Nevin Manimala

Clin Cancer Res. 2021 Jul 12:clincanres.CCR-21-2051-E.2021. doi: 10.1158/1078-0432.CCR-21-2051. Online ahead of print.

ABSTRACT

PURPOSE: The Cancer Immune Monitoring and Analysis Centers – Cancer Immunologic Data Commons (CIMAC-CIDC) network supported by the NCI Cancer Moonshot initiative was established to provide correlative analyses for clinical trials in cancer immunotherapy, using state-of-the-art technology. Fundamental to this initiative is implementation of multiplex immunohistochemical assays to define the composition and distribution of immune infiltrates within tumors in the context of their potential role as biomarkers. A critical unanswered question involves the relative fidelity of such assays to reliably quantify tumor associated immune cells across different platforms.

EXPERIMENTAL DESIGN: Three CIMAC sites compared across their laboratories: 1) image analysis algorithms, 2) image acquisition platforms, 3) multiplex staining protocols. Two distinct high-dimensional approaches were employed: multiplexed immunohistochemical consecutive staining on single slide (MICSSS) and multiplexed immunofluorescence (mIF). To eliminate variables potentially impacting assay performance, we completed a multistep harmonization process, first comparing assay performance using independent protocols followed by the integration of laboratory-specific protocols and finally, validating this harmonized approach in an independent set of tissues.

RESULTS: Data generated at the final validation step showed an inter-site Spearman correlation coefficient of {greater than or equal to}0.85 for each marker within and across tissue types, with an overall low average coefficient of variation {less than or equal to}0.1.

CONCLUSIONS: Our results support interchangeability of protocols and platforms to deliver robust, and comparable data using similar tissue specimens and confirm that CIMAC-CIDC analyses may therefore be used with confidence for statistical associations with clinical outcomes largely independent of site, antibody selection, protocol, and platform across different sites.

PMID:34253580 | DOI:10.1158/1078-0432.CCR-21-2051

Disabil Health J. 2021 Jul 7:101170. doi: 10.1016/j.dhjo.2021.101170. Online ahead of print.

ABSTRACT

BACKGROUND: Little is known about the exposure of youth with disability to cyber victimisation.

OBJECTIVE: /Hypothesis: To estimate the prevalence of peer cyber and non-cyber victimisation in a nationally representative sample of 14-year-old adolescents with and without disability and to determine whether gender moderates the relationship between disability and exposure to victimisation.

METHODS: Secondary analysis of data collected in Wave 6 of the UK’s Millennium Cohort Survey on 11,726 14-year-old adolescents living in the UK.

RESULTS: Adolescents with disability had higher prevalence of cyber and non-cyber victimisation than those with no disability. For cyber victimisation there was a statistically significant interaction between gender and disability, with evidence of increased cyber victimisation for adolescents with disability compared to those with no disability among girls, but not boys. For non-cyber victimisation there was no evidence of an interaction between gender and disability.

CONCLUSIONS: The prevalence of both cyber and non-cyber victimisation was higher among adolescents with disability than those with no disability. The association between disability and risk of exposure to peer cyber victimisation appears to be moderated by gender.

PMID:34253505 | DOI:10.1016/j.dhjo.2021.101170

J Plast Reconstr Aesthet Surg. 2021 Jun 9:S1748-6815(21)00291-6. doi: 10.1016/j.bjps.2021.05.039. Online ahead of print.

ABSTRACT

INTRODUCTION: The Breast Reconstruction Risk Assessment (BRA) Score tool is a risk calculator developed to predict the risk of complications in individual patients undergoing breast reconstruction. It was developed in a North American population exclusively undergoing immediate breast reconstruction. This study sought to assess the predictions of the BRA Score tool against the measured outcomes of surgery for an Australian public hospital population, including both immediate and delayed reconstructions.

METHOD: This was a retrospective cohort study of data from women at a single Australian public teaching hospital unit. Data from the Flinders Breast Reconstruction Database was retrieved and compared to BRA Scores calculated for each patient. Receiver operating curve area under the curve analysis was performed as well as Brier scores to compare predicted versus observed complications.

RESULTS: BRA Score predictions were reasonable or good (C-statistic >0.7, Brier score <0.09) for the complications of overall surgical complications, surgical site infection (SSI) and seroma at 30 days, and similarly accurate for prediction of the same complications for implant reconstructions at 12 months. There were similar findings between delayed and immediate reconstructions.

CONCLUSION: The BRA Score risk calculator is valid to detect some risks in both patients undergoing immediate and delayed breast reconstruction in an Australian public hospital setting. SSI is the best predicted complication and is well-predicted across both autologous and prosthetic reconstruction types.

PMID:34253489 | DOI:10.1016/j.bjps.2021.05.039

Cardiovasc Revasc Med. 2021 Jun 25:S1553-8389(21)00488-7. doi: 10.1016/j.carrev.2021.06.123. Online ahead of print.

ABSTRACT

BACKGROUND: Most pivotal transcatheter aortic valve replacement (TAVR) trials have excluded patients with bicuspid aortic stenosis (AS). This study compared TAVR in low-risk patients with bicuspid AS to those with tricuspid AS, incorporating data from prospective trials.

METHODS: We selected prospective US low-risk TAVR trials containing a bicuspid arm for this meta-analysis, examining outcomes at 30 days. Study results were pooled using a hierarchical Bayesian random-effects model.

RESULTS: Included were 3 Food and Drug Administration (FDA)-approved investigational device exemption (IDE) trials that enrolled a total of 1810 low-risk patients with symptomatic severe AS for TAVR. We compared 380 bicuspid patients with 1430 tricuspid patients. Event rates at 30 days overall were low, with similar mortality (odds ratio [OR], 0.38; 95% credible interval [CrI]: 0.08, 1.78; I², 0%), non-disabling stroke (OR, 0.45; 95% CrI: 0.15, 1.07; I², 9%), and permanent pacemaker implantation (OR, 0.86; 95% CrI: 0.41, 1.47; I², 59%). There were statistically significant differences in disabling stroke (OR, 0.16; 95% CrI: 0.01, 0.90; I², NA) and coronary obstruction (OR, 0.21; 95% CrI: 0.05, 0.91) that disappeared after sensitivity analysis by adding a single event to the tricuspid arm. Hemodynamics were similar at 30 days.

CONCLUSIONS: Preliminary data from the FDA-approved IDE trials of low-risk patients with bicuspid AS undergoing TAVR demonstrated 30-day outcomes comparable to low-risk tricuspid patients, except for a trend toward higher stroke in bicuspid patients. Randomized trials are warranted to reassure the safety and long-term outcome of TAVR in patients with severe bicuspid AS.

PMID:34253474 | DOI:10.1016/j.carrev.2021.06.123

Reprod Biomed Online. 2021 May 2:S1472-6483(21)00195-4. doi: 10.1016/j.rbmo.2021.04.019. Online ahead of print.

ABSTRACT

RESEARCH QUESTION: Is transvaginal hydrolaparoscopy (THL) non-inferior to hysterosalpingography (HSG) as a first-line tubal patency test in subfertile women in predicting the chance of conception leading to live birth?

DESIGN: A multicentre, randomized controlled trial in four teaching hospitals in the Netherlands, which randomized subfertile women scheduled for tubal patency testing to either THL or HSG as a first-line tubal patency test. The primary outcome was conception leading to live birth within 24 months after randomization.

RESULTS: A total of 149 women were randomized to THL and 151 to HSG. From the intention-to-treat population, 83 women from the THL group (58.5%) conceived and delivered a live born child within 24 months after randomization compared with 82 women (55.4%) in the HSG group (difference 3.0%, 95% CI -8.3 to 14.4). Time to conception leading to live birth was not statistically different between groups. Miscarriage occurred in 16 (11.3%) women in the THL group, versus 20 (13.5%) women in the HSG group (RR = 0.66, 95% CI 0.34 to 1.32, P = 0.237), and multiple pregnancies occurred in 12 (8.4%) women in the THL group compared with 19 (12.8%) women in the HSG group (RR = 0.84, 95% CI 0.46 to 1.55, P = 0.58). Ectopic pregnancy was diagnosed in two women in the HSG group (1.4%) and none in the THL group (P = 0.499).

CONCLUSION: In a preselected group of subfertile women with a low risk of tubal pathology, use of THL was not inferior to HSG as a first-line test for predicting conception leading to live birth.

PMID:34253451 | DOI:10.1016/j.rbmo.2021.04.019

Eur J Intern Med. 2021 Jul 10:S0953-6205(21)00242-9. doi: 10.1016/j.ejim.2021.06.031. Online ahead of print.

ABSTRACT

BACKGROUND: Interactions between chronic kidney disease (CKD) and several comorbidities may potentially affect prognosis of older hospitalized patients. This study aims at evaluating the prognostic interactions between estimated glomerular filtration rate (eGFR), anemia, sarcopenia, functional and cognitive dysfunction, and 3-year mortality among older patients discharged from acute care hospitals.

METHODS: Our series consisted of 504 older adults enrolled in a multicenter observational study carried out in twelve Acute Geriatric and Internal Medicine wards throughout Italy. CKD was defined as an eGFR< 60 ml/min/1.73 m². Anemia, Short Portable Status Mental Questionnaire (SPMSQ), Basic Activities of Daily Living (BADL), sarcopenia, and Charlson index were considered in the analysis. 3-year survival was investigated by Cox regression and prognostic interactions among study variables were assessed by survival tree analysis. Accuracy of different survival models was investigated by C-index.

RESULTS: eGFR < 30 mL/min/1.73 m², anemia, sarcopenia, SPMSQ ≥ 5, and impairment in 1 or more BADL were significantly associated with mortality. Survival tree analysis showed that patients with eGFR < 35.32 ml/min/1.73 m² and SPMSQ ≥ 5 had the highest risk of mortality [hazard ratio (HR): 5.49, 95%CI: 3.04-9.94] followed by those with eGFR < 35.32 ml/min/1.73 m², hemoglobin < 11.95 g/dL and SPMSQ < 5 (HR:3.65; 95%CI: 2.21-6.02) and those with eGFR 35.32-47.99 ml/min/1.73 m² and sarcopenia (HR:3.65; 95%CI: 1.99-6.69). Survival tree leaf node membership had good accuracy in predicting the study outcome (C-index: 0.73, 95%CI:0.70-0.76).

CONCLUSIONS: Interactions among study risk factors designed distinct risk profiles in older patients discharged from acute care hospitals, that may help identify patients needing targeted interventions and appropriate follow-up after discharge.

PMID:34253448 | DOI:10.1016/j.ejim.2021.06.031

Bioinformatics. 2021 Jul 12;37(Supplement_1):i161-i168. doi: 10.1093/bioinformatics/btab305.

ABSTRACT

MOTIVATION: The availability of human genomic data, together with the enhanced capacity to process them, is leading to transformative technological advances in biomedical science and engineering. However, the public dissemination of such data has been difficult due to privacy concerns. Specifically, it has been shown that the presence of a human subject in a case group can be inferred from the shared summary statistics of the group, e.g. the allele frequencies, or even the presence/absence of genetic variants (e.g. shared by the Beacon project) in the group. These methods rely on the availability of the target’s genome, i.e. the DNA profile of a target human subject, and thus are often referred to as the membership inference method.

RESULTS: In this article, we demonstrate the haplotypes, i.e. the sequence of single nucleotide variations (SNVs) showing strong genetic linkages in human genome databases, may be inferred from the summary of genomic data without using a target’s genome. Furthermore, novel haplotypes that did not appear in the database may be reconstructed solely from the allele frequencies from genomic datasets. These reconstructed haplotypes can be used for a haplotype-based membership inference algorithm to identify target subjects in a case group with greater power than existing methods based on SNVs.

AVAILABILITY AND IMPLEMENTATION: The implementation of the membership inference algorithms is available at https://github.com/diybu/Haplotype-based-membership-inferences.

PMID:34252973 | DOI:10.1093/bioinformatics/btab305

Bioinformatics. 2021 Jul 12;37(Supplement_1):i42-i50. doi: 10.1093/bioinformatics/btab336.

ABSTRACT

MOTIVATION: Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. Recently developed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network)-based models have shown promising results in improving drug sensitivity prediction. The primary idea behind REFINED-CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from CNN architectures. However, the mapping from a high dimensional vector to a compact 2D image depends on the a priori choice of the distance metric and projection scheme with limited empirical procedures guiding these choices.

RESULTS: In this article, we consider an ensemble of REFINED-CNN built under different choices of distance metrics and/or projection schemes that can improve upon a single projection based REFINED-CNN model. Results, illustrated using NCI60 and NCI-ALMANAC databases, demonstrate that the ensemble approaches can provide significant improvement in prediction performance as compared to individual models. We also develop the theoretical framework for combining different distance metrics to arrive at a single 2D mapping. Results demonstrated that distance-averaged REFINED-CNN produced comparable performance as obtained from stacking REFINED-CNN ensemble but with significantly lower computational cost.

AVAILABILITY AND IMPLEMENTATION: The source code, scripts, and data used in the paper have been deposited in GitHub (https://github.com/omidbazgirTTU/IntegratedREFINED).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34252971 | DOI:10.1093/bioinformatics/btab336

Bioinformatics. 2021 Jul 12;37(Supplement_1):i272-i279. doi: 10.1093/bioinformatics/btab272.

ABSTRACT

MOTIVATION: The high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem.

RESULTS: In this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions.

AVAILABILITY AND IMPLEMENTATION: EnHiC is publicly available at https://github.com/wmalab/EnHiC.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34252966 | DOI:10.1093/bioinformatics/btab272

Bioinformatics. 2021 Jul 12;37(Supplement_1):i443-i450. doi: 10.1093/bioinformatics/btab285.

ABSTRACT

MOTIVATION: Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly.

RESULTS: To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease.

AVAILABILITY AND IMPLEMENTATION: The source code is freely available at: https://github.com/mskspi/PathCNN.

PMID:34252964 | DOI:10.1093/bioinformatics/btab285