Impact of the STK11/KRAS co-mutation on the response to immunotherapy in a real-world pan-cancer cohort.

INTRODUCTION: Immune checkpoint inhibitors are highly effective in treating various cancers. We analyzed the significance of the KRAS/STK11 co-mutation in relation to the efficacy of immune checkpoint inhibitors in pan-cancer patient cohort. METHODS: We analyzed data from open-access research: MSK-IMPACT (molecular profiling data from patients receiving systemic antitumor therapy) and MSK-TMB (molecular profiling data from patients receiving immune checkpoint inhibitors). In both studies, high throughput sequencing was used for molecular profiling. RESULTS: A total of 10,336 patients receiving antitumor therapy (MSK-IMPACT study) and 1661 patients receiving immune checkpoint inhibitors (MSK-TMB study) were included in the analysis. Co-mutation STK11/KRAS was found in 156 (1.5%) and 46 (2.8%) patients in the two studies, respectively. Most patients with the STK11/KRAS co-mutation had non-small cell lung cancer (83% and 85% in the two studies, respectively). Among non-small cell lung cancer patients, the STK11 mutation was associated with a worse outcome for patients receiving systemic antitumor therapy, but not immune checkpoint inhibition therapy (HR for OS 1.90 [95% CI 1.36-2.65] and 1.44 [95% CI 0.88-2.37]). Co-mutation STK11/KRAS was also not associated with patient outcome in any of the studies (HR for OS 0.93 [95% CI 0.56-1.52] and 1.09 [95% CI 0.54-2.19]). High tumor mutational burden was associated with better outcome in the cohort of patients receiving immune checkpoint inhibitors. An analogous analysis among patients in the pan-cancer cohort (excluding patients with non-small cell lung cancer) showed STK11 mutations and high tumor mutational burden have a predictive role for the efficacy of immune checkpoint inhibitors, but not STK11/KRAS co-mutation. CONCLUSIONS: Co-mutation STK11/KRAS is common among patients with non-small cell lung cancer and is not an independent predictive marker for the efficacy of immune checkpoint inhibitors. Further studies are required to clarify the role of STK11 mutations in immune checkpoint inhibitor treatment response.

Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform.

Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.