Citations

Abstract Sequencing efforts have identified key oncogenic drivers and transcriptomic subtypes of high-grade gliomas (HGGs), but this knowledge has not resulted in individualized therapy or improved patient outcomes. Here, we used a functional genomics approach combining integrated genomic/transcriptomic analyses with patient-derived 3D micro-cancers (mCancers) to rapidly identify individualized therapeutic vulnerabilities and determine predictive response biomarkers. 30 primary/recurrent HGGs, including 20 glioblastomas, 5 astrocytomas, and 5 oligodendrogliomas, were included for drug testing. mCancers recapitulated the structural and molecular alterations, cellularity, and heterogeneity, of parental patient tumors. A total of 119 single-agent drugs and 183 drug combinations were tested, while a pre-selected drug panel comprising 25 glioma-relevant treatment strategies was tested across all cases. In general, mCancer efficacy corresponded well to historical responses to previously tested agents (EGFR, HDAC inhibitors, etc.). Combined, strong efficacy (i.e. >70% growth inhibition at Cmax) of single treatments targeting deregulated receptor tyrosine kinase (RTK), p53/apoptosis, cell cycle, DNA damage repair, IDH, or telomere maintainance pathways, was observed in 14 out of 30 cases. Surprisingly, transcriptional signatures, but not specific genetic alterations, were found to stratify drug responses. For example, mCancers enriched for the most aggressive mesenchymal (MES) and glycolytic/plurimetabolic (GPM) transcriptional subtypes exhibited stronger response to the BET inhibitor JQ1 (and other clinical BET inhibitors) and the DNA-PKcs/mTOR dual inhibitor CC-115. This therapeutic vulnerability was associated with enrichment of YAP/TAZ signaling, and the glioblastoma stem cell G-STEM signature. Notably, individual combination strategies were effective across tumors, with strong responses ranging from 38% to 77% of all tumors tested. Overall, this study underscores the value of functional genomics in identifying novel vulnerabilities of HGGs and associated response biomarkers. It also highlights the need for further clinical validation of several novel therapies and especially drug combination strategies with strong efficacy in the ex vivo setting.

Abstract BACKGROUND Treatment of high-grade glioma (HGG) remains a challenge. Several groups have developed ex vivo drug sensitivity assays to evaluate susceptibility of the tumor tissue to specific chemotherapeutics. This tool, in addition to molecular analysis of tumors might provide another layer of confidence in choosing treatments for patients with high-grade gliomas. METHODS 3D PredictTM platform (Kiyatec, Greenville, SC, USA) was tested at three academic medical centers in the United States. Twelve patients with newly diagnosed and recurrent HGGs were included. Tumor spheroids created ex vivo were exposed to twelve pre-selected drugs. Responses were reported as: no response, moderate response and response. We analyzed: demographics, histopathology, stage of disease, success rate and results of the ex vivo assay and clinical implications. RESULTS Majority of patients were women (67%). Median age was 58.5. Histology: GBM-83%, HGGs-17%. Eighty three percent of the samples represented recurrent disease. Sixty percent represented first recurrence while the remaining 40% represented 2nd or 3rd recurrence. Eight out of 12 tissue samples (67%) were successfully tested ex vivo. In six cases (75%), the information was used for immediate clinical decision making. In 2 cases, the decision was deferred until the next progression of the disease. The drugs chosen for treatment based on assay results included: olaparib (the assay was positive for response to rucaparib), temozolomide, lomustine, carboplatin and irinotecan. CONCLUSIONS This platform is easy to use in a standard clinical setting, the success rate for creating viable spheroids for drug sensitivity testing is high and the information obtained can be useful in clinical practice. Addition of other agents to the assay, correlation with molecular testing of the tumor tissue and attempt to assess response to drug combinations should be considered in further development of this technology. Prospective clinical trial is needed to validate this approach for routine clinical use.

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

Glioblastomas (GBM) are aggressive tumors that lack effective treatments. Here, we show that the Rho family guanine nucleotide exchange factor Syx promotes GBM cell growth both in vitro and in orthotopic xenografts derived from patients with GBM. Growth defects upon Syx depletion are attributed to prolonged mitosis, increased DNA damage, G2/M cell cycle arrest, and cell apoptosis, mediated by altered mRNA and protein expression of various cell cycle regulators. These effects are phenocopied by depletion of the Rho downstream effector Dia1 and are due, at least in part, to increased phosphorylation, cytoplasmic retention, and reduced activity of the YAP/TAZ transcriptional coactivators. Furthermore, targeting Syx signaling cooperates with radiation treatment and temozolomide (TMZ) to decrease viability in GBM cells, irrespective of their inherent response to TMZ. The data indicate that a Syx-RhoA-Dia1-YAP/TAZ signaling axis regulates cell cycle progression, DNA damage, and therapy resistance in GBM and argue for its targeting for cancer treatment.

Abstract Background: Mutations in RAS is reported in embryonal rhabdomyosarcoma (ERMS). Upregulation of IGF2/IGF1R axis and IGF2 overexpression is a common finding. Targeting RAS downstream is associated with high toxicity. Combination therapies targeting parallel pathways is necessary to overcome treatment resistance. Dual inhibition of RAS downstream and IGF2/IGF1R axis can be a potential mechanism of targeted treatment. The efficacy of this mechanism was studied using a microcancer 3D exvivo tumor cell viability assay in a KRAS mutant ERMS. Design: Tumor was collected for a high-risk pediatric ERMS patient. Comprehensive genomic analysis, for identification of targetable genomic alterations, was performed by integrating WES, MPseq and RNAseq. Tumor spheroids grown in 3D environment were tested against multiple single drugs and combinations. The tumor viability was assed employing the Cell Titer Glow Luminescent Cell Viability Assay. Results were expressed as nMATP and were shown as drug-response curve exhibiting the ATP concentration changes at Cmax. Results: Genomic findings included an oncogenic KRAS (G12D) mutation. BAY-293 has a partial cell killing response as a single agent. Given to the presence of IGF2 over-expression, IGF2/IGF1R pathway was targeted by linsitinib. Addition of BAY-293 to linsitinib led to enhanced effectiveness of this agent (Table 1). Conclusion: Combination of comprehensive genomic profiling and a laboratory-based 3D micro-cancer model using the patient’s own tumor is an effective way to determine drug efficacy. Targeting RAS pathway by single agents, increases the possibility of resistance. Dual targeting of downstream pathways by MEK and PI3K pathway inhibition is highly toxic, and the clinical usage of this combination is limited. Our results have indicated inhibition of IGF-1R/IGF2 axis in combination with other agents targeting RAS pathway, can be a potential method to address these limitations. ABEL 1. Summary of molecular alterations and targeting methods Molecular alteration Drug Target Kill% at Cmax KRAS (G12D) Mutation BAY-293 RAS-SOS1 interaction 55.3 Trametinib MEK1/2 88.3 Selumetinib MEK1/2 76 Paxalisib PI3K/mTOR 89.7 Samotolisib PI3K/mTOR 92.9 Copanlisib PI3K 95.2 Perifosine AKT 89.2 IGF2 Over-expression Linsitinib IGF-1R/InsR 84.1 BMS-754807 IGF-1R/InsR 67.4 NTRK2 mutation Larotrectinib NTRK 41.9 KMT2D mutation Panobinostat HDAC 85.3 Combinations Kill% at Cmax BAY-293+Selumetinib 100 BAY-293+Samotolisib 100 BAY-293+Paxalisib 99.7 BAY-293+Linsitinib 97.5 Selumetinib+Samotolisib 93.1 Citation Format: Dorsay Sadeghian, Wan-Hsin Lin, James Smadbeck, Alan Panheiter, Michael Barrett, Alexa F. Mccune, Giannoula Karagouga, Sarah Johnson, Ryan W. Feathers, Faye R. Harris, Lindsey Kinsella, Taylor Berry, John Cheville, Panos Anastasiadis, Candace Granberg, Patricio Gargollo, Vasmatzis George. Dual inhibition of RAS and the IGF2/IGF1R pathways: a potential targeting strategy for RAS-mutated embryonal rhabdomyosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3391.

SummaryLeukaemic stem cell (LSC) gene expression has recently been linked to prognosis in patients with acute myeloid leukaemia (17‐gene LSC score, LSC‐17) and myelodysplastic syndromes. Although chronic myelomonocytic leukaemia (CMML) is regarded as a stem cell disorder, the clinical and biological impact of LSCs on CMML patients remains elusive. Making use of multiple independent validation cohorts, we here describe a concise three‐gene expression signature (LSC‐3, derived from the LSC‐17 score) as an independent and robust prognostic factor for leukaemia‐free and overall survival in CMML. We propose that LSC‐3 could be used to supplement existing risk stratification systems, to improve prognostic performance and guide management decisions.

Colorectal cancer (CRC) is one of the most common cancers, and it frequently metastasizes to the liver and lymph nodes. Despite major advances in treatment modalities, CRC remains a poorly characterized biological malignancy, with high reported cases of deaths globally. Moreover, cancer stem cells (CSCs) and their microenvironment have been widely shown to promote colon cancer development, progression, and metastasis. Therefore, an understanding of the underlying mechanisms that contribute to the maintenance of CSCs and their markers in CRC is crucial in efforts to treat cancer metastasis and develop specific therapeutic targets for augmenting current standard treatments. Herein, we applied computational simulations using bioinformatics to identify potential theranostic markers for CRC. We identified the overexpression of vascular endothelial growth factor-α (VEGFA)/β-catenin/matrix metalloproteinase (MMP)-7/Cluster of Differentiation 44 (CD44) in CRC to be associated with cancer progression, stemness, resistance to therapy, metastasis, and poor clinical outcomes. To further investigate, we explored in silico molecular docking, which revealed potential inhibitory activities of LCC-21 as a potential multitarget small molecule for VEGF-A/CTNNB1/MMP7/CD44 oncogenic signatures, with the highest binding affinities displayed. We validated these finding in vitro and demonstrated that LCC-21 inhibited colony and sphere formation, migration, and invasion, and these results were further confirmed by a Western blot analysis in HCT116 and DLD-1 cells. Thus, the inhibitory effects of LCC-21 on these angiogenic and onco-immunogenic signatures could be of translational relevance as potential CRC biomarkers for early diagnosis.

Abstract Diffuse gliomas are aggressive brain tumors that lack effective therapies. Despite advances in the molecular characterization of gliomas, targeted treatments have failed to improve patient outcomes to date. Here, we employed a functional genomics approach to rapidly identify therapeutic vulnerabilities and provide a strategy to individualize patient treatment. Our process combined comprehensive genomic and transcriptomic tumor profiling, custom algorithms and visualization software for data integration, and preclinical 3-dimensional ex vivo models (mcancers) for drug screening to assess response to therapeutic agents targeting specific molecular alterations. A total of 32 cases, including 5 oligodendrogliomas (1 primary and 4 recurrent), 5 astrocytomas (1 primary and 4 recurrent), and 22 glioblastomas (5 primary and 17 recurrent), were included in the study. Immunohistochemistry and integrated genomics confirmed that mcancers maintain key molecular alterations, including mutations, chromosomal rearrangements and focal amplifications, cellularity, and heterogeneity, faithfully recapitulating the original tumor. A total of 119 single drugs and 183 combinations were tested, including a panel of 26-brain penetrant targeted agents (18 single drugs and 8 combinations) that was curated to target common glioma oncogenic pathways and was tested across all cases. A strong correlation was observed between therapeutic vulnerabilities of glioma mcancers to targeted agents (EGFR, MET, PDGFR, HDAC inhibitors, etc.) and clinical experience. Single agent activity of panel compounds in the ex vivo setting was generally poor, with only 35% mCancers exhibiting > 70% growth inhibition at Cmax. In contrast, combination strategies, especially ones including epigenetic drugs, were significantly more effective (affecting 22 out of 32 mCancers), underscoring the molecular complexity of gliomas and the need for clinical trials testing combination treatments. Our data identified a number of novel combination treatments with significant efficacy across glioma subtypes and molecular profiles, suggesting that our functional genomics pipeline can guide treatment selection and inform clinical trial enrollment for gliomas.

Abstract Glioblastoma (GBM) is the most aggressive adult brain tumor with no effective therapy. Despite advances on its molecular characterization, targeted therapeutics remain out of reach. This lack-of-progress is largely attributed to molecular and spatial heterogeneity and absence of clinically applicable models to guide patient care. Moreover, as standard surgical resection is aimed at contrast-enhancing (CE) tumor, the molecular landscape and treatment response of the remaining non-contrast enhancing (nCE) component is largely unknown despite its active role in tumor progression and local recurrence. Here, we aimed to identify the functional impact of tumor heterogeneity and to explore therapeutic vulnerabilities of both CE and nCE components. Four tumor regions (two CE and two nCE) from each of five GBM patients were collected under neuronavigation. The resulting tissue was subjected to generation of 3D tumor models (a brain tumor initiating-enriched cell line and a spheroid-based mcancer model) with comparable success in both CE and nCE samples. A panel of 25 brain penetrant drugs (alone and in combination) targeting GBM drivers (i.e. RTK/Ras/PI3K, RB/cell cycle, p53/apoptosis, DNA damage repair, transcription addiction) was developed and used to test for therapeutic vulnerabilities. From each tumor region, 4.5 ± 3.7 (average ± SD) different targeting strategies were identified exhibiting at least 70% growth inhibition at Cmax. Drug response heterogeneity was relatively rare, varied among cases, and was independent of tumor enhancement status. To our knowledge, this is the first study to systemically assess treatment response from both CE and nCE patient-derived GBM models. Ongoing studies include comprehensive genome and transcriptome profiling and pathway analysis from these different tumor regions. We postulate that interrogation of molecular landscapes and vulnerability profiles of both CE and nCE tumor regions, will enable us to personalize and prioritize effective targeted interventions for GBM treatment.