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.

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.

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.

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a very rare type of T-cell lymphoma, 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 (CNA) and mutational profiles for a subset (n=7) were determined. For comparison, CNAs for ALK-negative nodal-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 for BIA-ALCL as compared to other classes of ALCL, such as primary cutaneous ALCL, systemic type ALK-positive and -negative ALCL. Mutational patterns confirm that the IL6-JAK1-STAT3 pathway is deregulated. Although this is commonly observed across various types of T-cell lymphomas, the extent of deregulation however is significantly higher in BIA-ALCL as indicated by pSTAT3 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.

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] &lt;0.001) were more frequent in stage I FL. In contrast, programmed cell death protein 1–positive T cells, CD68+/CD163+ macrophages (P &lt; .001), BCL2 translocation (BCL2trl+) (P &lt; .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.

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We investigated whether outcome prediction of aggressive B-cell lymphoma patients 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 PET/CT of 323 patients: 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+MYC (wild type, single hit or double/triple hit), 3) IPI, MYC and MTV, 4) radiomics and 5) MYC+radiomics models was tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPV). IPI yielded a CV-AUC of 0.65±0.07 with a PPV of 29.6%. The IPI+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+radiomics model (50.0%) and increased with 20% compared to the IPI (29.6%). Adding radiomics features improved model performance and PPV and can therefore aid in identifying poor prognosis patients.

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