Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA476140 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7344550.fastq.gz | Fastqsanger.gz | |||
| SRR7344550_1.fastq.gz | Fastqsanger.gz | |||
| SRR7344550_2.fastq.gz | Fastqsanger.gz | |||
| SRR7344560_1.fastq.gz | Fastqsanger.gz | |||
| SRR7344560_2.fastq.gz | Fastqsanger.gz |
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Project description:Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains, where melanoma is at the forefront of its success. However, only a subset of patients with advanced tumors currently benefit from these therapies, which at times incur considerable side-effects and costs. Constructing such predictors of patient’s response has remained a serious challenge due to the complexity of the immune response and the shortage of large ICB-treated patient cohorts including both omics and response data. Here we build IMPRES, a predictor of ICB-response in melanoma which encompasses 15 pairwise transcriptomics relations between immune checkpoint genes. It is based on two key conjectures: (a) immune mechanisms underlining spontaneous regression in neuroblastoma can predict ICB response in melanoma, and (b) key immune interactions can be captured via specific pairwise relations of immune checkpoint genes’ expression. IMPRES is validated on 9 published datasets1–6 and on a newly generated dataset of 31 tumor samples treated with anti-PD-1 and 10 tumor samples treated with anti-CTLA-4 (some of these are treated with both antibodies), spanning 297 samples in total. It achieves an overall accuracy of AUC=0.83, outperforming existing predictors, capturing almost all true responders while misclassifying less than half of the non-responders. Future studies are warranted to determine the value of the approach presented here in other cancer types.
2018-08-20 | GSE115821 | GEO
Project description:DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
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Project description:Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains and has been very successful in treatment of melanoma. However, only a subset of patients with advanced tumors currently benefit from ICB therapies, which at times incur considerable side effects and costs. Constructing predictors of patient response has remained a serious challenge because of the complexity of the immune response and the shortage of large cohorts of ICB-treated patients that include both 'omics' and response data. Here we build immuno-predictive score (IMPRES), a predictor of ICB response in melanoma which encompasses 15 pairwise transcriptomics relations between immune checkpoint genes. It is based on two key conjectures: (i) immune mechanisms underlying spontaneous regression in neuroblastoma can predict melanoma response to ICB, and (ii) key immune interactions can be captured via specific pairwise relations of the expression of immune checkpoint genes. IMPRES is validated on nine published datasets1-6 and on a newly generated dataset with 31 patients treated with anti-PD-1 and 10 with anti-CTLA-4, spanning 297 samples in total. It achieves an overall accuracy of AUC = 0.83, outperforming existing predictors and capturing almost all true responders while misclassifying less than half of the nonresponders. Future studies are warranted to determine the value of the approach presented here in other cancer types.
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Project description:<p>Blockade of T cell coinhibitory molecules such as CTLA-4 and PD-1, can activate T cell antitumor response. Although these immune checkpoint blockades (CTLA-4 blockade and PD-1 blockade) have shown durable response, response rate is modest. Therefore, there is a need to find stable biomarkers predictive of response to immune checkpoint blockades and to understand underlying resistance mechanisms. We collected longitudinal tumor biopsies from a cohort of metastatic melanoma patients treated with sequential immune checkpoint blockades and performed whole exome sequencing of this cohort. The comprehensive genomic characterization of tumors enabled identification of higher copy number loss burden as a resistance mechanism and clonal T cell repertoire as a predictive biomarker.</p>
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Project description:In the version of this article originally published, there was an error in the URL linked to by an accession code in the data availability section of the methods. The erroneous URL was: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100351 . The correct URL is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115821 . The error has been corrected in the HTML and PDF versions of this article.
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Project description:Mitochondrial DNA (mtDNA) encodes essential machinery for respiration and metabolic homeostasis but is paradoxically among the most common targets of somatic mutations in the cancer genome, with truncating mutations in complex I genes being over-represented1 . While mtDNA mutations have been associated with both improved and worsened prognoses in several cancer lineages1–3, whether these mutations are drivers, or exert any functional effect on tumour biology remains controversial. Here we discover that complex I-encoding mtDNA mutations are sufficient to remodel the tumour immune landscape and therapeutic resistance to immune checkpoint blockade. Using mtDNA base editing technology we engineered recurrent truncating mutations in the mtDNA-encoded complex I gene, Mt-Nd5, into murine models of melanoma. Mechanistically, these mutations promoted utilisation of pyruvate as a terminal electron acceptor and increased glycolytic flux driven by an over-reduced NAD pool and NADH shuttling between GAPDH and MDH1, mediating a Warburg-like metabolic shift. In turn, without modifying tumour growth, this altered cancer cell-intrinsic metabolism reshaped the tumour microenvironment of mouse and human cancer in a mutation load-dependent fashion, encouraging an anti-tumour immune response. This subsequently sensitises both mouse and human cancers with high mtDNA mutant heteroplasmy to immune checkpoint blockade. Strikingly, patient lesions bearing >50% mtDNA mutation load demonstrated a >2.5-fold improved response rate to checkpoint inhibitor blockade. Taken together these data nominate mtDNA mutations as functional regulators of cancer metabolism and tumour biology, with potential for therapeutic exploitation and treatment stratification.
2023-11-08 | PXD039705 | Pride
Project description:Mitochondrial DNA (mtDNA) encodes essential machinery for respiration and metabolic homeostasis but is paradoxically among the most common targets of somatic mutations in the cancer genome, with truncating mutations in complex I genes being over-represented1 . While mtDNA mutations have been associated with both improved and worsened prognoses in several cancer lineages1–3, whether these mutations are drivers, or exert any functional effect on tumour biology remains controversial. Here we discover that complex I-encoding mtDNA mutations are sufficient to remodel the tumour immune landscape and therapeutic resistance to immune checkpoint blockade. Using mtDNA base editing technology we engineered recurrent truncating mutations in the mtDNA-encoded complex I gene, Mt-Nd5, into murine models of melanoma. Mechanistically, these mutations promoted utilisation of pyruvate as a terminal electron acceptor and increased glycolytic flux driven by an over-reduced NAD pool and NADH shuttling between GAPDH and MDH1, mediating a Warburg-like metabolic shift. In turn, without modifying tumour growth, this altered cancer cell-intrinsic metabolism reshaped the tumour microenvironment of mouse and human cancer in a mutation load-dependent fashion, encouraging an anti-tumour immune response. This subsequently sensitises both mouse and human cancers with high mtDNA mutant heteroplasmy to immune checkpoint blockade. Strikingly, patient lesions bearing >50% mtDNA mutation load demonstrated a >2.5-fold improved response rate to checkpoint inhibitor blockade. Taken together these data nominate mtDNA mutations as functional regulators of cancer metabolism and tumour biology, with potential for therapeutic exploitation and treatment stratification.
2023-11-08 | PXD044987 | Pride
Project description:Inhibition of immune-checkpoint targets including PD1 is clinically effective in a variety of cancers. However, only a subset of patients respond and complete response remains uncommon. Given the known role of metabolites in modulating immunity, we sought to understand how individual patients’ metabolic activities adapt to PD1 immune checkpoint blockade and how they associate with therapeutic benefits. To this end, we profiled metabolites in pre- and multiple on-treatment patient serum samples from three independent immunotherapy trials using hydrophilic interaction liquid chromatography coupled with either triple quadrupole MS multiple reaction monitoring or high resolution full scan MS detection. The study consisted of two Phase I trials (CA209-038, NCT01621490; CA209-009, NCT01358721) which included 78 patients with advanced melanoma and 91 patients with metastatic renal cell carcinoma (RCC) treated with nivolumab. To investigate the generalizability of our results, we also analyzed a large randomized Phase III trial (CheckMate 025, NCT01668784) with 743 RCC patients, among which 394 received nivolumab and 349 received everolimus.
2019-08-08 | ST001235 | MetabolomicsWorkbench
Project description:Inhibition of immune-checkpoint targets including PD1 is clinically effective in a variety of cancers. However, only a subset of patients respond and complete response remains uncommon. Given the known role of metabolites in modulating immunity, we sought to understand how individual patients’ metabolic activities adapt to PD1 immune checkpoint blockade and how they associate with therapeutic benefits. To this end, we profiled metabolites in pre- and multiple on-treatment patient serum samples from three independent immunotherapy trials using hydrophilic interaction liquid chromatography coupled with either triple quadrupole MS multiple reaction monitoring or high resolution full scan MS detection. The study consisted of two Phase I trials (CA209-038, NCT01621490; CA209-009, NCT01358721) which included 78 patients with advanced melanoma and 91 patients with metastatic renal cell carcinoma (RCC) treated with nivolumab. To investigate the generalizability of our results, we also analyzed a large randomized Phase III trial (CheckMate 025, NCT01668784) with 743 RCC patients, among which 394 received nivolumab and 349 received everolimus.
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Project description:A robust predictor for hERG channel blockade based on an ensemble of five deep learning models. The authors have collected a dataset from public sources, such as BindingDB and ChEMBL on hERG blockers and non-blockers. The cut-off for hERG blockade was set at IC50
Model Type: Predictive machine learning model.
Model Relevance: Prediction of hERG blockade
Model Encoded by: Miquel Duran-Frigola (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos2ta5
2024-07-18 | MODEL2407180003 | BioModels