A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors. Additionally, our data indicate that the signaling output upon loss or downregulation of one RAF isoform can be compensated by overexpression of other RAF isoforms. We furthermore demonstrate that, while single RAF inhibitors cannot efficiently inhibit ERK reactivation caused by RAF overexpression, a combination of two structurally distinct RAF inhibitors synergizes to robustly suppress pathway reactivation.

Deciphering the tumour immune microenvironment cell by cell

Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.

Small gene networks delineate immune cell states and characterize immunotherapy response in melanoma

Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed co-expression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n=19, discovery dataset) treated with ICIs, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells were associated with therapy response; and were characterized by differentially active and cell-state specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n=209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (p < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand-receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative co-stimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

The EMT transcription factor ZEB1 governs a fitness-promoting but vulnerable DNA replication stress response

The DNA damage response (DDR) and epithelial-to-mesenchymal transition (EMT) are two crucial cellular programs in cancer biology. While the DDR orchestrates cell-cycle progression, DNA repair, and cell death, EMT promotes invasiveness, cellular plasticity, and intratumor heterogeneity. Therapeutic targeting of EMT transcription factors, such as ZEB1, remains challenging, but tumor-promoting DDR alterations elicit specific vulnerabilities. Using multi-omics, inhibitors, and high-content microscopy, we discover a chemoresistant ZEB1-high-expressing sub-population (ZEB1^hi) with co-rewired cell-cycle progression and proficient DDR across tumor entities. ZEB1 stimulates accelerated S-phase entry via CDK6, inflicting endogenous DNA replication stress. However, DDR buildups involving constitutive MRE11-dependent fork resection allow homeostatic cycling and enrichment of ZEB1^hi cells during transforming growth factor β (TGF-β)-induced EMT and chemotherapy. Thus, ZEB1 promotes G1/S transition to launch a progressive DDR benefitting stress tolerance, which concurrently manifests a targetable vulnerability in chemoresistant ZEB1^hi cells. Our study thus highlights the translationally relevant intercept of the DDR and EMT.

Signaling Dynamics Regulating Crosstalks between T-Cell Activation and Immune Checkpoints

Immune checkpoint inhibitors (ICIs) targeting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) have been hailed as major advances in cancer therapeutics; however, in many cancers response rates remain low. Extensive research efforts are underway to improve the efficacy of ICIs. The signaling pathways regulated by immune checkpoints (ICs) may be an important lever as they interfere with T-cell activation when activated by ICIs. Here, we review the current understanding of T-cell receptor signaling and their intersection with IC signaling pathways. As these signaling processes are highly dynamic and controlled by intricate spatiotemporal mechanisms, we focus on aspects of kinetic regulation that are modulated by ICs. Recent advances in computational modeling and experimental methods that can resolve spatiotemporal dynamics provide insights that reveal molecular mechanisms and new potential approaches for improving the design and application of ICIs.