BackgroundRecently, a gene expression algorithm, TNBCtype, was developed that can divide triple-negative breast cancer (TNBC) into molecularly-defined subtypes. The algorithm has potential to provide predictive value for TNBC subtype-specific response to various treatments. TNBCtype used in a retrospective analysis of neoadjuvant clinical trial data of TNBC patients demonstrated that TNBC subtype and pathological complete response to neoadjuvant chemotherapy were significantly associated. Herein we describe an expression algorithm reduced to 101 genes with the power to subtype TNBC tumors similar to the original 2188-gene expression algorithm and predict patient outcomes.MethodsThe new classification model was built using the same expression data sets used for the original TNBCtype algorithm. Gene set enrichment followed by shrunken centroid analysis were used for feature reduction, then elastic-net regularized linear modeling was used to identify genes for a centroid model classifying all subtypes, comprised of 101 genes. The predictive capability of both this new “lean” algorithm and the original 2188-gene model were applied to an independent clinical trial cohort of 139 TNBC patients treated initially with neoadjuvant doxorubicin/cyclophosphamide and then randomized to receive either paclitaxel or ixabepilone to determine association of pathologic complete response within the subtypes.ResultsThe new 101-gene expression model reproduced the classification provided by the 2188-gene algorithm and was highly concordant in the same set of seven TNBC cohorts used to generate the TNBCtype algorithm (87 %), as well as in the independent clinical trial cohort (88 %), when cases with significant correlations to multiple subtypes were excluded.Clinical responses to both neoadjuvant treatment arms, found BL2 to be significantly associated with poor response (Odds Ratio (OR) =0.12, p =0.03 for the 2188-gene model; OR = 0.23, p < 0.03 for the 101-gene model). Additionally, while the BL1 subtype trended towards significance in the 2188-gene model (OR = 1.91, p = 0.14), the 101-gene model demonstrated significant association with improved response in patients with the BL1 subtype (OR = 3.59, p = 0.02).ConclusionsThese results demonstrate that a model using small gene sets can recapitulate the TNBC subtypes identified by the original 2188-gene model and in the case of standard chemotherapy, the ability to predict therapeutic response.
BackgroundTriple negative breast cancer (TNBC) is a heterogeneous disease that lacks unifying molecular alterations that can guide therapy decisions. We previously identified distinct molecular subtypes of TNBC (TNBCtype) using gene expression data generated on a microarray platform using frozen tumor specimens. Tumors and cell lines representing the identified subtypes have distinct enrichment in biologically relevant transcripts with differing sensitivity to standard chemotherapies and targeted agents. Since our initial discoveries, RNA-sequencing (RNA-seq) has evolved as a sensitive and quantitative tool to measure transcript abundance.MethodsTo demonstrate that TNBC subtypes were similar between platforms, we compared gene expression from matched specimens profiled by both microarray and RNA-seq from The Cancer Genome Atlas (TCGA). In the clinical care of patients with TNBC, tumor specimens collected for diagnostic purposes are processed by formalin fixation and paraffin-embedding (FFPE). Thus, for TNBCtype to eventually have broad and practical clinical utility we performed RNA-seq gene expression and molecular classification comparison between fresh-frozen (FF) and FFPE tumor specimens.ResultsAnalysis of TCGA showed consistent subtype calls between 91% of evaluable samples demonstrating conservation of TNBC subtypes across microarray and RNA-seq platforms. We compared RNA-seq performed on 21-paired FF and FFPE TNBC specimens and evaluated genome alignment, transcript coverage, differential transcript enrichment and concordance of TNBC molecular subtype calls. We demonstrate that subtype accuracy between matched FF and FFPE samples increases with sequencing depth and correlation strength to an individual TNBC subtype.ConclusionsTNBC subtypes were reliably identified from FFPE samples, with highest accuracy if the samples were less than 4 years old and reproducible subtyping increased with sequencing depth. To reproducibly subtype tumors using gene expression, it is critical to select genes that do not vary due to platform type, tissue processing or RNA isolation method. The majority of differentially expressed transcripts between matched FF and FFPE samples could be attributed to transcripts selected for by RNA enrichment method. While differentially expressed transcripts did not impact TNBC subtyping, they will provide guidance on determining which transcripts to avoid when implementing a gene set size reduction strategy.Trial registration NCT00930930 07/01/2009. Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-017-3237-1) contains supplementary material, which is available to authorized users.
Patients with lung cancers harboring an activating anaplastic lymphoma kinase (ALK) rearrangement respond favorably to ALK inhibitor therapy. Fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) are validated and widely used screening tests for ALK rearrangements but both methods have limitations. The ALK RGQ RT-PCR Kit (RT-PCR) is a single tube quantitative real-time PCR assay for high throughput and automated interpretation of ALK expression. In this study, we performed a direct comparison of formalin-fixed paraffin-embedded (FFPE) lung cancer specimens using all three ALK detection methods. The RT-PCR test (diagnostic cut-off ΔCt of ≤8) was shown to be highly sensitive (100%) when compared to FISH and IHC. Sequencing of RNA detected full-length ALK transcripts or EML4-ALK and KIF5B-ALK fusion variants in discordant cases in which ALK expression was detected by the ALK RT-PCR test but negative by FISH and IHC. The overall specificity of the RT-PCR test for the detection of ALK in cases without full-length ALK expression was 94% in comparison to FISH and sequencing. These data support the ALK RT-PCR test as a highly efficient and reliable diagnostic screening approach to identify patients with non-small cell lung cancer whose tumors are driven by oncogenic ALK.
BackgroundThe anaplastic lymphoma kinase (ALK) gene encodes a receptor tyrosine kinase, which was first identified as the fusion partner of the nucleophosmin (NPM1) gene in the recurrent t(2;5)(p23;q35) found in a subset of anaplastic large cell lymphoma (ALCL). Several distinct, non-NPM1, ALK fusions have subsequently been described in lymphomas and other tumor types. All of these fusions result in the constitutive expression and activation of ALK and ALK signaling pathways, ultimately leading to the malignant phenotype.Case reportA non-NPM1 fusion partner of ALK was identified in a 32-year-old Caucasian male ALCL patient whose disease was refractory to standard chemotherapy and autologous stem cell transplantation, and exhibited a poor response to a first-generation ALK inhibitor. Non-allele-specific ALK RT-qPCR revealed ALK overexpression and 5′ RACE PCR revealed that the patient’s lymphoma expressed a TRAF1-ALK fusion.ConclusionsWe report the case of an ALCL patient whose tumor harbored the newly recognized TRAF1-ALK fusion and describe the clinical outcome after treatment with an ALK inhibitor. The short survival of our patient may reflect a propensity toward aggressive behavior in lymphomas that express this ALK fusion.
Lung cancer is the main cause of cancer mortality worldwide. Approximately ∼80% of lung cancer cases are non-small cell lung cancer (NSCLC) in type and >50% of NSCLC are adenocarcinoma in histopathology. A shifting paradigm in the field of pulmonary oncology is the identification of molecular markers that are therapeutic targets and/or provide prognostic information. The recent recognition of the role biomarkers such as EGFR, KRAS, and ALK play in NSCLC adenocarcinoma patients demonstrates this trend. Although these three highly characterized molecular markers make up one-quarter to two-thirds of NSCLC adenocarcinoma patients, the remaining population, termed “Triple-Negative Lung Cancer” (TNLC), has yet to be fully defined. The complete absence of therapeutic targets for triple-negative lung cancer patients undeniably supports the need to identify up-regulated and mutated genes that better define this poor prognostic cohort. Recent genome-wide expression profiles subdivided TNLC patients into a poor prognostic group based on the overexpression of the DEP domain containing 1 gene (DEPDC1) (Okayama et al., 2011). We hypothesized that overexpression of DEPDC1 correlates with a specific sub-population of adenocarcinoma patients. Our initial studies indicate that DEPDC1 is constitutively expressed in both normal lung as well as lung cancer specimens. DEPDC1 transcripts are present as two variants (V1 and V2), with constitutive expression of the V1 variant in normal lung. We hypothesized that expression of the V2 transcript could be unique to NSCLC adenocarcinomas. Screening of FFPE NSCLC adenocarcinoma specimens using a proprietary qPCR approach demonstrated the presence of the DEPDC1 V2 variant but at levels that were not sufficient to serve as a sole biomarker. We therefore investigated whether the ratio of DEPDC1 V2 expression relative to V1 could serve as a biomarker in TNLC. Our preliminary data suggest that the ratio of V2 to V1 expression of DEPDC1 does in fact segregate TNLC into specific sub-populations. These studies have exciting diagnostic as well as therapeutic implications as Phase I/II studies using novel peptide vaccines derived against DEPDC1 have proven tolerable and efficacious for patients with bladder cancer and could potentially be extended to target these newly identified TNLC sub-populations. Citation Format: Brock L. Schweitzer, Kasey D. Lawrence, John Handshoe, Rachel Skelton, Lindsay Chatfield, Liquan Xue, Stephan W. Morris, David R. Hout. The unknown piece of the pie: Molecular markers in triple-negative lung cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1930. doi:10.1158/1538-7445.AM2013-1930
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