Composite biomarker discovery

Modern discovery and development of new patient treatments depends on clinical biomarkers from multiple data modalities, including clinical records, imaging, and molecular data, to identify personalized composite phenotypes. These phenotypes, and the modalities they are extracted from, operate at different biological, contextual and time scales. Multimodal representations can accelerate composite clinical biomarker detection and quantification and improve patient stratification to enable a better prediction of disease progression and treatment response.

Enabling technology

The generation of multimodal representations is facilitated by several IBM technologies that generate and integrate modality-specific encodings. These range from molecular analyses through deep learning on medical images, tissue images and clinical data, to multiple data integration and fusion techniques (see FuseMedML). While biomarkers from modality-specific representations can enhance clinical trials through patient stratification and disease staging, an integrated 360 view of the patient that also encodes the patient journey can accelerate the next-generation of discovery by revealing the connections between biomarkers across biological, contextual and time scales (Figure 1).

Diagnosis and response prediction in cancer

Using different modalities, our technologies succeeded in identifying novel biomarkers and disease mechanisms to better understand breast cancer. These include characterizing spatial heterogeneity, clonal evolution in drug resistance, and deconvolving cell-free DNA from multiple lesions3. We also demonstrated the value of integrating our deep learning frameworks across modalities. The patient representations derived from 3D mammograms and clinical records were shown to be useful for reducing radiologists’ workloads by 40% (Figure 2). Further, we showed that our technologies can leverage imaging and clinical data to enable the pre-biopsy histopathological subtyping of breast lesions and predict future metastases.