BatchFLEX: feature-level equalization of X-batch.

MOTIVATION: Integrative analysis of heterogeneous expression data remains challenging due to variations in platform, RNA quality, sample processing, and other unknown technical effects. Selecting the approach for removing unwanted batch effects can be a time-consuming and tedious process, especially for more biologically focused investigators. RESULTS: Here, we present BatchFLEX, a Shiny app that can facilitate visualization and correction of batch effects using several established methods. BatchFLEX can visualize the variance contribution of a factor before and after correction. As an example, we have analyzed ImmGen microarray data and enhanced its expression signals that distinguishes each immune cell type. Moreover, our analysis revealed the impact of the batch correction in altering the gene expression rank and single-sample GSEA pathway scores in immune cell types, highlighting the importance of real-time assessment of the batch correction for optimal downstream analysis. AVAILABILITY AND IMPLEMENTATION: Our tool is available through Github https://github.com/shawlab-moffitt/BATCH-FLEX-ShinyApp with an online example on Shiny.io https://shawlab-moffitt.shinyapps.io/batch_flex/.

Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancers.

Super enhancers (SEs) are broad enhancer domains usually containing multiple constituent enhancers that hold elevated activities in gene regulation. Disruption in one or more constituent enhancers causes aberrant SE activities that lead to gene dysregulation in diseases. To quantify SE aberrations, differential analysis is performed to compare SE activities between cell conditions. The state-of-art strategy in estimating differential SEs relies on overall activities and neglect the changes in length and structure of SEs. Here, we propose a novel computational method to identify differential SEs by weighting the combinatorial effects of constituent-enhancer activities and locations (i.e. internal dynamics). In addition to overall activity changes, our method identified four novel classes of differential SEs with distinct enhancer structural alterations. We demonstrate that these structure alterations hold distinct regulatory impact, such as regulating different number of genes and modulating gene expression with different strengths, highlighting the differentiated regulatory roles of these unexplored SE features. When compared to the existing method, our method showed improved identification of differential SEs that were linked to better discernment of cell-type-specific SE activity and functional interpretation.

The harsh microenvironment in early breast cancer selects for a Warburg phenotype.

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected low-glycolytic breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA sequencing analysis of WE clones identified the transcription factor KLF4 as potential inducer of the WE phenotype. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a WE phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics.

SUMMARY: Spatially resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics that can be compared against clinical information, spot-level cell deconvolution and spatially informed clustering, all using a new data object to store data and resulting analyses simultaneously. AVAILABILITY AND IMPLEMENTATION: The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. The Thrane study data included in spatialGE was made available from the public available from the website https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PARP1 and POLD2 as prognostic biomarkers for multiple myeloma in autologous stem cell transplant.

Multiple Myeloma (MM) is an incurable plasma cell malignancy often treated by autologous stem cell transplant (ASCT). Clinical response to ASCT has been associated with DNA repair efficiency. Here we interrogated the role of the base excision DNA repair (BER) pathway in MM response to ASCT. Across 450 clinical samples and six disease stages, expression levels of genes in the BER pathway were found to be highly upregulated during the development of MM. In a separate cohort of 559 patients with MM treated with ASCT, expression of BER pathway members MPG and PARP3 was positively associated with overall survival (OS) while expression of PARP1, POLD1, and POLD2 was negatively associated with OS. In a validation cohort of 356 patients with MM treated with ASCT, PARP1 and POLD2 findings were replicated. In patients with MM who never received ASCT (n=319), PARP1 and POLD2 were not associated with OS, suggesting that the prognostic effect of these genes may be treatment-dependent. In preclinical models of MM, synergy was observed in anti-tumor activity when poly (ADPribose) polymerase (PARP) inhibitors (olaparib, talazoparib) were used in combination with melphalan. The negative prognosis associated with PARP1 and POLD2 expression along with the apparent melphalan-sensitizing effect of PARP inhibition may suggest this pathway as a potential biomarker in patients with MM in the setting of ASCT. Further understanding of the role of the BER pathway in MM is vital to improve therapeutic strategies related to ASCT.

The relationship of lifetime history of depression on the ovarian tumor immune microenvironment.

BACKGROUND: Depression is associated with a higher ovarian cancer risk. Prior work suggests that depression can lead to systemic immune suppression, which could potentially alter the anti-tumor immune response. METHODS: We evaluated the association of pre-diagnosis depression with features of the anti-tumor immune response, including T and B cells and immunoglobulins, among women with ovarian tumor tissue collected in three studies, the Nurses' Health Study (NHS; n = 237), NHSII (n = 137) and New England Case-Control Study (NECC; n = 215). Women reporting depressive symptoms above a clinically relevant cut-point, antidepressant use, or physician diagnosis of depression at any time prior to diagnosis of ovarian cancer were considered to have pre-diagnosis depression. Multiplex immunofluorescence was performed on tumor tissue microarrays to measure immune cell infiltration. In pooled analyses, we estimated odds ratios (OR) and 95% confidence intervals (CI) for the positivity of tumor immune cells using a beta-binomial model comparing those with and without depression. We used Bonferroni corrections to adjust for multiple comparisons. RESULTS: We observed no statistically significant association between depression status and any immune markers at the Bonferroni corrected p-value of 0.0045; however, several immune markers were significant at a nominal p-value of 0.05. Specifically, there were increased odds of having recently activated cytotoxic (CD3+CD8+CD69+) and exhausted-like T cells (CD3+Lag3+) in tumors of women with vs. without depression (OR = 1.36, 95 %CI = 1.09-1.69 and OR = 1.24, 95 %CI = 1.01-1.53, respectively). Associations were comparable when considering high grade serous tumors only (comparable ORs = 1.33, 95 %CI = 1.05-1.69 and OR = 1.25, 95 %CI = 0.99-1.58, respectively). There were decreased odds of having tumor infiltrating plasma cells (CD138+) in women with vs. without depression (OR = 0.54, 95 %CI = 0.33-0.90), which was similar among high grade serous carcinomas, although not statistically significant. Depression was also related to decreased odds of having naïve and memory B cells (CD20+: OR = 0.54, 95 %CI = 0.30-0.98) and increased odds of IgG (OR = 1.22, 95 %CI = 0.97-1.53) in high grade serous carcinomas. CONCLUSION: Our results provide suggestive evidence that depression may influence ovarian cancer outcomes through changes in the tumor immune microenvironment, including increasing T cell activation and exhaustion and reducing antibody-producing B cells. Further studies with clinical measures of depression and larger samples are needed to confirm these results.

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer.

New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley's K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the findings from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.

Prospective Analyses of Sedentary Behavior in Relation to Risk of Ovarian Cancer.

We examined the association of sedentary behavior with risk of ovarian cancer overall, by tumor subtype, and by participant characteristics in the Nurses' Health Study (NHS) and Nurses' Health Study II (NHS II). A total of 69,558 NHS participants (1992-2016) and 104,130 NHS II participants (1991-2015) who reported on time spent sitting at home, at work, and while watching television were included in the analysis, which included 884 histologically confirmed ovarian cancer cases. Multivariable-adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for ovarian cancer by sitting time (no mutual adjustment for individual sitting types in primary analyses). We examined potential heterogeneity by tumor histological type (type I or II), body mass index (weight (kg)/height (m)2; < 25 or ≥25), and total physical activity (<15 or ≥15 metabolic equivalent of task-hours/week). We observed an increased risk of ovarian cancer for women who sat at work for 10-19 hours/week (HR = 1.25, 95% CI: 1.04, 1.51) and ≥20 hours/week (HR = 1.40, 95% CI: 1.14, 1.71) versus <5 hours/week. This association did not vary by body mass index, physical activity, or histotype (P for heterogeneity ≥ 0.43). No associations were observed for overall sitting, sitting while watching television, or other sitting at home. Longer sitting time at work was associated with elevated risk of ovarian cancer. Further investigations are required to confirm these findings and elucidate underlying mechanisms.

spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data.

SUMMARY: Multiplex immunofluorescence (mIF) staining combined with quantitative digital image analysis is a novel and increasingly used technique that allows for the characterization of the tumor immune microenvironment (TIME). Generally, mIF data is used to examine the abundance of immune cells in the TIME; however, this does not capture spatial patterns of immune cells throughout the TIME, a metric increasingly recognized as important for prognosis. To address this gap, we developed an R package spatialTIME that enables spatial analysis of mIF data, as well as the iTIME web application that provides a robust but simplified user interface for describing both abundance and spatial architecture of the TIME. The spatialTIME package calculates univariate and bivariate spatial statistics (e.g. Ripley's K, Besag's L, Macron's M and G or nearest neighbor distance) and creates publication quality plots for spatial organization of the cells in each tissue sample. The iTIME web application allows users to statistically compare the abundance measures with patient clinical features along with visualization of the TIME for one tissue sample at a time. AVAILABILITY AND IMPLEMENTATION: spatialTIME is implemented in R and can be downloaded from GitHub (https://github.com/FridleyLab/spatialTIME) or CRAN. An extensive vignette for using spatialTIME can also be found at https://cran.r-project.org/web/packages/spatialTIME/index.html. iTIME is implemented within a R Shiny application and can be accessed online (http://itime.moffitt.org/), with code available on GitHub (https://github.com/FridleyLab/iTIME). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.

BACKGROUND: Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems used to extract information from pathology reports are often narrow in scope or require extensive tuning. Consequently, there is growing interest in automated deep learning approaches. A powerful new NLP algorithm, bidirectional encoder representations from transformers (BERT), was published in late 2018. BERT set new performance standards on tasks as diverse as question answering, named entity recognition, speech recognition, and more. OBJECTIVE: The aim of this study is to develop a BERT-based system to automatically extract detailed tumor site and histology information from free-text oncological pathology reports. METHODS: We pursued three specific aims: extract accurate tumor site and histology descriptions from free-text pathology reports, accommodate the diverse terminology used to indicate the same pathology, and provide accurate standardized tumor site and histology codes for use by downstream applications. We first trained a base language model to comprehend the technical language in pathology reports. This involved unsupervised learning on a training corpus of 275,605 electronic pathology reports from 164,531 unique patients that included 121 million words. Next, we trained a question-and-answer (Q&A) model that connects a Q&A layer to the base pathology language model to answer pathology questions. Our Q&A system was designed to search for the answers to two predefined questions in each pathology report: What organ contains the tumor? and What is the kind of tumor or carcinoma? This involved supervised training on 8197 pathology reports, each with ground truth answers to these 2 questions determined by certified tumor registrars. The data set included 214 tumor sites and 193 histologies. The tumor site and histology phrases extracted by the Q&A model were used to predict International Classification of Diseases for Oncology, Third Edition (ICD-O-3), site and histology codes. This involved fine-tuning two additional BERT models: one to predict site codes and another to predict histology codes. Our final system includes a network of 3 BERT-based models. We call this CancerBERT network (caBERTnet). We evaluated caBERTnet using a sequestered test data set of 2050 pathology reports with ground truth answers determined by certified tumor registrars. RESULTS: caBERTnet's accuracies for predicting group-level site and histology codes were 93.53% (1895/2026) and 97.6% (1993/2042), respectively. The top 5 accuracies for predicting fine-grained ICD-O-3 site and histology codes with 5 or more samples each in the training data set were 92.95% (1794/1930) and 96.01% (1853/1930), respectively. CONCLUSIONS: We have developed an NLP system that outperforms existing algorithms at predicting ICD-O-3 codes across an extensive range of tumor sites and histologies. Our new system could help reduce treatment delays, increase enrollment in clinical trials of new therapies, and improve patient outcomes.

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.

Mendelian randomization analyses suggest a role for cholesterol in the development of endometrial cancer.

Blood lipids have been associated with the development of a range of cancers, including breast, lung and colorectal cancer. For endometrial cancer, observational studies have reported inconsistent associations between blood lipids and cancer risk. To reduce biases from unmeasured confounding, we performed a bidirectional, two-sample Mendelian randomization analysis to investigate the relationship between levels of three blood lipids (low-density lipoprotein [LDL] and high-density lipoprotein [HDL] cholesterol, and triglycerides) and endometrial cancer risk. Genetic variants associated with each of these blood lipid levels (P < 5 × 10-8 ) were identified as instrumental variables, and assessed using genome-wide association study data from the Endometrial Cancer Association Consortium (12 906 cases and 108 979 controls) and the Global Lipids Genetic Consortium (n = 188 578). Mendelian randomization analyses found genetically raised LDL cholesterol levels to be associated with lower risks of endometrial cancer of all histologies combined, and of endometrioid and non-endometrioid subtypes. Conversely, higher genetically predicted HDL cholesterol levels were associated with increased risk of non-endometrioid endometrial cancer. After accounting for the potential confounding role of obesity (as measured by genetic variants associated with body mass index), the association between genetically predicted increased LDL cholesterol levels and lower endometrial cancer risk remained significant, especially for non-endometrioid endometrial cancer. There was no evidence to support a role for triglycerides in endometrial cancer development. Our study supports a role for LDL and HDL cholesterol in the development of non-endometrioid endometrial cancer. Further studies are required to understand the mechanisms underlying these findings.

Molecular signatures of X chromosome inactivation and associations with clinical outcomes in epithelial ovarian cancer.

X chromosome inactivation (XCI) is a key epigenetic gene expression regulatory process, which may play a role in women's cancer. In particular tissues, some genes are known to escape XCI, yet patterns of XCI in ovarian cancer (OC) and their clinical associations are largely unknown. To examine XCI in OC, we integrated germline genotype with tumor copy number, gene expression and DNA methylation information from 99 OC patients. Approximately 10% of genes showed different XCI status (either escaping or being subject to XCI) compared with the studies of other tissues. Many of these genes are known oncogenes or tumor suppressors (e.g. DDX3X, TRAPPC2 and TCEANC). We also observed strong association between cis promoter DNA methylation and allele-specific expression imbalance (P = 2.0 × 10-10). Cluster analyses of the integrated data identified two molecular subgroups of OC patients representing those with regulated (N = 47) and dysregulated (N = 52) XCI. This XCI cluster membership was associated with expression of X inactive specific transcript (P = 0.002), a known driver of XCI, as well as age, grade, stage, tumor histology and extent of residual disease following surgical debulking. Patients with dysregulated XCI (N = 52) had shorter time to recurrence (HR = 2.34, P = 0.001) and overall survival time (HR = 1.87, P = 0.02) than those with regulated XCI, although results were attenuated after covariate adjustment. Similar findings were observed when restricted to high-grade serous tumors. We found evidence of a unique OC XCI profile, suggesting that XCI may play an important role in OC biology. Additional studies to examine somatic changes with paired tumor-normal tissue are needed.

GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis.

MOTIVATION: Missingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns. RESULTS: Three proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMSimpute using simulated data (with a wide range of 80 missing patterns) and metabolomics data from the Cancer Genome Atlas breast cancer and clear cell renal cell carcinoma studies. Using Pearson correlation and normalized root mean square errors between the true and imputed abundance, we compare its performance to K-nearest neighbors' type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum values. The results indicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across different missing patterns. In addition, GMSimpute is able to identify the features in downstream differential expression analysis with high accuracy when applied to the Cancer Genome Atlas datasets. AVAILABILITY AND IMPLEMENTATION: GMSimpute is on CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mitochondrial DNA sequence variation and risk of meningioma.

BACKGROUND: Risk factors for meningioma include female gender, African American race, high body mass index (BMI), and exposure to ionizing radiation. Although genome-wide association studies (GWAS) have identified two nuclear genome risk loci for meningioma (rs12770228 and rs2686876), the relation between mitochondrial DNA (mtDNA) sequence variants and meningioma is unknown. METHODS: We examined the association of 42 common germline mtDNA variants (minor allele frequency ≥ 5%), haplogroups, and genes with meningioma in 1080 controls and 478 meningioma cases from a case-control study conducted at medical centers in the southeastern United States. Associations were examined separately for meningioma overall and by WHO grade (n = 409 grade I and n = 69 grade II/III). RESULTS: Overall, meningioma was significantly associated with being female (OR 2.85; 95% CI 2.21-3.69), self-reported African American race (OR 2.38, 95% CI 1.41-3.99), and being overweight (OR 1.48; 95% CI 1.11-1.97) or obese (OR 1.70; 95% CI 1.25-2.31). The variant m.16362T > C (rs62581341) in the mitochondrial control region was positively associated with grade II/III meningiomas (OR 2.33; 95% CI 1.14-4.77), but not grade I tumors (OR 0.99; 95% CI 0.64-1.53). Haplogroup L, a marker for African ancestry, was associated with meningioma overall (OR 2.92; 95% CI 1.01-8.44). However, after stratifying by self-reported race, this association was only apparent among the few self-reported Caucasians with this haplogroup (OR 6.35; 95% CI 1.56-25.9). No other mtDNA variant, haplogroup, or gene was associated with meningioma. CONCLUSION: Common mtDNA variants and major mtDNA haplogroups do not appear to have associations with the odds of developing meningioma.

"I think that a brief conversation from their provider can go a very long way": Patient and provider perspectives on barriers and facilitators of genetic testing after ovarian cancer.

OBJECTIVE: Identify predisposing, enabling, and reinforcing factors impacting genetic counseling/testing among ovarian cancer patients guided by Green and Kreuter's PRECEDE-PROCEED model. METHODS: Gynecologic oncology providers (N = 4), genetic counselors (N = 4), and ovarian cancer patients (N = 9) completed semi-structured qualitative interviews exploring participants' knowledge of and experiences with genetic counseling/testing. Interviews were audio recorded, transcribed verbatim, and analyzed using inductive content analysis by two independent raters. RESULTS: Thematic analysis identified predisposing, enabling, and reinforcing factors impacting referral for and uptake of genetic counseling/testing. Predisposing factors included participant's knowledge, beliefs, and attitudes related to genetic counseling/testing. Both patients and providers also cited that insurance coverage and out-of-pocket cost are major concerns for ovarian cancer patients considering genetic testing. Finally, both patients and providers emphasized that genetic counseling/testing would provide additional information to an ovarian cancer patient. While providers emphasized that genetic testing results were useful for informing a patient's personal treatment plan, patients emphasized that this knowledge would be beneficial for their family members. CONCLUSION: Barriers to genetic testing for ovarian cancer patients exist at multiple levels, including the patient (e.g., knowledge, attitudes), the provider (e.g., workload, availability of services), the institution (e.g., difficulty with referrals/scheduling), and the healthcare system (e.g., insurance/cost). Interventions aiming to increase genetic testing among ovarian cancer patients will likely need to target multiple levels of influence. Future quantitative studies are needed to replicate these results. This line of work will inform specific multilevel intervention strategies that are adaptable to different practice settings, ultimately improving guideline concordant care.

Patterns and predictors of genetic referral among ovarian cancer patients at a National Cancer Institute-Comprehensive Cancer Center.

Germline mutations (eg, BRCA1/2) have prognostic and treatment implications for ovarian cancer (OVCA) patients. Thus, national guidelines recommend genetic testing for OVCA patients. The present study examines patterns and predictors of genetics referral in OVCA patients. Electronic medical record data were abstracted retrospectively from 557 OVCA patients treated from 1 January 2001 to 31 December 2015. Logistic regression models identified sociodemographic characteristics, disease/treatment characteristics, family history data, provider characteristics, and survival data that predicted genetics referral. Overall, 27.5% of patients received referral. Eleven variables predicting referral were selected during stepwise regression: younger age, White race, not having private insurance, professional school education, year of OVCA diagnosis, platinum sensitivity, female gynecologic oncologist, chemotherapy administered by a gynecologic oncologist, clinical trial enrollment, longer overall survival, and family history of OVCA. Genetics referral among OVCA patients was similar to rates reported nationwide. Unique predictive factors will contribute to quality improvement and should be validated at a multi-institutional level to ensure guideline concordant care is provided to all OVCA patients. Future research should identify both patient-level and provider-level factors associated with genetics referral.

Predictors of survival trajectories among women with epithelial ovarian cancer.

OBJECTIVE: Although ovarian cancer is a deadly disease, approximately a third of women survive ≥9 years after diagnosis. The factors associated with achieving long-term survival are not well understood. In this study, data from the Surveillance, Epidemiology, and End Results (SEER) program were used to determine predictors of survival trajectories among women with epithelial ovarian cancer and across histotype (high-grade serous carcinoma (HGSC) and non-HGSC). METHODS: Data on 35,868 women diagnosed with epithelial ovarian cancer in 2004-2016 were extracted from SEER. Extended Cox proportional hazards regression was used to estimate overall and histotype-specific associations between patient and tumor characteristics and all-cause mortality within each survival time (t) interval (t < 3, 3 ≤ t < 6, 6 ≤ t < 9, and 9 ≤ t < 13 years). RESULTS: Age at diagnosis, marital status, race/ethnicity, stage, and surgery were more strongly associated with mortality in the short-term survival period, and these associations waned with increasing survival time. Exceptions to this pattern were age >70 years at diagnosis, where a high risk of mortality was observed in both the t < 3 and t ≥ 9 year time periods, and non-Hispanic Asian/Pacific Islanders, where a more pronounced inverse association with mortality was observed in t ≥ 9 years after diagnosis. Similar associations were observed for HGSC, although the waning effect was not apparent for most characteristics. Mortality associations for non-HGSC were more pronounced for stage and race/ethnicity, primarily for non-Hispanic Asian/Pacific Islanders. CONCLUSIONS: Most patient and tumor characteristics were more strongly associated with mortality in the years following diagnosis, but have declining impact with increasing survival time. Given this waning effect, it is critical to identify factors impacting risk of mortality as ovarian cancer patients advance through the survival trajectory.

Differential roles of ERRFI1 in EGFR and AKT pathway regulation affect cancer proliferation.

AKT signaling is modulated by a complex network of regulatory proteins and is commonly deregulated in cancer. Here, we present a dual mechanism of AKT regulation by the ERBB receptor feedback inhibitor 1 (ERRFI1). We show that in cells expressing high levels of EGFR, ERRF1 inhibits growth and enhances responses to chemotherapy. This is mediated in part through the negative regulation of AKT signaling by direct ERRFI1-dependent inhibition of EGFR In cells expressing low levels of EGFR, ERRFI1 positively modulates AKT signaling by interfering with the interaction of the inactivating phosphatase PHLPP with AKT, thereby promoting cell growth and chemotherapy desensitization. These observations broaden our understanding of chemotherapy response and have important implications for the selection of targeted therapies in a cell context-dependent manner. EGFR inhibition can only sensitize EGFR-high cells for chemotherapy, while AKT inhibition increases chemosensitivity in EGFR-low cells. By understanding these mechanisms, we can take advantage of the cellular context to individualize antineoplastic therapy. Finally, our data also suggest targeting of EFFRI1 in EGFR-low cancer as a promising therapeutic approach.

Phase II trial of gemcitabine and nab-paclitaxel in patients with recurrent Ewing sarcoma: A report from the National Pediatric Cancer Foundation.

BACKGROUND: The combination of gemcitabine and docetaxel is often used to treat patients with recurrent sarcoma. Nab-paclitaxel is a taxane modified to improve drug exposure and increase intratumoral accumulation and, in combination with gemcitabine, is standard therapy for pancreatic cancer. Applying the dosages and schedule used for pancreatic cancer, we performed a phase II trial to assess the response rate of gemcitabine and nab-paclitaxel in patients with relapsed Ewing sarcoma. PROCEDURE: Using a Simon's two-stage design to identify a response rate of ≥ 35%, patients received nab-paclitaxel 125 mg/m2 followed by gemcitabine 1000 mg/m2 i.v. on days 1, 8, and 15 of four-week cycles. Immunohistochemical analysis of archival tissue was performed to identify possible biomarkers of response. RESULTS: Eleven patients from four institutions enrolled, with a median age of 22 years (range, 14-27). Patients were heavily pretreated (median 3 prior regimens, range, 1-7). Thirty-five cycles were administered (median 2, range, 1-8). Accrual was stopped after 11 patients, due to only one confirmed partial response. Two other patients had partial responses after two cycles, but withdrew because of adverse effects or progression before confirmation of continued response. The predominant toxicity was myelosuppression, and four (36%) patients were removed due to hematologic toxicity despite pegfilgrastim and dose reductions. Expression of secreted protein, acidic and rich in cysteine (SPARC) and CAV-1 in archival tumors was not predictive of clinical benefit in this small cohort of patients. CONCLUSIONS: In patients with heavily pretreated Ewing sarcoma, the confirmed response rate of 9% was similar to multi-institutional studies of gemcitabine and docetaxel.