Cancer Optimizer · Causal-inference platform

When the standard therapy fails, the answer is often in the mechanism, not the molecule.

An atlas of 209 cited failed cancer therapies and their documented resistance mechanisms, across 27 solid and blood cancers. For patients who have relapsed or become resistant, BiRAGAS re-reads the biology to separate the causal driver from the noise.

Explore the cancer atlas For treating physicians →

27cancer types

209cited pathways

209illustrations

7failure patterns

Example: feedback-reactivation resistance to a BRAF inhibitor in colorectal cancer.

The situation

Thousands of patients are in the same position

Across the oncology landscape, the same failures repeat: the right drug in the wrong (unenriched) population, a target that was a correlate rather than a driver, a pathway that simply routed around the block. These are reasoning failures a causal model is built to surface, and they leave real patients without options.

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Relapse & acquired resistance

A response that isn't a cure, the tumor evolves an escape route (a gatekeeper mutation, a bypass pathway, antigen loss).

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"The trial failed"

A drug abandoned for the average patient may still be right for a molecularly-defined subgroup that the trial averaged away.

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Out of standard options

When guidelines are exhausted, a mechanism-anchored re-read can identify the enriched subgroup or the missing combination partner.

Interactive atlas

Find the cancer. See the failed pathways.

Pick a cancer type from the list. Each failed therapy is shown with its illustrated resistance mechanism, its citation and ClinicalTrials.gov link, and the BiRAGAS failure pattern. Every figure downloads as PNG or JPEG.

Why BiRAGAS

Seven recurring failure patterns, and the causal capability that addresses each

The BiRAGAS commentary below is retrospective and illustrative: it names the class of signal a causal-inference platform is built to surface. It is not a claim that BiRAGAS prospectively predicted any specific trial.

Resistance taxonomy

Every failure, classified: the 13 resistance mechanisms

Each of the 209 pathways is labelled with the mechanism class that best explains its failure, most-represented first. Click any class to filter the atlas to it.

Resistance class	N	Definition & canonical oncology examples

For treating physicians

Is your patient in one of these situations?

If a patient has relapsed, become resistant, or exhausted standard options, a BiRAGAS causal evaluation re-reads their tumor biology to identify what actually drives the disease, and what a mechanism-anchored next step could look like.

Causal driver vs. correlate, separate the node whose perturbation changes the outcome from the associated noise.
In-silico perturbation, test whether blocking a node collapses the phenotype or the flux simply reroutes (redundancy).
Patient stratification, identify the molecular subgroup in whom the target is the true driver.
Multi-evidence validation, a defensible, cited evidence trail for every hypothesis, for discussion at tumor board.

Enroll a patient in a BiRAGAS causal evaluation

Physician-initiated, evaluation collaboration. We return a mechanism-anchored, cited analysis.

Contact BiRAGAS →

biragas@ayassbioscience.com

Take it with you

The full BiRAGAS Oncology Pathway Atlas

A professional booklet of every illustrated pathway, with a clickable index, citations, and BiRAGAS pattern for each, plus the underlying validated dataset.

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Oncology Pathway Atlas, Master Booklet (PDF)

209 illustrated failed-therapy pathways · clickable index · every figure cited and NCT/PMID-validated.

Download booklet (PDF)

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Data provenance

209 pathways · ClinicalTrials.gov + PubMed validated · 122/122 NCTs title-matched. Every illustration is an AI-assisted schematic with GeneCards-verified labels.