Impact of genetic counseling strategy on diagnostic yield and workload for genome-sequencing-based tumor diagnostics.

PURPOSE: Genome sequencing (GS) enables comprehensive molecular analysis of tumors and identification of hereditary cancer predisposition. According to guidelines, directly determining pathogenic germline variants (PGVs) requires pretest genetic counseling, which is cost-ineffective. Referral for genetic counseling based on tumor variants alone could miss relevant PGVs and/or result in unnecessary referrals. METHODS: We validated GS for detection of germline variants and simulated 3 strategies using paired tumor-normal GS data of 937 metastatic patients. In strategy-1, genetic counseling before tumor testing allowed direct PGV analysis. In strategy-2 and -3, germline testing and referral for post-test genetic counseling is based on tumor variants using Dutch (strategy-2) or Europen Society for Medical Oncology (ESMO) Precision Medicine Working Group (strategy-3) guidelines. RESULTS: In strategy-1, PGVs would be detected in 50 patients (number-needed-to counsel; NTC = 18.7). In strategy-2, 86 patients would have been referred for genetic counseling and 43 would have PGVs (NTC = 2). In strategy-3, 94 patients would have been referred for genetic counseling and 32 would have PGVs (NTC = 2.9). Hence, 43 and 62 patients, respectively, were unnecessarily referred based on a somatic variant. CONCLUSION: Both post-tumor test counseling strategies (2 and 3) had significantly lower NTC, and strategy-2 had the highest PGV yield. Combining pre-tumor test mainstreaming and post-tumor test counseling may maximize the clinically relevant PGV yield and minimize unnecessary referrals.

Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma.

We investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography-computed tomography of 323 patients, which included maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUVpeak between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients.

The path towards consensus genome classification of diffuse large B-cell lymphoma for use in clinical practice.

Diffuse large B-cell lymphoma (DLBCL) is a widely heterogeneous disease in presentation, treatment response and outcome that results from a broad biological heterogeneity. Various stratification approaches have been proposed over time but failed to sufficiently capture the heterogeneous biology and behavior of the disease in a clinically relevant manner. The most recent DNA-based genomic subtyping studies are a major step forward by offering a level of refinement that could serve as a basis for exploration of personalized and targeted treatment for the years to come. To enable consistent trial designs and allow meaningful comparisons between studies, harmonization of the currently available knowledge into a single genomic classification widely applicable in daily practice is pivotal. In this review, we investigate potential avenues for harmonization of the presently available genomic subtypes of DLBCL inspired by consensus molecular classifications achieved for other malignancies. Finally, suggestions for laboratory techniques and infrastructure required for successful clinical implementation are described.

Genomic and microenvironmental landscape of stage I follicular lymphoma, compared with stage III/IV.

Although the genomic and immune microenvironmental landscape of follicular lymphoma (FL) has been extensively investigated, little is known about the potential biological differences between stage I and stage III/IV disease. Using next-generation sequencing and immunohistochemistry, 82 FL nodal stage I cases were analyzed and compared with 139 FL stage III/IV nodal cases. Many similarities in mutations, chromosomal copy number aberrations, and microenvironmental cell populations were detected. However, there were also significant differences in microenvironmental and genomic features. CD8+ T cells (P = .02) and STAT6 mutations (false discovery rate [FDR] <0.001) were more frequent in stage I FL. In contrast, programmed cell death protein 1-positive T cells, CD68+/CD163+ macrophages (P < .001), BCL2 translocation (BCL2trl+) (P < .0001), and KMT2D (FDR = 0.003) and CREBBP (FDR = 0.04) mutations were found more frequently in stage III/IV FL. Using clustering, we identified 3 clusters within stage I, and 2 clusters within stage III/IV. The BLC2trl+ stage I cluster was comparable to the BCL2trl+ cluster in stage III/IV. The two BCL2trl- stage I clusters were unique for stage I. One was enriched for CREBBP (95%) and STAT6 (64%) mutations, without BLC6 translocation (BCL6trl), whereas the BCL2trl- stage III/IV cluster contained BCL6trl (64%) with fewer CREBBP (45%) and STAT6 (9%) mutations. The other BCL2trl- stage I cluster was relatively heterogeneous with more copy number aberrations and linker histone mutations. This exploratory study shows that stage I FL is genetically heterogeneous with different underlying oncogenic pathways. Stage I FL BCL2trl- is likely STAT6 driven, whereas BCL2trl- stage III/IV appears to be more BCL6trl driven.

Reproducibility of Gene Expression Signatures in Diffuse Large B-Cell Lymphoma.

Multiple gene expression profiles have been identified in diffuse large B-cell lymphoma (DLBCL). Besides the cell of origin (COO) classifier, no signatures have been reproduced in independent studies or evaluated for capturing distinct aspects of DLBCL biology. We reproduced 4 signatures in 175 samples of the HOVON-84 trial on a panel of 117 genes using the NanoString platform. The four gene signatures capture the COO, MYC activity, B-cell receptor signaling, oxidative phosphorylation, and immune response. Performance of our classification algorithms were confirmed in the original datasets. We were able to validate three of the four GEP signatures. The COO algorithm resulted in 94 (54%) germinal center B-cell (GCB) type, 58 (33%) activated B-cell (ABC) type, and 23 (13%) unclassified cases. The MYC-classifier revealed 77 cases with a high MYC-activity score (44%) and this MYC-high signature was observed more frequently in ABC as compared to GCB DLBCL (68% vs. 32%, p < 0.00001). The host response (HR) signature of the consensus clustering was present in 55 (31%) patients, while the B-cell receptor signaling, and oxidative phosphorylation clusters could not be reproduced. The overlap of COO, consensus cluster and MYC activity score differentiated six gene expression clusters: GCB/MYC-high (12%), GCB/HR (16%), GCB/non-HR (27%), COO-Unclassified (13%), ABC/MYC-high (25%), and ABC/MYC-low (7%). In conclusion, the three validated signatures identify distinct subgroups based on different aspects of DLBCL biology, emphasizing that each classifier captures distinct molecular profiles.

Chromosome 20 loss is characteristic of breast implant-associated anaplastic large cell lymphoma.

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a very rare type of T-cell lymphoma that is uniquely caused by a single environmental stimulus. Here, we present a comprehensive genetic analysis of a relatively large series of BIA-ALCL (n = 29), for which genome-wide chromosomal copy number aberrations (CNAs) and mutational profiles for a subset (n = 7) were determined. For comparison, CNAs for anaplastic lymphoma kinase (ALK)- nodal anaplastic large cell lymphomas (ALCLs; n = 24) were obtained. CNAs were detected in 94% of BIA-ALCLs, with losses at chromosome 20q13.13 in 66% of the samples. Loss of 20q13.13 is characteristic of BIA-ALCL compared with other classes of ALCL, such as primary cutaneous ALCL and systemic type ALK+ and ALK- ALCL. Mutational patterns confirm that the interleukin-6-JAK1-STAT3 pathway is deregulated. Although this is commonly observed across various types of T-cell lymphomas, the extent of deregulation is significantly higher in BIA-ALCL, as indicated by phosphorylated STAT3 immunohistochemistry. The characteristic loss of chromosome 20 in BIA-ALCL provides further justification to recognize BIA-ALCL as a separate disease entity. Moreover, CNA analysis may serve as a parameter for future diagnostic assays for women with breast implants to distinguish seroma caused by BIA-ALCL from other causes of seroma accumulation, such as infection or trauma.

ACE: absolute copy number estimation from low-coverage whole-genome sequencing data.

SUMMARY: Chromosomal copy number aberrations can be efficiently detected and quantified using low-coverage whole-genome sequencing, but analysis is hampered by the lack of knowledge on absolute DNA copy numbers and tumor purity. Here, we describe an analytical tool for Absolute Copy number Estimation, ACE, which scales relative copy number signals from chromosomal segments to optimally fit absolute copy numbers, without the need for additional genetic information, such as SNP data. In doing so, ACE derives an estimate of tumor purity as well. ACE facilitates analysis of large numbers of samples, while maintaining the flexibility to customize models and generate output of single samples. AVAILABILITY AND IMPLEMENTATION: ACE is freely available via www.bioconductor.org and at www.github.com/tgac-vumc/ACE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Benign and malignant tumors in Rubinstein-Taybi syndrome.

Rubinstein-Taybi syndrome (RSTS) is a multiple congenital anomalies syndrome associated with mutations in CREBBP (70%) and EP300 (5-10%). Previous reports have suggested an increased incidence of specific benign and possibly also malignant tumors. We identified all known individuals diagnosed with RSTS in the Netherlands until 2015 (n = 87) and studied the incidence and character of neoplastic tumors in relation to their CREBBP/EP300 alterations. The population-based Dutch RSTS data are compared to similar data of the Dutch general population and to an overview of case reports and series of all RSTS individuals with tumors reported in the literature to date. Using the Nationwide Network and Registry of Histopathology and Cytopathology in the Netherlands (PALGA Foundation), 35 benign and malignant tumors were observed in 26/87 individuals. Meningiomas and pilomatricomas were the most frequent benign tumors and their incidence was significantly elevated in comparison to the general Dutch population. Five malignant tumors were observed in four persons with RSTS (medulloblastoma; diffuse large-cell B-cell lymphoma; breast cancer; non-small cell lung carcinoma; colon carcinoma). No clear genotype-phenotype correlation became evident. The Dutch population-based data and reported case studies underscore the increased incidence of meningiomas and pilomatricomas in individuals with RSTS. There is no supporting evidence for an increased risk for malignant tumors in individuals with RSTS, however, due to the small numbers this risk may not be fully dismissed.

Prognostic relevance of CD163 and CD8 combined with EZH2 and gain of chromosome 18 in follicular lymphoma: a study by the Lunenburg Lymphoma Biomarker Consortium.

In follicular lymphoma, studies addressing the prognostic value of microenvironment-related immunohistochemical markers and tumor cell-related genetic markers have yielded conflicting results, precluding implementation in practice. Therefore, the Lunenburg Lymphoma Biomarker Consortium performed a validation study evaluating published markers. To maximize sensitivity, an end of spectrum design was applied for 122 uniformly immunochemotherapy-treated follicular lymphoma patients retrieved from international trials and registries. The criteria were: early failure, progression or lymphoma-related death <2 years versus long remission, response duration of >5 years. Immunohistochemical staining for T cells and macrophages was performed on tissue microarrays from initial biopsies and scored with a validated computer-assisted protocol. Shallow whole-genome and deep targeted sequencing was performed on the same samples. The 96/122 cases with complete molecular and immunohistochemical data were included in the analysis. EZH2 wild-type (P=0.006), gain of chromosome 18 (P=0.002), low percentages of CD8+ cells (P=0.011) and CD163+ areas (P=0.038) were associated with early failure. No significant differences in other markers were observed, thereby refuting previous claims of their prognostic significance. Using an optimized study design, this Lunenburg Lymphoma Biomarker Consortium study substantiates wild-type EZH2 status, gain of chromosome 18, low percentages of CD8+ cells and CD163+ area as predictors of early failure to immunochemotherapy in follicular lymphoma treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP [-like]), while refuting the prognostic impact of various other markers.