FGFR1 overexpression in non-small cell lung cancer is mediated by genetic and epigenetic mechanisms and is a determinant of FGFR1 inhibitor response.

Amplification of fibroblast growth factor receptor 1 (FGFR1) in non-small cell lung cancer (NSCLC) has been considered as an actionable drug target. However, pan-FGFR tyrosine kinase inhibitors did not demonstrate convincing clinical efficacy in FGFR1-amplified NSCLC patients. This study aimed to characterise the molecular context of FGFR1 expression and to define biomarkers predictive of FGFR1 inhibitor response. In this study, 635 NSCLC samples were characterised for FGFR1 protein expression by immunohistochemistry and copy number gain (CNG) by in situ hybridisation (n = 298) or DNA microarray (n = 189). FGFR1 gene expression (n = 369) and immune cell profiles (n = 309) were also examined. Furthermore, gene expression, methylation and microRNA data from The Cancer Genome Atlas (TCGA) were compared. A panel of FGFR1-amplified NSCLC patient-derived xenograft (PDX) models were tested for response to the selective FGFR1 antagonist M6123. A minority of patients demonstrated FGFR1 CNG (10.5%) or increased FGFR1 mRNA (8.7%) and protein expression (4.4%). FGFR1 CNG correlated weakly with FGFR1 gene and protein expression. Tumours overexpressing FGFR1 protein were typically devoid of driver alterations (e.g. EGFR, KRAS) and showed reduced infiltration of T-lymphocytes and lower PD-L1 expression. Promoter methylation and microRNA were identified as regulators of FGFR1 expression in NSCLC and other cancers. Finally, NSCLC PDX models demonstrating FGFR1 amplification and FGFR1 protein overexpression were sensitive to M6123. The unique molecular and immune features of tumours with high FGFR1 expression provide a rationale to stratify patients in future clinical trials of FGFR1 pathway-targeting agents.

Allele-specific multi-sample copy number segmentation in ASCAT.

MOTIVATION: Allele-specific copy number alterations are commonly used to trace the evolution of tumours. A key step of the analysis is to segment genomic data into regions of constant copy number. For precise phylogenetic inference, breakpoints shared between samples need to be aligned to each other. RESULTS: Here, we present asmultipcf, an algorithm for allele-specific segmentation of multiple samples that infers private and shared segment boundaries of phylogenetically related samples. The output of this algorithm can directly be used for allele-specific copy number calling using ASCAT. AVAILABILITY AND IMPLEMENTATION: asmultipcf is available as part of the ASCAT R package (version ≥2.5) from github.com/Crick-CancerGenomics/ascat/.

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer.

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.

KHS101 disrupts energy metabolism in human glioblastoma cells and reduces tumor growth in mice.

Pharmacological inhibition of uncontrolled cell growth with small-molecule inhibitors is a potential strategy for treating glioblastoma multiforme (GBM), the most malignant primary brain cancer. We showed that the synthetic small-molecule KHS101 promoted tumor cell death in diverse GBM cell models, independent of their tumor subtype, and without affecting the viability of noncancerous brain cell lines. KHS101 exerted cytotoxic effects by disrupting the mitochondrial chaperone heat shock protein family D member 1 (HSPD1). In GBM cells, KHS101 promoted aggregation of proteins regulating mitochondrial integrity and energy metabolism. Mitochondrial bioenergetic capacity and glycolytic activity were selectively impaired in KHS101-treated GBM cells. In two intracranial patient-derived xenograft tumor models in mice, systemic administration of KHS101 reduced tumor growth and increased survival without discernible side effects. These findings suggest that targeting of HSPD1-dependent metabolic pathways might be an effective strategy for treating GBM.

OncoNEM: inferring tumor evolution from single-cell sequencing data.

Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM's robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.