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At least 433 records · Page 24

Network-medicine framework for studying disease trajectories in U.S. veterans

A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.

60 APPLIED LIFE SCIENCES↗

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

60 APPLIED LIFE SCIENCES↗

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗

Proof-of-concept studies with a computationally designed M pro inhibitor as a synergistic combination regimen alternative to Paxlovid

As the SARS-CoV-2 virus continues to spread and mutate, it remains important to focus not only on preventing spread through vaccination but also on treating infection with direct-acting antivirals (DAA). The approval of Paxlovid, a SARS-CoV-2 main protease (M pro ) DAA, has been significant for treatment of patients. A limitation of this DAA, however, is that the antiviral component, nirmatrelvir, is rapidly metabolized and requires inclusion of a CYP450 3A4 metabolic inhibitor, ritonavir, to boost levels of the active drug. Serious drug–drug interactions can occur with Paxlovid for patients who are also taking other medications metabolized by CYP4503A4, particularly transplant or otherwise immunocompromised patients who are most at risk for SARS-CoV-2 infection and the development of severe symptoms. Developing an alternative antiviral with improved pharmacological properties is critical for treatment of these patients. By using a computational and structure-guided approach, we were able to optimize a 100 to 250 μM screening hit to a potent nanomolar inhibitor and lead compound, Mpro61. In this study, we further evaluate Mpro61 as a lead compound, starting with examination of its mode of binding to SARS-CoV-2 M pro . In vitro pharmacological profiling established a lack of off-target effects, particularly CYP450 3A4 inhibition, as well as potential for synergy with the currently approved alternate antiviral, molnupiravir. Development and subsequent testing of a capsule formulation for oral dosing of Mpro61 in B6-K18-hACE2 mice demonstrated favorable pharmacological properties, efficacy, and synergy with molnupiravir, and complete recovery from subsequent challenge by SARS-CoV-2, establishing Mpro61 as a promising potential preclinical candidate.

60 APPLIED LIFE SCIENCES↗

Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma

Abstract Background Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients. Objectives To determine if artificial intelligence and machine learning might help to stratify patients with cSCC by risk using more than solely clinical and histopathological factors. Methods We retrieved a retrospective cohort of 104 patients whose cSCCs had been excised with clear margins. Clinical and histopathological risk factors were evaluated. Haematoxylin and eosin-stained slides were scanned and analysed by an algorithm based on the stacked predictive sparse decomposition technique. Cellular morphometric biomarkers (CMBs) were identified via machine learning and used to derive a cellular morphometric risk score (CMRS) that classified cSCCs into clusters of differential prognoses. Concordance analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated and compared with results obtained with the Brigham and Women’s Hospital (BWH) staging system. The performance of the combination of the BWH staging system and the CMBs was also analysed. Results There were no differences among the CMRS groups in terms of clinical and histopathological risk factors and T-stage assignment, but there were significant differences in prognosis. Combining the CMRS with BWH staging systems increased distinctiveness and improved prognostic performance. C-indices were 0.91 local recurrence and 0.91 for nodal metastasis when combining the two approaches. The NPV was 94.41% and 96.00%, the PPV was 36.36% and 41.67%, and accuracy reached 86.75% and 89.16%, respectively, with the combined approach. Conclusions CMRS is helpful for cSCC risk stratification beyond classic clinical and histopathological risk features. Combining the information from the CMRS and the BWH staging system offers outstanding prognostic performance for patients with high-risk cSCC.

Pérez-Baena, Manuel J.↗

Symptoms and Systemic Drug Reactions in Persons Receiving Weekly Rifapentine Plus Isoniazid (3HP) Treatment for Latent Tuberculosis Infection

Abstract Background Three months of weekly rifapentine plus isoniazid (3HP) therapy for latent tuberculosis infection (LTBI) is recommended worldwide. The development of symptoms and systemic drug reactions (SDRs) on 3HP have not been fully characterized. We aimed to determine the patterns of symptom development and identify SDRs and associated factors in patients taking 3HP. Methods We analyzed symptoms data in participants receiving 3HP in the Tuberculosis Trials Consortium's iAdhere study (Study 33). We examined the patterns of symptom reporting across participants from baseline and 4 monthly visits. Bivariate analyses and multivariable regression models were used to identify factors associated with SDRs. Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated. Results Among 1002 participants receiving 3HP, 768 (77%) reported at least 1 symptom; 97% of these symptoms were grade 1 (79%) or grade 2 (18%). Most symptoms developed in the first month and resolved. A total of 111 (11%) participants had symptoms that met criteria for SDRs; however, 53 (48%) of these participants completed therapy. Factors associated with SDRs and discontinuation included female sex (RR: 2.05; 95% CI: 1.19–3.54), age ≥45 years (RR: 1.99; 95% CI: 1.19–3.31), and use of concomitant medications (RR: 2.26; 95% CI: 1.15–4.42). Conclusions Although most patients receiving 3HP reported symptoms, most were mild, occurred early, and resolved without stopping treatment. Among patients experiencing SDRs, nearly half were able to complete therapy. Patient and provider education should focus on differentiating severe reactions where 3HP should be stopped from minor symptoms that will resolve. Clinical Trials Registration. NCT01582711.

Immunology↗

Moving Beyond Mortality: Development and Application of a Desirability of Outcome Ranking (DOOR) Endpoint for Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia

Abstract Background Hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) are frequently caused by multidrug-resistant organisms. Patient-centered endpoints in clinical trials are needed to develop new antibiotics for HABP/VABP. Desirability of outcome ranking (DOOR) is a paradigm for the design, analysis, and interpretation of clinical trials based on a patient-centered, benefit-risk evaluation. Methods A multidisciplinary committee created an infectious diseases DOOR endpoint customized for HABP/VABP, incorporating infectious complications, serious adverse events, and mortality. We applied this to 2 previously completed, large randomized controlled trials for HABP/VABP. ZEPHyR compared vancomycin to linezolid and VITAL compared linezolid to tedizolid. For each trial, we evaluated the DOOR distribution and probability, including DOOR component and partial credit analyses. We also applied DOOR in subgroup analyses. Results In both trials, the HABP/VABP DOOR demonstrated similar overall clinical outcomes between treatment groups. In ZEPHyR, the probability that a participant treated with linezolid would have a more desirable outcome than a participant treated with vancomycin was 50.2% (95% confidence interval [CI], 45.1%­−55.3%). In VITAL, the probability that a participant treated with tedizolid would have a more desirable outcome than a participant treated with linezolid was 48.7% (95% CI, 44.8%–52.6%). The DOOR component analysis revealed that participants treated with tedizolid had a less desirable outcome than those treated with linezolid when considering clinical response alone. However, participants with decreased renal function had improved overall outcomes with tedizolid. Conclusions The HABP/VABP DOOR provided more granular information about clinical outcomes than is typically presented in clinical trials. HABP/VABP trials would benefit from prospectively using DOOR.

Immunology↗

Prevalence of erythromycin-resistant emm92 -type invasive group A streptococcal infections among injection drug users in West Virginia, United States, 2021–23

Abstract Background Increasing incidence of invasive group A Streptococcus (iGAS) disease has been reported in Europe and the USA over the past several years. Coupled with this are observations of higher rates of resistance to erythromycin and clindamycin. Objectives To characterize iGAS and pharyngitis isolates from West Virginia (WV), a US state outside of the national Active Bacteria Core surveillance purview, where risk factors associated with iGAS infections are prevalent. Methods Seventy-seven invasive group A Streptococcus isolates were collected from 67 unique patients at the J.W. Ruby Memorial Hospital Clinical Microbiology Laboratory in WV from 2021 to 2023. Invasive isolates and 20 unique pharyngitis isolates were tested for clindamycin and erythromycin susceptibility in the clinical laboratory. Patient demographic and clinical information was retrieved from patient electronic health records. Isolates were further characterized based on emm subtype and detection of MLSB resistance determinants. Results Twenty-six (39%) isolates were of a single emm92 type. All emm92 isolates were uniformly erythromycin/clindamycin resistant with inducible or constitutive MLSB resistance imparted by the plasmid-borne erm(T) gene. The majority of emm92 infections were associated with adult patients who reported IV drug use, whereas no pharyngitis infections were caused by an emm92 strain. Overall, 51 (76%) of the 67 iGAS isolates were determined to carry MLSB resistance. Conclusions Isolates of emm92 type (clonal subtype emm92.0) were associated with iGAS infections in adult IV drug users, but not with paediatric pharyngitis, and were uniformly resistant to erythromycin and clindamycin.

Powell, Lillie M.↗

Matrix Metalloproteinases as Candidate Antigenic Determinants for Anti‐Tumor Autoantibodies in Human Ovarian Cancer: A Post Hoc Analysis

Circulating antibodies in patients with cancer can facilitate the identification of accessible epitopes on autoantigens expressed by tumors. To identify previously unrecognized protein targets in ovarian cancer, we computationally assessed a heptapeptide consensus motif (VPELGHE, flanked by two cysteine residues yielding a cyclic nonapeptide under oxidizing conditions) previously discovered via phage display-based epitope mapping of autoantibodies in patients. Eight proteins associated with ovarian cancer encompass amino acid sequences similar to the consensus motif and were, therefore, considered as candidate native autoantigens. Among these candidate targets, however, matrix metalloproteinase 14 (MMP14) demonstrates gene expression that is both high and negatively correlated with survival in ovarian cancer patient cohorts. MMP14 protein levels are also stable in tumor versus non-tumor tissues. Moreover, the corresponding heptapeptide mimic in MMP14 occurs within an α-helical secondary structural element observed in its catalytic domain. These findings demonstrate that a subset of patient-derived autoantibodies may interact with a previously unknown antigenic epitope found in MMP14 and other MMPs, thereby providing opportunities for the development of new targeted agents.

Biochemistry & Molecular Biology↗

Analysis of implant loss risk factors after simultaneous guided bone regeneration: A retrospective study of 5404 dental implants

Abstract Purpose The purpose was to analyze the risk factors for implant loss after simultaneous guided bone regeneration (GBR). Materials and Methods Patients who underwent implant placement with simultaneous GBR between January 2011 and December 2018 were screened for this study. The cumulative survival rate (CSR) was calculated using the life table method. Log‐rank test and Kaplan–Meier survival estimates were used to identify potential risk factors for implant loss. The association between the investigated variables and implant loss was determined using hazard ratios (HRs) obtained from a multivariate Cox regression analysis. Results A total of 3973 patients with 5404 implants were included in this study. The CSRs of the implants at 1, 5, and 10 years were 99.6%, 98.9%, and 98.7%, respectively. Male patient (HR = 2.94, 95% CI: 1.41–6.14), periodontitis (HR = 4.26, 95% CI: 2.05–9.86), tissue‐level implants (HR = 3.02, 95% CI: 1.30–6.98), narrow implants (HR = 2.71, 95% CI: 1.12–6.57), and implant length ≤10 mm (HR = 2.91, 95% CI: 1.41–6.02) significantly increased the risk of implant loss ( p < 0.05). The risk of implant loss was significantly higher in the maxillary posterior region (HR = 2.26, 95% CI: 1.04–4.90) than in the maxillary anterior region ( p < 0.05). Compared to Straumann, Nobel (HR = 4.07, 95% CI: 1.75–9.44) and other implant systems (HR = 14.23, 95% CI: 4.32–46.85) showed a significantly higher risk of implant loss ( p < 0.05). Conclusion Male patient, periodontitis, maxillary posterior region, Nobel implant system, other implant systems, tissue‐level implants, narrow implants, and implant length ≤10 mm were considered risk factors for implant loss after simultaneous GBR.

Shen, Xiaoting↗

Implementation of rapid diagnostics assays for detection of histoplasmosis and cryptococcosis in central american people living with HIV

Abstract Objectives Histoplasmosis and cryptococcosis are important public health problems in people living with HIV (PLHIV) in Central America. Conventional laboratory assays, based on microscopy and culture, are not optimal for the diagnosis of either disease. However, antigen (Ag) assays are rapid and highly accurate for the diagnosis of these infections. Methods Laboratory surveillance of PLHIV was carried out in four hospitals in Panama, Honduras and Nicaragua, between 2015 and 2019. Detection of Histoplasma antigens in urine was performed by enzyme immunoassay (EIA), and Cryptococcus antigen detection in sera and cerebrospinal fluid specimens was performed by lateral flow assay (LFA). Results A total of 4,453 PLHIV with clinical suspicion of histoplasmosis ( n = 1,343) or cryptococcosis ( n = 3,110; 2,721 sera and 389 CSF) were tested. Of 1,343 patients suspected of having histoplasmosis, 269 (20%) were Histoplasma Ag positive. Of 3,110 patients tested using the Cryptococcus Ag assay, 329 (11%) were positive. Honduras reported the highest positivity rates (32% for Histoplasma Ag, and 16% for Cryptococcus Ag); Panama reported the largest number of patients testing positive using the Histoplasma Ag assay ( n = 201); and Nicaragua reported the largest number of patients testing positive using the Cryptococcus Ag assay ( n = 170). Conclusion Here, we show how the implementation of rapid diagnostics assays impacted case detection and was useful for the care of people with advanced HIV. Rapid and accurate diagnosis could reduce mortality associated with histoplasmosis and cryptococcosis in PLHIV.

Caceres, Diego H.↗

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity

ABSTRACT Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited. Here, we investigate capecitabine (CAP)-microbiome interactions through longitudinal metagenomic sequencing of stool from 56 advanced colorectal cancer patients. CAP significantly altered the gut microbiome, enriching for menaquinol (vitamin K2) biosynthesis genes. Transposon library screens, targeted gene deletions, and media supplementation revealed that menaquinol biosynthesis protectsEscherichia colifrom drug toxicity. Stool menaquinol gene and metabolite levels were associated with decreased peripheral sensory neuropathy. Machine learning models trained in this cohort predicted toxicities in an independent cohort. Taken together, these results suggest treatment-associated increases in microbial vitamin biosynthesis serve a chemoprotective role for bacterial and host cells. Further, our findings provide a foundation for in-depth mechanistic dissection, human intervention studies, and extension to other cancer treatments. IMPORTANCE Side effects are common during the treatment of cancer. The trillions of microbes found within the human gut are sensitive to anticancer drugs, but the effects of treatment-induced shifts in gut microbes for side effects remain poorly understood. We profiled gut microbes in colorectal cancer patients treated with capecitabine and carefully monitored side effects. We observed a marked expansion in genes for producing vitamin K2 (menaquinone). Vitamin K2 rescued gut bacterial growth and was associated with decreased side effects in patients. We then used information about gut microbes to develop a predictive model of drug toxicity that was validated in an independent cohort. These results suggest that treatment-associated increases in bacterial vitamin production protect both bacteria and host cells from drug toxicity, providing new opportunities for intervention and motivating the need to better understand how dietary intake and bacterial production of micronutrients like vitamin K2 influence cancer treatment outcomes.

Microbiology↗

Effect of vaccination on the case fatality rate for COVID-19 infections 2020–2021: multivariate modelling of data from the US Department of Veterans Affairs

Objectives: To evaluate the benefits of vaccination on the case fatality rate (CFR) for COVID-19 infections. Design, setting and participants: The US Department of Veterans Affairs has 130 medical centres. We created multivariate models from these data—339 772 patients with COVID-19—as of 30 September 2021. Outcome measures: The primary outcome for all models was death within 60 days of the diagnosis. Logistic regression was used to derive adjusted ORs for vaccination and infection with Delta versus earlier variants. Models were adjusted for confounding factors, including demographics, comorbidity indices and novel parameters representing prior diagnoses, vital signs/baseline laboratory tests and outpatient treatments. Patients with a Delta infection were divided into eight cohorts based on the time from vaccination to diagnosis. A common model was used to estimate the odds of death associated with vaccination for each cohort relative to that of unvaccinated patients. Results: 9.1% of subjects were vaccinated. 21.5% had the Delta variant. 18 120 patients (5.33%) died within 60 days of their diagnoses. The adjusted OR for a Delta infection was 1.87±0.05, which corresponds to a relative risk (RR) of 1.78. The overall adjusted OR for prior vaccination was 0.280±0.011 corresponding to an RR of 0.291. Raw CFR rose steadily after 10–14 weeks. The OR for vaccination remained stable for 10–34 weeks. Conclusions: Our CFR model controls for the severity of confounding factors and priority of vaccination, rather than solely using the presence of comorbidities. Our results confirm that Delta was more lethal than earlier variants and that vaccination is an effective means of preventing death. After adjusting for major selection biases, we found no evidence that the benefits of vaccination on CFR declined over 34 weeks. We suggest that this model can be used to evaluate vaccines designed for emerging variants.

59 BASIC BIOLOGICAL SCIENCES↗

Altered Regional Homogeneity and Functional Connectivity during Microlesion Period after Deep Brain Stimulation in Parkinson’s Disease

Background. Patients with Parkinson’s disease (PD) undergoing deep brain electrode implantation experience a temporary improvement in motor symptoms before the electrical stimulation begins. We usually call this the microlesion effect (MLE), but the mechanism behind it is not clear. Purpose. This study aimed to assess the alterations in brain functions at the regional and whole-brain levels, using regional homogeneity (ReHo) and functional connectivity (FC), during the postoperative microlesion period after deep brain stimulation (DBS) in PD patients. Method. Resting-state functional MRI data were collected from 27 PD patients before and after the first day of DBS and 12 healthy controls (HCs) in this study. The ReHo in combination with FC analysis was used to investigate the alterations of regional brain activity in all the subjects. Results. There were increased ReHo in the basal ganglia-thalamocortical circuit (left supplementary motor area and bilateral paracentral lobule), whereas decreased ReHo in the default mode network (DMN) (left angular gyrus, bilateral precuneus), prefrontal cortex (bilateral middle frontal gyrus), and the cerebello-thalamocortical (CTC) circuit (Cerebellum_crus2/1_L) after DBS. In addition, we also found abnormal FC in the lingual gyrus, cerebellum, and DMN. Conclusion. Microlesion of the thalamus caused by electrode implantation can alter the activity of the basal ganglia-thalamocortical circuit, prefrontal cortex, DMN, and CTC circuit and induce abnormal FC in the lingual gyrus, cerebellum, prefrontal cortex, and DMN among PD patients. The findings of this study contribute to the understanding of the mechanism of MLE.

Luo, Bei↗

COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination

Background: Studies have shown an increased risk of severe SARS-CoV-2–related (COVID-19) disease outcome and mortality for patients with cancer, but it is not well understood whether associations vary by cancer site, cancer treatment, and vaccination status. Methods: Using electronic health record data from an academic medical center, we identified a retrospective cohort of 260,757 individuals tested for or diagnosed with COVID-19 from March 10, 2020, to August 1, 2022. Of these, 52,019 tested positive for COVID-19 of whom 13,752 had a cancer diagnosis. We conducted Firth-corrected logistic regression to assess the association between cancer status, site, treatment, vaccination, and four COVID-19 outcomes: hospitalization, intensive care unit admission, mortality, and a composite “severe COVID” outcome. Results: Cancer diagnosis was significantly associated with higher rates of severe COVID, hospitalization, and mortality. These associations were driven by patients whose most recent initial cancer diagnosis was within the past 3 years. Chemotherapy receipt, colorectal cancer, hematologic malignancies, kidney cancer, and lung cancer were significantly associated with higher rates of worse COVID-19 outcomes. Vaccinations were significantly associated with lower rates of worse COVID-19 outcomes regardless of cancer status. Conclusions: Patients with colorectal cancer, hematologic malignancies, kidney cancer, or lung cancer or who receive chemotherapy for treatment should be cautious because of their increased risk of worse COVID-19 outcomes, even after vaccination. Impact: Additional COVID-19 precautions are warranted for people with certain cancer types and treatments. Further, significant benefit from vaccination is noted for both cancer and cancer-free patients.

60 APPLIED LIFE SCIENCES↗

Novel Anti-LY6G6D/CD3 T-Cell–Dependent Bispecific Antibody for the Treatment of Colorectal Cancer

Abstract New therapeutics and combination regimens have led to marked clinical improvements for the treatment of a subset of colorectal cancer. Immune checkpoint inhibitors have shown clinical efficacy in patients with mismatch-repair–deficient or microsatellite instability–high (MSI-H) metastatic colorectal cancer (mCRC). However, patients with microsatellite-stable (MSS) or low levels of microsatellite instable (MSI-L) colorectal cancer have not benefited from these immune modulators, and the survival outcome remains poor for the majority of patients diagnosed with mCRC. In this article, we describe the discovery of a novel T-cell–dependent bispecific antibody (TDB) targeting tumor-associated antigen LY6G6D, LY6G6D-TDB, for the treatment of colorectal cancer. RNAseq analysis showed that LY6G6D was differentially expressed in colorectal cancer with high prevalence in MSS and MSI-L subsets, whereas LY6G6D expression in normal tissues was limited. IHC confirmed the elevated expression of LY6G6D in primary and metastatic colorectal tumors, whereas minimal or no expression was observed in most normal tissue samples. The optimized LY6G6D-TDB, which targets a membrane-proximal epitope of LY6G6D and binds to CD3 with high affinity, exhibits potent antitumor activity both in vitro and in vivo. In vitro functional assays show that LY6G6D-TDB–mediated T-cell activation and cytotoxicity are conditional and target dependent. In mouse xenograft tumor models, LY6G6D-TDB demonstrates antitumor efficacy as a single agent against established colorectal tumors, and enhanced efficacy can be achieved when LY6G6D-TDB is combined with PD-1 blockade. Our studies provide evidence for the therapeutic potential of LY6G6D-TDB as an effective treatment option for patients with colorectal cancer.

60 APPLIED LIFE SCIENCES↗

Single reader between-cases AUC estimator with nested data

The area under the receiver operating characteristic curve (AUC) is widely used in evaluating diagnostic performance for many clinical tasks. It is still challenging to evaluate the reading performance of distinguishing between positive and negative regions of interest (ROIs) in the nested-data problem, where multiple ROIs are nested within the cases. To address this issue, we identify two kinds of AUC estimators, within-cases AUC and between-cases AUC. We focus on the between-cases AUC estimator, since our main research interest is in patient-level diagnostic performance rather than location-level performance (the ability to separate ROIs with and without disease within each patient). Another reason is that as the case number increases, the number of between-cases paired ROIs is much larger than the number of within-cases ROIs. We provide estimators for the variance of the between-cases AUC and for the covariance when there are two readers. We derive and prove the above estimators’ theoretical values based on a simulation model and characterize their behavior using Monte Carlo simulation results. We also provide a real-data example. Moreover, we connect the distribution-based simulation model with the simulation model based on the linear mixed-effect model, which helps better understand the sources of variation in the simulated dataset.

Health Care Sciences & Services↗

In search of autophagy biomarkers in breast cancer: Receptor status and drug agnostic transcriptional changes during autophagy flux in cell lines

Autophagy drives drug resistance and drug-induced cancer cell cytotoxicity. Targeting the autophagy process could greatly improve chemotherapy outcomes. The discovery of specific inhibitors or activators has been hindered by challenges with reliably measuring autophagy levels in a clinical setting. We investigated drug-induced autophagy in breast cancer cell lines with differing ER/PR/Her2 receptor status by exposing them to known but divergent autophagy inducers each with a unique molecular target, tamoxifen, trastuzumab, bortezomib or rapamycin. Differential gene expression analysis from total RNA extracted during the earliest sign of autophagy flux showed both cell- and drug-specific changes. We analyzed the list of differentially expressed genes to find a common, cell- and drug-agnostic autophagy signature. Twelve mRNAs were significantly modulated by all the drugs and 11 were orthogonally verified with Q-RT-PCR (Klhl24, Hbp1, Crebrf, Ypel2, Fbxo32, Gdf15, Cdc25a, Ddit4, Psat1, Cd22, Ypel3). The drug agnostic mRNA signature was similarly induced by a mitochondrially targeted agent, MitoQ. In-silico analysis on the KM-plotter cancer database showed that the levels of these mRNAs are detectable in human samples and associated with breast cancer prognosis outcomes of Relapse-Free Survival in all patients (RSF), Overall Survival in all patients (OS), and Relapse-Free Survival in ER + Patients (RSF ER + ). High levels of Klhl24, Hbp1, Crebrf, Ypel2, CD22 and Ypel3 were correlated with better outcomes, whereas lower levels of Gdf15, Cdc25a, Ddit4 and Psat1 were associated with better prognosis in breast cancer patients. This gene signature uncovers candidate autophagy biomarkers that could be tested during preclinical and clinical studies to monitor the autophagy process.

60 APPLIED LIFE SCIENCES↗