Pan-TRK Immunohistochemistry Is Highly Correlated With NTRK3 Gene Rearrangements in Salivary Gland Tumors

International audienc

Pan-TRK Immunohistochemistry Is Highly Correlated With NTRK3 Gene Rearrangements in Salivary Gland Tumors

International audienc

RT-MiS : Un Nouvel Outil Diagnostique Pan-Lymphomes

International audienc

An improved assay for detection of theranostic gene translocations and MET exon 14 skipping in thoracic oncology

International audienceTheranostic translocations may be difficult to detect by routine techniques, especially when specimens are exiguous. We recently demonstrated in a series of translocated lung adenocarcinomas that LD-RT-PCR (ligation-dependent reverse transcription polymerase chain reaction) assay could identify ALK, ROS1 and RET rearrangements with 64% sensitivity and 100% specificity. Here, we report an upgraded version of this assay used in a routine prospective cohort of lung carcinomas. Newly diagnosed lung carcinomas referred to the Rouen molecular platform between 15/05/2018 and 15/05/2019 for ALK and ROS1 IHC, genotyping (SNaPshot© +/- high-throughput genotyping) and sometimes FISH (standard routine process) were tested prospectively in parallel with the LD-RT-PCR assay designed to detect at one go ALK, ROS1 and RET translocations and MET exon 14 skipping. 413 tumors from 396 patients were included. LD-RT-PCR had a global sensitivity of 91.43% (standard routine process: 80%), with a specificity of 100%. It detected 15/18 ALK and 4/4 ROS1 translocated tumors, but also 6/6 tumors with MET exon 14 skipping retrieved by genotyping. In addition, it retrieved 7 alterations missed by the routine process, then confirmed by other means: 5 MET exon 14 skipping and 2 RET translocated tumors. Finally, it allowed to deny an effect on MET exon 14 skipping for 8 mutations detected by routine genotyping. We successfully implemented LD-RT-PCR in routine analysis. This technique is cheap, fast, sensitive, specific, and easily upgradable (e.g., NTRK translocations), but still requires IHC to be performed in parallel. Owing to its advantages, we recommend considering it, in parallel with IHC and genotyping, as an excellent cost-effective alternative, for the systematic testing of lung adenocarcinoma, to FISH and to more expensive and complex assays such as RNA-seq

Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers

International audienceAbstract Background Recently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct. Results Here, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization. Conclusion We provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI

Combining Gene Expression Profiling and Artificial Intelligence to Diagnose B-Cell Non-Hodgkin Lymphoma

International audienceIntroductionNon-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies associated with very diverse clinical behaviors. They rely on the activation of different signaling pathways for proliferation and survival which might be amenable to targeted therapies, increasing the need for precision diagnosis. Unfortunately, their accurate classification can be challenging, even for expert hemato-pathologists, and secondary reviews recurrently differ from initial diagnosis. To address this issue we have developed a pan-B-NHL classifier based on a middle throughput gene expression assay coupled with a random forest algorithm.Material and MethodsFive hundred ten B-NHL diagnosed according to the WHO criteria were studied, with 325 diffuse large B-cell lymphomas (DLBCL), 43 primary mediastinal B-cell lymphomas (PMBL), 55 follicular lymphomas (FL), 31 mantle cell lymphomas (MCL), 17 small lymphocytic lymphomas (SLL), 20 marginal zone lymphomas (MZL), 11 marginal zone lymphomas of mucosa-associated lymphoid tissue (MALT) and 8 lymphoplasmacytic lymphomas (LPL). To train and validate the predictor the samples were randomly split into a training (2/3) and an independent validation cohort (1/3). A panel of 137 genes was designed by purposely selecting the differentiation markers identified in the WHO classification for their capacity to provide diagnostic and prognostic information in NHLs. Gene expression profiles were generated by ligation dependent RT-PCR applied to RNA extracted from frozen or FFPE tissue and analyzed on a MiSeq sequencer. For analysis, the sequencing reads were de-multiplexed, aligned with the sequences of the LD-RTPCR probes and counted. Results were normalized using unique molecular indexes counts to correct PCR amplification biases.ResultsIn DLBCL, unsupervised gene expression analysis retrieved the expected GCB, ABC and PMBL signatures (Fig A). These tumors also showed higher expressions of the KI67 (proliferation), CD68 and CD163 (tumor associated macrophages), and PD-L1/2 (immune response) markers. We also observed that the dual expression of MYC and BCL2 at the mRNA level significantly associates with inferior PFS and OS, independent from the International Prognostic Index and from the GCB/ABC cell-of-origin signature, validating the capacity of the assay to identify these highly aggressive lymphomas (Fig C).Overall, low-grade lymphomas were characterized by a significant T cell component. FLs associated with the GCB (BCL6, MYBL1, CD10 and LMO2) and Tfh (CD3, CD5, CD28, ICOS, CD40L, CXCL13) signatures. Other small B-cell lymphomas tended to overexpress activated B-cell markers (LIMD1, TACI, IRF4,FOXP1...), and the expected CD5, CD10, CD23 and CCND1 differential expressions in SLL, MCL and MZL were correctly retrieved (Fig B). Surprisingly, our analysis revealed that the Ie-Ce sterile transcript, expressed from the IGH locus during IgE isotype switching, is almost exclusively expressed by FLs, constituting one of the most discriminant markers for this pathology.We next trained a random forest classifier to discriminate the 7 principal subtypes of B-NHLs. The training cohort comprised 162 DLBCLs (ABC or GCB), 28 PMBL, 35 FLs (grade 1-3A), 21 MCLs, 12 SLLs, and 25 NHLs grouped into the MZL category (13 MZLs, 8 MALT and 4 LPLs). The independent validation series comprised 90 DLBCLs classified as GCB or ABC DLBCLs by the Lymph2Cx assay, 15 PMBLs, 12 FLs (grade 1-3A), 10 MCLs, 5 SLLs and 14 MZLs (7 MZL, 3 MALT and 4 LPL). The RF algorithm classified all cases of the training series into the expected subtype, as well as 94.5% samples of the independent validation cohort (Fig D). For ABC and GCB DLBCLs, the concordance with the Lymph2Cx assay in the validation cohort was 94.3%.ConclusionWe have developed a comprehensive gene expression based solution which allows a systematic evaluation of multiple diagnostic and prognostic markers expressed by the tumor and by the microenvironment in B-NHLs. This assay, which does not require any specific platform, could be implemented in complement to histology in many diagnostic laboratories and, with the current development of targeted therapies, enable a more accurate and standardized B-NHL diagnosis. Together, our data illustrate how the integration of gene expression profiling and artificial intelligence can increase precision diagnosis in cancers

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma

International audienc