A single-cell based precision medicine approach using glioblastoma patient-specific models

Organoids for Functional Precision Medicine in Advanced Pancreatic Cancer

BACKGROUND & AIMS

Patient-derived organoids (PDOs) are promising tumor avatars that could enable ex vivo drug tests to personalize patients' treatments in the frame of functional precision oncology. However, clinical evidence remains scarce. This study aims to evaluate whether PDOs can be implemented in clinical practice to benefit patients with advanced refractory pancreatic ductal adenocarcinoma (PDAC).

METHODS

During 2021 to 2022, 87 patients were prospectively enrolled in an institutional review board-approved protocol. Inclusion criteria were histologically confirmed PDAC with the tumor site accessible. A panel of 25 approved antitumor therapies (chemogram) was tested and compared to patient responses to assess PDO predictive values and map the drug sensitivity landscape in PDAC.

RESULTS

Fifty-four PDOs were generated from 87 pretreated patients (take-on rate, 62%). The main PDO mutations were KRAS (96%), TP53 (88%), and CDKN2A/B (22%), with a 91% concordance rate with their tumor of origin. The mean turnaround time to chemogram was 6.8 weeks. In 91% of cases, ≥1 hit was identified (gemcitabine (n = 20 of 54), docetaxel (n = 18 of 54), and vinorelbine (n = 17 of 54), with a median of 3 hits/patient (range, 0-12). Our cohort included 34 evaluable patients with full clinical follow-up. We report a chemogram sensitivity of 83.3% and specificity of 92.9%. The overall response rate and progression-free survival were higher when patients received a hit treatment as compared to patients who received a nonhit drug (as part of routine management). Finally, we leveraged our PDO collection as a platform for drug validation and combo identification. We tested anti-KRASG12D (MRTX1133), alone or combined, and identified a specific synergy with anti-EGFR therapies in KRASG12D variants.

CONCLUSIONS

We report the largest prospective study aiming at implementing PDO-based functional precision oncology and identify very robust predictive values in this clinical setting. In a clinically relevant turnaround time, we identify putative hits for 91% of patients, providing unexpected potential survival benefits in this very aggressive indication. Although this remains to be confirmed in interventional precision oncology trials, PDO collection already provides powerful opportunities for drugs and combinatorial treatment development.

Spatial transcriptomics reveals segregation of tumor cell states in glioblastoma and marked immunosuppression within the perinecrotic niche

Glioblastoma (GBM) remains an untreatable malignant tumor with poor patient outcomes, characterized by palisading necrosis and microvascular proliferation. While single-cell technology made it possible to characterize different lineage of glioma cells into neural progenitor-like (NPC-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like) and mesenchymal like (MES-like) states, it does not capture the spatial localization of these tumor cell states. Spatial transcriptomics empowers the study of the spatial organization of different cell types and tumor cell states and allows for the selection of regions of interest to investigate region-specific and cell-type-specific pathways. Here, we obtained paired 10x Chromium single-nuclei RNA-sequencing (snRNA-seq) and 10x Visium spatial transcriptomics data from three GBM patients to interrogate the GBM microenvironment. Integration of the snRNA-seq and spatial transcriptomics data reveals patterns of segregation of tumor cell states. For instance, OPC-like tumor and NPC-like tumor significantly segregate in two of the three samples. Our differentially expressed gene and pathway analyses uncovered significant pathways in functionally relevant niches. Specifically, perinecrotic regions were more immunosuppressive than the endogenous GBM microenvironment, and perivascular regions were more pro-inflammatory. Our gradient analysis suggests that OPC-like tumor cells tend to reside in areas closer to the tumor vasculature compared to tumor necrosis, which may reflect increased oxygen requirements for OPC-like cells. In summary, we characterized the localization of cell types and tumor cell states, the gene expression patterns, and pathways in different niches within the GBM microenvironment. Our results provide further evidence of the segregation of tumor cell states and highlight the immunosuppressive nature of the necrotic and perinecrotic niches in GBM.

Haploinsufficiency of phosphodiesterase 10A activates PI3K/AKT signaling independent of PTEN to induce an aggressive glioma phenotype

Glioblastoma is universally fatal and characterized by frequent chromosomal copy number alterations harboring oncogenes and tumor suppressors. In this study, we analyzed exome-wide human glioblastoma copy number data and found that cytoband 6q27 is an independent poor prognostic marker in multiple data sets. We then combined CRISPR-Cas9 data, human spatial transcriptomic data, and human and mouse RNA sequencing data to nominate PDE10A as a potential haploinsufficient tumor suppressor in the 6q27 region. Mouse glioblastoma modeling using the RCAS/tv-a system confirmed that Pde10a suppression induced an aggressive glioma phenotype in vivo and resistance to temozolomide and radiation therapy in vitro. Cell culture analysis showed that decreased Pde10a expression led to increased PI3K/AKT signaling in a Pten-independent manner, a response blocked by selective PI3K inhibitors. Single-nucleus RNA sequencing from our mouse gliomas in vivo, in combination with cell culture validation, further showed that Pde10a suppression was associated with a proneural-to-mesenchymal transition that exhibited increased cell adhesion and decreased cell migration. Our results indicate that glioblastoma patients harboring PDE10A loss have worse outcomes and potentially increased sensitivity to PI3K inhibition.

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing non-genetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupts acquired resistance in GBM.

ACAT1 deficiency in myeloid cells promotes glioblastoma progression by enhancing the accumulation of myeloid-derived suppressor cells

Glioblastoma (GBM) is a highly aggressive and lethal brain tumor with an immunosuppressive tumor microenvironment (TME). In this environment, myeloid cells, such as myeloid-derived suppressor cells (MDSCs), play a pivotal role in suppressing antitumor immunity. Lipometabolism is closely related to the function of myeloid cells. Here, our study reports that acetyl-CoA acetyltransferase 1 (ACAT1), the key enzyme of fatty acid oxidation (FAO) and ketogenesis, is significantly downregulated in the MDSCs infiltrated in GBM patients. To investigate the effects of ACAT1 on myeloid cells, we generated mice with myeloid-specific (LyzM-cre) depletion of ACAT1. The results show that these mice exhibited a remarkable accumulation of MDSCs and increased tumor progression both ectopically and orthotopically. The mechanism behind this effect is elevated secretion of C-X-C motif ligand 1 (CXCL1) of macrophages (Mφ). Overall, our findings demonstrate that ACAT1 could serve as a promising drug target for GBM by regulating the function of MDSCs in the TME.

Personalized Treatment of Glioblastoma: Current State and Future Perspective

Glioblastoma (GBM) is the most aggressive glial tumor of the central nervous system. Despite intense scientific efforts, patients diagnosed with GBM and treated with the current standard of care have a median survival of only 15 months. Patients are initially treated by a neurosurgeon with the goal of maximal safe resection of the tumor. Obtaining tissue samples during surgery is indispensable for the diagnosis of GBM. Technological improvements, such as navigation systems and intraoperative monitoring, significantly advanced the possibility of safe gross tumor resection. Usually within six weeks after the surgery, concomitant radiotherapy and chemotherapy with temozolomide are initiated. However, current radiotherapy regimens are based on population-level studies and could also be improved. Implementing artificial intelligence in radiotherapy planning might be used to individualize treatment plans. Furthermore, detailed genetic and molecular markers of the tumor could provide patient-tailored immunochemotherapy. In this article, we review current standard of care and possibilities of personalizing these treatments. Additionally, we discuss novel individualized therapeutic options with encouraging results. Due to inherent heterogeneity of GBM, applying patient-tailored treatment could significantly prolong survival of these patients.

Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis

Glioblastoma multiforme (GBM), a grade IV glioma, is a challenging disease for patients and clinicians, with an extremely poor prognosis. These tumours manifest a high molecular heterogeneity, with limited therapeutic options for patients. Since GBM is a rare disease, sufficient statistically strong evidence is often not available to explore the roles of lesser-known GBM proteins. We present a network-based approach using centrality measures to explore some key, topologically strategic proteins for the analysis of GBM. Since network-based analyses are sensitive to changes in network topology, we analysed nine different GBM networks, and show that small but well-curated networks consistently highlight a set of proteins, indicating their likely involvement in the disease. We propose 18 novel candidates which, based on differential expression, mutation analysis, and survival analysis, indicate that they may play a role in GBM progression. These should be investigated further for their functional roles in GBM, their clinical prognostic relevance, and their potential as therapeutic targets.