TEAD Inhibitors Sensitize KRASG12C Inhibitors via Dual Cell Cycle Arrest in KRASG12C-Mutant NSCLC.

KRASG12C is one of the most common mutations detected in non-small cell lung cancer (NSCLC) patients, and it is a marker of poor prognosis. The first FDA-approved KRASG12C inhibitors, sotorasib and adagrasib, have been an enormous breakthrough for patients with KRASG12C mutant NSCLC; however, resistance to therapy is emerging. The transcriptional coactivators YAP1/TAZ and the family of transcription factors TEAD1-4 are the downstream effectors of the Hippo pathway and regulate essential cellular processes such as cell proliferation and cell survival. YAP1/TAZ-TEAD activity has further been implicated as a mechanism of resistance to targeted therapies. Here, we investigate the effect of combining TEAD inhibitors with KRASG12C inhibitors in KRASG12C mutant NSCLC tumor models. We show that TEAD inhibitors, while being inactive as single agents in KRASG12C-driven NSCLC cells, enhance KRASG12C inhibitor-mediated anti-tumor efficacy in vitro and in vivo. Mechanistically, the dual inhibition of KRASG12C and TEAD results in the downregulation of MYC and E2F signatures and in the alteration of the G2/M checkpoint, converging in an increase in G1 and a decrease in G2/M cell cycle phases. Our data suggest that the co-inhibition of KRASG12C and TEAD leads to a specific dual cell cycle arrest in KRASG12C NSCLC cells.

Simulation-based inference for non-parametric statistical comparison of biomolecule dynamics.

Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules. Recorded trajectories are often too short to reliably discern such multi-scale dynamics, which precludes unambiguous assessment of the type of random walk and its parameters. Furthermore, the motion of biomolecules may not be well described by a single, canonical random walk model. Here, we develop a two-step statistical testing scheme for comparing biomolecule dynamics observed in different experimental conditions without having to identify or make strong prior assumptions about the model generating the recorded random walks. We first train a graph neural network to perform simulation-based inference and thus learn a rich summary statistics vector describing individual trajectories. We then compare trajectories obtained in different biological conditions using a non-parametric maximum mean discrepancy (MMD) statistical test on their so-obtained summary statistics. This procedure allows us to characterise sets of random walks regardless of their generating models, without resorting to model-specific physical quantities or estimators. We first validate the relevance of our approach on numerically simulated trajectories. This demonstrates both the statistical power of the MMD test and the descriptive power of the learnt summary statistics compared to estimates of physical quantities. We then illustrate the ability of our framework to detect changes in α-synuclein dynamics at synapses in cultured cortical neurons, in response to membrane depolarisation, and show that detected differences are largely driven by increased protein mobility in the depolarised state, in agreement with previous findings. The method provides a means of interpreting the differences it detects in terms of single trajectory characteristics. Finally, we emphasise the interest of performing various comparisons to probe the heterogeneity of experimentally acquired datasets at different levels of granularity (e.g., biological replicates, fields of view, and organelles).

Variational inference of fractional Brownian motion with linear computational complexity.

We introduce a simulation-based, amortized Bayesian inference scheme to infer the parameters of random walks. Our approach learns the posterior distribution of the walks' parameters with a likelihood-free method. In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk. In the second step an invertible neural network generates the posterior distribution of the parameters from the learned summary statistics using variational inference. We apply our method to infer the parameters of the fractional Brownian motion model from single trajectories. The computational complexity of the amortized inference procedure scales linearly with trajectory length, and its precision scales similarly to the Cramér-Rao bound over a wide range of lengths. The approach is robust to positional noise, and generalizes to trajectories longer than those seen during training. Finally, we adapt this scheme to show that a finite decorrelation time in the environment can furthermore be inferred from individual trajectories.

Preclinical Activity of SAR408701: A Novel Anti-CEACAM5-maytansinoid Antibody-drug Conjugate for the Treatment of CEACAM5-positive Epithelial Tumors.

PURPOSE: Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) is a glycoprotein that has limited expression in normal adult tissues, but is overexpressed in carcinomas of the gastrointestinal tract, the genitourinary and respiratory systems, and breast cancer. As such, CEACAM5 is an attractive target for antibody-based therapies designed to selectively deliver cytotoxic drugs to certain epithelial tumors. Here, we describe preclinical data for a novel antibody-drug conjugate (ADC), SAR408701, which consists of an anti-CEACAM5 antibody (SAR408377) coupled to a maytansinoid agent DM4 via a cleavable linker. EXPERIMENTAL DESIGN: The specificity and binding affinity of SAR408701 to human and cynomolgus monkey CEACAM5 were tested in vitro. The cytotoxic activity of SAR408701 was assessed in CEACAM5-expressing tumor cell lines and using patient-derived xenograft mouse models of CEACAM5-positive tumors. Pharmacokinetic-pharmacodynamic and pharmacokinetic-efficacy relationships were established. SAR408701 toxicity was evaluated in cynomolgus monkey. RESULTS: SAR408701 bound selectively to human and cynomolgus monkey CEACAM5 with similar apparent Kd values (0.017 nmol/L and 0.024 nmol/L, respectively). Both in vitro and in vivo evaluations showed that SAR408701 has cytotoxic activity, leading to in vivo efficacy in single and repeated dosing. Single doses of SAR408701 induced significant increases in the tumor expression of phosphorylated histone H3, confirming the tubulin-targeting mechanism of action. The overall toxicity profile of SAR408701 in cynomolgus monkey was similar to that observed after intravenous administration of DM4 alone. CONCLUSIONS: On the basis of these preclinical data, the ADC SAR408701 is a promising candidate for development as a potential treatment for patients with CEACAM5-positive tumors.