Functional genomics of human clear cell sarcoma: genomic, transcriptomic and chemical biology landscape for clear cell sarcoma.

BACKGROUND: Systemic therapy for metastatic clear cell sarcoma (CCS) bearing EWSR1-CREB1/ATF1 fusions remains an unmet clinical need in children, adolescents, and young adults. METHODS: To identify key signaling pathway vulnerabilities in CCS, a multi-pronged approach was taken: (i) genomic and transcriptomic landscape analysis, (ii) integrated chemical biology interrogations, (iii) development of CREB1/ATF1 inhibitors, and (iv) antibody-drug conjugate testing (ADC). The first approach encompassed DNA exome and RNA deep sequencing of the largest human CCS cohort yet reported consisting of 47 patient tumor samples and 8 cell lines. RESULTS: Sequencing revealed recurrent mutations in cell cycle checkpoint, DNA double-strand break repair or DNA mismatch repair genes, with a correspondingly low to intermediate tumor mutational burden. DNA multi-copy gains with corresponding high RNA expression were observed in CCS tumor subsets. CCS cell lines responded to the HER3 ADC patritumab deruxtecan in a dose-dependent manner in vitro, with impaired long term cell viability. CONCLUSION: These studies of the genomic, transcriptomic and chemical biology landscape represent a resource 'atlas' for the field of CCS investigation and drug development. CHK inhibitors are identified as having potential relevance, CREB1 inhibitors non-dependence of CCS on CREB1 activity was established, and the potential utility of HER3 ADC being used in CCS is found.

Assessments of tumor metabolism with CEST MRI.

Chemical exchange saturation transfer (CEST) is a relatively new contrast mechanism for MRI. CEST MRI exploits a specific MR frequency (chemical shift) of a molecule while generating an image with good spatial resolution using standard MRI techniques, combining the specificity of MRS with the spatial resolution of MRI. Many CEST MRI acquisition methods have been developed to improve analyses of tumor metabolism. GluCEST, CrCEST, and LATEST can map glutamate, creatine, and lactate, which are important metabolites involved in tumor metabolism. GlucoCEST MRI tracks the pharmacokinetics of glucose transport and cell internalization within tumors. CatalyCEST MRI detects enzyme catalysis that changes a substrate CEST agent. AcidoCEST MRI measures extracellular pH of the tumor microenvironment by exploiting a ratio of two pH-dependent CEST signals. This review describes each technique, the technical issues involved with CEST MRI and each specific technique, and the merits and challenges associated with applying each CEST MRI technique to study tumor metabolism.

Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T1 relaxation, CEST, and DCE MRI. METHODS: The T1 relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766 T, MIA PaCa-2, and SU.86.86 pancreatic tumor models. We used each of these measurements as predictors for machine learning classifier algorithms. We also used principal component analysis to reduce the dimensionality of entire CEST spectra and DCE signal evolutions, which were then analyzed using classification methods. RESULTS: The T1 relaxation time constants, % CEST amplitudes at specific saturation frequencies, and the relative Ktrans and kep values from DCE MRI could not classify all three tumor types. However, the area under the curve from DCE signal evolutions could classify each tumor type. Principal component analysis was used to analyze the entire CEST spectrum and DCE signal evolutions, which predicted the correct tumor model with 87.5% and 85.1% accuracy, respectively. CONCLUSIONS: Machine learning applied to the entire CEST spectrum improved the classification of the three tumor models, relative to classifications that used % CEST values at single saturation frequencies. A similar improvement was not attained with machine learning applied to T1 relaxation times or DCE signal evolutions, relative to more simplistic analysis methods.

Preliminary Results that Assess Metformin Treatment in a Preclinical Model of Pancreatic Cancer Using Simultaneous [18F]FDG PET and acidoCEST MRI.

PURPOSE: We sought to determine if the synergy between evaluations of glucose uptake in tumors and extracellular tumor acidosis measured with simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) can improve longitudinal evaluations of the response to metformin treatment. PROCEDURES: A standard 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET protocol that evaluates glucose uptake in tumors, and a standard acidoCEST MRI protocol that measures extracellular pH (pHe) in tumors, were simultaneously performed to assess eight vehicle-treated (control) mice and eight metformin-treated mice 1 day before treatment, 1 day after initiating daily treatment with metformin, and 7 days after initiating treatment. Longitudinal changes in SUVmax and extracellular pH (pHe) were evaluated for each treatment group, and differences in SUVmax and pHe between metformin-treated and control groups were also evaluated. RESULTS: MRI acquisition protocols had little effect on the PET count rate, and the PET instrumentation had little effect on image contrast during acidoCEST MRI, verifying that [18F]FDG PET and acidoCEST MRI can be performed simultaneously. The average SUVmax of the tumor model had a significant decrease after 7 days of treatment with metformin, as expected. The average tumor pHe decreased after 7 days of metformin treatment, which reflected the inhibition of the consumption of cytosolic lactic acid caused by metformin. However, the average SUVmax of the tumor model was not significantly different between the metformin-treated and control groups after 7 days of treatment, and average pHe was also not significantly different between these groups. For comparison, the combination of average SUVmax and pHe measurements significantly differed between the treatment group and control group on Day 7. CONCLUSIONS: [18F]FDG PET and acidoCEST MRI studies can be performed simultaneously. The synergistic combination of assessing glucose uptake and tumor acidosis can improve differentiation of a drug-treated group from a control group during drug treatment of a tumor model.