Designing Clinical Trials for Combination Immunotherapy: A Framework for Glioblastoma

Singh, Kirit; Batich, Kristen A.; Wen, Patrick Y.; Tan, Aaron C.; Bagley, Stephen; Lim, Michael; Platten, Michael; Colman, Howard; Ashley, David M.; Chang, Susan M.; Rahman, Rifaquat; Galanis, Evanthia; Mansouri, Alireza; Puduvalli, Vinay K.; Reardon, David A.; Sahebjam, Solmaz; Sampson, John H.; Simes, John; Berry, Donald A.; Zadeh, Gelareh; Cloughesy, Timothy F.; Mehta, Minesh P.; Piantadosi, Steven; Weller, Michael; Heimberger, Amy B.; Khasraw, Mustafa

doi:10.1158/1078-0432.ccr-21-2681

“…Additional help comes from TAMs that show high levels of programmed cell death-ligand 1 (PD-L1). PD-L1 binds to receptors on the surface of T cells, restricting their activity [69,113].…”

Section: Immunotherapy Methodsmentioning

confidence: 99%

“…The fast expansion and associated infiltrating immune cells quickly eradicate antibody monotherapies by disrupting the antigen flow and immune cell traffic toward the tumor niche. The latest immunotherapy strategies utilize vaccines to enhance T-cell response [112,113].…”

Section: Progress In Gbm Treatmentmentioning

confidence: 99%

An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment

Verdugo

¹

,

Puerto

²

,

Medina

³

2022

Cancer Communications

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Glioblastoma multiforme (GBM) is the most aggressive and common malignant primary brain tumor. Patients with GBM often have poor prognoses, with a median survival of ∼15 months. Enhanced understanding of the molecular biology of central nervous system tumors has led to modifications in their classifications, the most recent of which classified these tumors into new categories and made some changes in their nomenclature and grading system. This review aims to give a panoramic view of the last 3 years’ findings in glioblastoma characterization, its heterogeneity, and current advances in its treatment. Several molecular parameters have been used to achieve an accurate and personalized characterization of glioblastoma in patients, including epigenetic, genetic, transcriptomic and metabolic features, as well as age‐ and sex‐related patterns and the involvement of several noncoding RNAs in glioblastoma progression. Astrocyte‐like neural stem cells and outer radial glial‐like cells from the subventricular zone have been proposed as agents involved in GBM of IDH‐wildtype origin, but this remains controversial. Glioblastoma metabolism is characterized by upregulation of the PI3K/Akt/mTOR signaling pathway, promotion of the glycolytic flux, maintenance of lipid storage, and other features. This metabolism also contributes to glioblastoma's resistance to conventional therapies. Tumor heterogeneity, a hallmark of GBM, has been shown to affect the genetic expression, modulation of metabolic pathways, and immune system evasion. GBM's aggressive invasion potential is modulated by cell‐to‐cell crosstalk within the tumor microenvironment and altered expressions of specific genes, such as ANXA2, GBP2, FN1, PHIP, and GLUT3. Nevertheless, the rising number of active clinical trials illustrates the efforts to identify new targets and drugs to treat this malignancy. Immunotherapy is still relevant for research purposes, given the amount of ongoing clinical trials based on this strategy to treat GBM, and neoantigen and nucleic acid‐based vaccines are gaining importance due to their antitumoral activity by inducing the immune response. Furthermore, there are clinical trials focused on the PI3K/Akt/mTOR axis, angiogenesis, and tumor heterogeneity for developing molecular‐targeted therapies against GBM. Other strategies, such as nanodelivery and computational models, may improve the drug pharmacokinetics and the prognosis of patients with GBM.

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“…Previous trials investigating neoantigen vaccination showed that vaccines can induce a significant increase of tumors infiltrating lymphocytes ( 144 ). Unfortunately, these infiltrating lymphocytes assume an exhausted phenotype.…”

Section: Targeting the Microenvironment In Glioblastomamentioning

confidence: 99%

“…Previous trials investigating neoantigen vaccination showed that vaccines can induce a significant increase of tumors infiltrating lymphocytes (144). Unfortunately, these infiltrating lymphocytes assume an exhausted Thus the coadministration of multi-epitope vaccines and immunecheckpoint inhibitors could be another promising approach (NCT02149225 GAPVAC trial) (144).…”

Section: Targeting the Microenvironment In Glioblastomamentioning

confidence: 99%

“…Previous trials investigating neoantigen vaccination showed that vaccines can induce a significant increase of tumors infiltrating lymphocytes (144). Unfortunately, these infiltrating lymphocytes assume an exhausted Thus the coadministration of multi-epitope vaccines and immunecheckpoint inhibitors could be another promising approach (NCT02149225 GAPVAC trial) (144). Strategies assessing coadministration of vaccines and CAR-T engineered with EGFRvIII and PD-1 are also under investigation (NCT04003649, NCT04201873, NCT02529072, NCT02287428) (145)(146)(147).…”

Section: Targeting the Microenvironment In Glioblastomamentioning

confidence: 99%

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Glioblastoma Microenvironment: From an Inviolable Defense to a Therapeutic Chance

Nunno¹,

Franceschi

²

,

Tosoni

³

et al. 2022

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Glioblastoma is an aggressive tumor and is associated with a dismal prognosis. The availability of few active treatments as well as the inexorable recurrence after surgery are important hallmarks of the disease. The biological behavior of glioblastoma tumor cells reveals a very complex pattern of genomic alterations and is partially responsible for the clinical aggressiveness of this tumor. It has been observed that glioblastoma cells can recruit, manipulate and use other cells including neurons, glial cells, immune cells, and endothelial/stromal cells. The final result of this process is a very tangled net of interactions promoting glioblastoma growth and progression. Nonetheless, recent data are suggesting that the microenvironment can also be a niche in which glioblastoma cells can differentiate into glial cells losing their tumoral phenotype. Here we summarize the known interactions between micro-environment and glioblastoma cells highlighting possible therapeutic implications.

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A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

Wei,

Lu,

Lai

et al. 2024

J Cancer Res Clin Oncol

0

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Purpose To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. Methods 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. Results The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ($${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$ F seq 1 , 2 , 3 , 4 ) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features $${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$ F seq 1 , 2 , 3 , 4 achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). Conclusion The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.

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Designing Clinical Trials for Combination Immunotherapy: A Framework for Glioblastoma

Cited by 21 publications

References 90 publications

An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment

An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment

Glioblastoma Microenvironment: From an Inviolable Defense to a Therapeutic Chance

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

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