EORTC-SPECTA Arcagen study, comprehensive genomic profiling and treatment adaptation of rare thoracic cancers.

Arcagen (NCT02834884) is a European prospective study aiming at defining the molecular landscape of rare cancers for treatment guidance. We present data from the cohort of rare thoracic tumors. Patients with advanced pleural mesothelioma (PM) or thymic epithelial tumors (TET) underwent genomic profiling with large targeted assay [>300 genes, tumor mutational burden (TMB), microsatellite instability (MSI) status] on formalin-fixed paraffin-embedded (FFPE) or plasma samples. EORTC molecular tumor board (MTB) advised for biomarker-guided treatments. 102 patients recruited from 8 countries between July 2019 and May 2022 were evaluable: 56 with PM, 46 with TET (23 thymomas, 23 thymic carcinomas). Molecular profiling was performed on 70 FFPE samples (42 PM, 28 TET), and 32 cases on ctDNA (14 PM, 18 TET), within a median turnaround time of 8 days from sample reception. We detected relevant molecular alterations in 66 out of 102 patients (65%; 79% PM, 48% TET), 51 of 70 FFPE samples (73%; 90% PM, 46% TET), and 15 of 32 plasma samples (47%; 43% PM, 50% TET). The most frequently altered genes were CDKN2A/B, BAP1, MTAP in PM and TP53, CDKN2A/B, SETD2 in TET. The TMB was low (mean 3.2 Muts/MB), 2 PM had MSI-high status. MTB advised molecular-guided treatment options in 32 situations, for 17 PM and 15 TET patients (75% clinical trial option, 22% off-label drug or compassionate use, 3% early access program). Molecular testing and MTB discussion were feasible for patients with rare thoracic cancers and allowed the broadening of treatment options for 30% of the cases.

Investigation of Clinically Significant Molecular Aberrations in Patients with Prostate Cancer: Implications for Personalized Treatment, Prognosis and Genetic Testing.

The data on tumor molecular profiling of European patients with prostate cancer is limited. Our aim was to evaluate the prevalence and prognostic and predictive values of gene alterations in unselected patients with prostate cancer. The presence of gene alterations was assessed in patients with histologically confirmed prostate cancer using the ForeSENTIA® Prostate panel (Medicover Genetics), targeting 36 clinically relevant genes and microsatellite instability testing. The primary endpoint was the prevalence of gene alterations in homologous recombination repair (HRR) genes. Overall, 196 patients with prostate cancer were evaluated (median age 72.2 years, metastatic disease in 141 (71.9%) patients). Gene alterations were identified in 120 (61%) patients, while alteration in HRR genes were identified in 34 (17.3%) patients. The most commonly mutated HRR genes were ATM (17, 8.7%), BRCA2 (9, 4.6%) and BRCA1 (4, 2%). The presence of HRR gene alterations was not associated with advanced stage (p = 0.21), age at diagnosis (p = 0.28), Gleason score (p = 0.17) or overall survival (HR 0.72; 95% CI: 0.41-1.26; p = 0.251). We identified clinically relevant somatic gene alterations in European patients with prostate cancer. These molecular alterations have prognostic significance and therapeutic implications and/or may trigger genetic testing in selected patients. In the era of precision medicine, prospective research on the predictive role of these alterations for innovative treatments or their combinations is warranted.

Molecular profile and clinical features of patients with gliomas using a broad targeted next generation-sequencing panel.

Gliomas are the most common malignant primary brain tumors characterized by poor prognosis. The genotyping of tumors using next generation sequencing (NGS) platforms enables the identification of genetic alterations that constitute diagnostic, prognostic and predictive biomarkers. The present study investigated the molecular profile of 32 tumor samples from 32 patients with high-grade gliomas by implementing a broad 80-gene targeted NGS panel while reporting their clinicopathological characteristics and outcomes. Subsequently, 14 of 32 tumor specimens were also genotyped using a 55-gene NGS panel to validate the diagnostic accuracy and clinical utility of the extended panel. The median follow-up was 19.2 months. In total, 129 genetic alterations including 33 structural variants were identified in 38 distinct genes. Among 96 variants (single nucleotide variants and insertions and deletions), 38 were pathogenic and 58 variants of unknown clinical significance. TP53 was the most frequently mutated gene, followed by PTEN and IDH1 genes. Glioma patients with IDH1 mutant tumors were younger and had significantly longer overall survival compared to patients with wild-type IDH1 tumors. Similarly, tumors with TP53 mutations were more likely observed in younger patients with glioma. Subsequently, a comparison of mutational profiles of samples analyzed by both panels was also performed. Implementation of the comprehensive pan-cancer and the MOL panels resulted in the identification of 37 and 15 variants, respectively. Of those, 13 were common. Comprehensive pan-cancer panel identified 24 additional variants, 22 of which were located in regions that were not targeted by the MOL panel. By contrast, the MOL panel identified two additional variants. Overall, the present study demonstrated that using an extended tumor profile assay instead of a glioma-specific tumor profile panel identified additional genetic changes that may be taken into consideration as potential therapeutic targets for glioma diagnosis and molecular classification.

Clinical Significance of Germline Cancer Predisposing Variants in Unselected Patients with Pancreatic Adenocarcinoma.

Our aim was to determine the prevalence, prognostic and predictive role of germline pathogenic/likely pathogenic variants (P/LPVs) in cancer predisposing genes in patients with pancreatic ductal adenocarcinoma (PDAC). Germline testing of 62 cancer susceptibility genes was performed on unselected patients diagnosed from 02/2003 to 01/2020 with PDAC, treated at Hellenic Cooperative Oncology Group (HeCOG)-affiliated Centers. The main endpoints were prevalence of P/LPVs and overall survival (OS). P/LPVs in PDAC-associated and homologous recombination repair (HRR) genes were identified in 22 (4.0%) and 42 (7.7%) of 549 patients, respectively. P/LPVs were identified in 16 genes, including ATM (11, 2.0%) and BRCA2 (6, 1.1%), while 19 patients (3.5%) were heterozygotes for MUTYH P/LPVs and 9 (1.6%) carried the low-risk allele, CHEK2 p.(Ile157Thr). Patients carrying P/LPVs had improved OS compared to non-carriers (22.6 vs. 13.9 months, p = 0.006). In multivariate analysis, there was a trend for improved OS in P/LPV carriers (p = 0.063). The interaction term between platinum exposure and mutational status of HRR genes was not significant (p-value = 0.35). A significant proportion of patients with PDAC carries clinically relevant germline P/LPVs, irrespectively of age, family history or disease stage. The predictive role of these P/LPVs has yet to be defined. ClinicalTrials.gov Identifier: NCT03982446.

Whole transcriptome sequence data of 5-FU sensitive and 5-FU resistant tumors generated in a mouse model of de novo carcinogenesis.

We have performed whole transcriptome sequencing of 5-FU resistant and 5-FU sensitive tumors generated in a mouse model of de novo carcinogenesis that closely recapitulates tumor initiation, progression and maintenance in vivo. Tumors were generated using the DMBA/TPA model of chemically induced carcinogenesis [1], tumor-bearing mice were subsequently treated with 5-FU, and tumor growth as well as response to treatment was monitored by measuring tumor volume twice a week. Based on these measurements, we selected two 5-FU resistant and two 5-FU sensitive tumors and performed whole transcriptome sequencing and in order to identify differentially expressed transcripts between the two sets. Data obtained is deposited and available through NCBI SRA (reference number SRP155180 - https://www.ncbi.nlm.nih.gov/sra/?term=SRP155180).

Whole-genome fetal and maternal DNA methylation analysis using MeDIP-NGS for the identification of differentially methylated regions.

DNA methylation is an epigenetic marker that has been shown to vary significantly across different tissues. Taking advantage of the methylation differences between placenta-derived cell-free DNA and maternal blood, several groups employed different approaches for the discovery of fetal-specific biomarkers. The aim of this study was to analyse whole-genome fetal and maternal methylomes in order to identify and confirm the presence of differentially methylated regions (DMRs). We have initially utilized methylated DNA immunoprecipitation (MeDIP) and next-generation sequencing (NGS) to identify genome-wide DMRs between chorionic villus sampling (CVS) and female non-pregnant plasma (PL) and peripheral blood (WBF) samples. Next, using specific criteria, 331 fetal-specific DMRs were selected and confirmed in eight CVS, eight WBF and eight PL samples by combining MeDIP and in-solution targeted enrichment followed by NGS. Results showed higher enrichment in CVS samples as compared to both WBF and PL samples, confirming the distinct methylation levels between fetal and maternal DNA for the selected DMRs. We have successfully implemented a novel approach for the discovery and confirmation of a significant number of fetal-specific DMRs by combining for the first time MeDIP and in-solution targeted enrichment followed by NGS. The implementation of this double-enrichment approach is highly efficient and enables the detailed analysis of multiple DMRs by targeted NGS. Also, this is, to our knowledge, the first reported application of MeDIP on plasma samples, which leverages the implementation of our enrichment methodology in the detection of fetal abnormalities in maternal plasma.

Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis.

Tumorigenesis is a complex, multistep process that depends on numerous alterations within the cell and contribution from the surrounding stroma. The ability to model macroscopic tumor evolution with high fidelity may contribute to better predictive tools for designing tumor therapy in the clinic. However, attempts to model tumor growth have mainly been developed and validated using data from xenograft mouse models, which fail to capture important aspects of tumorigenesis including tumor-initiating events and interactions with the immune system. In the present study, we investigate tumor growth and therapy dynamics in a mouse model of de novo carcinogenesis that closely recapitulates tumor initiation, progression and maintenance in vivo. We show that the rate of tumor growth and the effects of therapy are highly variable and mouse specific using a Gompertz model to describe tumor growth and a two-compartment pharmacokinetic/ pharmacodynamic model to describe the effects of therapy in mice treated with 5-FU. We show that inter-mouse growth variability is considerably larger than intra-mouse variability and that there is a correlation between tumor growth and drug kill rates. Our results show that in vivo tumor growth and regression in a double transgenic mouse model are highly variable both within and between subjects and that mathematical models can be used to capture the overall characteristics of this variability. In order for these models to become useful tools in the design of optimal therapy strategies and ultimately in clinical practice, a subject-specific modelling strategy is necessary, rather than approaches that are based on the average behavior of a given subject population which could provide erroneous results.

Multiprocess dynamic modeling of tumor evolution with bayesian tumor-specific predictions.

We propose a sequential probabilistic mixture model for individualized tumor growth forecasting. In contrast to conventional deterministic methods for estimation and prediction of tumor evolution, we utilize all available tumor-specific observations up to the present time to approximate the unknown multi-scale process of tumor growth over time, in a stochastic context. The suggested mixture model uses prior information obtained from the general population and becomes more individualized as more observations from the tumor are sequentially taken into account. Inference can be carried out using the full, possibly multimodal, posterior, and predictive distributions instead of point estimates. In our simulation study we illustrate the superiority of the suggested multi-process dynamic linear model compared to the single process alternative. The validation of our approach was performed with experimental data from mice. The methodology suggested in the present study may provide a starting point for personalized adaptive treatment strategies.