Cabozantinib monotherapy for advanced adrenocortical carcinoma: a single-arm, phase 2 trial.

BACKGROUND: Adrenocortical carcinoma is a rare malignancy with poor response to systemic chemotherapy. Mitotane is the only approved therapy for adrenocortical carcinoma. Cabozantinib is a multikinase inhibitor approved in multiple malignancies. This is the first prospective trial to explore the anti-tumour activity, safety, and pharmacokinetic profile of cabozantinib in patients with advanced adrenocortical carcinoma. METHODS: This investigator-initiated, single-arm, phase 2 trial in adult patients (aged ≥18 years) with advanced adrenocortical carcinoma was done at the University of Texas MD Anderson Cancer Center (Houston, TX, USA). Eligible patients had histologically confirmed adrenocortical carcinoma, were not candidates for surgery with curative intent, had measurable disease, had an estimated life expectancy of at least 3 months, and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 with adequate organ function. Patients who had used mitotane within 6 months of study participation were required to have a serum mitotane level of less than 2 mg/L. Patients were given oral cabozantinib 60 mg daily with the option of dose reduction to manage adverse events. The primary endpoint was progression-free survival at 4 months, assessed in all patients who received at least one dose of study drug per protocol. This study is registered with ClinicalTrials.gov, NCT03370718, and is now complete. FINDINGS: Between March 1, 2018, and May 31, 2021, we enrolled 18 patients (ten males and eight females), all of whom received at least one dose of study treatment. Of the 18 patients, eight (44%) had an ECOG performance status of 0, nine (50%) patients had a performance status of 1, and one (6%) patient had a performance status of 2. Median follow-up was 36·8 months (IQR 30·2-50·3). At 4 months, 13 (72·2%; 95% CI 46·5-90·3) of 18 patients had progression-free survival and median progression-free survival was 6 months (95% CI 4·3 to not reached). One patient remains on treatment. Treatment-related adverse events of grade 3 or worse occurred in 11 (61%) of 18 patients. The most common grade 3 adverse events were lipase elevation (three [17%] of 18 patients), elevated γ-glutamyl transferase concentrations (two [11%] patients), elevated alanine aminotransferase concentrations (two [11%] patients), hypophosphatemia (two [11%] patients), and hypertension (two [11%] patients). One (6%) of 18 patients had grade 4 hypertension. No treatment related deaths occurred on study. INTERPRETATION: Cabozantinib in advanced adrenocortical carcinoma showed promising efficacy with a manageable and anticipated safety profile. Further prospective studies with cabozantinib alone and in combination with immune checkpoint therapy are ongoing. FUNDING: Exelixis.

A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.

MOTIVATION: Multilevel molecular profiling of tumors and the integrative analysis with clinical outcomes have enabled a deeper characterization of cancer treatment. Mediation analysis has emerged as a promising statistical tool to identify and quantify the intermediate mechanisms by which a gene affects an outcome. However, existing methods lack a unified approach to handle various types of outcome variables, making them unsuitable for high-throughput molecular profiling data with highly interconnected variables. RESULTS: We develop a general mediation analysis framework for proteogenomic data that include multiple exposures, multivariate mediators on various scales of effects as appropriate for continuous, binary and survival outcomes. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption, while accommodating multiple exposures and high-dimensional mediators. We compare our approach to other methods in extensive simulation studies at a range of sample sizes, disease prevalence and number of false mediators. Using kidney renal clear cell carcinoma proteogenomic data, we identify genes that are mediated by proteins and the underlying mechanisms on various survival outcomes that capture short- and long-term disease-specific clinical characteristics. AVAILABILITY AND IMPLEMENTATION: Software is made available in an R package (https://github.com/longjp/mediateR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lead-Time Trajectory of CA19-9 as an Anchor Marker for Pancreatic Cancer Early Detection.

BACKGROUND & AIMS: There is substantial interest in liquid biopsy approaches for cancer early detection among subjects at risk, using multi-marker panels. CA19-9 is an established circulating biomarker for pancreatic cancer; however, its relevance for pancreatic cancer early detection or for monitoring subjects at risk has not been established. METHODS: CA19-9 levels were assessed in blinded sera from 175 subjects collected up to 5 years before diagnosis of pancreatic cancer and from 875 matched controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. For comparison of performance, CA19-9 was assayed in blinded independent sets of samples collected at diagnosis from 129 subjects with resectable pancreatic cancer and 275 controls (100 healthy subjects; 50 with chronic pancreatitis; and 125 with noncancerous pancreatic cysts). The complementary value of 2 additional protein markers, TIMP1 and LRG1, was determined. RESULTS: In the PLCO cohort, levels of CA19-9 increased exponentially starting at 2 years before diagnosis with sensitivities reaching 60% at 99% specificity within 0 to 6 months before diagnosis for all cases and 50% at 99% specificity for cases diagnosed with early-stage disease. Performance was comparable for distinguishing newly diagnosed cases with resectable pancreatic cancer from healthy controls (64% sensitivity at 99% specificity). Comparison of resectable pancreatic cancer cases to subjects with chronic pancreatitis yielded 46% sensitivity at 99% specificity and for subjects with noncancerous cysts, 30% sensitivity at 99% specificity. For prediagnostic cases below cutoff value for CA19-9, the combination with LRG1 and TIMP1 yielded an increment of 13.2% in sensitivity at 99% specificity (P = .031) in identifying cases diagnosed within 1 year of blood collection. CONCLUSION: CA19-9 can serve as an anchor marker for pancreatic cancer early detection applications.

Factors modulating 99m Tc-MAA planar lung dosimetry for 90 Y radioembolization.

PURPOSE: To investigate the accuracy and biases of predicted lung shunt fraction (LSF) and lung dose (LD) calculations via 99m Tc-macro-aggregated albumin (99m Tc-MAA) planar imaging for treatment planning of 90 Y-microsphere radioembolization. METHODS AND MATERIALS: LSFs in 52 planning and LDs in 44 treatment procedures were retrospectively calculated, in consecutive radioembolization patients over a 2 year interval, using 99m Tc-MAA planar and SPECT/CT imaging. For each procedure, multiple planar LSFs and LDs were calculated using different: (1) contours, (2) views, (3) liver 99m Tc-MAA shine-through compensations, and (4) lung mass estimations. The accuracy of each planar-based LSF and LD methodology was determined by calculating the median (range) absolute difference from SPECT/CT-based LSF and LD values, which have been demonstrated in phantom and patient studies to more accurately and reliably quantify the true LSF and LD values. RESULTS: Standard-of-care LSF using geometric mean of lung and liver contours had median (range) absolute over-estimation of 4.4 percentage points (pp) (0.9 to 11.9 pp) from SPECT/CT LSF. Using anterior views only decreased LSF errors (2.4 pp median, -1.1 to +5.7 pp range). Planar LD over-estimations decreased when using single-view versus geometric-mean LSF (1.3 vs. 2.6 Gy median and 7.2 vs. 18.5 Gy maximum using 1000 g lung mass) but increased when using patient-specific versus standard-man lung mass (2.4 vs. 1.3 Gy median and 11.8 vs. 7.2 Gy maximum using single-view LSF). CONCLUSIONS: Calculating planar LSF from lung and liver contours of a single view and planar LD using that same LSF and 1000 g lung mass was found to improve accuracy and minimize bias in planar lung dosimetry.

A Comprehensive Search of Non-Canonical Proteins in Non-Small Cell Lung Cancer and Their Impact on the Immune Response.

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

The efficacy of first-line chemotherapy in endocrine-resistant hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer.

PURPOSE: Combinations of endocrine therapy (ET) and targeted therapy (CDK4/6 or mTOR inhibitors) are standard of care for HR+/HER2- metastatic breast cancer (MBC). When ET is not effective, chemotherapy is commonly used. However, clinical outcomes of chemotherapy in the endocrine-resistant setting are limited. The purpose of this study was to identify predictive factors and the compare efficacies of chemotherapy agents in endocrine-resistant MBC. METHODS: We conducted a retrospective study of patients with HR+/HER2- MBC who received chemotherapy after progression on ET with or without targeted therapy at MD Anderson Cancer Center from 1999 to 2017. We collected baseline clinicopathological and all treatment data. Primary endpoint was time to treatment failure (TTF) of first-line chemotherapy for MBC. RESULTS: For the 1258 patients analyzed, mean age was 55.3 years (range 21-91). Previous treatment with targeted therapy was recorded for 390 patients (31%): 264 with CDK4/6 inhibitor, 205 with mTOR inhibitor, and 79 treated with both. The most frequent chemotherapy agents were capecitabine (48.9%) and taxanes (28.6%). After adjustment for all factors in a multivariate model, previous treatment with a CDK4/6 inhibitor had the strongest negative effect on TTF regardless of ET duration (hazard ratio [HR] 1.84; 95%CI 1.49-2.27; p < 0.001). Conversely, capecitabine had significantly longer median TTF than taxanes regardless of whether patients had prior exposure to taxanes in primary setting (6.1 vs 4.9 months; HR 0.64; 95%CI 0.55-0.75; p < 0.001). CONCLUSIONS: Previous exposure to CDK4/6 inhibitor had a negative predictive effect for the efficacy of chemotherapy. Capecitabine had the best efficacy against endocrine-resistant breast cancer.

Low-loss, extreme subdiffraction photon confinement via silicon carbide localized surface phonon polariton resonators.

Plasmonics provides great promise for nanophotonic applications. However, the high optical losses inherent in metal-based plasmonic systems have limited progress. Thus, it is critical to identify alternative low-loss materials. One alternative is polar dielectrics that support surface phonon polariton (SPhP) modes, where the confinement of infrared light is aided by optical phonons. Using fabricated 6H-silicon carbide nanopillar antenna arrays, we report on the observation of subdiffraction, localized SPhP resonances. They exhibit a dipolar resonance transverse to the nanopillar axis and a monopolar resonance associated with the longitudinal axis dependent upon the SiC substrate. Both exhibit exceptionally narrow linewidths (7-24 cm(-1)), with quality factors of 40-135, which exceed the theoretical limit of plasmonic systems, with extreme subwavelength confinement of (λ(res)3/V(eff))1/3 = 50-200. Under certain conditions, the modes are Raman-active, enabling their study in the visible spectral range. These observations promise to reinvigorate research in SPhP phenomena and their use for nanophotonic applications.

A novel method to guide biomarker combinations to optimize the sensitivity.

Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore, the application of logistic regression is limited in such settings. To this end, we have developed an improved regression framework, SMAGS, for binary classification that, for a given specificity, finds the linear decision rule that yields the maximum sensitivity. Furthermore, we employed the method for feature selection to find the features that are satisfying the sensitivity maximization goal. We compared our method with normal logistic regression by applying it to real clinical data as well as synthetic data. In the real application data (colorectal cancer dataset), we found 14% improvement of sensitivity at 98.5% specificity. Availability and implementation: Software is made available in Python ( https://github.com/smahmoodghasemi/SMAGS ).

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

BACKGROUND: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. PURPOSE: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. METHODS: We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. RESULTS: For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. CONCLUSIONS: Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

Quantitative imaging of protein secretions from single cells in real time.

Protein secretions from individual cells create spatially and temporally varying concentration profiles in the extracellular environment, which guide a wide range of biological processes such as wound healing and angiogenesis. Fluorescent and colorimetric probes for the detection of single cell secretions have time resolutions that range from hours to days, and as a result, little is known about how individual cells may alter their protein secretion rates on the timescale of minutes or seconds. Here, we present a label-free technique based upon nanoplasmonic imaging, which enabled the measurement of individual cell secretions in real time. When applied to the detection of antibody secretions from single hybridoma cells, the enhanced time resolution revealed two modes of secretion: one in which the cell secreted continuously and another in which antibodies were released in concentrated bursts that coincided with minute-long morphological contractions of the cell. From the continuous secretion measurements we determined the local concentration of antibodies at the sensing array closest to the cell and from the bursts we estimated the diffusion constant of the secreted antibodies through the extracellular media. The design also incorporates transmitted light and fluorescence microscopy capabilities for monitoring cellular morphological changes and intracellular fluorescent labels. We anticipate that this technique can be adapted as a general tool for the quantitative study of paracrine signaling in both adherent and nonadherent cell lines.

Quantitative LSPR imaging for biosensing with single nanostructure resolution.

Localized surface plasmon resonance (LSPR) imaging has the potential to map complex spatio-temporal variations in analyte concentration, such as those produced by protein secretions from live cells. A fundamental roadblock to the realization of such applications is the challenge of calibrating a nanoscale sensor for quantitative analysis. Here, we introduce a new, to our knowledge, LSPR imaging and analysis technique that enables the calibration of hundreds of individual gold nanostructures in parallel. The calibration allowed us to map the fractional occupancy of surface-bound receptors at individual nanostructures with nanomolar sensitivity and a temporal resolution of 225 ms. As a demonstration of the technique's applicability to molecular and cell biology, the calibrated array was used for the quantitative LSPR imaging of anti-c-myc antibodies harvested from a cultured 9E10 hybridoma cell line without the need for further purification or processing.

Mie resonance-enhanced light absorption in periodic silicon nanopillar arrays.

Mie-resonances in vertical, small aspect-ratio and subwavelength silicon nanopillars are investigated using visible bright-field µ-reflection measurements and Raman scattering. Pillar-to-pillar interactions were examined by comparing randomly to periodically arranged arrays with systematic variations in nanopillar diameter and array pitch. First- and second-order Mie resonances are observed in reflectance spectra as pronounced dips with minimum reflectances of several percent, suggesting an alternative approach to fabricating a perfect absorber. The resonant wavelengths shift approximately linearly with nanopillar diameter, which enables a simple empirical description of the resonance condition. In addition, resonances are also significantly affected by array density, with an overall oscillating blue shift as the pitch is reduced. Finite-element method and finite-difference time-domain simulations agree closely with experimental results and provide valuable insight into the nature of the dielectric resonance modes, including a surprisingly small influence of the substrate on resonance wavelength. To probe local fields within the Si nanopillars, µ-Raman scattering measurements were also conducted that confirm enhanced optical fields in the pillars when excited on-resonance.

Accumulation of CD11b⁺Gr-1⁺ cells in the lung, blood and bone marrow of mice infected with highly pathogenic H5N1 and H1N1 influenza viruses.

Infection with pathogenic influenza viruses is associated with intense inflammatory disease. Here, we investigated the innate immune response in mice infected with H5N1 A/Vietnam/1203/04 and with reassortant human H1N1 A/Texas/36/91 viruses containing the virulence genes hemagglutinin (HA), neuraminidase (NA) and NS1 of the 1918 pandemic virus. Inclusion of the 1918 HA and NA glycoproteins rendered a seasonal H1N1 virus capable of inducing an exacerbated host innate immune response similar to that observed for highly pathogenic A/Vietnam/1203/04 virus. Infection with 1918 HA/NA:Tx/91 and A/Vietnam/1203/04 were associated with severe lung pathology, increased cytokine and chemokine production, and significant immune cell changes, including the presence of CD11b(+)Gr-1(+) cells in the blood, lung and bone marrow. Significant differential gene expression in the lung included pathways for cell death, apoptosis, production and response to reactive oxygen radicals, as well as arginine and proline metabolism and chemokines associated with monocyte and neutrophil/granulocyte accumulation and/or activation. Arginase was produced in the lung of animals infected with A/Vietnam/1204. These results demonstrate that the innate immune cell response results in the accumulation of CD11b(+)Gr-1(+) cells and products that have previously been shown to contribute to T cell suppression.

Detection method has independent prognostic significance in the PLCO lung screening trial.

Prognostic models in cancer use patient demographic and tumor characteristics to predict survival and dynamic disease prognosis. Past work in breast cancer has shown that cancer detection method, screen-detected or symptom-detected, has prognostic significance. We investigate this phenomenon in the lung component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial. Patients were randomized to intervention, receiving four annual chest x-rays (CXRs), or to control, receiving usual care. Patients were followed for a total of approximately 13 years. In PLCO, lung cancer detection method has independent prognostic value exceeding that of variables commonly used in lung cancer prognostic models, including sex, histology, and age. Results are robust to cohort selection and type of predictive model. These results imply that detection method should be considered when developing prognostic models in lung cancer studies, and cancer registries should routinely collect cancer detection method.

Estimating causal effects with hidden confounding using instrumental variables and environments.

Recent works have proposed regression models which are invariant across data collection environments [24, 20, 11, 16, 8]. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.

Postoperative Management of Recurrence After Radiosurgery and Surgical Resection for Brain Metastases and Predicting Benefit From Adjuvant Radiation.

Stereotactic radiosurgery (SRS) is often used as upfront treatment for brain metastases. Progression or radionecrosis after SRS is common and can prompt resection. However, postoperative management strategies after resection for SRS failure vary widely, and no standard practice has been established. In this approved study, we retrospectively reviewed patients who received SRS for a brain metastasis followed by resection of the same lesion. We extracted patient-, disease-, and treatment-related variables and information on disease-related outcomes. Univariate and multivariate analyses of clinicopathologic variables were used to create a model to predict factors associated with local failure (LF). A total of 225 patients with brain metastases treated with SRS from 2009 to 2017 followed by surgical resection were identified. Overall, 65% of cases had gross total resection (GTR) on postoperative imaging review. Twenty-one patients (9.3%) received adjuvant radiation therapy to the surgical cavity, and 204 (90.7%) were observed. Of these 204 patients, 118 had GTR with evidence of tumor within the pathology specimen. With a median follow-up of 13 months after resection, 47 patients (40%) developed LF after surgery. After salvage resection of a brain metastasis initially treated with SRS, the observed LF rate was 40% among those who had a GTR and evidence of tumor on pathologic examination. This LF rate is sufficiently high that adjuvant radiation to the surgical bed after salvage resection should be considered in these cases when there is tumor in the pathology, even after a GTR.

Mortality Benefit of a Blood-Based Biomarker Panel for Lung Cancer on the Basis of the Prostate, Lung, Colorectal, and Ovarian Cohort.

PURPOSE: To investigate the utility of integrating a panel of circulating protein biomarkers in combination with a risk model on the basis of subject characteristics to identify individuals at high risk of harboring a lethal lung cancer. METHODS: Data from an established logistic regression model that combines four-marker protein panel (4MP) together with the Prostate, Lung, Colorectal, and Ovarian (PLCO) risk model (PLCOm2012) assayed in prediagnostic sera from 552 lung cancer cases and 2,193 noncases from the PLCO cohort were used in this study. Of the 552 lung cancer cases, 387 (70%) died of lung cancer. Cumulative incidence of lung cancer death and subdistributional and cause-specific hazard ratios (HRs) were calculated on the basis of 4MP + PLCOm2012 risk scores at a predefined 1.0% and 1.7% 6-year risk thresholds, which correspond to the current and former US Preventive Services Task Force screening criteria, respectively. RESULTS: When considering cases diagnosed within 1 year of blood draw and all noncases, the area under receiver operation characteristics curve estimate of the 4MP + PLCOm2012 model for risk prediction of lung cancer death was 0.88 (95% CI, 0.86 to 0.90). The cumulative incidence of lung cancer death was statistically significantly higher in individuals with 4MP + PLCOm2012 scores above the 1.0% 6-year risk threshold (modified χ2, 166.27; P < .0001). Corresponding subdistributional and lung cancer death-specific HRs for test-positive cases were 9.88 (95% CI, 6.44 to 15.18) and 10.65 (95% CI, 6.93 to 16.37), respectively. CONCLUSION: The blood-based biomarker panel in combination with PLCOm2012 identifies individuals at high risk of a lethal lung cancer.

A blood-based metabolomic signature predictive of risk for pancreatic cancer.

Emerging evidence implicates microbiome involvement in the development of pancreatic cancer (PaCa). Here, we investigate whether increases in circulating microbial-related metabolites associate with PaCa risk by applying metabolomics profiling to 172 sera collected within 5 years prior to PaCa diagnosis and 863 matched non-subject sera from participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cohort. We develop a three-marker microbial-related metabolite panel to assess 5-year risk of PaCa. The addition of five non-microbial metabolites further improves 5-year risk prediction of PaCa. The combined metabolite panel complements CA19-9, and individuals with a combined metabolite panel + CA19-9 score in the top 2.5th percentile have absolute 5-year risk estimates of >13%. The risk prediction model based on circulating microbial and non-microbial metabolites provides a potential tool to identify individuals at high risk of PaCa that would benefit from surveillance and/or from potential cancer interception strategies.

Modulating a prebiotic food source influences inflammation and immune-regulating gut microbes and metabolites: insights from the BE GONE trial.

BACKGROUND: Accessible prebiotic foods hold strong potential to jointly target gut health and metabolic health in high-risk patients. The BE GONE trial targeted the gut microbiota of obese surveillance patients with a history of colorectal neoplasia through a straightforward bean intervention. METHODS: This low-risk, non-invasive dietary intervention trial was conducted at MD Anderson Cancer Center (Houston, TX, USA). Following a 4-week equilibration, patients were randomized to continue their usual diet without beans (control) or to add a daily cup of study beans to their usual diet (intervention) with immediate crossover at 8-weeks. Stool and fasting blood were collected every 4 weeks to assess the primary outcome of intra and inter-individual changes in the gut microbiome and in circulating markers and metabolites within 8 weeks. This study was registered on ClinicalTrials.gov as NCT02843425, recruitment is complete and long-term follow-up continues. FINDINGS: Of the 55 patients randomized by intervention sequence, 87% completed the 16-week trial, demonstrating an increase on-intervention in diversity [n = 48; linear mixed effect and 95% CI for inverse Simpson index: 0.16 (0.02, 0.30); p = 0.02] and shifts in multiple bacteria indicative of prebiotic efficacy, including increased Faecalibacterium, Eubacterium and Bifidobacterium (all p < 0.05). The circulating metabolome showed parallel shifts in nutrient and microbiome-derived metabolites, including increased pipecolic acid and decreased indole (all p < 0.002) that regressed upon returning to the usual diet. No significant changes were observed in circulating lipoproteins within 8 weeks; however, proteomic biomarkers of intestinal and systemic inflammatory response, fibroblast-growth factor-19 increased, and interleukin-10 receptor-α decreased (p = 0.01). INTERPRETATION: These findings underscore the prebiotic and potential therapeutic role of beans to enhance the gut microbiome and to regulate host markers associated with metabolic obesity and colorectal cancer, while further emphasizing the need for consistent and sustainable dietary adjustments in high-risk patients. FUNDING: This study was funded by the American Cancer Society.

A new methodology for quantitative LSPR biosensing and imaging.

A new quantitative analysis methodology for localized surface plasmon resonance (LSPR) biosensing which determines surface-receptor fractional occupancy, as well as an LSPR imaging technique for the spatiotemporal mapping of binding events, is presented. Electron beam nanolithography was used to fabricate 20 × 20 arrays of gold nanostructures atop glass coverslips. A single biotinylated array was used to measure the association kinetics of neutravidin to the surface by spectroscopically determining the fractional occupancy as a function of time. By regenerating the same array, a reliable comparison of the kinetics could be made between control samples and neutravidin concentrations ranging from 1 µM to 50 nM. CCD-based imagery of the array, taken simultaneously with the spectroscopic measurements, reveals the binding of neutravidin to the surface as manifested by enhanced scattering over the majority of the resonance peak. The temporal resolution of the LSPR imaging technique was 200 ms and the spatial resolution was 8 µm(2).