Next-generation sequencing of immunoglobulin gene rearrangements for clonality assessment: a technical feasibility study by EuroClonality-NGS

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Formalin-Fixed, Paraffin-Embedded–Targeted Locus Capture:A Next-Generation Sequencing Technology for Accurate DNA-Based Gene Fusion Detection in Bone and Soft Tissue Tumors

Chromosomal rearrangements are important drivers in cancer, and their robust detection is essential for diagnosis, prognosis, and treatment selection, particularly for bone and soft tissue tumors. Current diagnostic methods are hindered by limitations, including difficulties with multiplexing targets and poor quality of RNA. A novel targeted DNA-based next-generation sequencing method, formalin-fixed, paraffin-embedded–targeted locus capture (FFPE-TLC), has shown advantages over current diagnostic methods when applied on FFPE lymphomas, including the ability to detect novel rearrangements. We evaluated the utility of FFPE-TLC in bone and soft tissue tumor diagnostics. FFPE-TLC sequencing was successfully applied on noncalcified and decalcified FFPE samples (n = 44) and control samples (n = 19). In total, 58 rearrangements were identified in 40 FFPE tumor samples, including three previously negative samples, and none was identified in the FFPE control samples. In all five discordant cases, FFPE-TLC could identify gene fusions where other methods had failed due to either detection limits or poor sample quality. FFPE-TLC achieved a high specificity and sensitivity (no false positives and negatives). These results indicate that FFPE-TLC is applicable in cancer diagnostics to simultaneously analyze many genes for their involvement in gene fusions. Similar to the observation in lymphomas, FFPE-TLC is a good DNA-based alternative to the conventional methods for detection of rearrangements in bone and soft tissue tumors.</p

One-fits-all pretreatment protocol facilitating Fluorescence in Situ Hybridization on formalin-fixed paraffin-embedded, fresh frozen and cytological slides

Background: The Fluorescence In Situ Hybridization (FISH) technique is a very useful tool for diagnostic and prognostic purposes in molecular pathology. However, clinical testing on patient tissue is challenging due to variables of tissue processing that can influence the quality of the results. This emphasizes the necessity of a standardized FISH protocol with a high hybridization efficiency. We present a pretreatment protocol that is easy, reproducible, cost-effective, and facilitates FISH on all types of patient material simultaneously with good quality results. During validation, FISH analysis was performed simultaneously on formalin-fixed paraffin-embedded, fresh frozen and cytological patient material in combination with commercial probes using our optimized one-fits-all pretreatment protocol. An optimally processed sample is characterized by strong specific signals, intact nuclear membranes, non-disturbing autofluorescence and a homogeneous DAPI staining. Results: In our retrospective cohort of 3881 patient samples, overall 93% of the FISH samples displayed good quality results leading to a patient diagnosis. All FISH were assessed on quality aspects such as adequacy and consistency of signal strength (brightness), lack of background and / or cross-hybridization signals, and additionally the presence of appropriate control signals were evaluated to assure probe accuracy. In our analysis 38 different FISH probes from 3 commercial manufacturers were used (Cytocell, Vysis and ZytoLight). The majority of the patients in this cohort displayed good signal quality and barely non-specific background fluorescence on all tissue types independent of which commercial probe was used. Conclusion: The optimized one-fits-all FISH method is robust, reliable and reproducible to deliver an accurate result for patient diagnostics in a lean workflow and cost-effective manner. This protocol can be used for widespread application in cancer and non-cancer diagnostics and research

Formalin-Fixed, Paraffin-Embedded–Targeted Locus Capture:A Next-Generation Sequencing Technology for Accurate DNA-Based Gene Fusion Detection in Bone and Soft Tissue Tumors

Chromosomal rearrangements are important drivers in cancer, and their robust detection is essential for diagnosis, prognosis, and treatment selection, particularly for bone and soft tissue tumors. Current diagnostic methods are hindered by limitations, including difficulties with multiplexing targets and poor quality of RNA. A novel targeted DNA-based next-generation sequencing method, formalin-fixed, paraffin-embedded–targeted locus capture (FFPE-TLC), has shown advantages over current diagnostic methods when applied on FFPE lymphomas, including the ability to detect novel rearrangements. We evaluated the utility of FFPE-TLC in bone and soft tissue tumor diagnostics. FFPE-TLC sequencing was successfully applied on noncalcified and decalcified FFPE samples (n = 44) and control samples (n = 19). In total, 58 rearrangements were identified in 40 FFPE tumor samples, including three previously negative samples, and none was identified in the FFPE control samples. In all five discordant cases, FFPE-TLC could identify gene fusions where other methods had failed due to either detection limits or poor sample quality. FFPE-TLC achieved a high specificity and sensitivity (no false positives and negatives). These results indicate that FFPE-TLC is applicable in cancer diagnostics to simultaneously analyze many genes for their involvement in gene fusions. Similar to the observation in lymphomas, FFPE-TLC is a good DNA-based alternative to the conventional methods for detection of rearrangements in bone and soft tissue tumors.</p

Towards diagnostic criteria for malignant deep penetrating melanocytic tumors using single nucleotide polymorphism array and next-generation sequencing

Cutaneous deep penetrating melanocytic neoplasms frequently simulate melanoma and might occasionally progress to metastatic melanoma. Distinguishing deep penetrating nevi (DPN) and deep penetrating melanocytomas (DPM) from malignant deep penetrating tumors (MDPT) is difficult based on histopathology alone, and diagnostic criteria for MDPT are currently lacking. Using a molecular workup, we aimed to provide readily available diagnostic tools for classification of deep penetrating tumors. We used clinical follow-up and Single Nucleotide Polymorphism (SNP) array for tumor classification of 20 deep penetrating neoplasms to identify associations with histopathological, immunohistochemistry, and NGS findings. Ten neoplasms were classified as MDPT, four as DPM, and six as DPN. Two MDPT showed metastases. The following parameters were statistically significantly associated with MDPT: severe nuclear atypia (risk ratio [RR] 2.9, p < 0.05), absence of a nevus component (RR 10.0, p = 0.04), positive PRAME expression (RR 9.0, p = 0.02), complete loss of p16 expression (RR 3.5, p = 0.003), TERT-p and APC mutations (RR 11.0, p = 0.01 and RR 2.7, p = 0.002, respectively), and ≥1 additional pathogenic mutation (RR 9.0, p = 0.02). Ki-67 expression ≥ 5% was not significantly associated with MDPTs, although it was <5% in all DPNs. Three MDPT did not show nuclear β-catenin expression despite having a CTNNB1 (n = 2) or an APC mutation (n = 1). Our findings suggest that complete loss of p16 and positive PRAME expression, a driver mutation in APC, ≥ 1 additional pathogenic mutation, especially in TERT-p, support an MDPT diagnosis in deep penetrating neoplasms. Besides severe nuclear atypia and possibly severe inflammation, we did not identify specific histopathological criteria for malignancy. Non-aberrant nuclear β-catenin expression might not exclude a deep penetrating signature in MDPT

Towards diagnostic criteria for malignant deep penetrating melanocytic tumors using single nucleotide polymorphism array and next-generation sequencing

Cutaneous deep penetrating melanocytic neoplasms frequently simulate melanoma and might occasionally progress to metastatic melanoma. Distinguishing deep penetrating nevi (DPN) and deep penetrating melanocytomas (DPM) from malignant deep penetrating tumors (MDPT) is difficult based on histopathology alone, and diagnostic criteria for MDPT are currently lacking. Using a molecular workup, we aimed to provide readily available diagnostic tools for classification of deep penetrating tumors. We used clinical follow-up and Single Nucleotide Polymorphism (SNP) array for tumor classification of 20 deep penetrating neoplasms to identify associations with histopathological, immunohistochemistry, and NGS findings. Ten neoplasms were classified as MDPT, four as DPM, and six as DPN. Two MDPT showed metastases. The following parameters were statistically significantly associated with MDPT: severe nuclear atypia (risk ratio [RR] 2.9, p < 0.05), absence of a nevus component (RR 10.0, p = 0.04), positive PRAME expression (RR 9.0, p = 0.02), complete loss of p16 expression (RR 3.5, p = 0.003), TERT-p and APC mutations (RR 11.0, p = 0.01 and RR 2.7, p = 0.002, respectively), and ≥1 additional pathogenic mutation (RR 9.0, p = 0.02). Ki-67 expression ≥ 5% was not significantly associated with MDPTs, although it was <5% in all DPNs. Three MDPT did not show nuclear β-catenin expression despite having a CTNNB1 (n = 2) or an APC mutation (n = 1). Our findings suggest that complete loss of p16 and positive PRAME expression, a driver mutation in APC, ≥ 1 additional pathogenic mutation, especially in TERT-p, support an MDPT diagnosis in deep penetrating neoplasms. Besides severe nuclear atypia and possibly severe inflammation, we did not identify specific histopathological criteria for malignancy. Non-aberrant nuclear β-catenin expression might not exclude a deep penetrating signature in MDPT

Multicenter Comparison of Molecular Tumor Boards in The Netherlands: Definition, Composition, Methods, and Targeted Therapy Recommendations

Background: Molecular tumor boards (MTBs) provide rational, genomics-driven, patient-tailored treatment recommendations. Worldwide, MTBs differ in terms of scope, composition, methods, and recommendations. This study aimed to assess differences in methods and agreement in treatment recommendations among MTBs from tertiary cancer referral centers in The Netherlands. Materials and Methods: MTBs from all tertiary cancer referral centers in The Netherlands were invited to participate. A survey assessing scope, value, logistics, composition, decision-making method, reporting, and registration of the MTBs was completed through on-site interviews with members from each MTB. Targeted therapy recommendations were compared using 10 anonymized cases. Participating MTBs were asked to provide a treatment recommendation in accordance with their own methods. Agreement was based on which molecular alteration(s) was considered actionable with the next line of targeted therapy. Results: Interviews with 24 members of eight MTBs revealed that all participating MTBs focused on rare or complex mutational cancer profiles, operated independently of cancer type–specific multidisciplinary teams, and consisted of at least (thoracic and/or medical) oncologists, pathologists, and clinical scientists in molecular pathology. Differences were the types of cancer discussed and the methods used to achieve a recommendation. Nevertheless, agreement among MTB recommendations, based on identified actionable molecular alteration(s), was high for the 10 evaluated cases (86%). Conclusion: MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational cancer profiles. We propose a “Dutch MTB model” for an optimal, collaborative, and nationally aligned MTB workflow. Implications for Practice: Interpretation of genomic analyses for optimal choice of target therapy for patients with cancer is becoming increasingly complex. A molecular tumor board (MTB) supports oncologists in rationalizing therapy options. However, there is no consensus on the most optimal setup for an MTB, which can affect the quality of recommendations. This study reveals that the eight MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational profiles. The Dutch MTB model is based on a collaborative and nationally aligned workflow with interinstitutional collaboration and data sharing

The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections

Background The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI. Results Various multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the 'bacterial' patients and 82% of the 'viral' patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus). Conclusions We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections