Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer.

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

Circulating cell-free DNA, peripheral lymphocyte subsets alterations and neutrophil lymphocyte ratio in assessment of COVID-19 severity.

Cell destruction results in plasma accumulation of cell-free DNA (cfDNA). Dynamic changes in circulating lymphocytes are features of COVID-19. We aimed to investigate if cfDNA level can serve in stratification of COVID-19 patients, and if cfDNA level is associated with alterations in lymphocyte subsets and neutrophil-to-lymphocyte ratio (NLR). This cross-sectional comparative study enrolled 64 SARS-CoV-2-positive patients. Patients were subdivided to severe and non-severe groups. Plasma cfDNA concentration was determined by real-time quantitative PCR. Lymphocyte subsets were assessed by flow cytometry. There was significant increase in cfDNA among severe cases when compared with non-severe cases. cfDNA showed positive correlation with NLR and inverse correlation with T cell percentage. cfDNA positively correlated with ferritin and C-reactive protein. The output data of performed ROC curves to differentiate severe from non-severe cases revealed that cfDNA at cut-off ≥17.31 ng/µl and AUC of 0.96 yielded (93%) sensitivity and (73%) specificity. In summary, excessive release of cfDNA can serve as sensitive COVID-19 severity predictor. There is an association between cfDNA up-regulation and NLR up-regulation and T cell percentage down-regulation. cfDNA level can be used in stratification and personalized monitoring strategies in COVID-19 patients.

Disease-Specific IgG Fc Glycosylation Ratios as Personalized Biomarkers to Differentiate Non-Small Cell Lung Cancer from Benign Lung Diseases.

PURPOSE: The authors aimed to separate Fc N-glycopeptides of disease-specific immunoglobulin G (DSIgG) as personalized biomarkers to distinguish non-small cell lung cancer (NSCLC) from benign lung diseases (BLDs). EXPERIMENTAL DESIGN: DSIgG from 509 BLDs patients and 477 NSCLC patients was isolated using native polyacrylamide gel electrophoresis and then the Fc glycosylation was determined using mass spectrometry. RESULTS: For the patients below 60 years of age, a combination of the glycopeptides ratios with one fucose residue difference of DSIgG1 and DSIgG2 can differentiate NSCLC from BLDs, with area under curve (AUC) values of >0.76, sensitivities of >87%, and specificities of >61%. For the patients above 60 years of age, a combination of the glycopeptides ratios with one monosaccharide residue of DSIgG2 can differentiate NSCLC from BLDs, with AUC values of >0.78, sensitivities of >91%, and specificities of >54%. For the same participants, the commonly used clinical biomarkers have AUC values of 0.5-0.621, sensitivities of 15.8-32.9%, and specificities of 75.7-90.5%. CONCLUSIONS: These findings indicate that these DSIgG Fc glycoforms are potential personalized biomarkers to differentiate NSCLC from BLDs.

The use of personalized biomarkers and liquid biopsies to monitor treatment response and disease recurrence in locally advanced rectal cancer after neoadjuvant chemoradiation.

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery is the mainstay treatment for locally advanced rectal cancer. Variable degrees of tumor regression are observed after nCRT and alternative treatment strategies, including close surveillance without immediate surgery, have been investigated to spare patients with complete tumor regression from potentially adverse outcomes of radical surgery. However, clinical and radiological assessment of response does not allow accurate identification of patients with complete response. In addition, surveillance for recurrence is similarly important for these patients, as early detection of recurrence allows salvage resections and adjuvant interventions. We report the use of liquid biopsies and personalized biomarkers for monitoring treatment response to nCRT and detecting residual disease and recurrence in patients with rectal cancer. We sequenced the whole-genome of four rectal tumors to identify patient-specific chromosomal rearrangements that were used to monitor circulating tumor DNA (ctDNA) in liquid biopsies collected at diagnosis and during nCRT and follow-up. We compared ctDNA levels to clinical, radiological and pathological response to nCRT. Our results indicate that personalized biomarkers and liquid biopsies may not be sensitive for the detection of microscopic residual disease. However, it can be efficiently used to monitor treatment response to nCRT and detect disease recurrence, preceding increases in CEA levels and radiological diagnosis. Similar good results were observed when assessing tumor response to systemic therapy and disease progression. Our study supports the use of personalized biomarkers and liquid biopsies to tailor the management of rectal cancer patients, however, replication in a larger cohort is necessary to introduce this strategy into clinical practice.