Hu, Li

Abstract Reprogramming of aged donor tissue cells into induced pluripotent stem cells (A-iPSC) preserved the epigenetic memory of aged-donor tissue, defined as genomic instability and poor tissue differentiation in our previous study. The unbalanced expression of RNA exosome subunits affects the RNA degradation complex function and is associated with geriatric diseases including premature aging and cancer progression. We hypothesized that the age-dependent progressive subtle dysregulation of EXOSC2 (exosome component 2) causes the aging traits (abnormal cell cycle and poor tissue differentiation). We used embryonic stem cells as a tool to study EXOSC2 function as the aging trait epigenetic memory determined in A-iPSC because these aging traits could not be studied in senesced aged cells or immortalized cancer cells. We found that the regulatory subunit of PP2A phosphatase, PPP2R5E, is a key target of EXOSC2 and this regulation is preserved in stem cells and cancer.

3079 Background: Elraglusib (9-ING-41), a novel inhibitor of GSK-3β, has been evaluated in >230 patients (pts) with advanced malignancies including metastatic melanoma in the 1801 phase 1/2 trial (NCT03678883). Pts with melanoma treated with single agent elraglusib demonstrated clinical benefit including a durable complete response despite multiple prior lines of therapy including checkpoint inhibitors (CPI). Methods: We collected data from numerous -omic modalities in the 1801 trial to characterize markers of elraglusib response with bioinformatics and machine learning (ML). Genomic panels were collected on 105 pts. The most frequent tumor types included colorectal cancer (n=35), pancreas (n=31), lung (n=15) and metastatic melanoma (n=12). Pts were treated with elraglusib as a single agent or in combination with chemotherapy. Pathway based features were generated by quantifying the percent of each Reactome pathway that was mutated. These pathway features were iteratively evaluated for their performance in shallow neural networks predicting patient RECIST with each model using 5-15 randomly selected features at a time. Publicly available genomic data from an anti-PD-1 trial in patients with metastatic melanoma (Hugo et al., 2017) was transformed into pathway-based features and input into the final model to predict elraglusib response in this population. Results: Our final pathway based genomic model for elraglusib response performed with a training accuracy of 93% and a test accuracy of 88% on the 1801 patients. We then tested the model on metastatic melanoma patients who were treated with anti-PD-1 therapy. We found that patients from Hugo et al. were predicted to respond to elraglusib (22/26). In addition, most patients with tumors resistant to anti-PD-1 therapy were also predicted to respond to elraglusib (10/12). Finally, we tested the model on metastatic melanoma patients treated with elraglusib in the 1801 study that had failed CPI and for whom -omic data was available. The model correctly identified 5/5 patients that demonstrated clinical benefit (1 CR, ongoing>1400 days; 4 prolonged OS of 107, 256, 357, and 556 days). Conclusions: We applied robust in-silico modeling developed using 1801 trial data to develop a rationale for the development of elraglusib in the treatment of metastatic melanoma including patients that failed or were refractory to anti-PD-1 treatment. This model correctly identified metastatic melanoma patients in 1801 with good clinical outcomes that had failed prior CPI treatment. Further, we present this in silico approach as a robust way to repurpose prior clinical study genomics and ML modeling for population enrichment during the clinical development of novel drugs. Clinical trial information: NCT03678883 .