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There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied artificial intelligence to comprehensive metabolic profiles to identify additional metabolites associated with treatment response. Using a deep learning model (DLM), a metabolite panel consisting of two polyamines as well as nine additional metabolites was developed for improved prediction of RCB-II/III. The DLM has potential clinical value for identifying TNBC patients who are unlikely to respond to NACT and who may benefit from other treatment modalities.

Abstract Background Cancer patients are more susceptible to an aggressive course of COVID-19. Developing biomarkers identifying cancer patients at high risk of COVID-19-related death could help determine who needs early clinical intervention. The miRNAs hosted in the genomic regions associated with the risk of aggressive COVID-19 could represent potential biomarkers for clinical outcomes. Patients and methods Plasma samples were collected at The University of Texas MD Anderson Cancer Center from cancer patients (N = 128) affected by COVID-19. Serum samples were collected from vaccinated healthy individuals (n = 23) at the Municipal Clinical Emergency Teaching Hospital in Timisoara, Romania. An in silico positional cloning approach was used to identify the presence of miRNAs at COVID-19 risk-associated genomic regions: CORSAIRs (COvid-19 RiSk AssocIated genomic Regions). The miRNA levels were measured by RT-qPCR. Results We found that miRNAs were enriched in CORSAIR. Low plasma levels of hsa-miR-150-5p and hsa-miR-93-5p were associated with higher COVID-19-related death. The levels of hsa-miR-92b-3p were associated with SARS-CoV-2 test positivity. Peripheral blood mononuclear cells (PBMC) increased secretion of hsa-miR-150-5p, hsa-miR-93-5p, and hsa-miR-92b-3p after in vitro TLR7/8- and T cell receptor (TCR)-mediated activation. Increased levels of these three miRNAs were measured in the serum samples of healthy individuals between one and nine months after the second dose of the Pfizer-BioNTech COVID-19 vaccine. SARS-CoV-2 infection of human airway epithelial cells influenced the miRNA levels inside their secreted extracellular vesicles. Conclusions MiRNAs are enriched at CORSAIR. Plasma miRNA levels can represent a potential blood biomarker for predicting COVID-19-related death in cancer patients.

Abstract Serial CA125 and second line transvaginal ultrasound (TVS) screening in the UKCTOCS indicated a shift towards detection of earlier stage ovarian cancer (OvCa), but did not yield a significant mortality reduction. There remains a need to establish additional biomarkers that can complement CA125 for even earlier and at a larger proportion of new cases. Using a cohort of plasma samples from 219 OvCa cases (59 stage I/II and 160 stage III/IV) and 409 female controls and a novel Sensitivity Maximization At A Given Specificity (SMAGS) method, we developed a blood-based metabolite-based test consisting of 7 metabolites together with CA125 for detection of OvCa. At a 98.5% specificity cutpoint, the metabolite test achieved sensitivity of 86.2% for detection of early-stage OvCa and was able to capture 64% of the cases with low CA125 levels (

Summary: Background: To determine whether an algorithm based on repeated measurements of a panel of four circulating protein biomarkers (4 MP) for lung cancer risk assessment results in improved performance over a single time measurement. Methods: We conducted data analysis of the 4 MP consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in pre-diagnostic sera from 2483 ever-smoker participants (389 cases and 2094 randomly selected non-cases) in the Prostate, Lung, Colorectal, Ovarian (PLCO) Study who had at least two sequential blood collections over 6 years. A parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history at each time point, was compared to a single-threshold (ST) method. Findings: Among ever-smoker participants, the PEB approach yielded an additional 4% improvement in the AUC compared to the ST approach (P-value: 0.009). When considering an ≥10 PY smoking history and at a fixing the specificity corresponding to 1% 6-year lung cancer risk, PEB resulted in significant improvement in the sensitivity (SenPEB:96.3% vs SenST:91.0%; P-value: 6.7e-3). The PEB algorithm identified 17 of the 35 cases that remained ST negative, at an average of 1.26 years before diagnosis. Ten case individuals who were positive based on ST at an average of 1.03 years prior to diagnosis were identified earlier by PEB, at an average of 2.70 years. Interpretation: An algorithm based on repeated measurements of the 4 MP improves sensitivity and results in an earlier detection of lung cancer compared to a single-threshold method. Funding: This study was supported by NIH Grant Nos. U01CA271888, U01CA194733, U01CA213285, NCI EDRN U01 CA200468, P30CA016672, and U24CA086368; the Cancer Prevention & Research Institute of Texas RP180505 and RP160693; the SPORE P50CA140388; the CCTS TR000371; and the generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Moon Shots Program and the Lyda Hill Foundation.

Motivation: Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies.Method: We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes.Results: We combined two prostate cancer microarray data sets, confirmed the appropriateness of the approach with the Kolmogorov-Smirnov goodness-of-fit test, and built several predictive models. The best logistic regression model with stepwise forward selection used 7 genes and had a misclassification rate of 31%. Models that combined LDA with different feature selection algorithms had misclassification rates between 19% and 33%, and the sets of genes in the models varied substantially during cross-validation. When we combined RGFS with LDA, the best model used two genes and had a misclassification rate of 15%.Availability: Affymetrix U95Av2 array data are available at http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi. The cDNA microarray data are available through the Stanford Microarray Database (http://cmgm.stanford.edu/pbrown/). GeneLink software is freely available at http://bioinformatics.mdanderson.org/GeneLink/. DNA-Chip Analyzer software is publicly available at http://biosun1.harvard.edu/complab/dchip/.

Motivation Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. Method We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes. Results We combined two prostate cancer microarray data sets, confirmed the appropriateness of the approach with the Kolmogorov-Smirnov goodness-of-fit test, and built several predictive models. The best logistic regression model with stepwise forward selection used 7 genes and had a misclassification rate of 31%. Models that combined LDA with different feature selection algorithms had misclassification rates between 19% and 33%, and the sets of genes in the models varied substantially during cross-validation. When we combined RGFS with LDA, the best model used two genes and had a misclassification rate of 15%. Availability Affymetrix U95Av2 array data are available at http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi . The cDNA microarray data are available through the Stanford Microarray Database ( http://cmgm.stanford.edu/pbrown/ ). GeneLink software is freely available at http://bioinformatics.mdanderson.org/GeneLink/ . DNA-Chip Analyzer software is publicly available at http://biosun1.harvard.edu/complab/dchip/ .

Oral mucositis (OM) is a common and clinically impactful side effect of cytotoxic cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy with or without concomitant chemotherapy. The etiology and pathogenic mechanisms of OM are complex, multifaceted and elicit both direct and indirect damage to the mucosa. In this narrative review, we describe studies that use various omics methodologies (genomics, transcriptomics, microbiomics and metabolomics) in attempts to elucidate the biological pathways associated with the development or severity of OM. Integrating different omics into multi-omics approaches carries the potential to discover links among host factors (genomics), host responses (transcriptomics, metabolomics), and the local environment (microbiomics).

The study aimed to determine how different baseline states of mind in each day (relaxed, ordinary, or stressful) affected the diurnal patterns of three commonly investigated salivary stress biomarkers: cortisol (sCort), alpha-amylase (sAA), and chromogranin A (sCgA). A total of 32 healthy volunteers collected saliva on three different mood days at six time points each day (awakening, 30 min after, 10:00, 12:00, 16:00, and 19:00 h). Pulse rates and subjective feeling of stress using a visual analog scale (VAS) were also recorded. The levels of sCort and sAA were highest on a stressful day at certain time points. The levels of sAA were lowest on a relaxing day in the afternoon. Surprisingly, sCgA levels showed an opposite pattern with the highest level seen on a relaxing day. Of note was that the majority of the participants chose a day during a meditation retreat as their relaxing day and participants practicing mindfulness manifested lower levels of sCort (p = 0.003) and sAA (p = 0.043) at 19:00 h compared with those choosing a general leisure day as their relaxing day. Different states of mind were associated with different courses of salivary stress biomarkers. sCort and sAA are the most reliable markers showing the expected trend with higher levels on a stressful day and lower levels on a relaxing day. While the current result cast doubt on the use of sCgA as a stress marker since it was the only marker that showed the opposite trend compared with those of the other two markers as well as pulse rates and VAS. Furthermore, this is the first study to demonstrate that mindfulness practice might have different effects on these biomarkers from just a general relaxed state of mind.

Abstract Background Identification of features is a critical task in microbiome studies that is complicated by the fact that microbial data are high dimensional and heterogeneous. Masked by the complexity of the data, the problem of separating signals (differential features between groups) from noise (features that are not differential between groups) becomes challenging and troublesome. For instance, when performing differential abundance tests, multiple testing adjustments tend to be overconservative, as the probability of a type I error (false positive) increases dramatically with the large numbers of hypotheses. Moreover, the grouping effect of interest can be obscured by heterogeneity. These factors can incorrectly lead to the conclusion that there are no differences in the microbiome compositions. Results We translate and represent the problem of identifying differential features, which are differential in two-group comparisons (e.g., treatment versus control), as a dynamic layout of separating the signal from its random background. More specifically, we progressively permute the grouping factor labels of the microbiome samples and perform multiple differential abundance tests in each scenario. We then compare the signal strength of the most differential features from the original data with their performance in permutations, and will observe a visually apparent decreasing trend if these features are true positives identified from the data. Simulations and applications on real data show that the proposed method creates a U-curve when plotting the number of significant features versus the proportion of mixing. The shape of the U-Curve can convey the strength of the overall association between the microbiome and the grouping factor. We also define a fragility index to measure the robustness of the discoveries. Finally, we recommend the identified features by comparing p-values in the observed data with p-values in the fully mixed data. Conclusions We have developed this into a user-friendly and efficient R-shiny tool with visualizations. By default, we use the Wilcoxon rank sum test to compute the p-values, since it is a robust nonparametric test. Our proposed method can also utilize p-values obtained from other testing methods, such as DESeq. This demonstrates the potential of the progressive permutation method to be extended to new settings.

Abstract Background The estimation of microbial networks can provide important insight into the ecological relationships among the organisms that comprise the microbiome. However, there are a number of critical statistical challenges in the inference of such networks from high-throughput data. Since the abundances in each sample are constrained to have a fixed sum and there is incomplete overlap in microbial populations across subjects, the data are both compositional and zero-inflated. Results We propose the COmpositional Zero-Inflated Network Estimation (COZINE) method for inference of microbial networks which addresses these critical aspects of the data while maintaining computational scalability. COZINE relies on the multivariate Hurdle model to infer a sparse set of conditional dependencies which reflect not only relationships among the continuous values, but also among binary indicators of presence or absence and between the binary and continuous representations of the data. Our simulation results show that the proposed method is better able to capture various types of microbial relationships than existing approaches. We demonstrate the utility of the method with an application to understanding the oral microbiome network in a cohort of leukemic patients. Conclusions Our proposed method addresses important challenges in microbiome network estimation, and can be effectively applied to discover various types of dependence relationships in microbial communities. The procedure we have developed, which we refer to as COZINE, is available online at https://github.com/MinJinHa/COZINE .

Abstract Background The PI3K/AKT pathway is activated through PIK3CA or AKT1 mutations and PTEN loss in breast cancer. We conducted a phase II trial with an allosteric AKT inhibitor MK-2206 in patients with advanced breast cancer who had tumors with PIK3CA/AKT1 mutations and/or PTEN loss/mutation. Methods The primary endpoint was objective response rate (ORR). Secondary endpoints were 6-month progression-free survival (6 m PFS), predictive and pharmacodynamic markers, safety, and tolerability. Patients had pre-treatment and on-treatment biopsies as well as collection of peripheral blood mononuclear cells (PBMC) and platelet-rich plasma (PRP). Next-generation sequencing, immunohistochemistry, and reverse phase protein arrays (RPPA) were performed. Results Twenty-seven patients received MK-2206. Eighteen patients were enrolled into the PIK3CA/AKT1 mutation arm (cohort A): 13 had PIK3CA mutations, four had AKT1 mutations, and one had a PIK3CA mutation as well as PTEN loss. ORR and 6 m PFS were both 5.6% (1/18), with one patient with HR+ breast cancer and a PIK3CA E542K mutation experiencing a partial response (on treatment for 36 weeks). Nine patients were enrolled on the PTEN loss/mutation arm (cohort B). ORR was 0% and 6 m PFS was 11% (1/9), observed in a patient with triple-negative breast cancer and PTEN loss. The study was stopped early due to futility. The most common adverse events were fatigue (48%) and rash (44%). On pre-treatment biopsy, PIK3CA and AKT1 mutation status was concordant with archival tissue testing. However, two patients with PTEN loss based on archival testing had PTEN expression on the pre-treatment biopsy. MK-2206 treatment was associated with a significant decline in pAKT S473 and pAKT T308 and PI3K activation score in PBMC and PRPs, but not in tumor biopsies. By IHC, there was no significant decrease in median pAKT S473 or Ki-67 staining, but a drop was observed in both responders. Conclusions MK-2206 monotherapy had limited clinical activity in advanced breast cancer patients selected for PIK3CA/AKT1 or PTEN mutations or PTEN loss. This may, in part, be due to inadequate target inhibition at tolerable doses in heavily pre-treated patients with pathway activation, as well as tumor heterogeneity and evolution in markers such as PTEN conferring challenges in patient selection. Trial registration ClinicalTrials.gov, NCT01277757. Registered 13 January 2011.

There is substantial interest in mining neoantigens for cancer applications. Non-canonical proteins resulting from frameshift mutations have been identified as neoantigens in cancer. We investigated the landscape of non-canonical proteins in non-small cell lung cancer (NSCLC) and their induced immune response in the form of autoantibodies. A database of cryptoproteins was computationally constructed and comprised all alternate open reading frames (altORFs) and ORFs identified in pseudogenes, noncoding RNAs, and untranslated regions of mRNAs that did not align with known canonical proteins. Proteomic profiles of seventeen lung adenocarcinoma (LUAD) cell lines were searched to evaluate the occurrence of cryptoproteins. To assess the immunogenicity, immunoglobulin (Ig)-bound cryptoproteins in plasmas were profiled by mass spectrometry. The specimen set consisted of plasmas from 30 newly diagnosed NSCLC cases, pre-diagnostic plasmas from 51 NSCLC cases, and 102 control plasmas. An analysis of LUAD cell lines identified 420 cryptoproteins. Plasma Ig-bound analyses revealed 90 cryptoproteins uniquely found in cases and 14 cryptoproteins that had a fold-change >2 compared to controls. In pre-diagnostic samples, 17 Ig-bound cryptoproteins yielded an odds ratio ≥2. Eight Ig-bound cryptoproteins were elevated in both pre-diagnostic and newly diagnosed cases compared to controls. Cryptoproteins represent a class of neoantigens that induce an autoantibody response in NSCLC.

Purpose: Patients with multiple endocrine neoplasia type 1 (MEN1) are predisposed to develop duodenopancreatic neuroendocrine tumors (dpNETs), and metastatic dpNET is the primary cause of disease-related mortality. Presently, there is a paucity of prognostic factors that can reliably identify patients with MEN1-related dpNETS who are at high risk of distant metastasis. In the current study, we aimed to establish novel circulating molecular protein signatures associated with disease progression. Experimental Design: Mass spectrometry-based proteomic profiling was conducted on plasmas procured through an international collaboration between MD Anderson Cancer Center, the National Institutes of Health, and the University Medical Center Utrecht from a cohort of 56 patients with MEN1 [14 with distant metastasis dpNETs (cases) and 42 with either indolent dpNETs or no dpNETs (controls)]. Findings were compared to proteomic profiles generated from serially collected plasmas from a mouse model of Men1-pancreatic neuroendocrine tumors (Men1fl/flPdx1-CreTg) and control mice (Men1fl/fl). Results: A total of 187 proteins were found to be elevated in MEN1 patients with distant metastasis compared to controls, including 9 proteins previously associated with pancreatic cancer and other neuronal proteins. Analyses of mouse plasmas revealed 196 proteins enriched for transcriptional targets of oncogenic MYCN, YAP1, POU5F1, and SMAD that were associated with disease progression in Men1fl/flPdx1-CreTg mice. Cross-species intersection revealed 19 proteins positively associated with disease progression in both human patients and in Men1fl/flPdx1-CreTg mice. Conclusions: Our integrated analyses identified novel circulating protein markers associated with disease progression in MEN1-related dpNET. [ABSTRACT FROM AUTHOR]