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- Karlsson, Anna, et al.
(författare)
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A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer
- 2019
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Ingår i: Scientific Reports. - : NATURE PUBLISHING GROUP. - 2045-2322. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Accurate histological classification and identification of fusion genes represent two cornerstones of clinical diagnostics in non-small cell lung cancer (NSCLC). Here, we present a NanoString gene expression platform and a novel platform-independent, single sample predictor (SSP) of NSCLC histology for combined, simultaneous, histological classification and fusion gene detection in minimal formalin fixed paraffin embedded (FFPE) tissue. The SSP was developed in 68 NSCLC tumors of adenocarcinoma (AC), squamous cell carcinoma (SqCC) and large-cell neuroendocrine carcinoma (LCNEC) histology, based on NanoString expression of 11 (CHGA, SYP, CD56, SFTPG, NAPSA, TTF-1, TP73L, KRT6A, KRT5, KRT40, KRT16) relevant genes for IHC-based NSCLC histology classification. The SSP was combined with a gene fusion detection module (analyzing ALK, RET, ROS1, MET, NRG1, and NTRK1) into a multicomponent NanoString assay. The histological SSP was validated in six cohorts varying in size (n = 11-199), tissue origin (early or advanced disease), histological composition (including undifferentiated cancer), and gene expression platform. Fusion gene detection revealed five EML4-ALK fusions, four KIF5B-RET fusions, two CD74-NRG1 fusion and three MET exon 14 skipping events among 131 tested cases. The histological SSP was successfully trained and tested in the development cohort (mean AUC = 0.96 in iterated test sets). The SSP proved successful in predicting histology of NSCLC tumors of well-defined subgroups and difficult undifferentiated morphology irrespective of gene expression data platform. Discrepancies between gene expression prediction and histologic diagnosis included cases with mixed histologies, true large cell carcinomas, or poorly differentiated adenocarcinomas with mucin expression. In summary, we present a proof-of-concept multicomponent assay for parallel histological classification and multiplexed fusion gene detection in archival tissue, including a novel platform-independent histological SSP classifier. The assay and SSP could serve as a promising complement in the routine evaluation of diagnostic lung cancer biopsies.
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- Karlsson, Anna K, et al.
(författare)
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Mutational and gene fusion analyses of primary large cell and large cell neuroendocrine lung cancer.
- 2015
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Ingår i: Oncotarget. - 1949-2553. ; 6:26, s. 22028-22037
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Tidskriftsartikel (refereegranskat)abstract
- Large cell carcinoma with or without neuroendocrine features (LCNEC and LC, respectively) constitutes 3-9% of non-small cell lung cancer but is poorly characterized at the molecular level. Herein we analyzed 41 LC and 32 LCNEC (including 15 previously reported cases) tumors using massive parallel sequencing for mutations in 26 cancer-related genes and gene fusions in ALK, RET, and ROS1. LC patients were additionally subdivided into three immunohistochemistry groups based on positive expression of TTF-1/Napsin A (adenocarcinoma-like, n = 24; 59%), CK5/P40 (squamous-like, n = 5; 12%), or no marker expression (marker-negative, n = 12; 29%). Most common alterations were TP53 (83%), KRAS (22%), MET (12%) mutations in LCs, and TP53 (88%), STK11 (16%), and PTEN (13%) mutations in LCNECs. In general, LCs showed more oncogene mutations compared to LCNECs. Immunomarker stratification of LC revealed oncogene mutations in 63% of adenocarcinoma-like cases, but only in 17% of marker-negative cases. Moreover, marker-negative LCs were associated with inferior overall survival compared with adenocarcinoma-like tumors (p = 0.007). No ALK, RET or ROS1 fusions were detected in LCs or LCNECs. Together, our molecular analyses support that LC and LCNEC tumors follow different tumorigenic paths and that LC may be stratified into molecular subgroups with potential implications for diagnosis, prognostics, and therapy decisions.
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- Lindquist, Kajsa Ericson, et al.
(författare)
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Clinical framework for next generation sequencing based analysis of treatment predictive mutations and multiplexed gene fusion detection in non-small cell lung cancer
- 2017
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Ingår i: Oncotarget. - : Impact Journals, LLC. - 1949-2553. ; 8:21, s. 34796-34810
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Tidskriftsartikel (refereegranskat)abstract
- Precision medicine requires accurate multi-gene clinical diagnostics. We describe the implementation of an Illumina TruSight Tumor (TST) clinical NGS diagnostic framework and parallel validation of a NanoString RNA-based ALK, RET, and ROS1 gene fusion assay for combined analysis of treatment predictive alterations in non-small cell lung cancer (NSCLC) in a regional healthcare region of Sweden (Scandinavia). The TST panel was clinically validated in 81 tumors (99% hotspot mutation concordance), after which 533 consecutive NSCLCs were collected during one-year of routine clinical analysis in the healthcare region (~90% advanced stage patients). The NanoString assay was evaluated in 169 of 533 cases. In the 533-sample cohort 79% had 1-2 variants, 12% >2 variants and 9% no detected variants. Ten gene fusions (five ALK, three RET, two ROS1) were detected in 135 successfully analyzed cases (80% analysis success rate). No ALK or ROS1 FISH fusion positive case was missed by the NanoString assay. Stratification of the 533-sample cohort based on actionable alterations in 11 oncogenes revealed that 66% of adenocarcinomas, 13% of squamous carcinoma (SqCC) and 56% of NSCLC not otherwise specified harbored ≥1 alteration. In adenocarcinoma, 10.6% of patients (50.3% if including KRAS) could potentially be eligible for emerging therapeutics, in addition to the 15.3% of patients eligible for standard EGFR or ALK inhibitors. For squamous carcinoma corresponding proportions were 4.4% (11.1% with KRAS) vs 2.2%. In conclusion, multiplexed NGS and gene fusion analyses are feasible in NSCLC for clinical diagnostics, identifying notable proportions of patients potentially eligible for emerging molecular therapeutics.
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- Winslow, Sofia, et al.
(författare)
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The expression pattern of matrix-producing tumor stroma is of prognostic importance in breast cancer
- 2016
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Ingår i: Bmc Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Background: There are several indications that the composition of the tumor stroma can contribute to the malignancy of a tumor. Here we utilized expression data sets to identify metagenes that may serve as surrogate marker for the extent of matrix production and vascularization of a tumor and to characterize prognostic molecular components of the stroma. Methods: TCGA data sets from six cancer forms, two breast cancer microarray sets and one mRNA data set of xenografted tumors were downloaded. Using the mean correlation as distance measure compact clusters with genes representing extracellular matrix production (ECM metagene) and vascularization (endothelial metagene) were defined. Explorative Cox modeling was used to identify prognostic stromal gene sets. Results: Clustering of stromal genes in six cancer data sets resulted in metagenes, each containing three genes, representing matrix production and vascularization. The ECM metagene was associated with poor prognosis in renal clear cell carcinoma and in lung adenocarcinoma but not in other cancers investigated. Explorative Cox modeling using gene pairs identified gene sets that in multivariate models were prognostic in breast cancer. This was validated in two microarray sets. Two notable genes are TCF4 and P4HA3 which were included in the sets associated with positive and negative prognosis, respectively. Data from laser-microdissected tumors, a xenografted tumor data set and from correlation analyses demonstrate the stroma specificity of the genes. Conclusions: It is possible to construct ECM and endothelial metagenes common for several cancer forms. The molecular composition of matrix-producing cells, rather than the extent of matrix production seem to be important for breast cancer prognosis.
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