Argenziano, Michael

Abstract Tumor cells require the mitotic spindle to drive chromosome separation in metaphase, which has made the spindle a compelling target for development of anti-mitotics. However, most of these spindle inhibitors target microtubules and can thereby be highly neurotoxic. This has motivated development of targeted anti-mitotics that inhibit non-microtubule components of the spindle, including kinesins and mitotic kinases. These drugs are not neurotoxic and prolong survival in tumor patients, but resistance limits their efficacy. We reported (Kenchappa et al., Cell Reports, 2022) that resistance to one of these—ispinesib, a highly specific inhibitor of the mitotic kinesin Kif11—requires two functions of STAT3: activation of transcription in the nucleus and of oxidative phosphorylation in the mitochondria. Inhibiting both functions reverses ispinesib resistance. Furthermore, combining ispinesib with a CNS-permeant STAT3 inhibitor significantly prolongs survival over ispinesib alone in both immunocompetent and PDX GBM models. We now report that resistance to two other targeted anti-mitotics—alisertib (Aurora Kinase inhibitor) and volasertib (Polo Like Kinase inhibitor)—works similarly and can likewise be reversed with STAT3 inhibitors. For all three of these anti-mitotics, resistance goes hand-in-hand with development of therapy-induced senescence (TIS) in approximately half of GBM cells. TIS cells secrete a variety of soluble factors, referred to as the senescence associated secretory phenotype (SASP), which stimulate stemness, angiogenesis, and immunosuppression. We propose that: 1) TIS cells require STAT3 for survival, and that they in turn maintain a drug resistant state by suppressing mitosis in the remaining, non senescent cells through components of the SASP; and 2) by killing TIS cells, STAT3 inhibitors restore anti-mitotic sensitivity in the remaining, non-senescent tumor cells. We propose that this STAT3/TIS model of resistance may also apply to how resistance develops to other GBM therapies, and may guide similar translational approaches to enhancing therapeutic efficacy.

Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of 14 months. GBMs are challenging to treat due to their heterogeneous nature. It has also been seen that these tumors have sex differences in their cellular subtypes as well as imaging. Radiomics has the potential to provide a non-invasive, spatial understanding of genetic and epigenetic diversity in these complex tumors and to aid in treatment planning. We have an ongoing study to obtain image-localized biopsies from GBM patients, allowing us to complete radiomic analysis and make connections between immunohistochemistry (IHC) and magnetic resonance imaging (MRI) features. We sought to determine if the patterns on imaging were correlated with underlying tumor biology. We focused on immunohistochemistry (IHC) markers of key features of tumor biology including SOX2 for stem-like tumor cells, CD68 for immune response and Ki67 for proliferation kinetics. Our study included 38 patients with a total of 99 biopsies (bxs): 27 males with 77 bxs and 11 females with 22 bxs. Biopsies were sectioned and stained for the SOX2, CD68, and KI67 markers. We computed 18 first-order radiomic features at each biopsy location for patients’ multimodal MRIs: T1W, T1Gd, T2W, FLAIR, apparent diffusion coefficient, diffusion weighted imaging (DWI) and susceptibility weighted imaging. We then performed correlation analysis between each radiomic feature and marker abundance for each IHC stain. Overall, we found sex-distinct patterns connecting imaging with these IHC markers. For example, amongst female patients, DWI held more prominent correlations with SOX2 than in males. Whereas there were more correlations between CD68 IHC abundance and T1Gd imaging features in males compared to females. Taken together, the overall patterns connecting locoregional imaging features to these IHC markers showed sex-distinct patterns suggesting the potential for sex to be an important biological variable when interpreting the biology underlying imaging changes.

Abstract While the allure of targeted therapies in oncology has been their high degree of specificity and potency for key tumor drivers, they have been disappointing in glioblastoma (GBM), even for drugs that are blood brain barrier permeable and CNS retained. This point is highlighted by the experience with mitotic spindle inhibitors, drugs which block the G2M transition and induce mitotic catastrophe—a phenotype characterized by cell enlargement and polyploidy that leads to apoptotic cell death. We have shown that one of these, a potent inhibitor of the mitotic kinesin Kif11 (ispinesib), is highly active against GBM tumor initiating cells and prolongs survival in murine models of this disease. However, tumors eventually progress, reflecting the development of drug resistance. Although ispinesib resistant GBM cells develop mitotic catastrophe, they become highly resistant to the apoptosis that typically follows and continue to proliferate. We find that this apoptosis resistance requires phosphorylation of the transcription factor STAT3 at two residues—Y705 and S727. Phosphorylation of Y705, mediated by SRC kinase, translocates STAT3 to the nucleus where it induces transcription of anti-apoptotic proteins. Phosphorylation at S727, mediated by EGFR, translocates STAT3 to the mitochondria where it blocks release of cytochrome c—the penultimate effector in apoptosis. Simultaneously inhibiting both SRC and EGFR with FDA-approved, CNS permeant inhibitors reverses this resistance and significantly prolongs survival in ispinesib-treated GBM-bearing mice. Furthermore, we find that resistance to several other mitotic inhibitors also utilizes this STAT3-driven mechanism and can likewise be reversed with combined EGFR and SRC inhibition. Thus, our work demonstrates how a promising therapeutic approach, which has been disappointing in GBM, can in fact be rendered effective by anticipating and prospectively treating ab initio the mechanism that drives treatment resistance.

Abstract Glioblastoma (GBM) is a devastating primary brain tumor known for its heterogeneity with a median survival of 15 months. Clinical imaging remains the primary modality to assess brain tumor response, but it is nearly impossible to distinguish between tumor growth and treatment response. Ki67 is a marker of active cell proliferation that shows inter- and intra-patient heterogeneity and should change under many therapies. In this work, we assessed the utility of a semi-supervised deep learning approach for regionally predicting high-vs-low Ki67 in GBM patients based on MRI. We used both labeled and unlabeled datasets to train the model. Labeled data included 114 MRI-localized biopsies from 43 unique GBM patients with available immunohistochemistry Ki67 labels. Unlabeled data included nine repeat routine pretreatment paired scans of newly-diagnosed GBM patients acquired within three days. Data augmentation techniques were utilized to enhance the size of our data and increase generalizability. Data was split between training, validation, and testing sets using 65-15-20 percent ratios. Model inputs were 16x16x3 patches around biopsies on T1Gd and T2 MRIs for labeled data, and around randomly selected patches inside the T2 abnormal region for unlabeled data. The network was a 4-conv layered VGG-inspired architecture. Training objective was accurate prediction of Ki67 in labeled patches and consistency in predictions across repeat unlabeled patches. We measured final model accuracy on held-out test samples. Our promising preliminary results suggest potential for deep learning in deconvolving the spatial heterogeneity of proliferative GBM subpopulations. If successful, this model can provide a non-invasive readout of cell proliferation and reveal the effectiveness of a given cytotoxic therapy dynamically during the patient's routine follow up. Further, the spatial resolution of our approach provides insights into the intra-tumoral heterogeneity of response which can be related to heterogeneity in localization of therapies (e.g. radiation therapy, drug dose heterogeneity).

Abstract Introduction Distinguishing between tumor and treatment effect in post-treatment glioma, although crucial for clinical management, is difficult because contrast-enhancing regions are mixtures of recurrent tumor and reactive tissue, and definitive histopathological criteria do not exist. This study disentangles the marked intra-tumoral heterogeneity in the treatment-recurrent setting by developing an unsupervised framework to algorithmically categorize intraoperative MRI-localized biopsies into three clinically-relevant tissue clusters based on joint analysis of RNA sequencing and histopathological data. Methods A retrospective cohort of 84 MRI-localized biopsies from 37 patients with post-treatment recurrent glioblastoma underwent mRNA extraction and quantification via PLATEseq protocol. For 48 of 84 biopsies, a neighboring piece of tissue underwent quantitative histopathology based on labeling index (LI) for SOX2, CD68, NeuN, Ki67, and H&E. Correlation between LIs and gene expression for these 48 samples was performed. Genes significantly correlated (p<0.05) with ≥1 marker were used for hierarchical clustering of correlation matrix, identifying three mutually-exclusive tissue-specific gene sets. These sets were then used to perform ssGSEA to categorize each of 84 biopsies into one of three tissue types. Results Correlation analysis identified 7779 genes significantly correlated with ≥1 histopathological marker. Clustering revealed three gene sets associated with specific markers: SetA-3688 genes associated with SOX2/Ki67/H&E; SetB-2418 genes associated with CD68; SetC-1673 genes associated with NeuN. ssGSEA using these sets categorized each biopsy into one of three tissue types: 27 biopsies enriched in SetA, 28 in SetB, and 29 in SetC. Conclusions Using MRI-localized biopsies with both RNAseq and histopathological data, this algorithmic approach allows development of three orthogonal tissue-specific gene sets that can be applied to characterize the heterogeneity in post-treatment recurrent glioma: SetA: genes correlated with SOX2/Ki67/H&E, representing recurrent tumor; SetB: genes correlated with CD68 (microglia) representing reactive tissue consistent with treatment effect; SetC: genes correlated with NeuN (neurons), representing infiltrated brain.