👤 M D Porta

Although KRASG12C inhibitors show clinical activity in patients with KRAS G12C mutated non-small cell lung cancer (NSCLC) and other solid tumor malignancies, response is limited by multiple mechanisms of resistance. The KRASG12C inhibitor JDQ443 shows enhanced preclinical antitumor activity combined with the SHP2 inhibitor TNO155, and the combination is currently under clinical evaluation. To identify rational combination strategies that could help overcome or prevent some types of resistance, we evaluated the duration of tumor responses to JDQ443 ± TNO155, alone or combined with the PI3Kα inhibitor alpelisib and/or the cyclin-dependent kinase 4/6 inhibitor ribociclib, in xenograft models derived from a KRASG12C-mutant NSCLC line and investigated the genetic mechanisms associated with loss of response to combined KRASG12C/SHP2 inhibition. Tumor regression by single-agent JDQ443 at clinically relevant doses lasted on average 2 weeks and was increasingly extended by the double, triple, or quadruple combinations. Growth resumption was accompanied by progressively increased KRAS G12C amplification. Functional genome-wide CRISPR screening in KRASG12C-dependent NSCLC lines with distinct mutational profiles to identify adaptive mechanisms of resistance revealed sensitizing and rescuing genetic interactions with KRASG12C/SHP2 coinhibition; FGFR1 loss was the strongest sensitizer, and PTEN loss the strongest rescuer. Consistently, the antiproliferative activity of KRASG12C/SHP2 inhibition was strongly enhanced by PI3K inhibitors. Overall, KRAS G12C amplification and alterations of the MAPK/PI3K pathway were predominant mechanisms of resistance to combined KRASG12C/SHP2 inhibitors in preclinical settings. The biological nodes identified by CRISPR screening might provide additional starting points for effective combination treatments. Identification of resistance mechanisms to KRASG12C/SHP2 coinhibition highlights the need for additional combination therapies for lung cancer beyond on-pathway combinations and offers the basis for development of more effective combination approaches. See related commentary by Johnson and Haigis, p. 4005. Show less

Weight loss in patients with metabolic syndrome has positive effects on cardiovascular and type 2 diabetes risks, but its effects on peripheral cytokines and lipid profiles in patients are still unclear. To determine the effects of diet-induced weight loss on metabolic parameters, lipids and cytokine profiles. Eighteen adult males with metabolic syndrome (defined according to IDF 2009) and Body Mass Index (BMI) between 25 and 35 kg/m After weight loss, a significant improvement in BMI, waist circumference, insulin, fasting blood glucose and HOMA-IR (homeostasis model assessment of insulin resistance) was observed. The analysis of LDL (low-density lipoprotein cholesterol) and HDL (high-density lipoprotein cholesterol) lipoproteins showed a change in their composition with a massive transfer of triacylglycerols from HDL to LDL. This was associated with a significant reduction in peripheral pro-inflammatory cytokines such as IL-6, TNF-α, IL-8 and MIP-1β, leading to an overall decreased inflammatory score. An interesting positive correlation was also observed among peripheral cytokines levels after diet and peripheral levels of CETP (cholesteryl ester transfer protein), an enzyme with a key role in lipid change. Weight loss through caloric restriction is associated with an improvement in peripheral lipid and cytokine profiles that may play a major role in improving cardiovascular risk. Show less

CircularRNAs (circRNAs) are non-coding RNAs which compete for microRNA (miRNA) binding, influencing the abundance and stability of other RNA species. Herein we have investigated the effect of circRNAs on the mir200-ZEB1 feedback loop in relationship with the aggressiveness of human melanoma cells. We first compared the level of expression of key factors in the mir200-ZEB1 feedback loop in primary human melanoma cells compared with their matching metastatic one and found a correlation between the aggressiveness of the cells and the level of expression of ZEB1 and SNAI1. We also analyzed factors in the mir200-ZEB1 feedback loop, including circZEB1, during the phenotypic switching of human melanoma cells. Our results showed a correlation between the level of ZEB1 and SNAI1 and the fraction of cancer stem cells in the population. The level of circZEB1 was, however, consistently high during the entire phenotypic transformation. To understand this result we propose a mathematical model of the regulatory circuit. According to the model, the experimental observations can be explained by the presence of a back-splicing factor limiting circRNA production. Show less