Development of an algorithm combining blood-based biomarkers, fecal immunochemical test, and age for population-based colorectal cancer screening.

BACKGROUND AND AIMS: Implementation of screening modalities have reduced the burden of colorectal cancer (CRC), but high false positive rates pose a major problem for colonoscopy capacity. We aimed to create a tailored screening algorithm that expands the fecal immunochemical test (FIT) with a blood specimen and current age to improve selection of individuals for diagnostic colonoscopy. METHODS: In this prospective multicenter study, 8 blood-based biomarkers (carcinoembryonic antigen, ferritin, high-sensitivity C-reactive protein, human epididymis protein 4, Cyfra21-1, hepsin, interleukin 8, and osteoprotegerin) were investigated in 1977 FIT-positive individuals from the Danish national CRC screening program undergoing follow-up colonoscopy. Specimens were analyzed on Architect i2000, Architect c8000 (both from Abbott, Chicago, IL, USA), or Luminex xMAP machines (MilliporeSigma, St. Louis, Mo, USA). FIT analyses and blood-based biomarker data were combined with clinical data (ie, age and colonoscopy findings) in a cross-validated logistic regression model (algorithm) benchmarked against a model solely using the FIT result (FIT model) applying different cutoffs for FIT positivity. RESULTS: The cohort included individuals with CRC (n = 240), adenomas (n = 938), or no neoplastic lesions (n = 799). The cross-validated algorithm combining the 8 biomarkers, quantitative FIT result, and age performed superior to the FIT model in discriminating CRC versus non-CRC individuals (area under the receiver operating characteristic curve, 0.77 vs 0.67, respectively; P < .001). When discriminating individuals with either CRC or high- or medium-risk adenomas versus low-risk adenomas or clean colorectum, the areas under the receiver operating characteristic curve were 0.68 versus 0.64 for the algorithm and FIT model, respectively. CONCLUSIONS: The algorithm presented here can improve patient allocation to colonoscopy, reducing colonoscopy burden without compromising cancer and adenoma detection rates or vice versa.

Exploring D-Lactate as a Biomarker for Acute Intestinal Necrosis in 2958 Patients: A Prospective Cross-Sectional Study.

BACKGROUND: Timely diagnosis of acute intestinal necrosis (AIN) is lifesaving, but challenging due to unclear clinical presentation. D-lactate has been proposed as an AIN biomarker. OBJECTIVES: We aimed to test the diagnostic performance in a clinical setting. METHODS: We performed a cross-sectional prospective study, including all adult patients with acute referral to a single tertiary gastrointestinal surgical department during 2015-2016 and supplemented by enrollment of high-risk in-hospital patients suspected of having AIN during 2016-2019. AIN was verified intraoperatively, and D-lactate was analyzed using an automatic spectrophotometric set-up. A D-lactate cut-off for AIN was estimated using the receiver operating characteristic curve. The performance according to patient subgroups was estimated using the area under the receiver operating characteristic curve (AUC). Given the exploratory nature of this study, a formal power calculation was not feasible. RESULTS: Forty-four AIN patients and 2914 controls were enrolled. The D-lactate cut-off was found to be 0.0925 mM. Due to lipemic interference, D-lactate could not be quantified in half of the patients, leaving 23 AIN patients and 1456 controls for analysis. The AUC for the diagnosis of AIN by D-lactate was 0.588 (95% confidence interval 0.475-0.712), with a sensitivity of 0.261 and specificity of 0.892. Analysis of high-risk patients showed similar results (AUC 0.579; 95% confidence interval 0.422-0.736). CONCLUSION: D-lactate showed low sensitivity for AIN in both average-risk and high-risk patients. Moreover, lipemic interference precluded valid spectrophotometric assessment of D-lactate in half of the patients, further disqualifying the clinical utility of D-lactate as a diagnostic marker for AIN.

Exploring I-FABP, endothelin-1 and L-lactate as biomarkers of acute intestinal necrosis: a case-control study.

OBJECTIVE: Acute intestinal necrosis (AIN) is a disease with devastating high mortality. AIN due to obstructed arterial blood flow has a blurred clinical presentation. Timely diagnosis is paramount, and a blood-based biomarker is warranted to increase patient survival. We aimed to assess intestinal fatty acid binding protein (I-FABP) and endothelin-1 as diagnostic biomarkers for AIN. To our knowledge, this is the first study exploring endothelin-1 in AIN patients from a general surgical population. DESIGN: We conducted a single-centre nested case-control study comparing acutely admitted AIN patients to age- and sex-matched non-AIN patients during 2015-2016. I-FABP and endothelin-1 were analysed using an enzyme-linked immunosorbent assay. L-lactate levels were also measured in all patients. Cut-offs were estimated using receiver operator characteristic curves, and the diagnostic performance was estimated using the area under the receiver operator characteristic curve (AUC). RESULTS: We identified 43 AIN patients and included 225 matched control patients. Median levels of I-FABP, endothelin-1 and L-lactate were 3550 (IQR: 1746-9235) pg/ml, 3.91 (IQR: 3.33-5.19) pg/ml and 0.92 (IQR: 0.74-1.45) mM in AIN patients and 1731 (IQR: 1124-2848) pg/ml, 2.94 (IQR: 2.32-3.82) pg/ml and 0.85 (IQR: 0.64-1.21) mM in control patients, respectively. The diagnostic performances of endothelin-1 and of I-FABP + endothelin-1 combined were moderate. Endothelin-1 alone revealed an AUC of 0.74 (0.67; 0.82). The sensitivity and specificity of endothelin-1 were 0.81 and 0.64, respectively. CONCLUSION: I-FABP and endothelin-1 are promising biomarkers for AIN, with moderate diagnostic performance compared with the commonly used biomarker L-lactate. PREREGISTRATION: ClinicalTrials.gov: NCT05665946.

Detection of circulating tumor DNA by tumor-informed whole-genome sequencing enables prediction of recurrence in stage III colorectal cancer patients.

INTRODUCTION: Circulating tumor (ctDNA) can be used to detect residual disease after cancer treatment. Detecting low-level ctDNA is challenging, due to the limited number of recoverable ctDNA fragments at any target loci. In response, we applied tumor-informed whole-genome sequencing (WGS), leveraging thousands of mutations for ctDNA detection. METHODS: Performance was evaluated in serial plasma samples (n = 1283) from 144 stage III colorectal cancer patients. Tumor/normal WGS was used to establish a patient-specific mutational fingerprint, which was searched for in 20x WGS plasma profiles. For reproducibility, paired aliquots of 172 plasma samples were analyzed in two independent laboratories. De novo variant calling was performed for serial plasma samples with a ctDNA level > 10 % (n = 17) to explore genomic evolution. RESULTS: WGS-based ctDNA detection was prognostic of recurrence: post-operation (Hazard ratio [HR] 6.75, 95 %CI 3.18-14.3, p < 0.001), post-adjuvant chemotherapy (HR 28.9, 95 %CI 10.1-82.8; p < 0.001), and during surveillance (HR 22.8, 95 %CI 13.7-37.9, p < 0.0001). The 3-year cumulative incidence of ctDNA detection in recurrence patients was 95 %. ctDNA was detected a median of 8.7 months before radiological recurrence. The independently analyzed plasma aliquots showed excellent agreement (Cohens Kappa=0.9, r = 0.99). Genomic characterization of serial plasma revealed significant evolution in mutations and copy number alterations, and the timing of mutational processes, such as 5-fluorouracil-induced mutations. CONCLUSION: Our study supports the use of WGS for sensitive ctDNA detection and demonstrates that post-treatment ctDNA detection is highly prognostic of recurrence. Furthermore, plasma WGS can identify genomic differences distinguishing the primary tumor and relapsing metastasis, and monitor treatment-induced genomic changes.

DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection.

Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.