Similar Datasets
Project description:Objective: In order to personalize standard therapies based on molecular profiles, we previously classified colorectal cancers (CRCs) into five distinct subtypes (CRCAssigner) and later into four consensus molecular subtypes (CMS) with different prognoses and treatment responses. For clinical application, here we developed an inexpensive multiplex biomarker-based assay. Design: Three cohorts of untreated fresh frozen CRC samples (n = 57) predominantly from primary tumours and profiled by microarray/RNA-Seq were analysed. A reduced 38-gene panel (CRCAssigner-38) was selected from the published 786-gene CRCAssigner signature (CRCAssigner-786) using an in-house gene selection approach. A customised NanoString Technologies nCounter platform-based assay (NanoCRCAssigner) was developed for comparison with different classifiers (CMS subtypes), platforms (microarrays and RNA-Seq), and gene sets (CRCAssigner-38 and CRCAssigner-786). An additional cohort of fresh-frozen paraffin embedded (FFPE) samples (n = 24) was subtyped using NanoCRCAssigner as a proof-of-concept. Results: NanoCRCAssigner classified fresh frozen samples (n = 48; except those showing a mixture of subtypes) into all five CRCAssigner subtypes with overall high concordance across platforms (>87%) and with CMS subtypes (81%) irrespective of variable tumour cellularity. The association of subtypes with their known molecular (microsatellite-instable and stemness), mutational (KRAS/BRAF), and clinical characteristics (including overall survival) further demonstrated assay validity. To reduce costs, we switched from the standard protocol to a low-cost protocol with a high Pearson correlation co-efficient (0.9) between protocols. Technical replicates were highly correlated (0.98). FFPE samples were also successfully classified and showed high reproducibility (0.96).
2019-03-04 | GSE101479 | GEO
Project description:Objective: In order to personalize standard therapies based on molecular profiles, we previously classified colorectal cancers (CRCs) into five distinct subtypes (CRCAssigner) and later into four consensus molecular subtypes (CMS) with different prognoses and treatment responses. For clinical application, here we developed an inexpensive multiplex biomarker-based assay.Objective: In order to personalise standard therapies based on molecular profiles, we previously classified colorectal cancers (CRCs) into five distinct subtypes (CRCAssigner) and later into four consensus molecular subtypes (CMS) with different prognoses and treatment responses. For clinical application, here we developed a low-cost multiplex biomarker assay. Design: Three cohorts of untreated fresh frozen CRC samples (n = 57) predominantly from primary tumours and profiled by microarray/RNA-Seq were analysed. A reduced 38-gene panel (CRCAssigner-38) was selected from the published 786-gene CRCAssigner signature (CRCAssigner-786) using an in-house gene selection approach. A customised NanoString Technologies nCounter platform-based assay (NanoCRCAssigner) was developed for comparison with different classifiers (CMS subtypes), platforms (microarrays and RNA-Seq), and gene sets (CRCAssigner-38 and CRCAssigner-786). An additional cohort of fresh-frozen paraffin embedded (FFPE) samples (n = 24) was subtyped using NanoCRCAssigner as a proof-of-concept. Results: NanoCRCAssigner classified fresh frozen samples (n = 48; except those showing a mixture of subtypes) into all five CRCAssigner subtypes with overall high concordance across platforms (>87%) and with CMS subtypes (81%) irrespective of variable tumour cellularity. The association of subtypes with their known molecular (microsatellite-instable and stemness), mutational (KRAS/BRAF), and clinical characteristics (including overall survival) further demonstrated assay validity. To reduce costs, we switched from the standard protocol to a low-cost protocol with a high Pearson correlation co-efficient (0.9) between protocols. Technical replicates were highly correlated (0.98). FFPE samples were also successfully classified and showed high reproducibility (0.96).
2019-03-04 | GSE101481 | GEO
Project description:Objective: In order to personalise standard therapies based on molecular profiles, we previously classified colorectal cancers (CRCs) into five distinct subtypes (CRCAssigner) and later into four consensus molecular subtypes (CMS) with different prognoses and treatment responses. For clinical application, here we developed a low-cost multiplex biomarker assay. Design: Three cohorts of primary (except one liver metastases) untreated and fresh frozen CRC samples (n=57) profiled by microarray and RNA-Seq were analyzed. A reduced 38-gene panel (CRCAssigner-38) was selected from the 786-gene CRCAssigner signature (CRCAssigner-786) using an in-house class prediction approach. A customised NanoString Technologies’ nCounter platform-based assay (NanoCRCAssigner) was developed for comparison with different classifiers (CMS subtypes), platforms (microarrays and RNA-Seq), and gene sets (CRCAssigner-38 and CRCAssigner-786). Results: NanoCRCAssigner classified samples (n=48; except those showing a mixture of subtypes) into all five CRCAssigner subtypes with overall high concordance across platforms (>87%) and with CMS subtypes (81%) irrespective of variable tumour cellularity. The association of subtypes with their known molecular (microsatellite-instable and stemness), mutational (KRAS/BRAF), and clinical characteristics (including overall survival) further demonstrated assay validity. To reduce costs, we switched from standard protocol to a low-cost protocol with a high Pearson correlation co-efficient (0.9) between protocols. Technical replicates were highly correlated (0.98).
2019-03-04 | GSE101588 | GEO
Project description:Objective: In order to personalise standard therapies based on molecular profiles, we previously classified colorectal cancers (CRCs) into five distinct subtypes (CRCAssigner) and later into four consensus molecular subtypes (CMS) with different prognoses and treatment responses. For clinical application, here we developed a low-cost multiplex biomarker assay. Design: Three cohorts of primary (except one liver metastases) untreated and fresh frozen CRC samples (n=57) profiled by microarray and RNA-Seq were analyzed. A reduced 38-gene panel (CRCAssigner-38) was selected from the 786-gene CRCAssigner signature (CRCAssigner-786) using an in-house class prediction approach. A customised NanoString Technologies’ nCounter platform-based assay (NanoCRCAssigner) was developed for comparison with different classifiers (CMS subtypes), platforms (microarrays and RNA-Seq), and gene sets (CRCAssigner-38 and CRCAssigner-786). Results: NanoCRCAssigner classified samples (n=48; except those showing a mixture of subtypes) into all five CRCAssigner subtypes with overall high concordance across platforms (>87%) and with CMS subtypes (81%) irrespective of variable tumour cellularity. The association of subtypes with their known molecular (microsatellite-instable and stemness), mutational (KRAS/BRAF), and clinical characteristics (including overall survival) further demonstrated assay validity. To reduce costs, we switched from standard protocol to a low-cost protocol with a high Pearson correlation co-efficient (0.9) between protocols. Technical replicates were highly correlated (0.98).
2019-03-04 | GSE101472 | GEO
Project description:A Low-cost Multiplex Biomarker Assay Stratifies Colorectal Cancer Patient Samples into Clinically-relevant Subtypes
| PRJNA395057 | ENA
Project description:Acute myeloid leukemia (AML) is an aggressive disease with complex and heterogeneous biology. Although several genomic classifications have been proposed, there is a growing interest in going beyond genomics to stratify AML. In this study, we profile the sphingolipid family of bioactive molecules in 213 primary AML samples and 30 common human AML cell lines. Using an integrative approach, we identify two distinct sphingolipid subtypes in AML characterized by a reciprocal abundance of hexosylceramide (Hex) and sphingomyelin (SM) species. The two Hex-SM clusters organize diverse samples more robustly than known AML driver mutations and are coupled to latent transcriptional states. Using transcriptomic data, we develop a machine-learning classifier to infer the Hex-SM status of AML cases in TCGA and BeatAML clinical repositories. The analyses show that the sphingolipid subtype with deficient Hex and abundant SM is enriched for leukemic stemness transcriptional programs and comprises an unappreciated high-risk subgroup with poor clinical outcomes. Our sphingolipid-focused examination of AML identifies patients least likely to benefit from standard of care and raises the possibility that sphingolipidomic interventions could switch the subtype of AML patients who otherwise lack targetable alternatives.
| S-EPMC10153188 | biostudies-literature
Project description:Molecular-based classifications of gastric cancer (GC) were recently proposed, but few of them robustly predict clinical outcomes. While mutation and expression signature of protein-coding genes were used in previous molecular subtyping methods, the noncoding genome in GC remains largely unexplored. Here, we developed the fast long-noncoding RNA analysis (FLORA) method to study RNA sequencing data of GC cases, and prioritized tumor-specific long-noncoding RNAs (lncRNAs) by integrating clinical and multi-omic data. We uncovered 1235 tumor-specific lncRNAs, based on which three subtypes were identified. The lncRNA-based subtype 3 (L3) represented a subgroup of intestinal GC with worse survival, characterized by prevalent TP53 mutations, chromatin instability, hypomethylation, and over-expression of oncogenic lncRNAs. In contrast, the lncRNA-based subtype 1 (L1) has the best survival outcome, while LINC01614 expression further segregated a subgroup of L1 cases with worse survival and increased chance of developing distal metastasis. We demonstrated that LINC01614 over-expression is an independent prognostic factor in L1 and network-based functional prediction implicated its relevance to cell migration. Over-expression and CRISPR-Cas9-guided knockout experiments further validated the functions of LINC01614 in promoting GC cell growth and migration. Altogether, we proposed a lncRNA-based molecular subtype of GC that robustly predicts patient survival and validated LINC01614 as an oncogenic lncRNA that promotes GC proliferation and migration.
| S-EPMC8062268 | biostudies-literature
Project description:PurposeLeiomyosarcoma is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for leiomyosarcoma. Recognition of different molecular subtypes is necessary to evaluate novel therapeutic options. In a previous study on 51 leiomyosarcomas, we identified three molecular subtypes in leiomyosarcoma. The current study was performed to determine whether the existence of these subtypes could be confirmed in independent cohorts.Experimental designNinety-nine cases of leiomyosarcoma were expression profiled with 3'end RNA-Sequencing (3SEQ). Consensus clustering was conducted to determine the optimal number of subtypes.ResultsWe identified 3 leiomyosarcoma molecular subtypes and confirmed this finding by analyzing publically available data on 82 leiomyosarcoma from The Cancer Genome Atlas (TCGA). We identified two new formalin-fixed, paraffin-embedded tissue-compatible diagnostic immunohistochemical markers; LMOD1 for subtype I leiomyosarcoma and ARL4C for subtype II leiomyosarcoma. A leiomyosarcoma tissue microarray with known clinical outcome was used to show that subtype I leiomyosarcoma is associated with good outcome in extrauterine leiomyosarcoma while subtype II leiomyosarcoma is associated with poor prognosis in both uterine and extrauterine leiomyosarcoma. The leiomyosarcoma subtypes showed significant differences in expression levels for genes for which novel targeted therapies are being developed, suggesting that leiomyosarcoma subtypes may respond differentially to these targeted therapies.ConclusionsWe confirm the existence of 3 molecular subtypes in leiomyosarcoma using two independent datasets and show that the different molecular subtypes are associated with distinct clinical outcomes. The findings offer an opportunity for treating leiomyosarcoma in a subtype-specific targeted approach.
| S-EPMC4526352 | biostudies-literature
Project description:Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond.
| S-EPMC6529539 | biostudies-literature
Project description:In this study we describe a multiplex PCR assay for the detection of nine clinically relevant antibiotic resistance genes of Staphylococcus aureus. Conditions were optimized to amplify fragments of mecA (encoding methicillin resistance), aacA-aphD (aminoglycoside resistance), tetK, tetM (tetracycline resistance), erm(A), erm(C) (macrolide-lincosamide-streptogramin B resistance), vat(A), vat(B), and vat(C) (streptogramin A resistance) simultaneously in one PCR amplification. An additional primer pair for the amplification of a fragment of the staphylococcal 16S rDNA was included as a positive control. The multiplex PCR assay was evaluated on 30 different S. aureus isolates, and the PCR results correlated with the phenotypic antibiotic resistance data obtained by the broth microdilution assay. The multiplex PCR assay offers a rapid, simple, and accurate identification of antibiotic resistance profiles and could be used in clinical diagnosis as well as for the surveillance of the spread of antibiotic resistance determinants in epidemiological studies.
| S-EPMC193808 | biostudies-literature