Summary of Study ST002235
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001425. The data can be accessed directly via it's Project DOI: 10.21228/M8HX4C This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002235 |
| Study Title | Application of Artificial Intelligence to Plasma Metabolomics Profiles to Predict Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer |
| Study Summary | Summary: There is a need for biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are unlikely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied artificial intelligence to comprehensive metabolic profiles to identify additional metabolites associated with treatment response. A deep learning model (DLM) consisting of two polyamines as well as nine additional metabolites was developed for improved prediction of RCB-II/III. The DLM has potential clinical value for identifying TNBC patients who are unlikely to respond to NACT and who may benefit from other treatment modalities. |
| Institute | University of Texas MD Anderson Cancer Center |
| Last Name | Cai |
| First Name | Yining |
| Address | 6767 Bertner Avenue, Houston, Texas, 77030 |
| ycai4@mdanderson.org | |
| Phone | 713-563-3096 |
| Submit Date | 2022-05-26 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Waters) |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-08-10 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN003645 |
|---|---|
| Analysis type | MS |
| Chromatography type | Reversed phase |
| Chromatography system | Waters XEVO-G2XSQTOF |
| Column | C18 |
| MS Type | ESI |
| MS instrument type | QTOF |
| MS instrument name | Waters Xevo-G2-XS |
| Ion Mode | POSITIVE |
| Units | ppm |
MS:
| MS ID: | MS003396 |
| Analysis ID: | AN003645 |
| Instrument Name: | Waters Xevo-G2-XS |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Data were processed using Progenesis QI (Non-linear, Waters). Peak picking and retention time alignment of LC-MS and MSe data were performed using Progenesis QI software (Non-linear, Waters) |
| Ion Mode: | POSITIVE |
