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PubMed2026

Unraveling the genomic blueprint of the Indian black soldier fly: From genome assembly to evolutionary insights.

The black soldier fly (BSF) (Hermetia illucens) has been renowned for its sustainable bioconversion capabilities, resulting in smart protein production with wide applications in animal feed, bioenergy, and biofertilizer. However, the genetic mechanisms underlying efficient bioconversion and productivity remain poorly understood. To advance strain-specific applications and strengthen genetic resource availability, we present the whole genome sequencing (WGS) data for an Indian isolate of black soldier fly. The assembled genome was 1.46 Gb with a scaffold N50 of 172.7 Mb, and a GC content of 42.6%. Furthermore, 64.17% of genomic sequences were masked as repeated, and 14,317 protein-coding sequences were identified. Variant analysis against the reference genome identified 34.44 million variants (∼33.25 million SNPs and ∼ 1.18 million INDELs), with the majority (99.3%) classified as MODIFIER, 0.54% as LOW impact, 0.14% as MODERATE, and only 0.003% as HIGH impact. Comparative genomic analysis with other related species revealed expansions of gene families in BSF associated with Immune effector (Antimicrobial peptides (AMPs), Lysozymes, and Peptidoglycan Recognition Protein (PGRP) and Detoxification (cytochrome P450 enzymes). Notably, AMPs in the Indian isolate showed enhanced copy number variation in defensin (27) and PGRP (40) compared to reference BSF, suggesting potential regional adaptations to pathogen exposure. Collectively, this genomic data provides an improved resource for evolutionary studies, functional genomics, and targeted genetic improvement of BSF for sustainable bioconversion applications.

Comparative genomics↗

PubMed2026

Integrated transcriptomic and functional characterization of Claudin-1 reveals its oncogenic and immunomodulatory roles in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest malignancies, driven by its invasive nature and lack of effective biomarkers. Disruption of the epithelial barrier, mediated by tight junction components, is a critical yet underexplored contributor to PDAC progression. Claudins, integral regulators of tight junction integrity, display altered expression across cancers, but their prognostic and immunomodulatory roles in PDAC remain unclear. We performed an integrative analysis of 177 RNA-Seq datasets from TCGA and GTEx to characterize Claudin family alterations in PDAC. Differential expression, copy number variation, methylation, and co-expression networks were analyzed alongside clinical and survival data. Prognostic significance was assessed using Kaplan - Meier and Cox regression analyses, while immune cell infiltration was examined using deconvolution algorithms. Functional validation of Claudin-1 was conducted in Capan-1 cells using CRISPR/Cas9 knockout, followed by proliferation, wound-healing, and Western blot assays. Ten Claudin genes were significantly dysregulated, with Claudin-1 and Claudin-4 frequently amplified and associated with advanced stage and poor survival. High Claudin-1 expression correlated with reduced immune infiltration, indicating an immune-excluded phenotype characterized by immune cells retained in the tumor stroma but largely absent from the tumor parenchyma. Claudin-1 knockout markedly inhibited proliferation, migration, and EMT, evidenced by downregulation of Snail and Slug and restoration of E-cadherin expression. This integrative transcriptomic and functional study identifies Claudin-1 as a key driver of PDAC aggressiveness and immune modulation. These findings establish Claudin-1 as a promising prognostic biomarker and therapeutic target for restoring epithelial integrity and counteracting immune evasion in pancreatic cancer.

Humans↗

PubMed2026

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell↗

PubMed2026

Utilization of long-read sequencing for the detection of structural rearrangements with AgileStructure.

MOTIVATION: Changes in genome organisation contribute to genetic disease when they disrupt gene function or regulation. Structural rearrangements may interrupt coding sequence or alter expression through promoter loss or gain, chromatin changes, copy-number variation, or disruption of short-range regulatory elements. Although short-read sequencing excels at detecting small variants, it performs poorly at resolving breakpoints of large rearrangements, especially in repetitive or low-complexity regions. Long-read sequencing overcomes these limitations, but analytical tools have not kept pace, making accurate identification and annotation of large structural variants challenging. RESULTS: We developed AgileStructure, a desktop application for locating and annotating large‑scale genomic rearrangements using aligned long‑read data. The software enables user‑guided exploration of breakpoint‑spanning reads, supporting accurate interpretation of complex events and filling a key gap in current structural variant analysis workflows. AVAILABILITY AND IMPLEMENTATION: Source code, binaries, user guide, and example aligned read data, are available on GitHub: https://github.com/msjimc/AgileStructure. An archived version is also available on Zenodo at https://doi.org/10.5281/zenodo.18610110.

Software↗

PubMed2026

Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.

MOTIVATION: Large scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding. RESULTS: To facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine-a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex 'cancer profiles' built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency. AVAILABILITY: The Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601.

Humans↗

PubMed2025

Genetic and phenotypic diversity of wine-associated Hanseniaspora species.

The genus Hanseniaspora includes apiculate yeasts commonly found in fruit- and fermentation-associated environments. Their genetic diversity and evolutionary adaptations remain largely unexplored despite their ecological and oenological significance. This study investigated the phylogenetic relationships, genome structure, selection patterns, and phenotypic diversity of Hanseniaspora species isolated primarily from Australian wine environments, focusing on Hanseniaspora uvarum, the most abundant non-Saccharomyces yeast in wine fermentation. A total of 151 isolates were sequenced, including long-read genomes for representatives of the main phylogenetic clades. Comparative genomics revealed ancestral chromosomal rearrangements between the slow-evolving lineage (SEL) and fast-evolving lineage (FEL) that could have contributed to their evolutionary split, as well as significant loss of genes associated with mRNA splicing, chromatid segregation and signal recognition particle protein targeting in the FEL. Pangenome analysis within H. uvarum identified extensive copy number variation, particularly in genes related to xenobiotic tolerance and nutrient transport. Investigation into the selective landscape following the FEL/SEL divergence identified diversifying selection in 229 genes in the FEL, with significant enrichment in genes within the lysine biosynthetic pathway. Furthermore, phenotypic screening of 116 isolates revealed substantial intraspecific diversity, with specific species exhibiting enhanced ethanol, osmotic, copper, SO₂, and cold tolerance.

Wine↗

PubMed2025

SegMantX: A Novel Tool for Detecting DNA Duplications Uncovers Prevalent Duplications in Plasmids.

Segmental duplications play an important role in genome evolution via their contribution to copy-number variation, gene-family diversification, and the emergence of novel functions. The detection of segmental duplications is challenging due to heterogeneous amelioration of sequence similarity among duplicates, which hinders the reconstruction of continuous sequence alignment. Here we introduce SegMantX, a novel approach for the identification of diverged segmental duplications in prokaryote genomes using local alignment chaining. In this approach, local alignments resulting from a preliminary sequence similarity search (e.g. BLASTn) are chained into continuous segments. Evaluating the performance of SegMantX using simulated sequences shows that the tool can detect diverged duplications beyond the sensitivity limits of standard alignment-based methods. Applying SegMantX to 6,784 enterobacterial plasmids, we find that 65% plasmids contain duplicated regions and gene duplications, most of which correspond either to dispersed, noncoding regions or duplicated mobile genetic elements (MGEs; e.g. transposons and insertion sequences). Furthermore, we demonstrate the applicability of SegMantX for the identification of diverged gene transfers between replicons and plasmid hybridization events. Our findings highlight MGEs as drivers of segmental duplications in plasmid evolution, leading to the amplification of their cargo genes, including antibiotic resistance genes. SegMantX provides a powerful framework for reconstructing diverged segmental duplications and other alignment problems.

Plasmids↗

PubMed1983

Nucleotide sequence of the genetically labile repeated elements 5' to the origin of mouse rRNA transcription.

We have determined the complete nucleotide sequence of a cloned Balb/c mouse rDNA NTS fragment containing 13 tandem copies of a 135 bp subrepeating segment. This repetitious region (VrDNA) lies close to the origin of ribosomal RNA transcription. Analyses of these VrDNA subrepeats from Balb/c and a related species, Mus pahari, reveal regions of inverted repeat DNA as well as large poly T tracts, either of which may be significant to the generation of the high levels of VrDNA copy number variation found in wild and inbred mice and/or the modulation of rRNA synthesis. Unlike the highly homogeneous subrepeats in the Xenopus laevis NTS repetitious regions, the VrDNA subrepeats differ from one another on the average by about 13%. Sequence analysis and Southern hybridization studies have also shown that, unlike the Xenopus and Drosophila NTS, extensive duplications of sequences found surrounding the mouse rRNA initiation site are found neither in the VrDNA region nor 6 kb further upstream in the NTS.

Animals↗

PubMed2026

Diverse haplotypes at a complex Solanum americanum locus confer resistance to Phytophthora infestans and P. capsici.

Plants encounter diverse pathogens and have evolved a two-layered innate immune system to detect pathogen molecules and activate defense mechanisms that restrict infection. Most cloned plant Resistance (R) genes encode NLR immune receptors. NLR genes are often found in clusters of paralogs with sequence and copy number variation; whether these NLR clusters evolve in response to single or multiple pathogens has been unclear. We report here the isolation of a Phytophthora capsici resistance gene, Rpc2, along with a novel P. infestans resistance gene, Rpi-amr5, from two Solanum americanum accessions. These orthologous genes reside in the Rpi-amr1 cluster, which has previously been associated with resistance to P. infestans. By screening RXLR effector libraries of P. infestans and P. capsici, we identified multiple effectors recognised by both NLRs. Our findings highlight the complexity of NLR clusters and evolution driven by interactions with multiple pathogens. This work will underpin efforts to elevate resistance against Phytophthora pathogens and enhances our understanding of NLR evolution.

Journal Article↗

PubMed1989

A tandem repeat sequence found in a heterogeneous fragment of UL of herpes simplex virus type 1.

We found a tandem repeat sequence in the region (designated BS7) in which restriction fragments BamHI D and SalI B overlap each other, near the centre of the unique long sequence (UL) of the herpes simplex virus type 1 (HSV-1) strain F genome. The SmaI physical map of BS7 was constructed, and the position of a heterogeneous SmaI subfragment from HSV-1 isolates and plaque-purified clones from a single strain was defined on the map. The maximum size difference in the SmaI subfragment was estimated to be 600 bp between these isolates and 100 bp between the clones. The 0.23 kb SmaI subfragment recloned from BS7 was sequenced, and was shown to contain a tandem repeat sequence consisting of 15 units of 12 bp, 5' TTGGGGCTGGGG 3'. These results suggest that the fragment length heterogeneity in the UL of HSV-1 isolates and clones is attributable to copy number variation of the tandem repeat sequence.

Base Sequence↗

PubMed2025

Replication stress increases de novo CNVs across the malaria parasite genome.

Changes in the copy number of large genomic regions, termed copy number variations (CNVs), contribute to important phenotypes. CNVs are readily identified using conventional approaches when present in a large fraction of the cell population. However, CNVs in only a few genomes are often overlooked but important; if beneficial, a de novo CNV that arises in a single genome can expand during selection to create a population of cells with novel characteristics. While single cell methods for studying de novo CNVs are increasing, we continue to lack information about CNV dynamics in rapidly evolving microbial populations. Here, we investigated de novo CNVs in the genome of the Plasmodium parasite that causes human malaria. The highly AT-rich P. falciparum genome readily accumulates CNVs that facilitate rapid adaptation. We employed low-input genomics and specialized computational tools to evaluate the impact of sub-lethal stress on the de novo CNV rate. We observed a significant increase in genome-wide de novo CNVs following treatment with an antimalarial compound that inhibits replication. De novo CNVs encompassed genes from various cellular pathways participating in human infection. This snapshot of CNV dynamics emphasizes the connection between replication stress, DNA repair, and CNV generation in this important microbial pathogen.

Journal Article↗

PubMed2025

Investigating the genomic landscape of mouse models of breast cancer metastasis.

Metastasis remains a major cause of cancer mortality. AbstractThis study, expanding upon previous findings in the MMTV-PyMT model, investigated four independent mouse models, representing luminal (MMTV-PyMT, MMTV-Myc), HER2-amplified (MMTV-Her2) and triple negative (C3(1)TAg) breast cancer subtypes. Consistent with previous results, limited evidence for metastasis-associated somatic point mutations was found for all models. We also found that oncogenic drivers significantly influenced the number and size of metastasis-specific copy number variations (MSCNVs), but common driver-independent MSCNVs were rare. Furthermore, analyzing a cohort with varying genetic backgrounds while maintaining a constant oncogenic driver (PyMT) revealed that genetic background profoundly impacts MSCNVs. Transcriptome analysis demonstrated that oncogenic drivers strongly shaped metastasis-specific gene expression (MSGE), with each driver exhibiting distinct expression profiles. In contrast, MSGE in the PyMT-F1 cohort was more variable across strains. Despite the diversity of MSCNV and MSGE, functional analysis revealed that both mechanisms converge on the modulation of key cellular processes, including immune responses, metabolism, and extracellular matrix interactions. These findings emphasize the complex interplay between oncogenic drivers and genetic background in shaping the genomic and transcriptional landscapes of metastatic lesions.

Journal Article↗

PubMed1992

Apparent heterozygote deficiencies observed in DNA typing data and their implications in forensic applications.

Restriction fragment length polymorphisms (RFLP) analysis using the Southern blot technique can be used to recognize copy number variation of variable number of tandem repeats (VNTR) of conserved core sequences at several regions of the human genome. This new class of polymorphisms reveals a high degree of genetic variation, useful for individual identification purposes. Criticisms against forensic applications of such DNA typing data include the limitation of employing Hardy-Weinberg expectation of genotype frequencies, since several surveys indicate apparent deficiency of heterozygosity (or excess homozygosity) in comparison with Hardy-Weinberg expectations. This research postulates an alternative explanation of deficiency of apparent heterozygosity which is caused by the inability to detect extremely small-sized alleles (called 'non-detectable' alleles) due to the sensitivity of Southern gel electrophoresis. We show that the presence of 'non-detectable' alleles can produce pseudo-homozygosity and their frequencies can be predicted from the observed proportional heterozygote deficiency. Furthermore, in the covert presence of such 'non-detectable' alleles, we show that the gene-count method provides over-estimates of allele frequencies in the sample population, and hence the Hardy-Weinberg predictions of genotype frequencies avoid wrongful bias against suspects in forensic applications of DNA typing data. Applications of this theory to population data on six VNTR loci in US Caucasians and US Blacks suggest that the presence of 'non-detectable' alleles could be the major cause of apparent heterozygote deficiency, and the current approaches of predicting the population frequency of specific DNA phenotypes are practically free of the possible wrongful bias in courtroom applications of DNA typing data.

Alleles↗

PubMed2026

Establishment of Stable Immortalized Human Choroidal Melanocytes for Ocular Research.

PURPOSE: The short lifespan of primary normal choroidal melanocytes (NCMs) in vitro represents a major barrier to mechanistic, functional, and translational studies of choroid biology and uveal melanoma (UM). This study aimed to establish and characterize immortalized human NCM lines that retain melanocytic function, maintain a non-cancerous profile, and are amenable to gene editing. METHODS: NCMs from four donors were immortalized by lentiviral transduction of cyclin-dependent kinase 4 (CDK4R24C), cyclin D1, and human telomerase reverse transcriptase (hTERT), establishing NCM-K4DT lines. Their morphology, melanocytic marker expression, proliferation, and functional properties (melanin synthesis and tyrosinase activity) were evaluated. Genomic stability was assessed by targeted mutation profiling, karyotyping, and copy number variation (CNV) analysis. The tumorigenicity was tested in immunodeficient mice. Plasmid-based CRISPR/Cas9 editing was performed to determine their suitability for gene editing. RESULTS: NCM-K4DT lines retained dendritic-shaped morphology, pigmentation, and expression of PMEL, TYRP1, Melan-A, and SOX10. Cells exhibited enhanced proliferative capacity with preserved cell cycle regulation. Melanin production and tyrosinase activity were comparable to primary NCMs. Genomic profiling confirmed the absence of UM-associated driver mutations and chromosomal abnormalities. In vivo growth assays demonstrated that NCM-K4DT lines did not form tumors within the 3-month observation period. Notably, NCM-K4DT cells were efficiently edited by CRISPR/Cas9. CONCLUSIONS: NCM-K4DT lines represent stable, non-cancerous, and genetically tractable models for studying choroidal melanocyte biology, modeling UM-associated mechanisms, and advancing therapeutic development in ocular research.

Humans↗

PubMed2025

Quinoxaline-based anti-schistosomal compounds have potent anti-plasmodial activity.

The human pathogens Plasmodium and Schistosoma are each responsible for over 200 million infections annually, especially in low- and middle-income countries. There is a pressing need for new drug targets for these diseases, driven by emergence of drug-resistance in Plasmodium and an overall dearth of drug targets against Schistosoma. Here, we explored the opportunity for pathogen-hopping by evaluating a series of quinoxaline-based anti-schistosomal compounds for their activity against P. falciparum. We identified compounds with low nanomolar potency against 3D7 and multidrug-resistant strains. In vitro resistance selections using wildtype and mutator P. falciparum lines revealed a low propensity for resistance. Only one of the series, compound 22, yielded resistance mutations, including point mutations in a non-essential putative hydrolase pfqrp1, as well as copy number amplification of a phospholipid-translocating ATPase, pfatp2, a potential target. Notably, independently generated CRISPR-edited mutants in pfqrp1 also showed resistance to compound 22 and a related analogue. Moreover, previous lines with pfatp2 copy number variations were similarly less susceptible to challenge with the new compounds. Finally, we examined whether the predicted hydrolase activity of PfQRP1 underlies its mechanism of resistance, showing that both mutation of the putative catalytic triad and a more severe loss of function mutation elicited resistance. Collectively, we describe a compound series with potent activity against two important pathogens and their potential target in P. falciparum.

Quinoxalines↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗

PubMed2026

Prognostic Role of Global DNA Methylation in Renal Cancer Reveals Decitabine Treatment Benefit.

BACKGROUND: Renal cancer presents a significant global health challenge due to its rising incidence and mortality rates. Often undetected in early stages, it complicates diagnosis and treatment. Current therapies face resistance and limited effectiveness, especially in advanced stages. The diverse subtypes of renal cancer highlight the need for new biomarkers and risk assessment tools for targeted treatments. OBJECTIVE: This study aims to assess the prognostic significance of global DNA methylation (GM) levels in renal cancer, identify new biomarkers, and evaluate the therapeutic potential of the DNA methyltransferase inhibitor decitabine. METHODS: Data on RNA sequencing, gene mutations, DNA methylation, and clinical outcomes were collected from TCGA and GEO databases. We calculated global DNA methylation scores (GMS) and categorized patients into high, intermediate, and low GMS groups. Survival analysis and genomic analyses were conducted to explore the relationships between GMS, clinical outcomes, and tumor characteristics. RESULTS: Higher GMS was identified as an independent prognostic factor associated with worse outcomes in renal cancer. Patients with elevated GMS showed increased mutations, copy number variations, and a more aggressive tumor phenotype. Treatment with decitabine was observed to reduce tumor hypermethylation and downregulate cell cycle pathway activity, indicating potential therapeutic benefits. CONCLUSION: Global DNA methylation plays a significant role in renal cancer prognosis. GMS may serve as valuable biomarkers for prognosis and personalized treatment strategies. Decitabine shows potential efficacy for high GMS patients, particularly through its impact on cell cycle regulation, underscoring the importance of personalized approaches in cancer treatment.

Humans↗

PubMed2026

A pan-cancer multi-omic SuperLearner for regulated cell death survival topologies.

INTRODUCTION: Regulated cell death (RCD) pathways influence tumor progression and immune modulation. We previously constructed a signature database mapping 25 RCD forms across seven multi-omic layers and 33 tumor types (CancerRCDShiny). Despite their ability to identify risk populations, translating these signatures into personalized clinical workflows requires a shift from cohort stratification to individualized risk mapping by modeling patient risk (survival topologies) to capture the non-linear dynamics of RCD signatures. METHODS: We engineered a pan-cancer multi-omic SuperLearner pipeline across 33 cancer types. Phase I performed zero-leakage harmonization and groupwise imputation to prevent cross-cohort amalgamation. Phase II deployed Elastic Net-regularized Cox regression as a CANARY diagnostic to map proportional hazards failures. Strata with a 35% missingness barrier entered Phase III, deploying a Quadripartite ensemble: Random Survival Forests, XGBoost, Survival-Boruta, and Multi-Task Logistic Regression, fused within an Elastic Net Multi-View Meta-Learner (MVL), with post-hoc TreeSHAP and LIME interpretability. RESULTS: The CANARY diagnostic demonstrated the structural invalidity of pan-cancer geometric proportional hazards. Across 96 admissible strata, Phase III executed algorithmic displacement: continuous multi-omic topologies suppressed static genomic mutations and copy number variations (85.7% vs. 0.0% apex retention). The MVL stabilized predictions against extreme variance; LIME surrogate validations (R 2 < 0.10) confirmed the systematic failure of linear interpretative proxies. N-dimensional TreeSHAP interaction mapping exposed synergistic and antagonistic rescue trajectories defining individualized Survival Topologies, which were invisible to additive models. The architecture was deployed as CancerRCDPredictor, a digital molecular tumor board with integrated LLM capabilities. The MVL SuperLearner achieved a median C-index of 0.749 (IQR: 0.722-0.836) across 96 modelable strata, with 95% bootstrap confidence intervals confirming precision (median width: 0.052) and permutation significance in 93.8% of strata (p < 0.001). External CPTAC validation across ten cancer types demonstrated significant cross-cohort generalizability in clear cell renal carcinoma (KIRC; C-index 0.675, p = 0.017) and modest performance across the remaining adequately powered cancers (median 0.582), underscoring the need for larger multi-institutional validation cohorts. CONCLUSION: This pan-cancer multi-omic SuperLearner bypasses linear topological failures, advancing beyond generalized stratification to establish a deterministically mapped architecture for predicting RCD-related survival topologies. Through the CancerRCDPredictor interface, multi-omic insights translate into individualized survival topology exploration, providing a foundation for future precision oncology validation.

SuperLearner↗