 KM plots (9 genes x 8 cohorts) available in Supplementary Figure S2.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistically Significant Caspase-Survival Associations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLineage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrug Implication\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPAF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-BLCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmac mimetics; APR-246 to restore APAF1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-BLCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBirinapant / LCL161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIDD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-LUSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePIDDosome reactivation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-PAAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRestore with APR-246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-PAAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMCL1 dependency; S63845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFLAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-HNSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquamous/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHDAC-I\u0026thinsp;+\u0026thinsp;APR-246 combination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-PAAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExecutioner reactivation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIRC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA-COAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u0026thinsp;=\u0026thinsp;worse OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYM155 / survivin inhibition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOS\u0026thinsp;=\u0026thinsp;overall survival. Log-rank test p-values from Kaplan-Meier analysis.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Drug-Target Network Overlay: A Computational Pharmacology Vulnerability Atlas\u003c/h2\u003e \u003cp\u003eThe drug-target network overlay integrates the pan-cancer caspase rewiring map with ten clinical-stage pharmacological agents to create a lineage-resolved, hotspot-resolved druggability atlas (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Three major vulnerability archetypes were identified. First, an IAP overload vulnerability defined by consistent pan-cancer BIRC5 upregulation with BIRC2/BIRC3 upregulation in specific lineages (particularly LUSC and HNSC), directly implicating smac mimetics (birinapant, LCL161) and survivin inhibitors (YM155). Second, a death receptor silencing vulnerability specific to adenocarcinomas defined by FAS and TNFRSF10A/B downregulation, addressable through APR-246/eprenetapopt to restore wtp53 transcriptional activity and re-induce FAS and DR4/DR5 expression. Third, a mitochondrial blockade vulnerability in adenocarcinomas (particularly PAAD and COAD) defined by BCL2 downregulation alongside poor prognosis with BCL2 loss (p\u0026thinsp;=\u0026thinsp;0.010), indicating a shift in anti-apoptotic dependency to MCL1, rationally addressable with S63845. The complete lineage-resolved, evidence-graded drug-vulnerability atlas derived from this analysis is presented (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.8 CCLE Cell Line Validation\u003c/h2\u003e \u003cp\u003eTo validate the TCGA tumor findings in an independent experimental system, the analysis was replicated in 1,684 cancer cell lines from the DepMap/CCLE resource (2024Q2 release), comprising 675 mutp53 lines and 1,009 wtp53 lines across 24 OncotreeLineage categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The CCLE data confirmed the TCGA findings with high fidelity: BBC3/PUMA, FAS, TNFRSF10B, and CRADD showed the strongest lineage-specific delta expression patterns, with adenocarcinoma-related lineages (Lung, Pancreas, Bowel) recapitulating the FAS downregulation and BIRC5 upregulation observed in TCGA. Squamous-related lineages (Head and Neck, Bladder/Urinary Tract, Cervix) confirmed the distinct pattern of CFLAR and BIRC3 elevation observed in squamous TCGA cohorts. The concordance between tumor-derived TCGA data and experimentally manipulable CCLE cell lines provides strong cross-platform validation that the mutp53 caspase rewiring patterns identified are intrinsic to cancer cell biology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study presents the first pan-cancer, hotspot-resolved computational pharmacology analysis of mutp53-driven caspase network rewiring, generating several findings of both biological and clinical significance. The analysis demonstrates that mutp53 does not simply abrogate caspase activity uniformly; rather, it differentially rewires a 34-gene caspase regulatory network in a manner summarized schematically in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); rather, it differentially rewires a 34-gene caspase regulatory network in a manner that is partially conserved across all eight cancer types (BIRC5 and E2F1 upregulation), lineage-specific in its directional effect on the death receptor and mitochondrial pathway arms, and hotspot-variant-resolved such that R175H, R248W, and R273H impose distinct caspase signatures.\u003c/p\u003e \u003cp\u003eThe consistent pan-cancer upregulation of BIRC5 (survivin) in mutp53 tumors is the most reproducible finding, confirmed in 6/8 cohorts at padj\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Survivin is a member of the IAP family that directly inhibits CASP3 and CASP9 while also functioning as a chromosomal passenger protein essential for cell division [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Its consistent upregulation in mutp53 tumors, combined with significant survival associations in COAD (p\u0026thinsp;=\u0026thinsp;0.043), identifies the BIRC5-mutp53 axis as a pan-cancer vulnerability with direct pharmacological implications.\u003c/p\u003e \u003cp\u003eThe lineage-specific directionality of FAS and death receptor pathway regulation is mechanistically interpretable through the lens of mutp53 GOF. In adenocarcinoma lineages, FAS downregulation by mutp53 is consistent with published reports that mutp53 can repress FAS transcription through aberrant co-factor interactions, and that loss of FAS is a major mechanism of chemotherapy resistance in colon and gastric cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The absence of this effect in squamous lineages suggests lineage-specific chromatin architecture and transcription factor availability, echoing the 2025 finding that mutp53 R172H in pancreatic ductal adenocarcinoma exploits super-enhancers in a cell-type-specific manner [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The ssGSEA finding of opposite HALLMARK_APOPTOSIS enrichment directions between adenocarcinoma and squamous/other mutp53 tumors directly challenges the assumption that mutp53 suppresses apoptosis as a lineage-agnostic statement.\u003c/p\u003e \u003cp\u003eThe hotspot-specific findings warrant careful mechanistic interpretation. R175H is a conformational mutant that globally destabilizes the p53 DNA-binding domain and exposes hydrophobic aggregation-prone regions, leading to broad transcriptional rewiring through co-factor sequestration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. R273H is a contact mutant that retains near-native protein folding but loses sequence-specific DNA binding, instead engaging non-canonical DNA sequences and transcription factor partnerships that drive a qualitatively distinct gene expression programme [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The finding that R175H produces more extensive bidirectional rewiring in adenocarcinomas while R273H produces predominantly silencing rewiring in squamous lineages is consistent with this structural distinction: conformational mutp53 may engage more co-factors through exposed protein surfaces while contact mutp53 more specifically redirects transcriptional activity in the lineage-specific chromatin context of squamous cancers.\u003c/p\u003e \u003cp\u003eThe clinical implications of this pharmacology atlas are immediate and specific. For mutp53 bladder cancer (BLCA), characterized by APAF1 downregulation (p\u0026thinsp;=\u0026thinsp;0.00018), XIAP upregulation (p\u0026thinsp;=\u0026thinsp;0.0036), and BCL2 downregulation, the data support a therapeutic strategy combining APR-246 (to restore APAF1 transcription) with a smac mimetic such as birinapant or LCL161 (to relieve XIAP-mediated CASP3/CASP9 blockade), creating a synergistic reversal of the mutp53-imposed dual apoptotic block. These lineage-specific drug-target pairings are consolidated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e as a precision pharmacotherapy reference for mutp53 cancers. For mutp53 pancreatic adenocarcinoma (PAAD) with BCL2 loss (survival p\u0026thinsp;=\u0026thinsp;0.010) and BAX downregulation (survival p\u0026thinsp;=\u0026thinsp;0.0041), the data indicate MCL1 dependency, making S63845 rationally indicated. For mutp53 HNSC with CFLAR upregulation and survival significance (p\u0026thinsp;=\u0026thinsp;0.028), consistent with FLIP-L as a direct p53 transcriptional target [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the data support combination of class-I HDAC inhibitors with APR-246, a strategy demonstrated to synergistically promote apoptosis in FLIP-depleted colorectal cancer cells.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant acknowledgment. The hotspot-specific analyses in some cohorts were constrained by small sample sizes (R175H n\u0026thinsp;=\u0026thinsp;1 in LUAD; R273H n\u0026thinsp;=\u0026thinsp;0 in BLCA), limiting statistical power for those specific cohort-hotspot combinations. Neither TCGA nor CCLE captures the full complexity of tumor microenvironment contributions to caspase regulation. The drug-target pharmacology predictions are computationally derived and require prospective experimental validation in appropriate mutp53-expressing cancer models before clinical application. The ssGSEA analysis was limited to two apoptosis gene sets due to the small size of the input expression matrix. Despite these limitations, the multi-platform validation approach across TCGA tumors, CCLE cell lines, and three independent analytical methods substantially mitigates these constraints.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eMutant TP53 rewires the caspase regulatory network in a manner that is simultaneously pan-cancer (BIRC5/survivin upregulation across 6/8 cohorts), lineage-specific (FAS/death receptor silencing in adenocarcinomas; CFLAR/FLIP elevation in squamous cancers), and hotspot-resolved (R273H preferentially silences the caspase network in squamous lineages while R175H produces broader bidirectional rewiring in adenocarcinomas). The clinical survival significance of APAF1, XIAP, BCL2, BAX, PIDD1, CFLAR, and BIRC5 in specific mutp53 cancer types translates directly into evidence-grounded pharmacological hypotheses. This pan-cancer computational pharmacology atlas, validated in TCGA tumors, CCLE cell lines, and three independent analytical platforms, provides the first systematic framework for matching specific mutp53 cancer types to specific apoptosis-restoring pharmacological agents, advancing the field from the generic observation that mutp53 suppresses apoptosis toward precision pharmacotherapy stratified by lineage, hotspot, and specific caspase vulnerability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eDev Sudersan Venkatesan conceived, designed, performed, analyzed, interpreted, and wrote all aspects of this study. The author read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthics Statement\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available de-identified datasets. No human subject\u0026rsquo;s ethics approval was required. TCGA data were accessed through the GDC portal under open-access data policy. CCLE/DepMap data were accessed under the DepMap public data use policy.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe author acknowledges the TCGA Research Network and the DepMap/CCLE consortium for generating and making publicly available the datasets used in this study.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eAll primary data used in this study are publicly available. TCGA RNA-sequencing data and somatic mutation data were downloaded from the GDC Data Portal (https://portal.gdc.cancer.gov) using TCGAbiolinks v2.32. CCLE/DepMap expression, mutation, and metadata files were downloaded from the DepMap portal, 2024Q2 release (https://depmap.org/portal/download). Gene set data were retrieved from MSigDB via msigdbr. The complete R analysis pipeline will be deposited at Zenodo upon acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLevine AJ, Oren M. The first 30 years of p53: growing ever more complex. Nature Reviews Cancer. 2009;9(10):749-758. doi:10.1038/nrc2723\u003c/li\u003e\n \u003cli\u003eKastenhuber ER, Lowe SW. Putting p53 in context. Cell. 2017;170(6):1062-1078. doi:10.1016/j.cell.2017.08.028\u003c/li\u003e\n \u003cli\u003eVousden KH, Prives C. Blinded by the light: the growing complexity of p53. 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Human Mutation. 2007;28(6):622-629. doi:10.1002/humu.20495\u003c/li\u003e\n \u003cli\u003eSchulz-Heddergott R, Stark N, Edmunds SJ, et al. Therapeutic ablation of gain-of-function mutant p53 in colorectal cancer inhibits Stat3-mediated tumor growth and invasion. Cancer Cell. 2018;34(2):298-314. doi:10.1016/j.ccell.2018.07.004\u003c/li\u003e\n \u003cli\u003eSouers AJ, Leverson JD, Boghaert ER, et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nature Medicine. 2013;19(2):202-208. doi:10.1038/nm.3048\u003c/li\u003e\n \u003cli\u003eKotschy A, Szlavik Z, Murray J, et al. The MCL1 inhibitor S63845 is tolerable and effective in diverse cancer models. Nature. 2016;538(7626):477-482. doi:10.1038/nature19830\u003c/li\u003e\n \u003cli\u003eFulda S, Vucic D. Targeting IAP proteins for therapeutic intervention in cancer. Nature Reviews Drug Discovery. 2012;11(2):109-124. doi:10.1038/nrd3627\u003c/li\u003e\n \u003cli\u003eAshkenazi A, Pai RC, Fong S, et al. Safety and antitumor activity of recombinant soluble Apo2 ligand. Journal of Clinical Investigation. 1999;104(2):155-162. doi:10.1172/JCI6926\u003c/li\u003e\n \u003cli\u003eGarcia-Manero G, Goldberg AD, Winer ES, et al. Eprenetapopt combined with venetoclax and azacitidine in TP53-mutated acute myeloid leukaemia: a phase 1, dose-finding and expansion study. The Lancet Haematology. 2023;10(4):e272-e283. doi:10.1016/S2352-3026(22)00403-3\u003c/li\u003e\n \u003cli\u003eSallman DA, DeZern AE, Garcia-Manero G, et al. Eprenetapopt (APR-246) and azacitidine in TP53-mutant myelodysplastic syndromes. Journal of Clinical Oncology. 2021;39(14):1584-1594. doi:10.1200/JCO.20.02341\u003c/li\u003e\n \u003cli\u003eAltieri DC. Survivin \u0026mdash; the inconvenient IAP. Seminars in Cell and Developmental Biology. 2015;39:91-96. doi: 10.1016/j.semcdb.2014.12.007\u003c/li\u003e\n \u003cli\u003eMuller PA, Vousden KH. p53 mutations in cancer. Nature Cell Biology. 2013;15(1):2-8. doi:10.1038/ncb2641\u003c/li\u003e\n \u003cli\u003eAno Bom AP, Rangel LP, Costa DC, et al. Mutant p53 aggregates into prion-like amyloid oligomers and fibrils: implications for cancer. Journal of Biological Chemistry. 2012;287(33):28152-28162. doi:10.1074/jbc.M112.340638\u003c/li\u003e\n \u003cli\u003eBaugh EH, Ke H, Levine AJ, Bhautani RA, Chan CS. Why are there hotspot mutations in the TP53 gene in human cancers? Cell Death and Differentiation. 2018;25(1):154-160. doi:10.1038/cdd.2017.180\u003c/li\u003e\n \u003cli\u003eColaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Research. 2016;44(8):e71. doi:10.1093/nar/gkv1507\u003c/li\u003e\n \u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15(12):550. doi:10.1186/s13059-014-0550-8\u003c/li\u003e\n \u003cli\u003eHanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7. doi:10.1186/1471-2105-14-7\u003c/li\u003e\n \u003cli\u003eGhandi M, Huang FW, Jane-Valbuena J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569(7757):503-508. doi:10.1038/s41586-019-1186-3\u003c/li\u003e\n \u003cli\u003eLiberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Systems. 2015;1(6):417-425. doi:10.1016/j.cels.2015.12.004\u003c/li\u003e\n \u003cli\u003eTherneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York: Springer; 2000. doi:10.1007/978-1-4757-3294-8\u003c/li\u003e\n \u003cli\u003eSallman DA, Komrokji RS, DeZern AE, et al. Long-term follow-up and combined phase 2 results of eprenetapopt and azacitidine in patients with TP53 mutant MDS/AML. HemaSphere. 2025;9(7):e70164. doi:10.1002/hem3.70164\u003c/li\u003e\n \u003cli\u003eFujihara KM, Corrales Benitez M, Cabalag CS, et al. SLC7A11 is a superior determinant of APR-246 (eprenetapopt) response than TP53 mutation status. Molecular Cancer Therapeutics. 2021;20(10):1858-1867. doi:10.1158/1535-7163.MCT-21-0006\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mutant TP53, caspase network, pan-cancer, lineage-specific, R175H, R248W, R273H, apoptosis resistance, venetoclax, TRAIL, smac mimetics, APR-246, computational pharmacology, TCGA, CCLE, ssGSEA","lastPublishedDoi":"10.21203/rs.3.rs-9309751/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9309751/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eTP53 is mutated in approximately 50% of all human cancers, and mutant p53 (mutp53) proteins acquire gain-of-function (GOF) activities that actively suppress apoptosis. However, the mechanism by which mutp53 specifically rewires the complete caspase regulatory network spanning initiator caspases, executioner caspases, the PIDDosome complex, the pseudo-caspase FLIP (CFLAR), inhibitor of apoptosis proteins (IAPs), death receptors, and BH3-only proteins across distinct cancer lineages has never been systematically mapped at pan-cancer scale. Furthermore, whether individual hotspot variants (R175H, R248W, R273H) produce distinct caspase regulatory signatures remains unknown.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eRNA-sequencing data (STAR counts) and somatic mutation data from 3,739 tumors across eight TCGA cohorts (LUAD, COAD, STAD, PAAD, LUSC, HNSC, BLCA, LIHC) were integrated to perform DESeq2 differential expression analysis comparing mutp53 (missense; n=1,202) versus wild-type p53 tumors, stratified by adenocarcinoma versus squamous/other lineage. Hotspot-resolved analyses were performed for R175H (n=59), R248W (n=33), and R273H (n=35). Spearman correlation, Kaplan-Meier survival analysis, Cox proportional hazards regression, ssGSEA apoptosis pathway scoring, and CCLE cell line validation (675 mutp53 vs 1,009 wtp53 lines across 24 lineages) were performed. A drug-target pharmacology network overlay mapped computationally identified vulnerabilities to clinical-stage apoptosis-restoring agents.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eMutp53 consistently upregulated BIRC5 (survivin) and E2F1 across all eight cohorts, while FAS (TNFRSF6), BCL2, and HRK showed lineage-specific downregulation predominantly in adenocarcinomas. Hotspot analysis revealed that R175H and R248W produced bidirectional rewiring in adenocarcinomas, while R273H caused predominantly caspase network silencing in squamous/other lineages. ssGSEA demonstrated opposite apoptosis pathway enrichment directions between lineages: HALLMARK_APOPTOSIS was paradoxically enriched in adenocarcinoma mutp53 tumors but depleted in squamous/other mutp53 tumors. Clinically significant survival associations were identified for APAF1 in TCGA-BLCA (p=0.00018), XIAP in TCGA-BLCA (p=0.0036), BAX in TCGA-PAAD (p=0.0041), BCL2 in TCGA-PAAD (p=0.010), PIDD1 in TCGA-LUSC (p=0.0026), and CFLAR in TCGA-HNSC (p=0.028). CCLE validation confirmed the TCGA findings across independent cell lines.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eMutp53 rewires the caspase regulatory network in a lineage-specific and hotspot-resolved manner. This pan-cancer computational pharmacology analysis provides a vulnerability atlas directly informing rational drug combinations: venetoclax for BCL2-dependent adenocarcinomas, smac mimetics for XIAP/BIRC2-dependent bladder cancer, and TRAIL agonists combined with APR-246 for FAS-silenced squamous cancers, representing the first systematic integration of mutp53 caspase network biology with precision pharmacotherapy.\u003c/p\u003e","manuscriptTitle":"Mutant TP53 Hotspot Variants Differentially Rewire the Caspase Regulatory Network Across Cancer Lineages: A Pan-Cancer Computational Pharmacology Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 11:46:41","doi":"10.21203/rs.3.rs-9309751/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5ba6727-df70-4069-9bdf-b42eefba5d38","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65661674,"name":"Epigenetics \u0026 Genomics"},{"id":65661675,"name":"Evolutionary Genetics"}],"tags":[],"updatedAt":"2026-04-06T11:46:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 11:46:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9309751","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9309751","identity":"rs-9309751","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}