TPES: tumor purity estimation from SNVs.

MOTIVATION: Tumor purity (TP) is the proportion of cancer cells in a tumor sample. TP impacts on the accurate assessment of molecular and genomics features as assayed with NGS approaches. State-of-the-art tools mainly rely on somatic copy-number alterations (SCNA) to quantify TP and therefore fail when a tumor genome is nearly euploid, i.e. 'non-aberrant' in terms of identifiable SCNAs. RESULTS: We introduce a computational method, tumor purity estimation from single-nucleotide variants (SNVs), which derives TP from the allelic fraction distribution of SNVs. On more than 7800 whole-exome sequencing data of TCGA tumor samples, it showed high concordance with a range of TP tools (Spearman's correlation between 0.68 and 0.82; >9 SNVs) and rescued TP estimates of 1, 194 samples (15%) pan-cancer. AVAILABILITY AND IMPLEMENTATION: TPES is available as an R package on CRAN and at https://bitbucket.org/l0ka/tpes.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Immunotherapy drives mesenchymal tumor cell state shift and TME immune response in glioblastoma patients.

BACKGROUND: Glioblastoma is a highly aggressive type of brain tumor for which there is no curative treatment available. Immunotherapies have shown limited responses in unselected patients, and there is an urgent need to identify mechanisms of treatment resistance to design novel therapy strategies. METHODS: Here we investigated the phenotypic and transcriptional dynamics at single-cell resolution during nivolumab immune checkpoint treatment of glioblastoma patients. RESULTS: We present the integrative paired single-cell RNA-seq analysis of 76 tumor samples from patients in a clinical trial of the PD-1 inhibitor nivolumab and untreated patients. We identify a distinct aggressive phenotypic signature in both tumor cells and the tumor microenvironment in response to nivolumab. Moreover, nivolumab-treatment was associated with an increased transition to mesenchymal stem-like tumor cells, and an increase in TAMs and exhausted and proliferative T cells. We verify and extend our findings in large external glioblastoma dataset (n = 298), develop a latent immune signature and find 18% of primary glioblastoma samples to be latent immune, associated with mesenchymal tumor cell state and TME immune response. Finally, we show that latent immune glioblastoma patients are associated with shorter overall survival following immune checkpoint treatment (P = .0041). CONCLUSIONS: We find a resistance mechanism signature in one fifth of glioblastoma patients associated with a tumor-cell transition to a more aggressive mesenchymal-like state, increase in TAMs and proliferative and exhausted T cells in response to immunotherapy. These patients may instead benefit from neuro-oncology therapies targeting mesenchymal tumor cells.

Glioblastoma cells increase expression of notch signaling and synaptic genes within infiltrated brain tissue.

Glioblastoma remains one of the deadliest brain malignancies. First-line therapy consists of maximal surgical tumor resection, accompanied by chemotherapy and radiotherapy. Malignant cells escape surgical resection by migrating into the surrounding healthy brain tissue, where they give rise to the recurrent tumor. Based on gene expression, tumor cores can be subtyped into mesenchymal, proneural, and classical tumors, each being associated with differences in genetic alterations and cellular composition. In contrast, the adjacent brain parenchyma where infiltrating malignant cells escape surgical resection is less characterized in patients. Using spatial transcriptomics (n = 11), we show that malignant cells within proneural or mesenchymal tumor cores display spatially organized differences in gene expression, although such differences decrease within the infiltrated brain tissue. Malignant cells residing in infiltrated brain tissue have increased expression of genes related to neurodevelopmental pathways and glial cell differentiation. Our findings provide an updated view of the spatial landscape of glioblastomas and further our understanding of the malignant cells that infiltrate the healthy brain, providing new avenues for the targeted therapy of these cells after surgical resection.

Allele-specific genomic data elucidate the role of somatic gain and copy-number neutral loss of heterozygosity in cancer.

Pan-cancer studies sketched the genomic landscape of the tumor types spectrum. We delineated the purity- and ploidy-adjusted allele-specific profiles of 4,950 patients across 27 tumor types from the Cancer Genome Atlas (TCGA). Leveraging allele-specific data, we reclassified as loss of heterozygosity (LOH) 9% and 7% of apparent copy-number wild-type and gain calls, respectively, and overall observed more than 18 million allelic imbalance somatic events at the gene level. Reclassification of copy-number events revealed associations between driver mutations and LOH, pointing out the timings between the occurrence of point mutations and copy-number events. Integrating allele-specific genomics and matched transcriptomics, we observed that allele-specific gene status is relevant in the regulation of TP53 and its targets. Further, we disclosed the role of copy-neutral LOH in the impairment of tumor suppressor genes and in disease progression. Our results highlight the role of LOH in cancer and contribute to the understanding of tumor progression.