Predicting clinical outcomes using cancer progression associated signatures.
Ontology highlight
ABSTRACT:
SUBMITTER: Mamrot J
PROVIDER: S-EPMC8057277 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Approximately 90% of pre-clinically validated drugs fail in clinical trials due to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database “humanized” by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process.
2020-01-09 | GSE142068 | GEO
Project description:Predicting Human Clinical Outcomes using Mouse Multi-Organ Transcriptome
| PRJNA595856 | ENA
Project description:BackgroundEarly detection of gastric cancer (GC) remains challenging. We aimed to examine urine proteomic signatures and identify protein biomarkers that predict the progression of gastric lesions and risk of GC.MethodsA case-control study was initially designed, covering subjects with GC and gastric lesions of different stages. Subjects were aged 40-69 years, without prior diagnosis of renal or urological diseases. We enrolled a total of 255 subjects, with 123 in the discovery stage from Linqu, China, a high-risk area for GC and 132 in the validation stage from Linqu and Beijing. A prospective study was further designed for a subset of 60 subjects with gastric lesions, which were followed for 297-857 days.FindingsWe identified 43 differentially expressed urine proteins in subjects with GC vs. mild or advanced gastric lesions. Baseline urinary levels of ANXA11, CDC42, NAPA and SLC25A4 were further positively associated with risk of gastric lesion progression. Three of them, except for SLC25A4, also had higher expression in GC than non-GC tissues. Integrating these four proteins showed outstanding performance in predicting the progression of gastric lesions (AUC (95% CI): 0.92 (0.83-1.00)) and risk of GC (AUC (95% CI): 0.81 (0.73-0.89) and 0.84 (0.77-0.92) for GC vs. mild or advanced gastric lesions respectively).InterpretationThis study revealed distinct urine proteomic profiles and a panel of proteins that may predict the progression of gastric lesions and risk of GC. These biomarkers in a non-invasive approach may have translational significance for defining high-risk populations of GC and its early detection.FundingFunders are listed in the Acknowledgement.
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Project description:TP63 is required to maintain stem cell pluripotency and suppresses the metastatic potential of cancer cells through multiple mechanisms. These functions are differentially regulated by individual isoforms, necessitating a deeper understanding of how the distinct transcriptional programs controlled by these isoforms affect cancer progression and outcomes. In this study, we conducted a pan-cancer analysis of The Cancer Genome Atlas to identify transcriptional networks regulated by TAp63 and ΔNp63 using transcriptomes derived from epidermal cells of TAp63-/- and ΔNp63-/- mice. Analysis of 17 cancer developmental and 27 cancer progression signatures revealed a consistent tumor suppressive pattern for TAp63. In contrast, we identified pleiotropic roles for ΔNp63 in tumor development and found that its regulation of Lef1 was crucial for its oncogenic role. ΔNp63 performed a distinctive role as suppressor of tumor progression by cooperating with TAp63 to modulate key biological pathways, principally cell-cycle regulation, extracellular matrix remodeling, epithelial-to-mesenchymal transition, and the enrichment of pluripotent stem cells. Importantly, these TAp63 and ΔNp63 signatures prognosticated progression and survival, even within specific stages, in bladder and renal carcinomas as well as low-grade gliomas. These data describe a novel approach for understanding transcriptional activities of TP63 isoforms across a large number of cancer types, potentially enabling identification of patient subsets most likely to benefit from therapies predicated on manipulating specific TP63 isoforms.Significance: Transcriptomic analyses of patient samples and murine knockout models highlight the prognostic role of several critical mechanisms of tumor suppression that are regulated by TP63. Cancer Res; 78(2); 451-62. ©2017 AACR.
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Project description:Molecular signatures are a powerful approach to characterize novel small molecules and derivatized small molecule libraries. While new experimental techniques are being developed in diverse model systems, informatics approaches lag behind these exciting advances. We propose an analysis pipeline for signature based drug annotation. We develop an integrated strategy, utilizing supervised and unsupervised learning methodologies that are bridged by network based statistics. Using this approach we can: 1, predict new examples of drug mechanisms that we trained our model upon; 2, identify "New" mechanisms of action that do not belong to drug categories that our model was trained upon; and 3, update our training sets with these "New" mechanisms and accurately predict entirely distinct examples from these new categories. Thus, not only does our strategy provide statistical generalization but it also offers biological generalization. Additionally, we show that our approach is applicable to diverse types of data, and that distinct biological mechanisms characterize its resolution of categories across different data types. As particular examples, we find that our predictive resolution of drug mechanisms from mRNA expression studies relies upon the analog measurement of a cell stress-related transcriptional rheostat along with a transcriptional representation of cell cycle state; whereas, in contrast, drug mechanism resolution from functional RNAi studies rely upon more dichotomous (e.g., either enhances or inhibits) association with cell death states. We believe that our approach can facilitate molecular signature-based drug mechanism understanding from different technology platforms and across diverse biological phenomena.
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Project description:Approximately 90% of pre-clinically validated drugs fail in clinical trials owing to unanticipated clinical outcomes, costing over several hundred million US dollars per drug. Despite such critical importance, translating pre-clinical data to clinical outcomes remain a major challenge. Herein, we designed a modality-independent and unbiased approach to predict clinical outcomes of drugs. The approach exploits their multi-organ transcriptome patterns induced in mice and a unique mouse-transcriptome database "humanized" by machine learning algorithms and human clinical outcome datasets. The cross-validation with small-molecule, antibody, and peptide drugs shows effective and efficient identification of the previously known outcomes of 5,519 adverse events and 11,312 therapeutic indications. In addition, the approach is adaptable to deducing potential molecular mechanisms underlying these outcomes. Furthermore, the approach identifies previously unsuspected repositioning targets. These results, together with the fact that it requires no prior structural or mechanistic information of drugs, illustrate its versatile applications to drug development process.
| S-EPMC7033637 | biostudies-literature
Project description:INTRODUCTION:Prostate cancer is the most common cancer in men in the UK, with nearly 40?000 diagnosed in 2014; and it is the second most common cause of male cancer-related mortality. The clinical conundrum is that most men live with prostate cancer rather than die from it, while existing treatments have significant associated morbidity. Recent studies have shown very low mortality rates (1% after a median of 10-year follow-up) and no treatment-related reductions in mortality, in men with localised prostate cancer. This study will identify prognostic factors associated with prostate cancer progression to help differentiate aggressive from more indolent tumours in men with localised disease at diagnosis, and so inform the decision to adopt conservative (active surveillance) or radical (surgery or radiotherapy) management strategies. METHODS AND ANALYSIS:The Clinical Practice Research Datalink (CPRD) contains 57?318 men who were diagnosed with prostate cancer between 1 January 1987 and 31 December 2016. These men will be linked to the Office for National Statistics (ONS) and the National Cancer Registration and Analysis Service registry databases for mortality, TNM stage, Gleason grade and treatment data. Men with a diagnosis date prior to 1 January 1987 and men with lymph node or distant metastases at diagnosis will be excluded. A priori determined prognostic factors potentially associated with prostate cancer mortality, the end point of cancer progression, will be measured at baseline, and the participants followed through to development of cancer progression, death or the end of the follow-up period (31 December 2016). Cox proportional hazards regression will be used to estimate crude and mutually adjusted HRs. Mortality risk will be predicted using flexible parametric survival models that can accurately fit the shape of the hazard function. ETHICS AND DISSEMINATION:This study protocol has approval from the Independent Scientific Advisory Committee for the UK Medicines and Healthcare products Regulatory Agency Database Research (protocol 17_041). The findings will be presented in peer-reviewed journals and local CPRD researcher meetings.
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Project description:BackgroundThe role of the immune response in influencing leptospirosis clinical outcomes is not yet well understood. We hypothesized that acute-phase serum cytokine responses may play a role in disease progression, risk for death, and severe pulmonary hemorrhage syndrome (SPHS).Methodology/principal findingsWe performed a case-control study design to compare cytokine profiles in patients with mild and severe forms of leptospirosis. Among patients hospitalized with severe disease, we compared those with fatal and nonfatal outcomes. During active outpatient and hospital-based surveillance we prospectively enrolled 172 patients, 23 with mild disease (outpatient) and 149 with severe leptospirosis (hospitalized). Circulating concentrations of pro- and anti-inflammatory cytokines at the time of patient presentation were measured using a multiplex bead array assay. Concentrations of IL-1?, IL-2, IL-4, IL-6, IL-8, IL-10, IL-17A, and TNF-? were significantly higher (P<0.05) in severe disease compared to mild disease. Among severe patients, levels of IL-6 (P<0.001), IL-8 (P = 0.0049) and IL-10 (P<0.001), were higher in fatal compared to non-fatal cases. High levels of IL-6 and IL-10 were independently associated (P<0.05) with case fatality after adjustment for age and days of symptoms. IL-6 levels were higher (P = 0.0519) among fatal cases who developed SPHS than among who did not.Conclusion/significanceThis study shows that severe cases of leptospirosis are differentiated from mild disease by a "cytokine storm" process, and that IL-6 and IL-10 may play an immunopathogenic role in the development of life-threatening outcomes in human leptospirosis.
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Project description:Cancer-associated fibroblasts (CAFs) are actively involved in cancer progression through generating extracellular matrix and orchestrating the crosstalk within the tumor microenvironment (TME). This study aimed to develop and validate a CAF-derived lncRNA (long non-coding RNA) (CAFDL) signature for predicting clinical outcomes in colorectal cancer (CRC). Clinical data and transcriptomic profiles of 2,320 patients with CRC from The Cancer Genome Atlas (TCGA)-COAD and TCGA-READ datasets and 16 Gene Expression Omnibus datasets were included in this study. CAFDLs were identified using weighted gene co-expression network analysis. The CAFDL signature was constructed using the least absolute shrinkage and selection operator analysis in the TCGA-CRC training set. Multiple CRC cohorts and pan-cancer cohorts were used to validated the CAFDL signature. Patients with high CAFDL scores had significantly worse overall survival and disease-free survival than patients with low CAFDL scores in all CRC cohorts. In addition, non-responders to fluorouracil, leucovorin, and oxaliplatin (FOLFOX)/fluorouracil, leucovorin, and irinotecan (FOLFIRI) chemotherapy, chemoradiotherapy, bevacizumab, and immune checkpoint inhibitors had significantly higher CAFDL scores compared with responders. Pan-cancer analysis showed that CAFDL had prognostic predictive power in multiple cancers such as lung adenocarcinoma, breast invasive carcinoma, stomach adenocarcinoma, and thyroid carcinoma. The CAFDL signature was positively correlated with transforming growth factor-beta (TGF-β) signaling, epithelial-mesenchymal transition, and angiogenesis pathways but negatively correlated with the expression of immune checkpoints such as PDCD1, CD274, and CTLA4. The CAFDL signature reflects CAF properties from a lncRNA perspective and effectively predicts clinical outcomes in CRC and across pan-cancer. The CAFDL signature can serve as a useful tool for risk stratification and provide new insights into the underlying mechanisms of CAFs in cancer immunity.
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