Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatially preserved multi-region transcriptomic subtyping and biomarkers of outcome with chemoimmunotherapy in extensive-stage small cell lung cancer [CANTABRICO_DSP cohort]

ABSTRACT: Transcriptomic subtyping holds promise for personalized therapy in extensive stage small cell lung cancer (ES-SCLC). In this study, we aimed to assess intratumoral transcriptomic subtype diversity and to identify biomarkers associated with long-term chemoimmunotherapy benefit in human ES-SCLC. Our work highlights that high intratumoral heterogeneity, lack of consistent association with outcome, and unclear subtype-specific target expression are major challenges for SCLC subtype-based precision oncology. Pre-existing IFNϒ-driven immunity and mitochondrial metabolism seem key correlates of long-term efficacy for chemoimmunotherapy in ES-SCLC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE261345 | GEO | 2024/04/09

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Shared Molecules

Only show the datasets with similarity scores above: 0.5

Threshold

0.5

Similar Datasets

Spatially preserved multi-region transcriptomic subtyping and biomarkers of outcome with chemoimmunotherapy in extensive-stage small cell lung cancer [IMfirst_DSP cohort]

Project description:Transcriptomic subtyping holds promise for personalized therapy in extensive stage small cell lung cancer (ES-SCLC). In this study, we aimed to assess intratumoral transcriptomic subtype diversity and to identify biomarkers associated with long-term chemoimmunotherapy benefit in human ES-SCLC. Our work highlights that high intratumoral heterogeneity, lack of consistent association with outcome, and unclear subtype-specific target expression are major challenges for SCLC subtype-based precision oncology. Pre-existing IFNϒ-driven immunity and mitochondrial metabolism seem key correlates of long-term efficacy for chemoimmunotherapy in ES-SCLC.

2024-04-09 | GSE261348 | GEO

scRNA-seq of two triple knockout SCLC mouse models for subtyping

Project description:Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes, while the fourth is a previously undescribed neuroendocrine variant (NEv2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

2019-09-20 | GSE137749 | GEO

Spatially preserved multi-region transcriptomic subtyping and biomarkers of outcome with chemoimmunotherapy in extensive-stage small cell lung cancer [CANTABRICO_DSP cohort]

Project description:Spatially preserved multi-region transcriptomic subtyping and biomarkers of outcome with chemoimmunotherapy in extensive-stage small cell lung cancer [CANTABRICO_DSP cohort]

| PRJNA1086530 | ENA

Spatially preserved multi-region transcriptomic subtyping and biomarkers of outcome with chemoimmunotherapy in extensive-stage small cell lung cancer [IMfirst_DSP cohort]

Project description:Spatially preserved multi-region transcriptomic subtyping and biomarkers of outcome with chemoimmunotherapy in extensive-stage small cell lung cancer [IMfirst_DSP cohort]

| PRJNA1086533 | ENA

Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes

Project description:Small-cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, each with unique therapeutic vulnerabilities. Implementing subtyping in the clinic has remained challenging due to limited tissue availability, particularly for longitudinal monitoring. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that there would be subtype-specific patterns of DNA methylation that could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts of totally 179 SCLC patients and machine learning approaches, we developed a highly accurate DNA methylation-based classifier (SCLC-DMC) that could distinguish SCLC subtypes using clinical tumor samples with 95.8% accuracy in the testing set compared to mRNA-based profiling. We further adjusted the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC) we could demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. Furthermore, methylation-based subtyping predicted response to a wide variety of drugs in preclinical models and clinical outcomes were indistinguishable in cohorts of patients subtyped using mRNA or SCLC-DMC. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and guide precision SCLC therapy.

2024-01-22 | GSE241673 | GEO

Purity Independent Subtyping of Tumors (PurIST), a clinically robust single-sample classifier for tumor subtyping in pancreatic cancer (RNA-Seq)

Project description:Purpose: Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption. Methods: We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema. Results: We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX. Conclusions: The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

2019-12-10 | GSE131050 | GEO

scRNA-seq of triple knockout SCLC mouse models for subtyping

Project description:scRNA-seq of triple knockout SCLC mouse models for subtyping

| PRJNA872011 | ENA

Purity Independent Subtyping of Tumors (PurIST), a clinically robust single sample classifier for tumor subtyping in pancreatic cancer (NanoString)

Project description:We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a Single Sample Classifier (SSC) using penalized logistic regression based on the most robust and replicable schema. We demonstrate that a tumor-intrinsic two subtype schema is most robust, replicable, and clinically relevant. We developed purity independent subtyping of tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX. Keywords: Expression profiling by array

2020-02-21 | GSE131051 | GEO

Molecular subtyping of breast cancer by transcription profiling

Project description:Breast cancer was one of the first cancer types where molecular subtyping led to explanation of interpersonal heterogeneity and resulted in improvement of treatment regimen. Several multigene classifiers have been developed and in particular those defining molecular signatures of early breast cancers possess significant prognostic information. Hence since 2014, molecular subtyping of primary breast cancers was implemented as a part of routine diagnostics with direct impact of therapy assignment. In this study, we evaluate direct and potential benefits of molecular subtyping in low-risk breast cancers as well as present the advantages of a robust molecular signature in regard to patient work-up among high-risk breast cancers.

2017-12-01 | E-MTAB-5724 | biostudies-arrayexpress

Lineage transcription factors ASCL1, NKX2-1, and PROX1 are enriched at super enhancers and co-regulate subtype-specific genes in small cell lung cancer [HiCHIP]

Project description:Heterogeneity between tumors is a major barrier to the treatment of small cell lung cancer (SCLC). Identification of molecular markers to characterize and classify tumors can facilitate the diagnosis and development of targeted therapies. Here, we analyzed genomic regions, called super enhancers (SE), across multiple SCLC cell lines and patient-derived xenograft models, and find SE enrichment is sufficient to classify SCLC models into recently defined SCLC molecular subtypes SCLC-A, SCLC-N, and SCLC-P defined by the transcription factors ASCL1, NEUROD1, and POU2F3, respectively. 3D chromatin structure analysis identified genes associated with the SE that assemble into subtype-specific tumor-signatures with genes functioning in diverse processes. Focusing on the SCLC-A subtype, transcription factors NKX2-1 and PROX1 were identified as ASCL1-interacting proteins. All three factors bind overlapping genomic regions within SEs in SCLC-A cell line models. Nevertheless, combined loss of all three factors suppresses growth of SCLC-A similar to that seen with ASCL1 loss alone, continuing to place ASCL1 as a key dependency factor in a subset of SCLC. Focusing on genes co-regulated by the three transcription factors, two SCLC-A subtype-specific cell surface proteins, SCN3A and KCNB2, were identified. Loss of either channel alone did not disrupt SCLC-A growth in vitro, but they may serve as diagnostic tools in subtyping SCLC.

2021-05-01 | GSE151001 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data