Construction and analysis of the NCI-EDRN breast cancer reference set for circulating markers of disease

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R² > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

Fucosylation genes as circulating biomarkers for lung cancer

PURPOSE

Fucosyltransferases (FUTs) catalyze fucosylation, which plays a central role in biological processes. Aberrant fucosylation is associated with malignant transformation. Here we investigated whether transcriptional levels of genes coding the FUTs in plasma could provide cell-free circulating biomarkers for lung cancer.

METHODS

mRNA expression of all 13 Futs (Fut1-11, Pofut1, and Pofut2) was evaluated by PCR assay in 48 lung tumor tissues and the 48 matched noncancerous lung tissues, and plasma of 64 lung cancer patients and 32 cancer-free individuals to develop plasma Fut biomarkers. The developed plasma Fut biomarkers were validated in an independent cohort of 40 lung cancer patients and 20 controls for their diagnostic performance.

RESULTS

Four of the 13 Futs showed a different transcriptional level in 48 lung tumor tissues compared with the 48 matched nonconscious tissues (all < 0.05). Two (Fut8, and Pofut1) of the four Futs had a higher plasma level in 64 lung cancer patients compared with 32 control subjects, and consistent with that in lung tissue specimens. Combined analysis of the two Futs produced 81% sensitivity and 86% specificity for diagnosis of lung cancer, and was independent of stage and histology of lung tumors. The diagnostic performance of the two plasma biomarkers was successfully validated in the different cohort of 40 lung cancer patients and 20 control individuals.

CONCLUSION

The fucosylation genes may provide new circulating biomarkers for the early detection of lung cancer.

A Plasma Long Noncoding RNA Signature for Early Detection of Lung Cancer

The early detection of lung cancer is a major clinical challenge. Long noncoding RNAs (lncRNAs) have important functions in tumorigenesis. Plasma lncRNAs directly released from primary tumors or the circulating cancer cells might provide cell-free cancer biomarkers. The objective of this study was to investigate whether the lncRNAs could be used as plasma biomarkers for early-stage lung cancer. By using droplet digital polymerase chain reaction, we determined the diagnostic performance of 26 lung cancer–associated lncRNAs in plasma of a development cohort of 63 lung cancer patients and 33 cancer-free individuals, and a validation cohort of 39 lung cancer patients and 28 controls. In the development cohort, 7 of the 26 lncRNAs were reliably measured in plasma. Two (SNHG1 and RMRP) displayed a considerably high plasma level in lung cancer patients vs. cancer-free controls (all P < .001). Combined use of the plasma lncRNAs as a biomarker signature produced 84.13% sensitivity and 87.88% specificity for diagnosis of lung cancer, independent of stage and histological type of lung tumor, and patients' age and sex (all P > .05). The diagnostic value of the plasma lncRNA signature for lung cancer early detection was confirmed in the validation cohort. The plasma lncRNA signature may provide a potential blood-based assay for diagnosing lung cancer at the early stage. Nevertheless, a prospective study is warranted to validate its clinical value.

Tissue and plasma proteomics for early stage cancer detection

The pursuit of novel and effective biomarkers is essential in the struggle against cancer, which is a leading cause of mortality worldwide. Here we discuss the relative advantages and disadvantages of the most frequently used proteomics techniques, concentrating on the latest advances and application of tissue and plasma proteomics for novel cancer biomarker discovery.

Cell Cycle Model System for Advancing Cancer Biomarker Research

Progress in understanding the complexity of a devastating disease such as cancer has underscored the need for developing comprehensive panels of molecular markers for early disease detection and precision medicine applications. The present study was conducted to assess whether a cohesive biological context can be assigned to protein markers derived from public data mining, and whether mass spectrometry can be utilized to screen for the co-expression of functionally related biomarkers to be recommended for further exploration in clinical context. Cell cycle arrest/release experiments of MCF7/SKBR3 breast cancer and MCF10 non-tumorigenic cells were used as a surrogate to support the production of proteins relevant to aberrant cell proliferation. Information downloaded from the scientific public domain was queried with bioinformatics tools to generate an initial list of 1038 cancer-associated proteins. Mass spectrometric analysis of cell extracts identified 352 proteins that could be matched to the public list. Differential expression, enrichment, and protein-protein interaction analysis of the proteomic data revealed several functionally-related clusters of relevance to cancer. The results demonstrate that public data derived from independent experiments can be used to inform biological research and support the development of molecular assays for probing the characteristics of a disease.

A plasma miRNA signature for lung cancer early detection

The early detection of lung cancer continues to be a major clinical challenge. Using whole-transcriptome next-generation sequencing to analyze lung tumor and the matched noncancerous tissues, we previously identified 54 lung cancer-associated microRNAs (miRNAs). The objective of this study was to investigate whether the miRNAs could be used as plasma biomarkers for lung cancer. We determined expressions of the lung tumor-miRNAs in plasma of a development cohort of 180 subjects by using reverse transcription PCR to develop biomarkers. The development cohort included 92 lung cancer patients and 88 cancer-free smokers. We validated the biomarkers in a validation cohort of 64 individuals comprising 34 lung cancer patients and 30 cancer-free smokers. Of the 54 miRNAs, 30 displayed a significant different expression level in plasma of the lung cancer patients vs. cancer-free controls (all P < 0.05). A plasma miRNA signature (miRs-126, 145, 210, and 205-5p) with the best prediction was developed, producing 91.5% sensitivity and 96.2% specificity for lung cancer detection. Diagnostic performance of the plasma miRNA signature had no association with stage and histological type of lung tumor, and patients' age, sex, and ethnicity (all p > 0.05). The plasma miRNA signature was reproducibly confirmed in the validation cohort. The plasma miRNA signature may provide a blood-based assay for diagnosing lung cancer at the early stage, and thereby reduce the associated mortality and cost.

A multiplex platform for the identification of ovarian cancer biomarkers

Background

Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.

Methods

We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek^® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.

Results

Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek^® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.

Conclusions

These data demonstrate that the Proseek^® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-017-9169-6) contains supplementary material, which is available to authorized users.

A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules

Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.

Crucial considerations for pipelines to validate circulating biomarkers for breast cancer

Despite decades of progress in breast imaging, breast cancer remains the second most common cause of cancer mortality in women. The rapidly proliferative breast cancers that are associated with high relapse rates and mortality frequently present in younger women, in unscreened individuals, or in the intervals between screening mammography. Biomarkers exist for monitoring metastatic disease, such as CEA, CA27.29 and CA15-3, but there are no circulating biomarkers clinically available for early detection, prognosis, or monitoring for clinical relapse. There has been significant progress in the discovery of potential circulating biomarkers, including proteins, autoantibodies, nucleic acids, exosomes, and circulating tumor cells, but the vast majority of these biomarkers have not progressed beyond initial research discovery, and none have yet been approved for clinical use in early stage disease. Here, the authors review the crucial considerations of developing pipelines for the rapid evaluation of circulating biomarkers for breast cancer.

Evaluating Serum Markers for Hormone Receptor-Negative Breast Cancer

INTRODUCTION

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in females worldwide. Death rates have been declining, largely as a result of early detection through mammography and improved treatment, but mammographic screening is controversial because of over-diagnosis of breast disease that might not require treatment, and under-diagnosis of cancer in women with dense breasts. Breast cancer screening could be improved by pairing mammography with a tumor circulating marker, of which there are currently none. Given genomic similarities between the basal breast cancer subtype and serous ovarian cancer, and given our success in identifying circulating markers for ovarian cancer, we investigated the performance in hormone receptor-negative breast cancer detection of both previously identified ovarian serum markers and circulating markers associated with transcripts that were differentially expressed in breast cancer tissue compared to healthy breast tissue from reduction mammaplasties.

METHODS

We evaluated a total of 15 analytes (13 proteins, 1 miRNA, 1 autoantibody) in sera drawn at or before breast cancer surgery from 43 breast cancer cases (28 triple-negative-TN-and 15 hormone receptor-negative-HRN-/ HER2-positive) and 87 matched controls.

RESULTS

In the analysis of our whole cohort of breast cancer cases, autoantibodies to TP53 performed significantly better than the other selected 14 analytes showing 25.6% and 34.9% sensitivity at 95% and 90% specificity respectively with AUC: 0.7 (p<0.001). The subset of 28 TN cancers showed very similar results. We observed no correlation between anti-TP53 and the 14 other markers; however, anti-TP53 expression correlated with Body-Mass-Index. It did not correlate with tumor size, positive lymph nodes, tumor stage, the presence of metastases or recurrence.

CONCLUSION

None of the 13 serum proteins nor miRNA 135b identified women with HRN or TN breast cancer. TP53 autoantibodies identified women with HRN breast cancer and may have potential for early detection, confirming earlier reports. TP53 autoantibodies are long lasting in serum but may be affected by storage duration. Autoantibodies to TP53 might correlate with Body-Mass-Index.