Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.79 for the most precise model (FDR = 0.01). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Heterogeneity in leukemia cells that escape drug-induced senescence-like state.

Erythropoietin (EPO) suppresses drug-induced apoptosis in EPO-receptor-positive leukemia cells and allows cells to persist after drug treatment by promoting cellular senescence. Importantly a small proportion of senescent cells can re-enter the cell cycle and resume proliferation after drug treatment, resulting in disease recurrence/persistence. Using a single-cell assay to track individual cells that exit a drug-induced senescence-like state, we show that cells exhibit asynchronous exit from a senescent-like state, and display different rates of proliferation. Escaped cells retain sensitivity to drug treatment, but display inter-clonal variability. We also find heterogeneity in gene expression with some of the escaped clones retaining senescence-associated gene expression. Senescent leukemia cells exhibit changes in gene expression that affect metabolism and senescence-associated secretory phenotype (SASP)-related genes. Herein, we generate a senescence gene signature and show that this signature is a prognostic marker of worse overall survival in AML and multiple other cancers. A portion of senescent leukemia cells depend on lysosome activity; chloroquine, an inhibitor of lysosome activity, promotes senolysis of some senescent leukemia cells. Our study indicates that the serious risks associated with the use of erythropoietin-stimulating agents (ESAs) in anemic cancer patients may be attributed to their ability to promote drug-tolerant cancer cells through the senescence program.

A pan-cancer clinical platform to predict immunotherapy outcomes and prioritize immuno-oncology combinations in early-phase trials.

BACKGROUND: Immunotherapy is effective, but current biomarkers for patient selection have proven modest sensitivity. Here, we developed VIGex, an optimized gene signature based on the expression level of 12 genes involved in immune response with RNA sequencing. METHODS: We implemented VIGex using the nCounter platform (Nanostring) on a large clinical cohort encompassing 909 tumor samples across 45 tumor types. VIGex was developed as a continuous variable, with cutoffs selected to detect three main categories (hot, intermediate-cold and cold) based on the different inflammatory status of the tumor microenvironment. FINDINGS: Hot tumors had the highest VIGex scores and exhibited an increased abundance of tumor-infiltrating lymphocytes as compared with the intermediate-cold and cold. VIGex scores varied depending on tumor origin and anatomic site of metastases, with liver metastases showing an immunosuppressive tumor microenvironment. The predictive power of VIGex-Hot was observed in a cohort of 98 refractory solid tumor from patients treated in early-phase immunotherapy trials and its clinical performance was confirmed through an extensive metanalysis across 13 clinically annotated gene expression datasets from 877 patients treated with immunotherapy agents. Last, we generated a pan-cancer biomarker platform that integrates VIGex categories with the expression levels of immunotherapy targets under development in early-phase clinical trials. CONCLUSIONS: Our results support the clinical utility of VIGex as a tool to aid clinicians for patient selection and personalized immunotherapy interventions. FUNDING: BBVA Foundation; 202-2021 Division of Medical Oncology and Hematology Fellowship award; Princess Margaret Cancer Center.

Predictors of residual disease after debulking surgery in advanced stage ovarian cancer.

Objective: Optimal debulking with no macroscopic residual disease strongly predicts ovarian cancer survival. The ability to predict likelihood of optimal debulking, which may be partially dependent on tumor biology, could inform clinical decision-making regarding use of neoadjuvant chemotherapy. Thus, we developed a prediction model including epidemiological factors and tumor markers of residual disease after primary debulking surgery. Methods: Univariate analyses examined associations of 11 pre-diagnosis epidemiologic factors (n=593) and 24 tumor markers (n=204) with debulking status among incident, high-stage, epithelial ovarian cancer cases from the Nurses' Health Studies and New England Case Control study. We used Bayesian model averaging (BMA) to develop prediction models of optimal debulking with 5x5-fold cross-validation and calculated the area under the curve (AUC). Results: Current aspirin use was associated with lower odds of optimal debulking compared to never use (OR=0.52, 95%CI=0.31-0.86) and two tissue markers, ADRB2 (OR=2.21, 95%CI=1.23-4.41) and FAP (OR=1.91, 95%CI=1.24-3.05) were associated with increased odds of optimal debulking. The BMA selected aspirin, parity, and menopausal status as the epidemiologic/clinical predictors with the posterior effect probability ≥20%. While the prediction model with epidemiologic/clinical predictors had low performance (average AUC=0.49), the model adding tissue biomarkers showed improved, but weak, performance (average AUC=0.62). Conclusions: Addition of ovarian tumor tissue markers to our multivariable prediction models based on epidemiologic/clinical data slightly improved the model performance, suggesting debulking status may be in part driven by tumor characteristics. Larger studies are warranted to identify those at high risk of poor surgical outcomes informing personalized treatment.

The impact of violating the independence assumption in meta-analysis on biomarker discovery.

With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner.

Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.

Oxidized mitochondrial DNA released after inflammasome activation is a disease biomarker for myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are heterogeneous hematopoietic stem cell malignancies that can phenotypically resemble other hematologic disorders. Thus, tools that may add to current diagnostic practices could aid in disease discrimination. Constitutive innate immune activation is a pathogenetic driver of ineffective hematopoiesis in MDS through Nod-like receptor protein 3 (NLRP3)-inflammasome-induced pyroptotic cell death. Oxidized mitochondrial DNA (ox-mtDNA) is released upon cytolysis, acts as a danger signal, and triggers inflammasome oligomerization via DNA sensors. By using immortalized bone marrow cells from murine models of common MDS somatic gene mutations and MDS primary samples, we demonstrate that ox-mtDNA is released upon pyroptosis. ox-mtDNA was significantly increased in MDS peripheral blood (PB) plasma compared with the plasma of healthy donors, and it was significantly higher in lower-risk MDS vs higher-risk MDS, consistent with the greater pyroptotic cell fraction in lower-risk patients. Furthermore, ox-mtDNA was significantly higher in MDS PB plasma compared with all other hematologic malignancies studied, with the exception of chronic lymphocytic leukemia (CLL). Receiver operating characteristic/area under the curve (ROC/AUC) analysis demonstrated that ox-mtDNA is a sensitive and specific biomarker for patients with MDS compared with healthy donors (AUC, 0.964), other hematologic malignancies excluding CLL (AUC, 0.893), and reactive conditions (AUC, 0.940). ox-mtDNA positively and significantly correlated with levels of known alarmins S100A9, S100A8, and apoptosis-associated speck-like protein containing caspase recruitment domain (CARD) specks, which provide an index of medullary pyroptosis. Collectively, these data indicate that quantifiable ox-mtDNA released into the extracellular space upon inflammasome activation serves as a biomarker for MDS and the magnitude of pyroptotic cell death.

Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments.

High-throughput sequencing (HTS) has revolutionized researchers' ability to study the human transcriptome, particularly as it relates to cancer. Recently, HTS technology has advanced to the point where now one is able to sequence individual cells (i.e., "single-cell sequencing"). Prior to single-cell sequencing technology, HTS would be completed on RNA extracted from a tissue sample consisting of multiple cell types (i.e., "bulk sequencing"). In this chapter, we review the various bioinformatics and statistical methods used in the processing, quality control, and analysis of bulk and single-cell RNA sequencing methods. Additionally, we discuss how these methods are also being used to study tumor heterogeneity.

Prospective CYP2C19-Guided Voriconazole Prophylaxis in Patients With Neutropenic Acute Myeloid Leukemia Reduces the Incidence of Subtherapeutic Antifungal Plasma Concentrations.

A risk mitigation strategy was implemented to determine if a higher prophylactic voriconazole dosage in patients with CYP2C19 rapid metabolizer neutropenic acute myeloid leukemia (AML) reduces the incidence of subtherapeutic trough concentrations. Patients with AML (n = 263) were preemptively genotyped for CYP2C19*2, *3, and *17 alleles as part of a single-center prospective, interventional, quality improvement study. CYP2C19 rapid metabolizers (CYP2C19*1/*17) were recommended to receive interventional voriconazole 300 mg twice daily, ultrarapid metabolizers (CYP2C19*17/*17) were recommended to avoid voriconazole, and all others received the standard prophylactic dosage of 200 mg twice daily. In this real-world setting, 202 patients (76.8%) were prescribed prophylactic voriconazole, and of these patients 176 (87.1%) received CYP2C19-guided prophylactic dosing. Voriconazole trough concentrations were obtained for 41 of the 58 (70.7%) CYP2C19 rapid metabolizers prescribed prophylactic voriconazole. Interventional voriconazole resulted in higher plasma trough concentrations (median 2.7 µg/mL) compared with the standard prophylactic dosage (median 0.6 µg/mL; P = 0.001). Subtherapeutic concentrations were avoided in 83.8% of CYP2C19 rapid metabolizers receiving interventional dosage compared to 46.2% receiving standard dosage (P = 0.02). CYP2C19 genotyping to preemptively guide prophylactic voriconazole dosing is feasible and may be a potential strategy for reducing the risk of subtherapeutic trough concentrations that potentiate breakthrough fungal infections.

Statistical genomics in rare cancer.

Rare cancers make of more than 20% of cancer cases. Due to the rare nature, less research has been conducted on rare cancers resulting in worse outcomes for patients with rare cancers compared to common cancers. The ability to study rare cancers is impaired by the ability to collect a large enough set of patients to complete an adequately powered genomic study. In this manuscript we outline analytical approaches and public genomic datasets that have been used in genomic studies of rare cancers. These statistical analysis approaches and study designs include: gene set / pathway analyses, pedigree and consortium studies, meta-analysis or horizontal integration, and integration of multiple types of genomic information or vertical integration. We also discuss some of the publicly available resources that can be leveraged in rare cancer genomic studies.

Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines.

Cancer cell lines (CCLs) have been widely used to study of cancer. Recent studies have called into question the reliability of data collected on CCLs. Hence, we set out to determine CCLs that tend to be overly sensitive or resistant to a majority of drugs utilizing a nonlinear mixed-effects (NLME) modeling framework. Using drug response data collected in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), we determined the optimal functional form for each drug. Then, a NLME model was fit to the drug response data, with the estimated random effects used to determine sensitive or resistant CCLs. Out of the roughly 500 CCLs studies from the CCLE, we found 17 cell lines to be overly sensitive or resistant to the studied drugs. In the GDSC, we found 15 out of the 990 CCLs to be excessively sensitive or resistant. These results can inform researchers in the selection of CCLs to include in drug studies. Additionally, this study illustrates the need for assessing the dose-response functional form and the use of NLME models to achieve more stable estimates of drug response parameters.

Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing.

Normalization of RNA-Seq data has proven essential to ensure accurate inferences and replication of findings. Hence, various normalization methods have been proposed for various technical artifacts that can be present in high-throughput sequencing transcriptomic studies. In this study, we set out to compare the widely used library size normalization methods (UQ, TMM, and RLE) and across sample normalization methods (SVA, RUV, and PCA) for RNA-Seq data using publicly available data from The Cancer Genome Atlas (TCGA) cervical cancer study. Additionally, an extensive simulation study was completed to compare the performance of the across sample normalization methods in estimating technical artifacts. Lastly, we investigated the effect of reduction in degrees of freedom in the normalized data and their impact on downstream differential expression analysis results. Based on this study, the TMM and RLE library size normalization methods give similar results for CESC dataset. In addition, the simulated datasets results show that the SVA ("BE") method outperforms the other methods (SVA "Leek", PCA) by correctly estimating the number of latent artifacts. Moreover, ignoring the loss of degrees of freedom due to normalization results in an inflated type I error rates. We recommend adjusting not only for library size differences but also the assessment of known and unknown technical artifacts in the data, and if needed, complete across sample normalization. In addition, we suggest that one includes the known and estimated latent artifacts in the design matrix to correctly account for the loss in degrees of freedom, as opposed to completing the analysis on the post-processed normalized data.

Estimating the local false discovery rate via a bootstrap solution to the reference class problem.

Methods of estimating the local false discovery rate (LFDR) have been applied to different types of datasets such as high-throughput biological data, diffusion tensor imaging (DTI), and genome-wide association (GWA) studies. We present a model for LFDR estimation that incorporates a covariate into each test. Incorporating the covariates may improve the performance of testing procedures, because it contains additional information based on the biological context of the corresponding test. This method provides different estimates depending on a tuning parameter. We estimate the optimal value of that parameter by choosing the one that minimizes the estimated LFDR resulting from the bias and variance in a bootstrap approach. This estimation method is called an adaptive reference class (ARC) method. In this study, we consider the performance of ARC method under certain assumptions on the prior probability of each hypothesis test as a function of the covariate. We prove that, under these assumptions, the ARC method has a mean squared error asymptotically no greater than that of the other method where the entire set of hypotheses is used and assuming a large covariate effect. In addition, we conduct a simulation study to evaluate the performance of estimator associated with the ARC method for a finite number of hypotheses. Here, we apply the proposed method to coronary artery disease (CAD) data taken from a GWA study and diffusion tensor imaging (DTI) data.

Assessment of ASC specks as a putative biomarker of pyroptosis in myelodysplastic syndromes: an observational cohort study.

BACKGROUND: NLRP3 inflammasome-directed pyroptotic cell death drives ineffective haemopoiesis in myelodysplastic syndromes. During inflammasome assembly, the apoptosis-associated speck-like protein containing a CARD (PYCARD, commonly known as ASC) adaptor protein polymerises into large, filamentous clusters termed ASC specks that are released upon cytolysis. Specks are resistant to proteolytic degradation because of their prion-like structure, and therefore might serve as a biomarker for pyroptotic cell death in myelodysplastic syndromes. METHODS: This observational cohort study was done at the H Lee Moffitt Cancer Center (Tampa, FL, USA). Patients with myelodysplastic syndromes, healthy controls, and patients with non-myelodysplastic syndrome haematological cancers or type 2 diabetes were recruited. We used confocal and electron microscopy to visualise, and flow cytometry to quantify, ASC specks in peripheral blood and bone marrow plasma samples. Speck percentages were compared by t test or ANOVA, correlations were assessed by Spearman's rank correlation coefficient, and biomarker efficiency was assessed by receiver operating characteristics and area under the curve (AUC) analysis. FINDINGS: Between Jan 1, 2005, and Jan 12, 2017, we obtained samples from 177 patients with myelodysplastic syndromes and 29 healthy controls for the discovery cohort, and 113 patients with myelodysplastic syndromes and 31 healthy controls for the validation cohort. We also obtained samples from 22 patients with del(5q) myelodysplastic syndromes, 230 patients with non-myelodysplastic syndrome haematological cancers and 23 patients with type 2 diabetes. After adjustment for glucose concentration, the log10-transformed mean percentage of peripheral blood plasma-derived ASC specks was significantly higher in the 177 patients with myelodysplastic syndromes versus the 29 age-matched, healthy donors (-0·41 [SD 0·49] vs -0·67 [0·59], p=0·034). The percentages of ASC specks in samples from patients with myelodysplastic syndromes were significantly greater than those in samples from individuals with every other haematological cancer studied (all p<0·05) except myelofibrosis (p=0·19). The findings were confirmed in the independent validation cohort (p<0·0001). Peripheral blood plasma danger-associated molecular pattern protein S100-A8 and protein S100-A9 concentrations from 144 patients with myelodysplastic syndromes from the discovery cohort directly correlated with ASC speck percentage (r=0·4, p<0·0001 for S100-A8 and r=0·2, p=0·017 for S100-A9). Patients with at least two somatic gene mutations had a significantly greater mean percentage of peripheral blood plasma ASC specks than patients with one or no mutation (-0·22 [SD 0·63] vs -0·53 [0·44], p=0·008). The percentage of plasma ASC specks was a robust marker for pyroptosis in myelodysplastic syndromes (AUC=0·888), in which a cutoff of 0·80 maximised sensitivity at 0·84 (95% CI 0·65-0·91) and specificity at 0·87 (0·58-0·97). INTERPRETATION: Our results underscore the pathobiological relevance of ASC specks and suggest that ASC specks are a sensitive and specific candidate plasma biomarker that provides an index of medullary pyroptotic cell death and ineffective haemopoiesis in patients with myelodysplastic syndromes. FUNDING: T32 Training Grant (NIH/NCI 5T32 CA115308-08), Edward P Evans Foundation, The Taub Foundation Grants Program, the Flow Cytometry, Analytic Microscopy, and Tissue Core Facilities at the H Lee Moffitt Cancer Center and Research Institute, a National Cancer Institute-designated Comprehensive Cancer Center (P30-CA076292).

Expression of Sestrin Genes in Radiotherapy for Prostate Cancer and Its Association With Fatigue: A Proof-of-Concept Study.

Genetic factors that influence inflammation and energy production/expenditure in cells may affect patient outcomes following treatment with external beam radiation therapy (EBRT). Sestrins, stress-inducible genes with antioxidant properties, have recently been implicated in several behaviors including fatigue. This proof-of-concept study explored whether the sestrin family of genes ( SESN1, SESN2, and SESN3) were differentially expressed from baseline to the midpoint of EBRT in a sample of 26 Puerto Rican men with nonmetastatic prostate cancer. We also examined whether changes in expression of these genes were associated with changes in fatigue scores during EBRT. METHOD: Participants completed the 13-item Functional Assessment of Cancer Therapy-Fatigue subscale, Spanish version. Whole blood samples were collected at baseline and at the midpoint of EBRT. Gene expression data were analyzed using the limma package in the R (version R 2.14.0.) statistical software. Linear models and empirical Bayes moderation, adjusted for radiation fraction (total number of days of prescribed radiation treatment), were used to examine potential associations between changes in gene expression and change in fatigue scores. RESULTS: Expression of SESN3 (adjusted p < .01, log fold change -0.649) was significantly downregulated during EBRT, whereas the expressions of SESN1 and SESN2 remained unchanged. After adjustment for radiation fraction, change in SESN3 expression was associated with change in fatigue during EBRT (false discovery rate <.01). CONCLUSIONS: Downregulation of SESN3, a novel pharmacoactive stress response gene, was associated with fatigue intensification during EBRT. SESN3 may serve as an interventional target and a biomarker for the cellular and molecular events associated with EBRT-related fatigue.