Cancer Biomarker Detection via Segmented Genetic Analysis

The patent segments the diagnostic process into multiple layers: first identifying recurrent copy number alterations using array CGH, then further characterizing them with FISH and karyotype analysis. This segmented approach allows precise identification of genetic alterations while managing complexity by breaking down the diagnostic workflow into manageable stages, each contributing specific precision at different levels of resolution.

The patent performs preliminary action by first identifying recurrent copy number alterations using array CGH before proceeding to more time-consuming FISH and karyotype analyses. This preliminary screening step filters out cases that don't require extensive analysis, thereby reducing overall diagnostic time while maintaining precision for cases that do show alterations. The preliminary identification of CNAs guides subsequent focused investigations.

The patent implements feedback by correlating genetic alteration data (from array CGH, FISH, and karyotype analysis) with clinical outcome data to develop prognostic models. The results feed back into refining the diagnostic criteria and identifying new genetic markers. This continuous feedback loop improves reliability in predicting clinical outcomes by systematically learning from accumulated data while managing complexity through iterative model refinement.

The patent applies local quality by tailoring treatment strategies to specific patient groups based on their genetic profiles. Instead of uniform treatment, patients with identified recurrent copy number alterations receive customized therapeutic approaches (such as targeted therapies or clinical trials) while others receive standard immunochemotherapy. This localized customization improves overall treatment effectiveness by matching therapy to patient-specific characteristics while managing complexity through risk-stratified management.