Genomic characterization and detection of potential therapeutic targets for peritoneal mesothelioma in current practice

Peritoneal mesothelioma (PeM) is an aggressive tumor with limited treatment options. The current study aimed to evaluate the value of next generation sequencing (NGS) of PeM samples in current practice. Foundation Medicine F1CDx NGS was performed on 20 tumor samples. This platform assesses 360 commonly somatically mutated genes in solid tumors and provides a genomic signature. Based on the detected mutations, potentially effective targeted therapies were identified. NGS was successful in 19 cases. Tumor mutational burden (TMB) was low in 10 cases, and 11 cases were microsatellite stable. In the other cases, TMB and microsatellite status could not be determined. BRCA1 associated protein 1 (BAP1) mutations were found in 32% of cases, cyclin dependent kinase inhibitor 2A/B (CDKN2A/B) and neurofibromin 2 (NF2) mutations in 16%, and ataxia-telangiectasia mutated serine/threonine kinase (ATM) in 11%. Based on mutations in the latter two genes, potential targeted therapies are available for approximately a quarter of cases (i.e., protein kinase inhibitors for three NF2 mutated tumors, and polyADP-ribose polymerase inhibitors for two ATM mutated tumors). Extensive NGS analysis of PeM samples resulted in the identification of potentially effective targeted therapies for about one in four patients. Although these therapies are currently not available for patients with PeM, ongoing developments might result in new treatment options in the future.</p

In-depth molecular analysis of combined and co-primary pulmonary large cell neuroendocrine carcinoma and adenocarcinoma

Up to 14% of large cell neuroendocrine carcinomas (LCNECs) are diagnosed in continuity with nonsmall cell lung carcinoma. In addition to these combined lesions, 1% to 7% of lung tumors present as co-primary tumors with multiple synchronous lesions. We evaluated molecular and clinicopathological characteristics of combined and co-primary LCNEC-adenocarcinoma (ADC) tumors. Ten patients with LCNEC-ADC (combined) and five patients with multiple synchronous ipsilateral LCNEC and ADC tumors (co-primary) were included. DNA was isolated from distinct tumor parts, and 65 cancer genes were analyzed by next generation sequencing. Immunohistochemistry was performed including neuroendocrine markers, pRb, Ascl1 and Rest. Pure ADC (N = 37) and LCNEC (N = 17) cases were used for reference. At least 1 shared mutation, indicating tumor clonality, was found in LCNEC- and ADC-parts of 10/10 combined tumors but only in 1/5 co-primary tumors. A range of identical mutations was observed in both parts of combined tumors: 8/10 contained ADC-related (EGFR/KRAS/STK11 and/or KEAP1), 4/10 RB1 and 9/10 TP53 mutations. Loss of pRb IHC was observed in 6/10 LCNEC- and 4/10 ADC-parts. The number and intensity of expression of Ascl1 and neuroendocrine markers increased from pure ADC (low) to combined ADC (intermediate) and combined and pure LCNEC (high). The opposite was true for Rest expression. In conclusion, all combined LCNEC-ADC tumors were clonally related indicating a common origin. A relatively high frequency of pRb inactivation was observed in both LCNEC- and ADC-parts, suggesting an underlying role in LCNEC-ADC development. Furthermore, neuroendocrine differentiation might be modulated by Ascl1(+) and Rest(-) expression

TP53 Mutations in Serum Circulating Cell-Free Tumor DNA As Longitudinal Biomarker for High-Grade Serous Ovarian Cancer

The aim of this study was to determine an optimal workflow to detect TP53 mutations in baseline and longitudinal serum cell free DNA (cfDNA) from high-grade serous ovarian carcinomas (HGSOC) patients and to define whether TP53 mutations are suitable as biomarker for disease. TP53 was investigated in tissue and archived serum from 20 HGSOC patients by a next-generation sequencing (NGS) workflow alone or combined with digital PCR (dPCR). AmpliSeq™-focused NGS panels and customized dPCR assays were used for tissue DNA and longitudinal cfDNAs, and Oncomine NGS panel with molecular barcoding was used for baseline cfDNAs. TP53 missense mutations were observed in 17 tissue specimens and in baseline cfDNA for 4/8 patients by AmpliSeq, 6/9 patients by Oncomine, and 4/6 patients by dPCR. Mutations in cfDNA were detected in 4/6 patients with residual disease and 3/4 patients with disease progression within six months, compared to 5/11 patients with no residual disease and 6/13 patients with progression after six months. Finally, mutations were detected at progression in 5/6 patients, but not during chemotherapy. NGS with molecular barcoding and dPCR were most optimal workflows to detect TP53 mutations in baseline and longitudinal serum cfDNA, respectively. TP53 mutations were undetectable in cfDNA during treatment but re-appeared at disease progression, illustrating its promise as a biomarker for disease monitoring

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Prevalence, clinical and molecular characteristics of early stage EGFR-mutated lung cancer in a real-life West-European cohort: Implications for adjuvant therapy

Objectives: The landmark ADAURA study recently demonstrated a significant disease-free survival benefit of adjuvant osimertinib in patients with resected EGFR-mutated lung adenocarcinoma. However, data on prevalence rates and stage distribution of EGFR mutations in non-small cell lung cancer in Western populations are limited since upfront EGFR testing in early stage lung adenocarcinoma is not common practice. Here, we present a unique, real-world, unselected cohort of lung adenocarcinoma to aid in providing a rationale for routine testing of early stage lung cancers for EGFR mutations in the West-European population. Material and methods: We performed routine unbiased testing of all cases, regardless of TNM stage, with targeted next-generation sequencing on 486 lung adenocarcinoma cases between 01- January 2014 and 01 February 2020. Clinical and pathological data, including co-mutations and morphology, were collected. EGFR-mutated cases were compared to KRAS-mutated cases to investigate EGFR-specific characteristics. Results: In total, 53 of 486 lung adenocarcinomas (11%) harboured an EGFR mutation. In early stages (stage 0-IIIA), the prevalence was 13%, versus 9% in stage IIIB-IV. Nine out of 130 (7%) stage IB-IIIA patients fit the ADAURA criteria. Early stage cases harboured more L858R mutations (p = 0.02), fewer exon 20 insertions (p = 0.048), fewer TP53 co-mutations (p = 0.007), and were more frequently never smokers (p = 0.04) compared to late stage cases with EGFR mutations. The KRAS-mutated cases were distributed more evenly across TNM stages compared to the EGFR-mutated cases. Conclusion: As (neo-)adjuvant targeted therapy regimes enter the field of lung cancer treatment, molecular analysis of early stage non-small cell lung cancer becomes relevant. Testing for EGFR mutations in early stage lung adenocarcinoma holds a substantial yield in our population, as our number needed to test ratio for adjuvant osimertinib was 14.4. The observed differences between early and late stage disease warrant further analysis to work towards better prognostic stratification and more personalised treatment

Tobacco Smoking-Related Mutational Signatures in Classifying Smoking-Associated and Nonsmoking-Associated NSCLC

Introduction: Patient-reported smoking history is frequently used as a stratification factor in NSCLC-directed clinical research. Nevertheless, this classification does not fully reflect the mutational processes in a tumor. Next-generation sequencing can identify mutational signatures associated with tobacco smoking, such as single-base signature 4 and indel-based signature 3. This provides an opportunity to redefine the classification of smoking- and nonsmoking-associated NSCLC on the basis of individual genomic tumor characteristics and could contribute to reducing the lung cancer stigma. Methods: Whole genome sequencing data and clinical records were obtained from three prospective cohorts of metastatic NSCLC (N = 316). Relative contributions and absolute counts of single-base signature 4 and indel-based signature 3 were combined with relative contributions of age-related signatures to divide the cohort into smoking-associated (“smoking high”) and nonsmoking-associated (“smoking low”) clusters. Results: The smoking high (n = 169) and smoking low (n = 147) clusters differed considerably in tumor mutational burden, signature contribution, and mutational landscape. This signature-based classification overlapped considerably with smoking history. Yet, 26% of patients with an active smoking history were included in the smoking low cluster, of which 52% harbored an EGFR/ALK/RET/ROS1 alteration, and 4% of patients without smoking history were included in the smoking high cluster. These discordant samples had similar genomic contexts to the rest of their respective cluster. Conclusions: A substantial subset of metastatic NSCLC is differently classified into smoking- and nonsmoking-associated tumors on the basis of smoking-related mutational signatures than on the basis of smoking history. This signature-based classification more accurately classifies patients on the basis of genome-wide context and should therefore be considered as a stratification factor in clinical research