Background Proteomic studies are typically conducted using flash-frozen (FF) samples utilizing tandem mass spectrometry (MS). However, FF specimens are comprised of multiple cell types, making it difficult to ascertain the proteomic profiles of specific cells. Conversely, OCT-embedded (Optimal Cutting Temperature compound) specimens can undergo laser microdissection (LMD) to capture and study specific cell types separately from the cell mixture. In the current study, we compared proteomic data obtained from FF and OCT samples to determine if samples that are stored and processed differently produce comparable results. Methods Proteins were extracted from FF and OCT-embedded invasive breast tumors from 5 female patients. FF specimens were lysed via homogenization (FF/HOM) while OCT-embedded specimens underwent LMD to collect only tumor cells (OCT/LMD-T) or both tumor and stromal cells (OCT/LMD-TS) followed by incubation at 37 °C. Proteins were extracted using the illustra triplePrep kit and then trypsin-digested, TMT-labeled, and processed by two-dimensional liquid chromatography-tandem mass spectrometry (2D LC–MS/MS). Proteins were identified and quantified with Proteome Discoverer v1.4 and comparative analyses performed to identify proteins that were significantly differentially expressed amongst the different processing methods. Results Among the 4,950 proteins consistently quantified across all samples, 216 and 171 proteins were significantly differentially expressed (adjusted p-value < 0.05; |log2 FC|> 1) between FF/HOM vs. OCT/LMD-T and FF/HOM vs. OCT/LMD-TS, respectively, with most proteins being more highly abundant in the FF/HOM samples. PCA and unsupervised hierarchical clustering analysis with these 216 and 171 proteins were able to distinguish FF/HOM from OCT/LMD-T and OCT/LMD-TS samples, respectively. Similar analyses using significantly differentially enriched GO terms also discriminated FF/HOM from OCT/LMD samples. No significantly differentially expressed proteins were detected between the OCT/LMD-T and OCT/LMD-TS samples but trended differences were detected. Conclusions The proteomic profiles of the OCT/LMD-TS samples were more similar to those from OCT/LMD-T samples than FF/HOM samples, suggesting a strong influence from the sample processing methods. These results indicate that in LC–MS/MS proteomic studies, FF/HOM samples exhibit different protein expression profiles from OCT/LMD samples and thus, results from these two different methods cannot be directly compared.
Cancer biomarker discovery is critically dependent on the integrity of biofluid and tissue samples acquired from study participants. Multi-omic profiling of candidate protein, lipid, and metabolite biomarkers is confounded by timing and fasting status of sample collection, participant demographics and treatment exposures of the study population. Contamination by hemoglobin, whether caused by hemolysis during sample preparation or underlying red cell fragility, contributes 0–10 g/L of extraneous protein to plasma, serum, and Buffy coat samples and may interfere with biomarker detection and validation. We analyzed 617 plasma, 701 serum, and 657 buffy coat samples from a 7-year longitudinal multi-omic biomarker discovery program evaluating 400+ participants with or at risk for pancreatic cancer, known as Project Survival. Hemolysis was undetectable in 93.1% of plasma and 95.0% of serum samples, whereas only 37.1% of buffy coat samples were free of contamination by hemoglobin. Regression analysis of multi-omic data demonstrated a statistically significant correlation between hemoglobin concentration and the resulting pattern of analyte detection and concentration. Although hemolysis had the greatest impact on identification and quantitation of the proteome, distinct differentials in metabolomics and lipidomics were also observed and correlated with severity. We conclude that quality control is vital to accurate detection of informative molecular differentials using OMIC technologies and that caution must be exercised to minimize the impact of hemolysis as a factor driving false discovery in large cancer biomarker studies.
As directed by the Council of the Aerospace Medical Association, the Commercial Spaceflight Working Group has developed the following position paper concerning medical issues for commercial suborbital spaceflight crewmembers. This position paper has been approved by the AsMA Council to become a policy of the AsMA.
Breast cancer (BrCA) therapeutic selection routinely incorporates clinicopathologic information along with immunohistochemistry (IHC) for ER/PR/HER2/Ki-67. However, this is incomplete and has shortcomings that are seen in clinical outcome differences even within the same subtype. Herein, we analyzed the proteome of 116 HER2-negative primary BrCA samples and subsequently validated a 34-proteogenomic signature in 5,963 BrCA tumor samples from TCGA, METABRIC, and GSE96058 that demonstrated a metabolic enrichment signature impacting overall survival, progression free survival, and response to therapy. The 34-proteogenomic signature selected ER+ BrCA tumors for upstaging to a more triple negative pathophysiological phenotype, herein referred to as Luminal/TN-like (L/T), impacting likelihood for chemotherapy consideration and other therapeutic modalities rather than hormonal therapy alone. Further, analysis of 9,530 tumors across 33 types of cancers in TCGA demonstrated the 34 proteogenomic signature utility in the reclassification of other cancer types into different risk groups.
Introduction: Classification of breast cancer can incorporate immunohistochemical (IHC) detection of ER/PR/HER2/KI67 to stratify the subtypes. High throughput proteomics analysis allows for the expansion of biomarker discovery within the subtypes. We evaluated a cohort of 109 tumors characterized as ER+ (Luminal A and Luminal B1; HER2+ and ER low (1-10%) cases were excluded) compared to ER-/HER2- tumors. Utilizing an integrated bioinformatics approach, we developed a proteomic marker signature to reclassify tumors into ER+(like) and ER-(like) tumors. CPTAC (Proteomic)/TCGA (RNAseq) datasets and larger METBRIC and GSE96058 cohorts were used to validate this marker signature. The selected biomarkers demonstrated significant differences impacting survival outcome. Methods: Clinical IHC subtyping of core biopsies was used to select a cohort of patients with ER+/HER2- and ER-/HER2- primary tumors from flash-frozen surgical samples. The positive/negative status of ER/PR/HER2 was defined using updated ASCO 2020 guidelines. Ki-67 status was determined using the 2011 St. Gallen's International Expert Consensus recommendations. Proteomic analysis was performed using Thermo Q-Exactive+ LC MS/MS analysis. Differential analysis was applied to select the significantly altered proteins between ER+ and ER- cases, Univariate survival analysis was engaged to filter informative protein/genes using TCGA RNA-Seq data. Nearest centroid analysis was deployed to define the classifier to predict novel molecular subtypes. Results/Conclusions: We selected 34 proteins/genes from 164 significantly differentially expressed proteins for further analysis. The centroid model constructed with the 34 proteins defined 2 groups: ER+(like) and ER-(like). An additional 4 groups were defined across subtypes: luminal tumors classified both by IHC and marker signature (LL), luminal tumors classified by IHC but marker signature more like triple negative (LT), triple negative tumors classified by IHC but marker signature more like luminal (TL), and triple negative classified by both IHC and marker signature (TT). This marker signature segregated close to 5000 tumors across CPTAC, TCGA, METABRIC and GSE96058 cohorts. Survival analysis in these groups of patients revealed differences in radiation, hormone/radiation, hormone therapy, and hormone/radiation/chemotherapy treatments. In summary using proteomics data we identified a 34 gene/protein marker signature, validated in large external cohorts and exhibited impact on survival and response to therapy. Further, this signature was enriched in metabolism and microenvironmental associated factors that could represent novel targets or development combination strategies based on this signature. Citation Format: Guisong Wang, Punit Shah, Rick Searfoss, Leigh Fantacone-Campbell, Jeffrey A. Hooke, Brenda Deyarmin, Rebecca N. Zingmark, Stella Somiari, Jianfang Liu, Leonid Kvecher, Bradley Mostoller, Lori A. Sturtz, Praven-Kumar Raj-Kumar, Elder Granger, Linda Vahdat, Mary L. Cutler, Chas Bountra, Rangaprasad Sarangarajan, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Niven R. Narain, Craig D. Shriver. Reclassification of ER+ (luminal A/luminal B1 minus ER low)-like and ER- like breast tumors based on proteomic/gene and clinical outcome signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1188.
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