# Real world prediction and biological characterization of sotorasib sensitivity using multimodal AI and liquid biopsy genomic inputs

### Authors
Maayan Baron$^{1}$, Momeneh Foroutan$^{1}$, Sunil Kumar$^{1}$, Zong Miao$^{1}$, Ripple Khera$^{1}$, Felicia Kuperwaser$^{1}$, Dillon Tracy$^{1}$, Kevin Freisen$^{1}$, Brandon Funkhouser$^{1}$, Nathaniel Tann$^{1}$, Fahad Khan$^{1}$, Nick Lee$^{1}$, Sean Klei$^{1}$, Jordan Wolinsky$^{1}$, Kevin Shah$^{2}$, Aaron Hardin$^{2}$, Jean-François Martini$^{2}$, Jean-Michel Rouly$^{1}$, Allen Chao$^{1}$, Anshu Jain$^{1}$, Jeff Sherman$^{1}$, Emily Vucic$^{1}$

1. Zephyr AI, McLean, VA  
2. Guardant Health, Palo Alto, CA

### Summary
**Heterogeneous Response to KRAS $^{G12C}$ Inhibitors Highlights the Need for Functional Biomarkers Beyond Mutation Status**

- KRAS $^{G12C}$ inhibitors (e.g., sotorasib) have expanded treatment options in NSCLC, yet clinical benefit remains heterogeneous and often short-lived.
- KRAS $^{G12C}$ mutation status alone is insufficient to explain response heterogeneity, suggesting that functional KRAS dependency is not fully captured by current companion diagnostics.
- Reduced benefit from KRAS inhibition can arise through: MAPK pathway reactivation; alternative RTK-driven signaling; tumor plasticity; and EMT-associated states.
- These functional tumor states are not effectively captured by single-gene biomarkers.
- Liquid biopsy NGS is increasingly used at treatment initiation and longitudinally in clinical trials and routine care, creating an opportunity to infer functional drug sensitivity from routine clinical data across the patient journey.
- Multimodal AI approaches that capture complex tumor biology relevant to drug response from clinically available inputs may enable improved and streamlined patient stratification and more precise deployment of KRAS and other therapies.

### Study Objectives
- Evaluate whether a biologically interpretable multimodal AI model, AIM-Bx (Figure 1), can predict real-world benefit from sotorasib using minimal clinical features, commercially available liquid biopsy DNA inputs, and drug structure properties in NSCLC (Table 1).

### Clinically Available Inputs
- ctDNA-derived genomic inputs from the Guardant360 CDx liquid biopsy assay.

### Drug Response
Supports prospective & retrospective real-world clinical validation.

### Explainable Drug Response Predictions
For each patient & drug, AIM-Bx predicts...

### AI-enabled Stratification Using ctDNA Inputs Identifies Clinically Meaningful Benefit From Sotorasib in Real-World NSCLC patients

| Characteristic                     | Overall (N=39)      |
|-------------------------------------|---------------------|
| Age, yr                             |                     |
| Median (range)                     | 62.8 (45.5-83.9)    |
| Treatment age group, no. (%)       |                     |
| <65 yr                             | 23 (59.0)          |
| ≥65 yr                             | 16 (41.0)          |
| Sex, no. (%)                       |                     |
| Male                                | 9 (23.1)           |
| Female                              | 30 (76.9)          |
| Ethnicity, no. (%)                 |                     |
| Not Hispanic or Latino             | 26 (66.7)          |
| Unknown                             | 13 (33.3)          |
| Smoking history, no. (%)           |                     |
| Former smoker                      | 27 (69.2)          |
| Current smoker                      | 8 (20.5)           |
| Smoking history unknown             | 4 (10.3)           |
| Cancer subtype, no. (%)            |                     |
| Lung adenocarcinoma                | 38 (97.4)          |
| Lung squamous cell carcinoma       | 1 (2.6)           |
| Guardant Health sequencing panel, no. (%) |           |
| Guardant360 CDx                     | 26 (66.7)          |
| GuardantOMNI                       | 13 (33.3)          |
| Line of therapy for sotorasib, no. (%) |                  |
| First-line                          | 9 (23.1)           |
| Second-line or later                | 30 (76.9)          |
| Prior systemic therapy pre-sotorasib therapy, no. (%) | |
| Not applicable (first-line sotorasib) | 9 (23.1)    |
| Chemotherapy only                  | 9 (23.1)           |
| Chemotherapy + immune checkpoint inhibitor | 18 (46.2)  |

### Predicted Network- and Program-Level Tumor States Define Sensitivity to KRAS Inhibition Beyond KRAS $^{G12C}$ Dependency

Figure 2. AIM-Bx stratifies real-world NSCLC patients by clinical response to sotorasib. (A-B) Kaplan-Meier analyses of real-world progression-free survival (rwPFS) and overall survival (rwOS) in sotorasib-treated NSCLC patients stratified by model-predicted sensitivity. Predicted-sensitive patients demonstrated improved rwPFS (14 vs. 4 months) and rwOS (14 vs. 7 months; log-rank p < 0.05). (C-D) Univariable Cox analyses for rwPFS and rwOS adjusting for clinical and molecular variables available at treatment initiation. Model-predicted sensitivity was the only variable significantly associated with improved outcomes. (E) Aggregate feature importance highlighting genomic alterations contributing to model.

### Conclusion
**Multimodal AI Identifies Functional Target Dependence Beyond Genomic Biomarkers Using Routine Clinical Inputs**

Multimodal AI-enabled software operating on routine clinical data enables biologically grounded, scalable stratification for sotorasib and other KRAS inhibitors, with broader applicability to targeted therapies.