Hybrid phenotype mining method for investigating off-target protein and underlying side effects of anti-tumor immunotherapy

Inducing mismatch repair deficiency sensitizes immune-cold neuroblastoma to anti-CTLA4 and generates broad anti-tumor immune memory

Immune checkpoint blockade can induce potent and durable responses in patients with highly immunogenic mismatch repair-deficient tumors; however, these drugs are ineffective against immune-cold neuroblastoma tumors. To establish a role for a T cell-based therapy against neuroblastoma, we show that T cell and memory T cell-dependent gene expression are associated with improved survival in high-risk neuroblastoma patients. To stimulate anti-tumor immunity and reproduce this immune phenotype in neuroblastoma tumors, we used CRISPR-Cas9 to knockout MLH1-a crucial molecule in the DNA mismatch repair pathway-to induce mismatch repair deficiency in a poorly immunogenic murine neuroblastoma model. Induced mismatch repair deficiency increased the expression of proinflammatory genes and stimulated T cell infiltration into neuroblastoma tumors. In contrast to adult cancers with induced mismatch repair deficiency, neuroblastoma tumors remained unresponsive to anti-PD1 treatment. However, anti-CTLA4 therapy was highly effective against these tumors. Anti-CTLA4 therapy promoted immune memory and T cell epitope spreading in cured animals. Mechanistically, the effect of anti-CTLA4 therapy against neuroblastoma tumors with induced mismatch repair deficiency is CD4+ T cell dependent, as depletion of these cells abolished the effect. Therefore, a therapeutic strategy involving mismatch repair deficiency-based T cell infiltration of neuroblastoma tumors combined with anti-CTLA4 can serve as a novel T cell-based treatment strategy for neuroblastoma.

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as “omics” data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.