Salvatori, Rebecca

Abstract Purpose: Monitoring disease progression in patients with high-grade gliomas (HGG) is challenging due to treatment-related changes in imaging and the requirement for neurosurgical intervention to obtain diagnostic tissue. DNA junctions in HGG often amplify oncogenes, making these DNA fragments potentially more abundant in blood than monoallelic mutations. In this study, we piloted a cell-free DNA approach for disease detection in the plasma of patients with HGG by leveraging patient-specific DNA junctions associated with oncogene amplifications. Experimental Design: Whole-genome sequencing of grade 3 or 4 isocitrate dehydrogenase–mutant or wild-type astrocytomas was utilized to identify amplified junctions. Individualized qPCR assays were developed using patient-specific primers designed for the amplified junction. ctDNA levels containing these junctions were measured in patient plasma samples. Results: Unique amplified junctions were evaluated by individualized semi-qPCR assays in presurgical plasma of 18 patients, 15 with tumor-associated focal amplifications and three without tumor-associated focal amplifications. high copy-number junctions were robustly detected in the plasma of 14 of 15 (93.3%) patients with amplified junctions and none of the controls. Changes in junction abundance correlated with disease trajectory in serial plasma samples from five patients, including increased abundance of amplified junctions preceding radiographic disease progression. Conclusions: In patients with grade 3 or 4 astrocytomas who had tumor-associated amplifications, patient-specific amplified junctions were successfully detected in assayed plasma from most patients. Longitudinal analysis of plasma samples correlated with disease trajectory, including cytoreduction and progression.

Abstract Accurate response assessment in patients with high-grade gliomas (HGGs) is complicated by post-treatment radiographic changes that are equivocal for treatment effect versus progression. Plasma-based liquid biopsy assays for circulating tumor DNA (ctDNA) have had limited sensitivity to date, due in part to the presence of the blood-brain barrier that limits the diffusion of tumor-derived analytes out of the central nervous system. To address this challenge, we developed a ctDNA plasma-based assay to detect individualized tumor-derived amplified junctions in patients with HGGs. These junctions occur due to large-scale chromosomal rearrangements, such as those associated with epidermal growth factor receptor (EGFR) or cyclin-dependent kinase 4 (CDK4) amplification. To identify these amplified junctions, whole genome sequencing was performed on resected tissue. Primers were designed to flank junction breakpoints and were then utilized on each patient’s plasma samples to detect the amplified junction via qPCR. Tumor-specific amplified junctions were robustly detected in plasma from 14/14 (100%) patients with IDH-mutant or wild-type grade 4 astrocytomas whose tumor genomes demonstrated amplification-related junctions, located at chromosomes 7, 11, or 12. In comparison, ctDNA could not be detected in three control cases that did not have an amplification or did not have high copy number junctions. Importantly, serial plasma samples obtained prior to and at disease recurrence in 4 patients demonstrated an increasing abundance of amplified junctions that accurately correlated with disease progression in 4/4 patients (100%). In two of these patients, increasing abundance of amplified junctions preceded radiographic changes compatible with disease progression by approximately 2 or 4 months, respectively. In conclusion, tumor-specific amplified junctions could reliably be detected in plasma from patients with IDH-mutant or wild-type grade 4 astrocytomas and correlated with changes in disease burden. Further work is currently ongoing to evaluate these promising results in a larger cohort of patients with serial plasma samples.

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.

PURPOSE The investigation of multiple molecular targets with next-generation sequencing (NGS) has entered clinical practice in oncology, yielding to a paradigm shift from the histology-centric approach to the mutational model for personalized treatment. Accordingly, most of the drugs recently approved in oncology are coupled to specific biomarkers. One potential tool for implementing the mutational model of precision oncology in daily practice is represented by the Molecular Tumor Board (MTB), a multidisciplinary team whereby molecular pathologists, biologists, bioinformaticians, geneticists, medical oncologists, and pharmacists cooperate to generate, interpret, and match molecular data with personalized treatments. PATIENTS AND METHODS Since May 2020, the institutional MTB set at Fondazione IRCCS Istituto Nazionale Tumori of Milan met weekly via teleconference to discuss molecular data and potential therapeutic options for patients with advanced/metastatic solid tumors. RESULTS Up to October 2021, among 1,996 patients evaluated, we identified >10,000 variants, 43.2% of which were functionally relevant (pathogenic or likely pathogenic). On the basis of functionally relevant variants, 711 patients (35.6%) were potentially eligible to targeted therapy according to European Society of Medical Oncology Scale for Clinical Actionability of Molecular Targets tiers, and 9.4% received a personalized treatment. Overall, larger NGS panels (containing >50 genes) significantly outperformed small panels (up to 50 genes) in detecting actionable gene targets across different tumor types. CONCLUSION Our real-world data provide evidence that MTB is a valuable tool for matching NGS data with targeted treatments, eventually implementing precision oncology in clinical practice.

Aims Reticulin stain is used routinely in the histological evaluation of hepatocellular carcinoma (HCC). The goal of this study was to assess whether the histological reticulin proportionate area (RPA) in HCCs predicts tumour‐related outcomes. Methods and results We developed and validated a supervised artificial intelligence (AI) model that utilises a cloud‐based, deep‐learning AI platform (Aiforia Technologies, Helsinki, Finland) to specifically recognise and quantify the reticulin framework in normal livers and HCCs using routine reticulin staining. We applied this reticulin AI model to a cohort of consecutive HCC cases from patients undergoing curative resection between 2005 and 2015. A total of 101 HCC resections were included (median age = 68 years, 64 males, median follow‐up time = 49.9 months). AI model RPA reduction of > 50% (compared to normal liver tissue) was predictive of metastasis [hazard ratio (HR) = 3.76, P = 0.004, disease‐free survival (DFS, HR = 2.48, P < 0.001) and overall survival (OS), HR = 2.80, P = 0.001]. In a Cox regression model, which included clinical and pathological variables, RPA decrease was an independent predictor of DFS and OS and the only independent predictor of metastasis. Similar results were found in the moderately differentiated HCC subgroup (WHO grade 2), in which reticulin quantitative analysis was an independent predictor of metastasis, DFS and OS. Conclusion Our data indicate that decreased RPA is a strong predictor of various HCC‐related outcomes, including within the moderately differentiated subgroup. Reticulin, therefore, may represent a novel and important prognostic HCC marker, to be further explored and validated.