Circulating KRAS G12D but not G12V is associated with survival in metastatic pancreatic ductal adenocarcinoma.

While high circulating tumor DNA (ctDNA) levels are associated with poor survival for multiple cancers, variant-specific differences in the association of ctDNA levels and survival have not been examined. Here we investigate KRAS ctDNA (ctKRAS) variant-specific associations with overall and progression-free survival (OS/PFS) in first-line metastatic pancreatic ductal adenocarcinoma (mPDAC) for patients receiving chemoimmunotherapy ("PRINCE", NCT03214250), and an independent cohort receiving standard of care (SOC) chemotherapy. For PRINCE, higher baseline plasma levels are associated with worse OS for ctKRAS G12D (log-rank p = 0.0010) but not G12V (p = 0.7101), even with adjustment for clinical covariates. Early, on-therapy clearance of G12D (p = 0.0002), but not G12V (p = 0.4058), strongly associates with OS for PRINCE. Similar results are obtained for the SOC cohort, and for PFS in both cohorts. These results suggest ctKRAS G12D but not G12V as a promising prognostic biomarker for mPDAC and that G12D clearance could also serve as an early biomarker of response.

Colorectal Cancer Screening in Free Clinics: A Systematic Review.

OBJECTIVE: There are significant inequities in colorectal cancer (CRC) screening and outcomes. Via literature review, we assessed CRC screening rates for the vulnerable populations served by free clinics. METHODS: A systematic review was conducted for publications on CRC screening in free clinics. Outcomes included CRC screening characteristics, population demographics, and limitations. A methodological quality assessment was completed. RESULTS: Out of 63 references, six studies were included, representing 8,844 participants. Black or Hispanic participants were the plurality in all but one study. All participants were uninsured. Median CRC screening rate was 48.4% (range 6.6-78.9%). Screening methods included colonoscopy, fecal occult blood test, flexible sigmoidoscopy, and fecal immunochemical test. Clinics offering only one screening method had a mean screening rate of 7.2% while those with multiple methods had a screening rate of 65.4%. CONCLUSION: Access to multiple CRC screening modalities correlates with higher screening rates in free clinics. More work is needed to increase CRC screening in free clinics.

Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program.

PURPOSE: We developed machine learning (ML) models to assess demographic and socioeconomic status (SES) variables' value in predicting continued participation in a low-dose CT lung cancer screening (LCS) program. MATERIALS AND METHODS: 480 LCS subjects were retrospectively examined for the following outcomes: (#1) no follow-up (single LCS scan) vs. multiple follow-ups (220 and 260 subjects respectively) and (#2) absent or delayed (>1 month past the due date) follow-up vs timely follow-up (356 and 124 subjects respectively). We quantified the contributions of 14 socioeconomic, demographic, and clinical predictors to LCS adherence, and validated and compared prediction performances of multivariate logistic regression (MLR), support vector machine (SVM) and shallow neural network (NN) models. RESULTS: For outcome #1, age, sex, race, insurance status, personal cancer history, and median household income were found to be associated with returning for follow-ups. For outcome #2, age, sex, race, and insurance status were significant predictor of absent/delayed LCS follow-up. Across 5-fold cross-validation, the MLR model achieved an average AUC of 0.732 (95% CI, 0.661-0.803) for outcome #1 and 0.633 (95% CI, 0.602-0.664) for outcome #2 and is the model with best predictive performance overall, whereas NN and SVM tended to overfit training data and fell short on testing data performance for either outcome. CONCLUSIONS: We identified significant predictors of LCS adherence, and our ML models can predict which subjects are at higher risk of receiving no or delayed LCS follow-ups. Our results could inform data-driven interventions to engage vulnerable populations and extend the benefits of LCS.

Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models.

OBJECTIVE: To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features. METHODS: We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors' contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance. RESULTS: On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840. CONCLUSION: Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.

Quantum dot-based fluorescence immunosorbent assay for the rapid detection of bacitracin zinc in feed samples.

Bacitracin zinc (BAC), a polypeptide antibiotic, is utilized as a feed additive due to its ability to promote growth in animals. However, the abuse of BAC can lead to a great threat to food safety. Therefore, there is an urgent need to develop a rapid and sensitive detection method. In this study, a monoclonal antibody (mAb) against BAC with excellent sensitivity and specificity was obtained. For the first time, quantum dots (QDs) were conjugated with the prepared mAb against BAC and rabbit anti-mouse antibody to fabricate a direct and an indirect competitive fluorescence-linked immunosorbent assay (dc-FLISA and ic-FLISA) to detect BAC. The IC50 of dc-FLISA and ic-FLISA were 0.28 ng/ml and 0.17 ng/ml, respectively. The limits of detection were 0.0016 ng/ml and 0.001 ng/ml, respectively, and the detection ranges were 0.0016-46.50 ng/ml and 0.001-35.65 ng/ml, respectively. In addition, the recovery rate of the two methods ranged from 93.5% to 112.0%, and the coefficient of variation (CV) was less than 10%. Therefore, the methods developed in this work have the merits of low cost, simple operation, and high sensitivity, which provide an effective analytical tool for BAC residue detection in feed samples.

Optimization of Sources of Circulating Cell-Free DNA Variability for Downstream Molecular Analysis.

Circulating cell-free DNA (ccfDNA) is used increasingly as a cancer biomarker for prognostication, as a correlate for tumor volume, or as input for downstream molecular analysis. Determining optimal blood processing and ccfDNA quantification are crucial for ccfDNA to serve as an accurate biomarker as it moves into the clinical realm. Whole blood was collected from 50 subjects, processed to plasma, and used immediately or frozen at -80°C. Plasma ccfDNA was extracted and concentration was assessed by real-time quantitative PCR (qPCR), fluorimetry, and droplet digital PCR (ddPCR). For the 24 plasma samples from metastatic pancreatic cancer patients, the variant allele fractions (VAF) of KRAS G12/13 pathogenic variants in circulating tumor DNA (ctDNA) were measured by ddPCR. Using a high-speed (16,000 × g) or slower-speed (4100 × g) second centrifugation step showed no difference in ccfDNA yield or ctDNA VAF. A two- versus three-spin centrifugation protocol also showed no difference in ccfDNA yield or ctDNA VAF. A higher yield was observed from fresh versus frozen plasma by qPCR and fluorimetry, whereas a higher yield was observed for frozen versus fresh plasma by ddPCR, however, no difference was observed in ctDNA VAF. Overall, our findings suggest factors to consider when implementing a ccfDNA extraction and quantification workflow in a research or clinical setting.