Circulating Protein Biomarkers for Use in Pancreatic Ductal Adenocarcinoma Identification

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Identification of biomarkers and potential therapeutic targets for pancreatic cancer by proteomic analysis in two prospective cohorts

Pancreatic cancer (PC) is the deadliest malignancy due to late diagnosis. Aberrant alterations in the blood proteome might serve as biomarkers to facilitate early detection of PC. We designed a nested case-control study of incident PC based on a prospective cohort of 38,295 elderly Chinese participants with ∼5.7 years' follow-up. Forty matched case-control pairs passed the quality controls for the proximity extension assay of 1,463 serum proteins. With a lenient threshold of p < 0.005, we discovered regenerating family member 1A (REG1A), REG1B, tumor necrosis factor (TNF), and phospholipase A2 group IB (PLA2G1B) in association with incident PC, among which the two REG1 proteins were replicated using the UK Biobank Pharma Proteomics Project, with effect sizes increasing steadily as diagnosis time approaches the baseline. Mendelian randomization analysis further supported the potential causal effects of REG1 proteins on PC. Taken together, circulating REG1A and REG1B are promising biomarkers and potential therapeutic targets for the early detection and prevention of PC.

Circulating immune-related proteins associated with immune checkpoint inhibitor efficacy in patients with pancreatic ductal adenocarcinoma

BACKGROUND

Most patients with pancreatic ductal adenocarcinoma (PDAC) do not benefit from immune checkpoint inhibitor treatment. However, the phase II study CheckPAC (NCT02866383) showed a clinical benefit (CB) rate of 37% and a response rate of 14% in patients with metastatic PDAC receiving stereotactic radiation therapy and nivolumab with or without ipilimumab. Translational studies were initiated to characterize the patients who would benefit from this treatment. Here, we evaluated the association between treatment outcome and 92 circulating immuno-oncology-related proteins in patients from the CheckPAC trial.

MATERIALS AND METHODS

The study included 78 patients with chemoresistant metastatic PDAC treated with nivolumab ± ipilimumab combined with radiotherapy. Proteins were measured in serum samples collected at baseline and on treatment with the use of the Olink Target 96 Immuno-Oncology panel. A cohort of 234 patients with metastatic PDAC treated with first-line chemotherapy were also included.

RESULTS

High levels of Fas ligand (FASLG) and galectin 1 (Gal-1) and low levels of C-C motif chemokine 4 were associated with CB. High FASLG and Gal-1 were associated with longer progression-free survival in univariable analysis. In the multivariable Cox regression analysis, the association was significant for Gal-1 (P < 0.001) but not significant for FASLG (P = 0.06). A focused unsupervised hierarchal clustering analysis, including T-cell activation and immune checkpoint-related proteins, identified clusters of patients with higher CB rate and higher tumor expression of leukocyte or T-cell markers (CD3, CD45, granzyme B). Thirty-six proteins increased significantly during immunotherapy. Several proteins (including FASLG, checkpoint proteins, and immune activation markers) increased independently of response during immunotherapy but did not increase in the cohort of patients treated with chemotherapy.

CONCLUSIONS

Circulating levels of immune-related proteins like FASLG and Gal-1 might be used to predict the efficacy of checkpoint inhibitors in patients with metastatic PDAC.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Amplification-Free, High-Throughput Nanoplasmonic Quantification of Circulating MicroRNAs in Unprocessed Plasma Microsamples for Earlier Pancreatic Cancer Detection

Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that is often detected at an advanced stage. Earlier diagnosis of PDAC is key to reducing mortality. Circulating biomarkers such as microRNAs are gaining interest, but existing technologies require large sample volumes, amplification steps, extensive biofluid processing, lack sensitivity, and are low-throughput. Here, we present an advanced nanoplasmonic sensor for the highly sensitive, amplification-free detection and quantification of microRNAs (microRNA-10b, microRNA-let7a) from unprocessed plasma microsamples. The sensor construct utilizes uniquely designed -ssDNA receptors attached to gold triangular nanoprisms, which display unique localized surface plasmon resonance (LSPR) properties, in a multiwell plate format. The formation of -ssDNA/microRNA duplex controls the nanostructure-biomolecule interfacial electronic interactions to promote the charge transfer/exciton delocalization processes and enhance the LSPR responses to achieve attomolar (10^-18 M) limit of detection (LOD) in human plasma. This improve LOD allows the fabrication of a high-throughput assay in a 384-well plate format. The performance of nanoplasmonic sensors for microRNA detection was further assessed by comparing with the qRT-PCR assay of 15 PDAC patient plasma samples that shows a positive correlation between these two assays with the Pearson correlation coefficient value >0.86. Evaluation of >170 clinical samples reveals that oncogenic microRNA-10b and tumor suppressor microRNA-let7a levels can individually differentiate PDAC from chronic pancreatitis and normal controls with >94% sensitivity and >94% specificity at a 95% confidence interval (CI). Furthermore, combining both oncogenic and tumor suppressor microRNA levels significantly improves differentiation of PDAC stages I and II versus III and IV with >91% and 87% sensitivity and specificity, respectively, in comparison to the sensitivity and specificity values for individual microRNAs. Moreover, we show that the level of microRNAs varies substantially in pre- and post-surgery PDAC patients (n = 75). Taken together, this ultrasensitive nanoplasmonic sensor with excellent sensitivity and specificity is capable of assaying multiple biomarkers simultaneously and may facilitate early detection of PDAC to improve patient care.

Prognostic value of circulating proteins in patients undergoing surgery for pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer death. Less than 20% of patients are diagnosed with resectable disease. Identifying truly resectable disease is challenging because 20%-40% of the patients subjected to resection are found to have advanced disease during surgery. The aim of our study was to identify panels of circulating proteins that could be used to distinguish patients with unresectable PDAC from patients with resectable PDAC and to identify prognostic signatures for both groups.

METHODS

We measured 92 circulating immuno-oncology-related proteins using the proximity extension assay from Olink Proteomics in 273 patients eligible for surgery for PDAC. Two bioinformaticians worked independently of one another on the same data. LASSO and Ridge regression were used in the statistical analyses.

RESULTS

One protein index for determining resectability had an AUC value of 0.66. Several indices for prognosis had AUC values between 0.50 and 0.75 and were therefore not better than existing prognostic markers.

DISCUSSION

Our study did not reveal any new high-performing protein panels that could be used to identify patients with inoperable PDAC before surgery. The panel of 92 proteins investigated has previously been found to be applicable for diagnostic use in patients with PDAC, but it does not seem to warrant further investigation regarding resectability in the subgroup of patients with PDAC referred to surgery.

Combining sCD163 with CA 19-9 Increases the Predictiveness of Pancreatic Ductal Adenocarcinoma

The objective of this study was to evaluate the diagnostic and prognostic potential of soluble CD163 (sCD163) in patients with pancreatic ductal adenocarcinoma (PDAC). Preoperative serum samples from 255 patients with PDAC were analyzed for sCD163 using a commercially available enzyme-linked immunosorbent assay. The diagnostic value of sCD163 was evaluated using receiver operating characteristic (ROC) curves. The prognostic significance of sCD163 was evaluated by Cox regression analysis and Kaplan-Meier survival curves. sCD163 was significantly increased in patients with PDAC, across all stages, compared to healthy subjects (stage 1: p value = 0.033; stage 2-4: p value ≤ 0.0001). ROC curves showed that sCD163 combined with CA 19-9 had the highest diagnostic potential compared to sCD163 and CA 19-9 alone both in patients with local PDAC and patients with advanced PDAC. Univariate and multivariate analysis showed no association between sCD163 and overall survival. This study found elevated levels of circulating sCD163 in patients with PDAC, regardless of stage, compared to healthy subjects. This suggests that sCD163 may have a clinical value as a novel diagnostic biomarker in PDAC.

Development and validation of circulating protein signatures as diagnostic biomarkers for biliary tract cancer

Background & Aims

Biliary tract cancer (BTC) is associated with a dismal prognosis, partly because it is typically diagnosed late, highlighting the need for diagnostic biomarkers. The purpose of this project was to identify and validate multiprotein signatures that could differentiate patients with BTC from non-cancer controls.

Methods

In this study, we included treatment-naïve patients with BTC, healthy controls, and patients with benign conditions including benign biliary tract disease. Participants were divided into three non-overlapping cohorts: a case-control-based discovery cohort (BTC = 186, controls = 249); a case-control-based validation cohort (validation cohort 1: BTC = 113, controls = 241); and a cohort study-based validation cohort including participants (BTC = 8, controls = 132) referred for diagnostic work-up for suspected cancer (validation cohort 2). Immuno-Oncology (I-O)-related proteins were measured in serum and plasma using a proximity extension assay (Olink Proteomics). Lasso and Ridge regressions were used to generate protein signatures of I-O-related proteins and carbohydrate antigen 19-9 (CA19-9) in the discovery cohort.

Results

Sixteen protein signatures, including 2 to 82 proteins, were generated. All signatures included CA19-9 and chemokine C-C motif ligand 20. Signatures discriminated between patients with BTC vs. controls, with AUCs ranging from 0.95 to 0.99 in the discovery cohort and 0.94 to 0.97 in validation cohort 1. In validation cohort 2, AUCs ranged from 0.84 to 0.94. Nine signatures achieved a specificity of 82% to 84% while keeping a sensitivity of 100% in validation cohort 2. All signatures performed better than CA19-9, and signatures including >15 proteins showed the best performance.

Conclusion

The study demonstrated that it is possible to generate protein signatures that can successfully differentiate patients with BTC from non-cancer controls.

Impact and implications

We attempted to find blood sample-based protein profiles that could differentiate patients with biliary tract cancer from those without cancer. Several profiles were found and tested in different groups of patients. The profiles were successful at identifying most patients with biliary tract cancer, pointing towards the utility of multiprotein signatures in this context.

Determination of temporal reproducibility and variability of cancer biomarkers in serum and EDTA plasma samples using a proximity extension assay

Background

Proximity extension assay (PEA) is a novel antibody-based proteomic technology. Sparse data have been published concerning the matrix effect of serum vs. ethylenediamine tetraacetic acid (EDTA) plasma and the reproducibility of results obtained using PEA technology.

Methods

We analyzed samples with the PEA-based 92-plex Olink® immuno-oncology (I-O) assay. To estimate the matrix effect, we analyzed paired serum and EDTA plasma samples from 12 patients with biliary tract cancer. To evaluate the reproducibility, we used data from 7 studies, where 6–8 serum samples from patients with pancreatic cancer were used as bridging samples on 3 versions of the panel over a 2.5-years period.

Results

For the study of serum vs. plasma, 80 proteins were evaluable. The mean serum to EDTA plasma ratio ranged from 0.41–3.01. For 36 proteins, the serum and plasma values were not comparable due to high variability of the ratio, poor correlation, or possible concentration effect. For the bridging samples, the mean intra-study inter-assay coefficient of variation (CV) ranged from 11.3% to 26.1%. The mean inter-study CV was 42.0% before normalization and 26.2% after normalization. Inter-study results were well correlated (r ≥ 0.93), especially for studies using the same version of the panel (r ≥ 0.99).

Conclusion

For 44 of 92 proteins included in the Olink® I-O panel, the variation between results obtained using serum and EDTA plasma was constant and results were well correlated. Furthermore, samples could be stored for several years and used on different versions of the same PEA panel without it effecting results.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09380-y.

Circulating Protein Biomarkers for Prognostic Use in Patients with Advanced Pancreatic Ductal Adenocarcinoma Undergoing Chemotherapy

Patients with advanced pancreatic ductal adenocarcinoma (PDAC) have a dismal prognosis. We aimed to find a prognostic protein signature for overall survival (OS) in patients with advanced PDAC, and to explore whether early changes in circulating-protein levels could predict survival. We investigated 92 proteins using the Olink Immuno-Oncology panel in serum samples from 363 patients with advanced PDAC. Protein panels for several survival cut-offs were developed independently by two bioinformaticians using LASSO and Ridge regression models. Two panels of proteins discriminated patients with OS < 90 days from those with OS > 2 years. Index I (CSF-1, IL-6, PDCD1, TNFRSF12A, TRAIL, TWEAK, and CA19-9) had AUCs of 0.99 (95% CI: 0.98−1) (discovery cohort) and 0.89 (0.74−1) (replication cohort). For Index II (CXCL13, IL-6, PDCD1, and TNFRSF12A), the corresponding AUCs were 0.97 (0.93−1) and 0.82 (0.68−0.96). Four proteins (ANGPT2, IL-6, IL-10, and TNFRSF12A) were associated with survival across all treatment groups. Longitudinal samples revealed several changes, including four proteins that were also part of the prognostic signatures (CSF-1, CXCL13, IL-6, TNFRSF12A). This study identified two circulating-protein indices with the potential to identify patients with advanced PDAC with very short OS and with long OS.

Circulating proteins and risk of pancreatic cancer: a case-subcohort study among Chinese adults

BACKGROUND

Pancreatic cancer has a very poor prognosis. Biomarkers that may help predict or diagnose pancreatic cancer may lead to earlier diagnosis and improved survival.

METHODS

The prospective China Kadoorie Biobank (CKB) recruited 512 891 adults aged 30-79 years during 2004-08, recording 702 incident cases of pancreatic cancer during 9 years of follow-up. We conducted a case-subcohort study measuring 92 proteins in 610 cases and a subcohort of 623 individuals, using the OLINK immuno-oncology panel in stored baseline plasma samples. Cox regression with the Prentice pseudo-partial likelihood was used to estimate adjusted hazard ratios (HRs) for risk of pancreatic cancer by protein levels.

RESULTS

Among 1233 individuals (including 610 cases), several chemokines, interleukins, growth factors and membrane proteins were associated with risk of pancreatic cancer, with adjusted HRs per 1 standard deviation (SD) of 0.86 to 1.86, including monocyte chemotactic protein 3 (MCP3/CCL7) {1.29 [95% CI (confidence interval) (1.10, 1.51)]}, angiopoietin-2 (ANGPT2) [1.27 (1.10, 1.48)], interleukin-18 (IL18) [1.24 (1.07, 1.43)] and interleukin-6 (IL6) [1.21 (1.06, 1.38)]. Associations between some proteins [e.g. matrix metalloproteinase-7 (MMP7), hepatocyte growth factor (HGF) and tumour necrosis factor receptor superfamily member 9 [TNFRSF9)] and risk of pancreatic cancer were time-varying, with higher levels associated with higher short-term risk. Within the first year, the discriminatory ability of a model with known risk factors (age, age squared, sex, region, smoking, alcohol, education, diabetes and family history of cancer) was increased when several proteins were incorporated (weighted C-statistic changed from 0.85 to 0.99; P for difference = 4.5 × 10-5), although only a small increase in discrimination (0.77 to 0.79, P = 0.04) was achieved for long-term risk.

CONCLUSIONS

Several plasma proteins were associated with subsequent diagnosis of pancreatic cancer. The potential clinical utility of these biomarkers warrants further investigation.

Serum Proteomics in Patients with Head and Neck Cancer: Peripheral Blood Immune Response to Treatment

In this real-world study, the aims were to prospectively evaluate the expression of inflammatory proteins in serum collected from head and neck cancer patients before and after treatment, and to assess whether there were differences in expression associated with treatment modalities. The mixed study cohort consisted of 180 patients with head and neck cancer. The most common tumor sites were the oropharynx (n = 81), the oral cavity (n = 53), and the larynx (n = 22). Blood tests for proteomics analysis were carried out before treatment, 7 weeks after the start of treatment, and 3 and 12 months after the termination of treatment. Sera were analyzed for 83 proteins using an immuno-oncology biomarker panel (Olink, Uppsala, Sweden). Patients were divided into four treatment groups: surgery alone (Surg group, n = 24), radiotherapy with or without surgery (RT group, n = 94), radiotherapy with concomitant cisplatin (CRT group, n = 47), and radiotherapy with concomitant targeted therapy (RT Cetux group, n = 15). For the overall cohort, the expression levels of 15 of the 83 proteins changed significantly between the pretreatment sample and the sample taken 7 weeks after the start of treatment. At 7 weeks after the start of treatment, 13 proteins showed lower expression in the CRT group compared to the RT group. The majority of the inflammatory proteins had returned to their pretreatment levels after 12 months. It was clearly demonstrated that cisplatin-based chemoradiation has immunological effects in patients with head and neck cancer. This analysis draws attention to several inflammatory proteins that are of interest for further studies.

Recent advances in understanding pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is an intractable cancer and a leading cause of cancer deaths worldwide. Over 90% of patients die within 1 year of diagnosis. Deaths from PDAC are increasing and it remains a cancer of substantial unmet need. A number of factors contribute to its poor prognosis: namely, late presentation, early metastases and limited systemic therapy options because of chemoresistance. A variety of research approaches underway are aimed at improving patient survival. Here, we review high-risk groups and efforts for early detection. We examine recent developments in the understanding of complex molecular and metabolic alterations which accompany PDAC. We explore artificial intelligence and biological targets for therapy and examine the role of tumour stroma and the immune microenvironment. We also review recent developments with respect to the PDAC microbiome. It is hoped that current research efforts will translate into earlier diagnosis, improvements in treatment and better outcomes for patients.

A Linear Discriminant Analysis Model Based on the Changes of 7 Proteins in Plasma Predicts Response to Anlotinib Therapy in Advanced Non-Small Cell Lung Cancer Patients

Background

Anlotinib is a multi-targeted tyrosine kinase inhibitor mainly targeting angiogenesis signaling. The predictive marker of anlotinib’s efficacy remains elusive. This study was designed to explore the predictive marker of anlotinib in non-small cell lung cancer (NSCLC).

Methods

We prospectively enrolled 52 advanced NSCLC patients who underwent at least one line of targeted therapy or chemotherapy between August 2018 and March 2020. Patients were divided into durable responders (DR) and non-durable responders (NDR) based on the median progression-free survival (PFS, 176 days). The Olink Immuno-Oncology panel (92 proteins) was used to explore the predictive protein biomarkers in plasma samples before treatment (baseline) and on the first treatment evaluation (paired).

Results

At baseline, the response to anlotinib was not significantly associated with age, gender, smoke history, histology, oligo-metastases, EGFR mutations, and other clinical characteristics. The results of PFS-related protein biomarkers at baseline were all not satisfying. Then we assessed the changes of 92 proteins levels in plasma on the first treatment evaluation. We obtained a Linear discriminant analysis (LDA) model based on 7 proteins, with an accuracy of 100% in the original data and an accuracy of 89.2% in cross validation. The 7 proteins were CD70, MIC-A/B, LAG3, CAIX, PDCD1, MMP12, and PD-L2. Multivariate Cox analysis further showed that the changes of CD70 (HR 25.48; 95% CI, 4.90–132.41, P=0.000) and MIC-A/B (HR 15.04; 95% CI, 3.81–59.36, P=0.000) in plasma were the most significant prognostic factors for PFS.

Conclusion

We reported herein a LDA model based on the changes of 7 proteins levels in plasma before and after treatment, which could predict anlotinib responders among advanced NSCLC patients with an accuracy of 100%. Further studies are warranted to verify the prediction performance of the LDA model.

Proteomics in support of immunotherapy: contribution to model-based precision medicine

INTRODUCTION

Proteomics encompasses a wide and expanding range of methods to identify, characterize, and quantify thousands of proteins from a variety of biological samples, including blood samples, tumors, and tissues. Such methods are supportive of various forms of immunotherapy applied to chronic conditions such as allergies, autoimmune diseases, cancers, and infectious diseases.

AREAS COVERED

In support of immunotherapy, proteomics based on mass spectrometry has multiple specific applications related to (i) disease modeling and patient stratification, (ii) antigen/ autoantigen/neoantigen/ allergen identification, (iii) characterization of proteins and monoclonal antibodies used for immunotherapeutic or diagnostic purposes, (iv) identification of biomarkers and companion diagnostics and (v) monitoring by immunoproteomics of immune responses elicited in the course of the disease or following immunotherapy.

EXPERT OPINION

Proteomics contributes as an enabling technology to an evolution of immunotherapy toward a precision medicine approach aiming to better tailor treatments to patients' specificities in multiple disease areas. This trend is favored by a better understanding through multi-omics profiling of both the patient's characteristics, his/her immune status as well as of the features of the immunotherapeutic drug.

An Original Ferroptosis-Related Gene Signature Effectively Predicts the Prognosis and Clinical Status for Colorectal Cancer Patients

Background

Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Ferroptosis is a newly defined form of cell death, distinguished by different morphology, biochemistry, and genetics, and involved in CRC progression and treatment. This study aims to establish a predictive model to elucidate the relationship between ferroptosis and prognosis of CRC patients, to explore the potential value of ferroptosis in therapeutic options.

Methods

The ferroptosis-related genes were obtained from the GeneCards and FerrDb websites. The limma R package was used to screen the differential ferroptosis-related genes (DEGs) in CRC from The Cancer Genome Atlas (TCGA) dataset. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regressions were to establish the 10-gene prognostic signature. The survival and receiver operating characteristic (ROC) curves were illustrated to evaluate the predictive effect of the signature. Besides, independent prognostic factors, downstream functional enrichment, drug sensitivity, somatic mutation status, and immune feature were analyzed. Moreover, all these conclusions were verified by using multiple datasets in International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO).

Results

Ten ferroptosis-related gene signature (TFAP2C, SLC39A8, NOS2, HAMP, GDF15, FDFT1, CDKN2A, ALOX12, AKR1C1, ATP6V1G2) was established to predict the prognosis of CRC patients by Lasso cox analysis, demonstrating a good performance on Receiver operating characteristic (ROC) and Kaplan–Meier (K–M) analyses. The CRC patients in the high- or low-risk group showed significantly different fractions of immune cells, such as macrophage cells and CD8+ T cells. Drug sensitivity and somatic mutation status like TP53 were also closely associated with the risk scores.

Conclusions

In this study, we identified a novel ferroptosis-related 10-gene signature, which could effectively predict the prognosis and survival time of CRC patients, and provide meaningful clinical implications for targeted therapy or immunotherapy. Targeting ferroptosis is a good therapeutic option for CRC patients. Further studies are needed to reveal the underlying mechanisms of ferroptosis in CRC.