Welcome to the CancerRCDPredictor. This tool bridges 96 predictive models across Pan-Cancer cohorts (spanning OS, DSS, PFI, and DFI metrics) using a frozen, pre-computed SuperLearner Multi-View architecture.
This application operates as an advanced Clinical Diagnostic Engine. It utilizes pre-rendered survival geometries to prevent server exhaustion while delivering high-fidelity, manuscript-grade interaction topologies.
At the core of this framework lies an exhaustive machine learning pipeline designed to bypass linear topological failures and predict individualized clinical non-proportional hazard trajectories. By deploying a Quadripartite Ensemble (RSF, XGBoost, Boruta, and MTLR) fused via a Multi-View Meta-Learner (MVL), the algorithm structurally audited over 12,613 multi-omic signatures spanning 7 distinct omic layers. Rather than relying on simple additive prognostic markers, this advanced SuperLearner isolates the true predictive power of non-linear biological geometries. By extracting N-dimensional TreeSHAP topologies, the application dynamically exposes the exact lethal and protective trajectories per patient—singularizing the specific biological markers that act as primary non-proportional hazard drivers or robust protective shields.
Machine Learning survival geometries (like SHAP and LIME) can be dense. This section serves as a static guide to interpreting penalty vs. protection axes.
1. The SHAP BeeSwarm
A right-sided (positive) SHAP value indicates a lethality driver (increased non-proportional hazard). A left-sided (negative) value indicates a protective shield (decreased non-proportional hazard).
2. SHAP Topologies (Synergy, Antagonism, & Bifurcation)
By plotting two interacting genes against their SHAP values, we decode their biological synergy, antagonism, or bifurcation across the clinical non-proportional hazard domain.
3. Individual Patient Trajectories (Precision Oncology)
SHAP Waterfall/Force Plots decompile the predictive logic for a single patient against a population baseline. The visualized bars directly represent specific multi-omic signatures (both continuous expression vectors and discrete genomic states like mutations/CNVs) acting as vectors of non-proportional hazard or protection. The predictive weight of each signature is defined by its breadth (width), orientation along the axis, and its top-to-bottom sequence in the trajectory.
Orange bars represent omic signatures imposing aggressive non-proportional hazard penalties (lethality), while purple bars represent signatures forcing deep negative non-proportional hazard pushes (protective shields).
Phase I: Harmonization
Raw multi-omic inputs strictly harmonized and dimensionally mapped via LiSHMOM logic.
Phase II: CANARY Protocol
Cohorts violating Proportional Hazards (PH) geometry structurally quarantined via CoxNet.
Phase III: Ensemble Synthesis
Topologies decoded using a Quadripartite Ensemble synthesized by a Multi-View ElasticNet SuperLearner.
Select a specific machine learning algorithmic framework to instantly project its specific mathematical Importance and Validation payload across the Golden 150 biological signatures.
Explore the Time-dependent ROC (AUROC) horizons for the 96 finalized models.
Figure 9: Dual TimeROC
HOW TO READ: AUROC PERFORMANCE
The X-axis represents False Positive Rate (1-Specificity), while the Y-axis tracks True Positive Rate (Sensitivity). The colored curves represent the discriminatory capability (AUC) of the MVL framework across three time horizons (1-year, 3-year, 5-year). The dotted diagonal represents random effect (AUC 0.50).
(A) Lush Multi-Omic Prognostic Stability (LGG DSS): Testing the algorithm's decentralized quad-core resilience, this panel demonstrates the framework generating and maintaining a high plateau of clinical discrimination across a deeply fragmented multi-omic terrain. By democratically balancing 25.0% trust across all four learning architectures, the SuperLearner successfully flattens prognostic entropy over time (maintaining an AUC of 0.931 at 1-year, 0.900 at 3-year, and 0.802 at 5-year progression nodes). This prevents the chronological predictive degradation typically seen in Lower Grade Gliomas.
(B) Supreme Algorithmic Convergence (READ OS): Powered by a near-total 95.7% sparsity-aware XGBoost hierarchy, this panel maps the temporal diagnostic trajectory when continuous omic parameters align perfectly into deterministic geometric axes. The framework achieves flawless instantaneous prognostic authority (AUC = 1.000 at 1-year) and successfully anchors a virtually impenetrable predictive barrier (AUC = 0.996) out to the 3-year tracking horizon, before encountering expected entropy drop-offs at extended boundaries (AUC = 0.842 at 5-year).
Explore the macro-level non-proportional hazard drivers isolated across the 96 finalized prognostic models.
Pedagogical Exemplars
Select a specific cohort exemplar from the manuscript to explore its macro-level non-proportional hazard drivers.
Explore specific multi-omic interactions mapping clinical non-proportional hazard interception at the cohort level.
Figure 8: Interaction Topologies
HOW TO READ: PANELS A, B, C
(A) Synergy (LUAD DSS): The partner mutation acts as a potent catalyst, violently accelerating the patient cloud upward into extreme lethality as the primary driver increases across the x-axis.
(B) Antagonism (LUAD DSS): The partner mutation acts as a functional buffer, physically rescuing the patient cloud by forcing the mortality trajectory back down into the protective zone.
(C) Bifurcation (SKCM OS): The primary driver dictates the absolute clinical hemisphere (x-axis), while the distinct modulating partner stratifies the internal density of the clouds.
This module isolates the 150 elite biological signatures (Table S12) that were universally retained by all four Machine Learning algorithms (RSF, XGBoost, Boruta, MTLR).
Explore specific multi-omic interactions mapping clinical non-proportional hazard interception at the individual patient level.
Clinical Probabilities: Analyze dynamic patient-specific survival and event probabilities across sequential 1, 3, and 5-year clinical landmarks using the Phase III algorithmic ensemble.
Non-Proportional Hazard Trajectory Selector
Select a patient trajectory to singularize the signatures driving the prediction toward lethality or pulling it back to safety.
CancerRCDPredictor Diagnostic Report (Precision Oncology)
Model Selection
CancerRCDPredictor Diagnostic Report (Precision Oncology)
Navigate the completely sequestered internal validation cohort. View the Dual-Track continuous algorithmic trajectories mapping 1, 3, and 5-year clinical probabilities.
A searchable datatable containing all prognostic signatures, categorized by their biological omic layer origin.
This engine converts the 12,613 high-dimensional prognostic signatures (Table S8) into human-readable clinical diagnostic reports.
Navigate and download the supporting raw datasets, trained model bundles, and analytical matrices directly from the ZIMA server architecture.
Directory Contents
If you use this prediction tool or the underlying ML methodologies in your research, please cite our manuscript:
@article{CancerRCDPredictor2026,
title={A pan-cancer quadripartite machine learning ensemble for decoding prognostic multi-omic topographies},
author={Emanuell Rodrigues de Souza and Higor Almeida Cordeiro Nogueira and Victor dos Santos Lopes and Enrique Medina-Acosta},
journal={Under Review},
year={2026}
}
Meet the team behind the Phase I-III computational pipelines.
