Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes, as well as differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor’s clonal evolution during treatment.

Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies1–5. This proposition, however, is complicated by spatial and temporal heterogeneity6–14. Here we study genomic and expression profiles across 127 multi-sector or longitudinal specimens from 52 glioblastoma (GBM) patients. Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, while geographically separated multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated to genetic similarity, and multifocal tumors enriched with PIK3CA mutations have a heterogeneous drug response pattern. Importantly, we show that targeting truncal events is more efficacious in reducing tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multi-sector biopsies can inform targeted therapeutic interventions for GBM patients.

Graphical Abstract Highlights d The human pancreas contains CD8 + TRMs exhibiting tissuespecific molecular signatures d Pancreas TRMs express high levels of PD-1 yet maintain strong effector function d During homeostasis, pancreas TRMs are regulated by PD-L1 + tissue macrophages d In chronic pancreatitis, TRM PD-1 levels and PD-L1 + macrophage density are reduced

Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under active study in the field of cancer biology. Its rapid progression and the relative time cost of obtaining molecular data make other readily-available forms of data, such as images, an important resource for actionable measures in patients. Our goal is to utilize information given by medical images taken from GBM patients in statistical settings. To do this, we design a novel statistic-the smooth Euler characteristic transform (SECT)-that quantifies magnetic resonance images (MRIs) of tumors. Due to its well-defined inner product structure, the SECT can be used in a wider range of functional and nonparametric modeling approaches than other previously proposed topological summary statistics. When applied to a cohort of GBM patients, we find that the SECT is a better predictor of clinical outcomes than both existing tumor shape quantifications and common molecular assays. Specifically, we demonstrate that SECT features alone explain more of the variance in GBM patient survival than gene expression, volumetric features, and morphometric features. The main takeaways from our findings are thus twofold. First, they suggest that images contain valuable information that can play an important role in clinical prognosis and other medical decisions. Second, they show that the SECT is a viable tool for the broader study of medical imaging informatics.

An open question in aggressive cancers such as melanoma is how malignant cells can shift the immune system to pro-tumourigenic functions. Here we identify Midkine (MDK) as a melanoma-secreted driver of an "inflamed", but immune evasive, microenvironment that defines poor patient prognosis and resistance to immune checkpoint blockade. Mechanistically, MDK was found to control the transcriptome of melanoma cells allowing for a coordinated activation of NF-B and downregulation of interferon-associated pathways. The resulting MDK-modulated secretome educated macrophages towards tolerant phenotypes that promoted CD8 + -T cell dysfunction. In contrast, genetic targeting of MDK sensitized melanoma cells to anti-PD1/PDL1 treatment. Emphasizing the translational relevance of these findings, the expression profile of MDK-depleted tumours was enriched in key indicators of good response to immune checkpoint blockers in independent patient cohorts. Together, these data reveal that MDK acts as an internal modulator of autocrine and paracrine signals that maintain immune suppression in aggressive melanomas.