Tumour-agnostic kinase inhibitors
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Systemic HER3 ligand-mimicking nanobioparticles enter the brain and reduce intracranial tumour growth
Crossing the blood–brain barrier (BBB) and reaching intracranial tumours is a clinical challenge for current targeted interventions including antibody-based therapies, contributing to poor patient outcomes. Increased cell surface density of human epidermal growth factor receptor 3 (HER3) is associated with a growing number of metastatic tumour types and is observed on tumour cells that acquire resistance to a growing number of clinical targeted therapies. Here we describe the evaluation of HER3-homing nanobiological particles (nanobioparticles (NBPs)) on such tumours in preclinical models and our discovery that systemic NBPs could be found in the brain even in the absence of such tumours. Our subsequent studies described here show that HER3 is prominently associated with both mouse and human brain endothelium and with extravasation of systemic NBPs in mice and in human-derived BBB chips in contrast to non-targeted agents. In mice, systemically delivered NBPs carrying tumoricidal agents reduced the growth of intracranial triple-negative breast cancer cells, which also express HER3, with improved therapeutic profile compared to current therapies and compared to agents using traditional BBB transport routes. As HER3 associates with a growing number of metastatic tumours, the NBPs described here may offer targeted efficacy especially when such tumours localize to the brain.
Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles
Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics.
Characterization of the landscape of the intratumoral microbiota reveals that Streptococcus anginosus increases the risk of gastric cancer initiation and progression
As a critical component of the tumour immune microenvironment (TIME), the resident microbiota promotes tumorigenesis across a variety of cancer types. Here, we integrated multiple types of omics data, including microbiome, transcriptome, and metabolome data, to investigate the functional role of intratumoral bacteria in gastric cancer (GC). The microbiome was used to categorize GC samples into six subtypes, and patients with a high abundance of Streptococcus or Pseudomonas had a markedly worse prognosis. Further assays revealed that Streptococcus anginosus (SA) promoted tumour cell proliferation and metastasis while suppressing the differentiation and infiltration of CD8+ T cells. However, antibiotic treatment significantly suppressed tumorigenesis in SA+ mice in vivo. We further demonstrated that the SA arginine pathway increased the abundance of ornithine, which may be a major contributor to reshaping of the TIME. Our findings demonstrated that SA, a novel risk factor, plays significant roles in the initiation and progression of GC, suggesting that SA might be a promising target for the diagnosis and treatment of GC.
Semi-mechanistic efficacy model for PARP + ATR inhibitors—application to rucaparib and talazoparib in combination with gartisertib in breast cancer PDXs
Promising cancer treatments, such as DDR inhibitors, are often challenged by the heterogeneity of responses in clinical trials. The present work aimed to build a computational framework to address those challenges.
A functional single-cell metabolic survey identifies Elovl1 as a target to enhance CD8+ T cell fitness in solid tumours
Reprogramming T cell metabolism can improve intratumoural fitness. By performing a CRISPR/Cas9 metabolic survey in CD8+ T cells, we identified 83 targets and we applied single-cell RNA sequencing to disclose transcriptome changes associated with each metabolic perturbation in the context of pancreatic cancer. This revealed elongation of very long-chain fatty acids protein 1 (Elovl1) as a metabolic target to sustain effector functions and memory phenotypes in CD8+ T cells. Accordingly, Elovl1 inactivation in adoptively transferred T cells combined with anti-PD-1 showed therapeutic efficacy in resistant pancreatic and melanoma tumours. The accumulation of saturated long-chain fatty acids in Elovl1-deficient T cells destabilized INSIG1, leading to SREBP2 activation, increased plasma membrane cholesterol and stronger T cell receptor signalling. Elovl1-deficient T cells increased mitochondrial fitness and fatty acid oxidation, thus withstanding the metabolic stress imposed by the tumour microenvironment. Finally, ELOVL1 in CD8+ T cells correlated with anti-PD-1 response in patients with melanoma. Altogether, Elovl1 targeting synergizes with anti-PD-1 to promote effective T cell responses.
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