Key residues in the VDAC2-BAK complex can be targeted to modulate apoptosis.

BAK and BAX execute intrinsic apoptosis by permeabilising the mitochondrial outer membrane. Their activity is regulated through interactions with pro-survival BCL-2 family proteins and with non-BCL-2 proteins including the mitochondrial channel protein VDAC2. VDAC2 is important for bringing both BAK and BAX to mitochondria where they execute their apoptotic function. Despite this important function in apoptosis, while interactions with pro-survival family members are well characterised and have culminated in the development of drugs that target these interfaces to induce cancer cell apoptosis, the interaction between BAK and VDAC2 remains largely undefined. Deep scanning mutagenesis coupled with cysteine linkage identified key residues in the interaction between BAK and VDAC2. Obstructive labelling of specific residues in the BH3 domain or hydrophobic groove of BAK disrupted this interaction. Conversely, mutating specific residues in a cytosol-exposed region of VDAC2 stabilised the interaction with BAK and inhibited BAK apoptotic activity. Thus, this VDAC2-BAK interaction site can potentially be targeted to either inhibit BAK-mediated apoptosis in scenarios where excessive apoptosis contributes to disease or to promote BAK-mediated apoptosis for cancer therapy.

T-cell help in the tumor microenvironment enhances rituximab-mediated NK-cell ADCC.

ABSTRACT: Rituximab (RTX) and other monoclonal antibodies (mAbs) that bind directly to malignant cells are of great clinical value but are not effective for all patients. A major mechanism of action of RTX is antibody-dependent cellular cytotoxicity (ADCC) mediated by natural killer (NK) cells. Prior in vitro studies in our laboratory demonstrated that T cells contribute to maintaining the viability and cytotoxic potential of NK cells activated by anti-CD20-coated target B cells. Here, we conducted studies using a novel mouse model and clinical correlative analysis to assess whether T-cell help contribute to RTX-mediated NK-cell ADCC in the tumor microenvironment (TME) in vivo. A humanized mouse model was developed using Raji lymphoma cells and normal donor peripheral blood mononuclear cells that allows for control of T-cell numbers in the lymphoma TME. In this model, NK-cell viability and CD16 and CD25 expression dropped after RTX in the absence of T cells but increased in the presence of T cells. RTX therapy was more effective when T cells were present and was ineffective when NK cells were depleted. In patients with indolent lymphoma, fine needle aspirates were obtained before and ∼1 week after treatment with a RTX-containing regimen. There was a strong correlation between CD4+ T cells as well as total T cells in the pretherapy TME and an increase in NK-cell CD16 and CD25 expression after RTX. We conclude that T-cell help in the TME enhances RTX-mediated NK-cell viability and ADCC.

A bivalent Adenovirus-Vectored Vaccine induces a robust humoral response, but does not protect cynomolgus macaques against a lethal challenge with Sudan virus.

The most recent Sudan virus (SUDV) outbreak in Uganda was first detected in September 2022 and resulted in 164 laboratory-confirmed cases and 77 deaths. There are no approved vaccines against SUDV. Here, we investigated the protective efficacy of ChAdOx1-biEBOV in cynomolgus macaques using a prime or a prime-boost regimen. ChAdOx1-biEBOV is a replication-deficient simian adenovirus vector encoding SUDV and Ebola virus (EBOV) glycoproteins (GPs). Intramuscular vaccination induced SUDV and EBOV GP-specific IgG responses and neutralizing antibodies. Upon challenge with SUDV, vaccinated animals showed signs of disease like those observed in control animals, and no difference in survival outcomes were measured among all three groups. Viral load in blood samples and in tissue samples obtained after necropsy were not significantly different between groups. Overall, this study highlights the importance of evaluating vaccines in multiple animal models and demonstrates the importance of understanding protective efficacy in both animal models and human hosts.

The Precision Between Transcutaneous Carbon Dioxide Versus PaCO2 in Infants Undergoing Therapeutic Hypothermia.

BACKGROUND: Infants with hypoxic-ischemic encephalopathy are often treated with therapeutic hypothermia and high-frequency ventilation. Fluctuations in PaCO2 during therapeutic hypothermia are associated with poor neurodevelopmental outcomes. Transcutaneous CO2 monitors offer a noninvasive estimate of PaCO2 represented by transcutaneously measured partial pressure of carbon dioxide (PtcCO2 ). We aimed to assess the precision between PtcCO2 and PaCO2 values in neonates undergoing therapeutic hypothermia. METHODS: This was a retrospective chart review of 10 neonates who underwent therapeutic hypothermia requiring respiratory support over 2 y. A range of 2-27 simultaneous PtcCO2 and PaCO2 pairs of measurements per neonate were analyzed via linear mixed models and a Bland-Altman plot for multiple observations per neonate. RESULTS: A linear mixed-effect model demonstrated that PtcCO2 and PaCO2 (controlling for sex) were similar. The 95% CI of the mean difference ranged from -2.3 to 5.7 mm Hg (P = .41). However, precision was poor as the PtcCO2 ranged from > 18 mm Hg to < 13 mm Hg than PaCO2 values for 95% of observations. CONCLUSIONS: The neonates' PtcCO2 was as much as 18 mm Hg higher to 13 mm Hg lower than the PaCO2 95% of the time. Transcutaneous CO2 monitoring may not be a good trending tool, nor is it appropriate for estimating PaCO2 in patients undergoing therapeutic hypothermia.

Magnetic Resonance Imaging of Iron Metabolism with T2* Mapping Predicts an Enhanced Clinical Response to Pharmacologic Ascorbate in Patients with GBM.

PURPOSE: Pharmacologic ascorbate (P-AscH-) is hypothesized to be an iron (Fe)-dependent tumor-specific adjuvant to chemoradiation in treating glioblastoma (GBM). This study determined the efficacy of combining P-AscH- with radiation and temozolomide in a phase II clinical trial while simultaneously investigating a mechanism-based, noninvasive biomarker in T2* mapping to predict GBM response to P-AscH- in humans. PATIENTS AND METHODS: The single-arm phase II clinical trial (NCT02344355) enrolled 55 subjects, with analysis performed 12 months following the completion of treatment. Overall survival (OS) and progression-free survival (PFS) were estimated with the Kaplan-Meier method and compared across patient subgroups with log-rank tests. Forty-nine of 55 subjects were evaluated using T2*-based MRI to assess its utility as an Fe-dependent biomarker. RESULTS: Median OS was estimated to be 19.6 months [90% confidence interval (CI), 15.7-26.5 months], a statistically significant increase compared with historic control patients (14.6 months). Subjects with initial T2* relaxation < 50 ms were associated with a significant increase in PFS compared with T2*-high subjects (11.2 months vs. 5.7 months, P < 0.05) and a trend toward increased OS (26.5 months vs. 17.5 months). These results were validated in preclinical in vitro and in vivo model systems. CONCLUSIONS: P-AscH- combined with temozolomide and radiotherapy has the potential to significantly enhance GBM survival. T2*-based MRI assessment of tumor iron content is a prognostic biomarker for GBM clinical outcomes. See related commentary by Nabavizadeh and Bagley, p. 255.

Prioritization of Fluorescence In Situ Hybridization (FISH) Probes for Differentiating Primary Sites of Neuroendocrine Tumors with Machine Learning.

Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not been thoroughly investigated using machine learning (ML) techniques. We trained ML models on FISH assay metrics from 85 sbNET and 59 pNET samples for primary site prediction. Exploring multiple methods for imputing missing data, the impute-by-median dataset coupled with a support vector machine model achieved the highest classification accuracy of 93.1% on a held-out test set, with the top importance variables originating from the ERBB2 FISH probe. Due to the greater interpretability of decision tree (DT) models, we fit DT models to ten dataset splits, achieving optimal performance with k-nearest neighbor (KNN) imputed data and a transformation to single categorical biomarker probe variables, with a mean accuracy of 81.4%, on held-out test sets. ERBB2 and MET variables ranked as top-performing features in 9 of 10 DT models and the full dataset model. These findings offer probabilistic guidance for FISH testing, emphasizing the prioritization of the ERBB2, SMAD4, and CDKN2A FISH probes in diagnosing NET primary sites.

Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.

Convolutional neural networks (CNNs) have a proven track record in medical image segmentation. Recently, Vision Transformers were introduced and are gaining popularity for many computer vision applications, including object detection, classification, and segmentation. Machine learning algorithms such as CNNs or Transformers are subject to an inductive bias, which can have a significant impact on the performance of machine learning models. This is especially relevant for medical image segmentation applications where limited training data are available, and a model's inductive bias should help it to generalize well. In this work, we quantitatively assess the performance of two CNN-based networks (U-Net and U-Net-CBAM) and three popular Transformer-based segmentation network architectures (UNETR, TransBTS, and VT-UNet) in the context of HNC lesion segmentation in volumetric [F-18] fluorodeoxyglucose (FDG) PET scans. For performance assessment, 272 FDG PET-CT scans of a clinical trial (ACRIN 6685) were utilized, which includes a total of 650 lesions (primary: 272 and secondary: 378). The image data used are highly diverse and representative for clinical use. For performance analysis, several error metrics were utilized. The achieved Dice coefficient ranged from 0.833 to 0.809 with the best performance being achieved by CNN-based approaches. U-Net-CBAM, which utilizes spatial and channel attention, showed several advantages for smaller lesions compared to the standard U-Net. Furthermore, our results provide some insight regarding the image features relevant for this specific segmentation application. In addition, results highlight the need to utilize primary as well as secondary lesions to derive clinically relevant segmentation performance estimates avoiding biases.

Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation.

There are strong correlations between the microbiome and human disease, including cancer. However, very little is known about potential mechanisms associated with malignant transformation in microbiome-associated gynecological cancer, except for HPV-induced cervical cancer. Our hypothesis is that differences in bacterial communities in upper genital tract epithelium may lead to selection of specific genomic variation at the cellular level of these tissues that may predispose to their malignant transformation. We first assessed differences in the taxonomic composition of microbial communities and genomic variation between gynecologic cancers and normal samples. Then, we performed a correlation analysis to assess whether differences in microbial communities selected for specific single nucleotide variation (SNV) between normal and gynecological cancers. We validated these results in independent datasets. This is a retrospective nested case-control study that used clinical and genomic information to perform all analyses. Our present study confirms a changing landscape in microbial communities as we progress into the upper genital tract, with more diversity in lower levels of the tract. Some of the different genomic variations between cancer and controls strongly correlated with the changing microbial communities. Pathway analyses including these correlated genes may help understand the basis for how changing bacterial landscapes may lead to these cancers. However, one of the most important implications of our findings is the possibility of cancer prevention in women at risk by detecting altered bacterial communities in the upper genital tract epithelium.

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer.

Purpose: The study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP). Methods: The DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed. Results: There was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and 'dead' group in their Kaplan-Meyer curves (p = 0.019). Conclusion: Deep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT. Summary: While current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients.

Which smokers enroll in a hospital based smoking cessation trial? Survey of smoking related behaviors, quit attempts, and motivation to quit.

BACKGROUND: Understanding smoking behaviors in hospital patients who smoke may improve inpatient cessation treatments. This study aimed to describe smoking-related behaviors, past-quit attempts, and self-reported difficulties experienced in quitting among those who enrolled in a smoking cessation trial of varenicline. METHODS: Baseline data were obtained from adult hospitalized smokers (average ≥ 10 cigarettes/day in 4-weeks prior to hospitalization) who enrolled in a randomized, placebo-controlled trial of varenicline ± nicotine lozenges at five Australian public hospitals. A logistic regression model tested the association between participant characteristics and quitting in the previous 12 months. RESULTS: Participants' (n = 320; 57% male, 52.5 ± 12.1 years old) motivation and confidence in quitting were high. A total of 120 participants (37.5%) had attempted quitting in the previous 12-months. Prior hospitalization (P = .008) and employment status (P = .015) were significantly associated with past quit attempts. No statistically significant differences were noted in the reason for hospitalization or the level of nicotine dependence between participants who attempted quitting in the previous 12 months and their counterparts. Smoking cessation pharmacotherapy was used by 55% of those attempting to quit; nicotine replacement therapy (65.2%) and varenicline (16.7%) most common. Stress or anxiety, urges to smoke and a lack of motivation were the difficulties experienced in past quit attempts. CONCLUSIONS: Those who had a prior hospitalization and were unemployed had significantly greater odds of reporting past quit attempts. Further research is needed to investigate the degree of adherence among inpatient smokers with the smoke-free hospital policies and the frequency of NRT provision and uptake on admission.

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes.

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue. METHODS: In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform. RESULTS: The prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances. CONCLUSIONS: Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

Cardiac Adverse Events Associated With Chemo-Radiation Versus Chemotherapy for Resectable Stage III Non-Small-Cell Lung Cancer: A Surveillance, Epidemiology and End Results-Medicare Study.

Background We compared cardiac outcomes for surgery-eligible patients with stage III non-small-cell lung cancer treated adjuvantly or neoadjuvantly with chemotherapy versus chemo-radiation therapy in the Surveillance, Epidemiology and End Results-Medicare database. Methods and Results Patients were age 66+, had stage IIIA/B resectable non-small-cell lung cancer diagnosed between 2007 and 2015, and received adjuvant or neoadjuvant chemotherapy or chemo-radiation within 121 days of diagnosis. Patients having chemo-radiation and chemotherapy only were propensity-score matched and followed from day 121 to first cardiac outcome, noncardiac death, radiation initiation by patients who received chemotherapy only, fee-for-service enrollment interruption, or December 31, 2016. Cause-specific hazard ratios (HRs) and competing risks subdistribution HRs were estimated. The primary outcome was the first of these severe cardiac events: acute myocardial infarction, other hospitalized ischemic heart disease, hospitalized heart failure, percutaneous coronary intervention/coronary artery bypass graft, cardiac death, or urgent/inpatient care for pericardial disease, conduction abnormality, valve disorder, or ischemic heart disease. With median follow-up of 13 months, 70 of 682 patients who received chemo-radiation (10.26%) and 43 of 682 matched patients who received chemotherapy only (6.30%) developed a severe cardiac event (P=0.008) with median time to first event 5.45 months. Chemo-radiation increased the rate of severe cardiac events (cause-specific HR: 1.62 [95% CI, 1.11-2.37] and subdistribution HR: 1.41 [95% CI, 0.97-2.04]). Cancer severity appeared greater among patients who received chemo-radiation (noncardiac death cause-specific HR, 2.53 [95% CI, 1.93-3.33] and subdistribution HR, 2.52 [95% CI, 1.90-3.33]). Conclusions Adding radiation therapy to chemotherapy is associated with an increased risk of severe cardiac events among patients with resectable stage III non-small-cell lung cancer for whom survival benefit of radiation therapy is unclear.

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case−control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

Single-chain insulin analogs threaded by the insulin receptor αCT domain.

Insulin is a mainstay of therapy for diabetes mellitus, yet its thermal stability complicates global transportation and storage. Cold-chain transport, coupled with optimized formulation and materials, prevents to some degree nucleation of amyloid and hence inactivation of hormonal activity. These issues hence motivate the design of analogs with increased stability, with a promising approach being single-chain insulins (SCIs), whose C domains (foreshortened relative to proinsulin) resemble those of the single-chain growth factors (IGFs). We have previously demonstrated that optimized SCIs can exhibit native-like hormonal activity with enhanced thermal stability and marked resistance to fibrillation. Here, we describe the crystal structure of an ultrastable SCI (C-domain length 6; sequence EEGPRR) bound to modules of the insulin receptor (IR) ectodomain (N-terminal α-subunit domains L1-CR and C-terminal αCT peptide; "microreceptor" [µIR]). The structure of the SCI-µIR complex, stabilized by an Fv module, was determined using diffraction data to a resolution of 2.6 Å. Remarkably, the αCT peptide (IR-A isoform) "threads" through a gap between the flexible C domain and the insulin core. To explore such threading, we undertook molecular dynamics simulations to 1) compare threaded with unthreaded binding modes and 2) evaluate effects of C-domain length on these alternate modes. The simulations (employing both conventional and enhanced sampling simulations) provide evidence that very short linkers (C-domain length of -1) would limit gap opening in the SCI and so impair threading. We envisage that analogous threading occurs in the intact SCI-IR complex-rationalizing why minimal C-domain lengths block complete activity-and might be exploited to design novel receptor-isoform-specific analogs.

Hospital Rurality and Gene Expression Profiling for Early-Stage Breast Cancer among Iowa Residents (2010-2018).

Objective: Given the challenges rural cancer patients face in accessing cancer care as well as the slower diffusion and adoption of new medical technologies among rural providers, the aim of our study was to examine trends in gene expression profiling (GEP) testing and evaluate the association between hospital rurality and receipt of GEP testing. Methods: Data from the Iowa Cancer Registry (ICR) were used to identify women with newly diagnosed, histologically confirmed breast cancer from 2010 through 2018 who met eligibility criteria for GEP testing. Patients were allocated to the hospitals where their most definitive surgical treatment was received, and Rural-Urban Commuting Area codes were used to categorize hospitals into urban (N = 43), large rural (N = 16), and small rural (N = 48). Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression to evaluate the association between hospital rurality and GEP test use, adjusting for demographic and clinical characteristics. The association between test result and treatment received was assessed among patients who received Oncotype DX (ODX) testing. Results: Of 6,726 patients eligible for GEP test use, 46% (N = 3,069) underwent testing with 95% receiving ODX. While overall GEP testing rates increased over time from 42% between 2010 and 2012 to 51% between 2016 and 2018 (P trend < 0.0001), use continued to be the lowest among patients treated at hospitals in small rural areas. The odds of GEP testing remained significantly lower among patients treated at hospitals located in small rural areas (aOR 0.55; 95% CI 0.43-0.71), after adjusting for demographic and clinical characteristics. ODX recurrence scores were highly correlated with chemotherapy use across all strata of hospital rurality. Conclusions: GEP testing continues to be underutilized, especially among those treated at small rural hospitals. Targeted interventions aimed at increasing rates of GEP testing to ensure the appropriate use of adjuvant chemotherapy may improve health outcomes and lower treatment-related costs.

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer.

Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon's clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

Pharmacological ascorbate improves the response to platinum-based chemotherapy in advanced stage non-small cell lung cancer.

PURPOSE: Platinum-based chemotherapy with or without immunotherapy is the mainstay of treatment for advanced stage non-small cell lung cancer (NSCLC) lacking a molecular driver alteration. Pre-clinical studies have reported that pharmacological ascorbate (P-AscH-) enhances NSCLC response to platinum-based therapy. We conducted a phase II clinical trial combining P-AscH- with carboplatin-paclitaxel chemotherapy. EXPERIMENTAL DESIGN: Chemotherapy naïve advanced stage NSCLC patients received 75 g ascorbate twice per week intravenously with carboplatin and paclitaxel every three weeks for four cycles. The primary endpoint was to improve tumor response per Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 compared to the historical control of 20%. The trial was conducted as an optimal Simon's two-stage design. Blood samples were collected for exploratory analyses. RESULTS: The study enrolled 38 patients and met its primary endpoint with an objective response rate of 34.2% (p = 0.03). All were confirmed partial responses (cPR). The disease control rate was 84.2% (stable disease + cPR). Median progression-free and overall survival were 5.7 months and 12.8 months, respectively. Treatment-related adverse events (TRAE) included one grade 5 (neutropenic fever) and five grade 4 events (cytopenias). Cytokine and chemokine data suggest that the combination elicits an immune response. Immunophenotyping of peripheral blood mononuclear cells demonstrated an increase in effector CD8 T-cells in patients with a progression-free survival (PFS) ≥ 6 months. CONCLUSIONS: The addition of P-AscH- to platinum-based chemotherapy improved tumor response in advanced stage NSCLC. P-AscH- appears to alter the host immune response and needs further investigation as a potential adjuvant to immunotherapy.

Rural-urban differences in secular trends of locoregional treatment for ductal carcinoma in situ: A patterns of care analysis.

PRECIS: Omission of PORT following BCS remains high among rural patients despite evidence that PORT leads to a significant reduction in the risk of local recurrence. Further research is needed to examine the impact of rural residence on treatment choices and develop methods to ensure equitable care among all breast cancer patients. BACKGROUND: Despite national guidelines, debate exists among clinicians regarding the optimal approach to treatment for patients diagnosed with ductal carcinoma in situ (DCIS). While regional variation in practice patterns has been well documented, population-based information on rural-urban treatment differences is lacking. METHODS: Data from the SEER Patterns of Care studies were used to identify women diagnosed with histologically confirmed DCIS who underwent cancer-directed surgery in the years 1991, 1995, 2000, 2005, 2010, and 2015. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using weighted multivariable logistic regression to evaluate cancer-directed surgery and use of post-operative radiation therapy (PORT). RESULTS: Of the 3337 patients who met inclusion criteria, 27% underwent mastectomy, 26% underwent breast-conserving surgery (BCS) without PORT, and 47% underwent BCS with PORT. After adjustment for other covariates, there was no difference in the likelihood of receiving mastectomy between rural and urban patients (aOR = 0.65; 95% CI 0.37-1.14). However, rural residents were more likely than urban residents to have mastectomy during 1991/1995 (aOR = 1.78; 95% CI 1.09-2.91; pinteraction  = 0.022). Across all diagnosis years, patients residing in rural areas were less likely to receive PORT following BCS compared to urban patients (aOR = 0.35; 95% CI 0.18-0.67). CONCLUSIONS: Omission of PORT following BCS remains high among rural patients despite evidence that PORT leads to a significant reduction in the risk of local recurrence. Further research is needed to examine the impact of rural residence on treatment choices and develop methods to ensure equitable care among all breast cancer patients.

Discovery, synthesis and exploration of N-benzylsulfonyl-2-phenylazepanes as inhibitors of Bim expression in a mouse embryonic fibroblast model.

Chronic activation of beta-adrenergic receptors by the sympathetic nervous system results in the apoptosis of cardiomyocytes. Due to the inability of cardiomyocytes to regenerate, this can result in heart failure. Upregulation of the pro-apoptotic protein Bim has been implicated as the cause of cardiomyocyte apoptosis. Beta blockers are the frontline drug used to negate this apoptotic pathway, as no direct inhibitors of Bim expression currently exist. Unfortunately, treatment of heart failure using beta blockers is not optimal. Therefore, direct inhibition of Bim expression is an attractive strategy to provide protection against stress-induced apoptosis of cardiomyocytes. Herein we explore a class of N-benzylsulfonyl-2-phenylazepanes to obtain anti-apoptotic compounds capable of reducing Bim expression levels to 7% of the control at 10 µM in cardiomyocytes under conditions of chronic beta-adrenergic receptor activation with little inhibitory effect upon protein kinase A activity and minimal toxicity.