Functional Genetic Screening Enables Theranostic Molecular Imaging in Cancer.

Purpose: Targeted therapies for cancer have accelerated the need for functional imaging strategies that inform therapeutic efficacy. This study assesses the potential of functional genetic screening to integrate therapeutic target identification with imaging probe selection through a proof-of-principle characterization of a therapy-probe pair using dynamic nuclear polarization (DNP)-enhanced magnetic resonance spectroscopic imaging (MRSI).

Experimental Design: CRISPR-negative selection screens from a public dataset were used to identify the relative dependence of 625 cancer cell lines on 18,333 genes. Follow-up screening was performed in hepatocellular carcinoma with a focused CRISPR library targeting imaging-related genes. Hyperpolarized [1-C]-pyruvate was injected before and after lactate dehydrogenase inhibitor (LDHi) administration in male Wistar rats with autochthonous hepatocellular carcinoma. MRSI evaluated intratumoral pyruvate metabolism, while T-weighted segmentations quantified tumor growth.

Results: Genetic screening data identified differential metabolic vulnerabilities in 17 unique cancer types that could be imaged with existing probes. Among these, hepatocellular carcinoma required lactate dehydrogenase (LDH) for growth more than the 29 other cancer types in this database. LDH inhibition led to a decrease in lactate generation ( < 0.001) and precipitated dose-dependent growth inhibition ( < 0.01 overall, < 0.05 for dose dependence). Intratumoral alanine production after inhibition predicted the degree of growth reduction ( < 0.001).

Conclusions: These findings demonstrate that DNP-MRSI of LDH activity using hyperpolarized [1-C]-pyruvate is a theranostic strategy for hepatocellular carcinoma, enabling quantification of intratumoral LDHi pharmacodynamics and therapeutic efficacy prediction. This work lays the foundation for a novel theranostic platform wherein functional genetic screening informs imaging probe selection to quantify therapeutic efficacy on a cancer-by-cancer basis.