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Objective: Onset and exacerbation of autoimmune, inflammatory or steroid‐responsive conditions have been reported following the remission of Cushing syndrome, leading to challenges in distinguishing a new condition versus expected symptomatology following remission. We describe a case of a 42‐year‐old man presenting with new‐onset sarcoidosis diagnosed 12 months following the surgical cure of Cushing syndrome and synthesise existing literature reporting on de novo conditions presenting after Cushing syndrome remission. Methods: A scoping review was conducted in Medline, Epub, Ovid and PubMed. Case reports and case series detailing adult patients presenting with new‐onset conditions following Cushing syndrome remission were included. Results: In total, 1641 articles were screened, 138 full‐text studies were assessed for eligibility, and 43 studies were included, of which 84 cases (including our case) were identified. Most patients were female (85.7%), and the median reported age was 39.5 years old (IQR = 13). Thyroid diseases were the most commonly reported conditions (48.8%), followed by sarcoidosis (15.5%). Psoriasis, lymphocytic hypophysitis, idiopathic intracranial hypertension, multiple sclerosis, rheumatoid arthritis, lupus and seronegative arthritis were reported in more than one case. The median duration between Cushing remission and de novo condition diagnosis was 4.1 months (IQR = 3.75). Of those patients, 59.5% were receiving corticosteroid therapy at the time of onset. Conclusion: Our scoping review identified several cases of de novo conditions emerging following the remission of Cushing syndrome. They occurred mostly in women and within the year following remission. Clinicians should remain aware that new symptoms, particularly in the first year following the treatment of Cushing syndrome, may be manifestations of a wide range of conditions aside from adrenal insufficiency or glucocorticoid withdrawal syndrome. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICI) can achieve remarkable responses in urothelial cancer (UC), which may depend on tumor microenvironment (TME) characteristics. However, the relationship between the TME, usually characterized by immune cell density, and response to ICI is unclear. Here, we quantify the TME immune cell densities and spatial relationships (SRs) of 24 baseline UC samples, obtained before pre-operative combination ICI treatment, using multiplex immunofluorescence. We describe SRs by approximating the first nearest-neighbor distance distribution with a Weibull distribution and evaluate the association between TME metrics and ipilimumab+nivolumab response. Immune cell density does not discriminate between response groups. However, the Weibull SR metrics of CD8+ T cells or macrophages to their closest cancer cell positively associate with response. CD8+ T cells close to B cells are characteristic of non-response. We validate our SR response associations in a combination ICI cohort of head and neck tumors. Our data confirm that SRs, in contrast to density metrics, are strong biomarkers of response to pre-operative combination ICIs.Spatial positioning of cells within the tumour microenvironment may have a function in the success of immune checkpoint immunotherapy (ICI). Here the authors analyse spatial relationships from immunohistochemistry samples prior to ICI therapy and show that CD8 T cell or macrophage proximity to cancer cells is associated with better responses. [ABSTRACT FROM AUTHOR]

Combinations of anti-cancer drugs can overcome resistance and provide new treatments1,2. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS–TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.A survey of potency and efficacy of 2,025 clinically relevant two-drug combinations against 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines identifies rare synergistic effects of anticancer drugs, informing rational combination treatments for specific cancer subtypes. [ABSTRACT FROM AUTHOR]

Candidate immune biomarkers have been proposed for predicting response to immunotherapy in urothelial cancer (UC). Yet, these biomarkers are imperfect and lack predictive power. A comprehensive overview of the tumor immune contexture, including Tertiary Lymphoid structures (TLS), is needed to better understand the immunotherapy response in UC. We analyzed tumor sections by quantitative multiplex immunofluorescence to characterize immune cell subsets in various tumor compartments in tumors without pretreatment and tumors exposed to preoperative anti-PD1/CTLA-4 checkpoint inhibitors (NABUCCO trial). Pronounced immune cell presence was found in UC invasive margins compared to tumor and stroma regions. CD8+PD1+ T-cells were present in UC, particularly following immunotherapy. The cellular composition of TLS was assessed by multiplex immunofluorescence (CD3, CD8, FoxP3, CD68, CD20, PanCK, DAPI) to explore specific TLS clusters based on varying immune subset densities. Using a k-means clustering algorithm, we found five distinct cellular composition clusters. Tumors unresponsive to anti-PD-1/CTLA-4 immunotherapy showed enrichment of a FoxP3+ T-cell-low TLS cluster after treatment. Additionally, cluster 5 (macrophage low) TLS were significantly higher after pre-operative immunotherapy, compared to untreated tumors. We also compared the immune cell composition and maturation stages between superficial (submucosal) and deeper TLS, revealing that superficial TLS had more pronounced T-helper cells and enrichment of early TLS than TLS located in deeper tissue. Furthermore, superficial TLS displayed a lower fraction of secondary follicle like TLS than deeper TLS. Taken together, our results provide a detailed quantitative overview of the tumor immune landscape in UC, which can provide a basis for further studies. [ABSTRACT FROM AUTHOR]

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents. [ABSTRACT FROM AUTHOR]

Resistance to drugs targeting the androgen receptor (AR) signaling axis remains an important challenge in the treatment of prostate cancer patients. Activation of alternative growth pathways is one mechanism used by cancer cells to proliferate despite treatment, conferring drug resistance. Through a kinome-centered CRISPR-Cas9 screen in CWR-R1 prostate cancer cells, we identified activated BRAF signaling as a determinant for enzalutamide resistance. Combined pharmaceutical targeting of AR and MAPK signaling resulted in strong synergistic inhibition of cell proliferation. The association between BRAF activation and enzalutamide resistance was confirmed in two metastatic prostate cancer patients harboring activating mutations in the BRAF gene, as both patients were unresponsive to enzalutamide. Our findings suggest that co-targeting of the MAPK and AR pathways may be effective in patients with an activated MAPK pathway, particularly in patients harboring oncogenic BRAF mutations. These results warrant further investigation of the response to AR inhibitors in BRAF-mutated prostate tumors in clinical settings. [ABSTRACT FROM AUTHOR]

Dishing out treatment recommendations: The number of treatment options for cancer patients keeps expanding, but it remains difficult to predict which tumors will be sensitive to which treatments. As a result, most patients receive treatment according to standardized protocols. With this approach, some patients respond to treatment, but others only experience side effects. To help address this situation, Ooft et al. developed a method of testing drugs in patient-derived organoids, biopsy-derived cells from individual patients grown in a dish. In a clinical study, the responses of organoids to irinotecan correlated with patients' responses to the drug, suggesting that organoids could help avoid giving irinotecan to patients who would not benefit. There is a clear and unmet clinical need for biomarkers to predict responsiveness to chemotherapy for cancer. We developed an in vitro test based on patient-derived tumor organoids (PDOs) from metastatic lesions to identify nonresponders to standard-of-care chemotherapy in colorectal cancer (CRC). In a prospective clinical study, we show the feasibility of generating and testing PDOs for evaluation of sensitivity to chemotherapy. Our PDO test predicted response of the biopsied lesion in more than 80% of patients treated with irinotecan-based therapies without misclassifying patients who would have benefited from treatment. This correlation was specific to irinotecan-based chemotherapy, however, and the PDOs failed to predict outcome for treatment with 5-fluorouracil plus oxaliplatin. Our data suggest that PDOs could be used to prevent cancer patients from undergoing ineffective irinotecan-based chemotherapy. [ABSTRACT FROM AUTHOR]

Background: The usefulness of physical examination findings for pulmonary hypertension (PH) is not well established. The purpose of this study was to evaluate prospectively the diagnostic performance of the physical examination for detecting PH.Methods: Consecutive patients undergoing right-sided heart catheterization (n = 116) were examined by an attending physician, medical resident, and medical student in a blinded fashion. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) were calculated for each physical finding. Jugular venous pulsation (JVP) height was compared with right atrial pressure (RAP) by using linear regression. The association between physical findings and PH was assessed using univariate and multivariate logistic regression.Results: The prevalence of PH was 87%. Only a JVP > 3 cm (positive LR, 2.5; 95% CI, 1.2-5.4) and pulmonic regurgitation murmur (specificity, 100%; 95% CI, 79%-100%) helped rule in PH. The absence of JVP > 3 cm (negative LR, 0.4; 95% CI, 0.3-0.6) and absence of loud pulmonic component of the second heart sound (negative LR, 0.5; 95% CI, 0.3-0.9) had modest usefulness in excluding PH. JVP correlated with RAP (r = 0.59; P < .001) but tended to lead to underestimation of RAP (mean bias, -3.4 cm H2O; 95% limits of agreement, -14.0 to 7.2). The presence of JVP > 3 cm and a parasternal heave discriminated PH (area under the curve [AUC] = 0.75). The combination of JVP > 3 cm, heave, and peripheral edema discriminated severe PH (mean pulmonary arterial pressure ≥ 45 mm Hg; AUC = 0.82).Conclusions: Individual physical examination findings have inadequate diagnostic usefulness for PH. No combination of findings can be used to exclude PH, but the presence of high JVP, peripheral edema, and parasternal heave suggests severe PH. [ABSTRACT FROM AUTHOR]

Purpose: In the I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis 2), the pan–erythroblastic oncogene B inhibitor neratinib was available to all hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) subtypes and graduated in the HR-negative/HER2-positive signature. We hypothesized that neratinib response may be predicted by baseline HER2 epidermal growth factor receptor (EGFR) signaling activation/phosphorylation levels independent of total levels of HER2 or EGFR proteins. Materials and Methods: Complete experimental and response data were available for between 130 and 193 patients. In qualifying analyses, which used logistic regression and treatment interaction analysis, 18 protein/phosphoprotein, 10 mRNA, and 12 DNA biomarkers that related to HER family signaling were evaluated. Exploratory analyses used Wilcoxon rank sum and t tests without multiple comparison correction. Results: HER pathway DNA biomarkers were either low prevalence or nonpredictive. In expression biomarker analysis, only one gene (STMN1) was specifically associated with response to neratinib in the HER2-negative subset. In qualifying protein/phosphoprotein analyses that used reverse phase protein microarrays, six HER family markers were associated with neratinib response. After analysis was adjusted for HR/HER2 status, EGFR Y1173 (pEGFR) showed a significant biomarker-by-treatment interaction (P =.049). Exploratory analysis of HER family signaling in patients with triple-negative (TN) disease found that activation of EGFR Y1173 (P =.005) and HER2 Y1248 (pHER2) (P =.019) were positively associated with pathologic complete response. Exploratory analysis in this pEGFR/pHER2–activated TN subgroup identified elevated levels of estrogen receptor α (P <.006) in these patients. Conclusion: Activation of HER family phosphoproteins associates with response to neratinib, but only EGFR Y1173 and STMN1 appear to add value to the graduating signature. Activation of HER2 and EGFR in TN tumors may identify patients whose diseases respond to neratinib and implies that there is a subset of patients with TN disease who paradoxically exhibit HER family signaling activation and may achieve clinical benefit with neratinib; this concept must be validated in future studies. [ABSTRACT FROM AUTHOR]

Summary Diffuse and uncontrollable brain invasion is a hallmark of glioblastoma (GBM), but its mechanism is understood poorly. We developed a 3D ex vivo organotypic model to study GBM invasion. We demonstrate that invading GBM cells upregulate a network of extracellular matrix (ECM) components, including multiple collagens, whose expression correlates strongly with grade and clinical outcome. We identify interferon regulatory factor 3 (IRF3) as a transcriptional repressor of ECM factors and show that IRF3 acts as a suppressor of GBM invasion. Therapeutic activation of IRF3 by inhibiting casein kinase 2 (CK2)—a negative regulator of IRF3—downregulated the expression of ECM factors and suppressed GBM invasion in ex vivo and in vivo models across a panel of patient-derived GBM cell lines representative of the main molecular GBM subtypes. Our data provide mechanistic insight into the invasive capacity of GBM tumors and identify a potential therapy to inhibit GBM invasion. [ABSTRACT FROM AUTHOR]

Motivation: Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways. Results: To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression. [ABSTRACT FROM AUTHOR]

Although platinum-based drugs are widely used chemotherapeutics for cancer treatment, the determinants of tumor cell responsiveness remain poorly understood. We show that the loss of subunits LRRC8A and LRRC8D of the heteromeric LRRC8 volume-regulated anion channels (VRACs) increased resistance to clinically relevant cisplatin/ carboplatin concentrations. Under isotonic conditions, about 50% of cisplatin uptake depended on LRRC8A and LRRC8D, but neither on LRRC8C nor on LRRC8E. Cell swelling strongly enhanced LRRC8-dependent cisplatin uptake, bolstering the notion that cisplatin enters cells through VRAC. LRRC8A disruption also suppressed drug-induced apoptosis independently from drug uptake, possibly by impairing VRAC-dependent apoptotic cell volume decrease. Hence, by mediating cisplatin uptake and facilitating apoptosis, VRAC plays a dual role in the cellular drug response. Incorporation of the LRRC8D subunit into VRAC substantially increased its permeability for cisplatin and the cellular osmolyte taurine, indicating that LRRC8 proteins form the channel pore. Our work suggests that LRRC8D-containing VRACs are crucial for cell volume regulation by an important organic osmolyte and may influence cisplatin/carboplatin responsiveness of tumors. [ABSTRACT FROM AUTHOR]

The development of targeted inhibitors, like vemurafenib, has greatly improved the clinical outcome of BRAFV600E metastatic melanoma. However, resistance to such compounds represents a formidable problem. Using whole-exome sequencing and functional analyses, we have investigated the nature and pleiotropy of vemurafenib resistance in a melanoma patient carrying multiple drug-resistant metastases. Resistance was caused by a plethora of mechanisms, all of which reactivated the MAPK pathway. In addition to three independent amplifications and an aberrant form of BRAF V600E, we identified a new activating insertion in MEK1. This MEK1 T55delins RT mutation could be traced back to a fraction of the pre-treatment lesion and not only provided protection against vemurafenib but also promoted local invasion of transplanted melanomas. Analysis of patient-derived xenografts ( PDX) from therapy-refractory metastases revealed that multiple resistance mechanisms were present within one metastasis. This heterogeneity, both inter- and intra-tumorally, caused an incomplete capture in the PDX of the resistance mechanisms observed in the patient. In conclusion, vemurafenib resistance in a single patient can be established through distinct events, which may be preexisting. Furthermore, our results indicate that PDX may not harbor the full genetic heterogeneity seen in the patient's melanoma. [ABSTRACT FROM AUTHOR]

Challenge tests are used to assess the resilience of human beings to perturbations by analyzing responses to detect functional abnormalities. Well known examples are allergy tests and glucose tolerance tests. Increasingly, metabolomics analysis of blood or serum samples is used to analyze the biological response of the individual to these challenges. The information content of such metabolomics challenge test data involves both the disturbance and restoration of homeostasis on a metabolic level and is thus inherently different from the analysis of steady state data. It opens doors to study the variation of resilience between individuals beyond the classical biomarkers; preferably in terms of underlying biological processes. We review challenge tests in which metabolomics was used to analyze the biological response. Specifically, we describe strategies to perform statistical analyses on the responses and we will show some examples of these strategies applied to a postprandial challenge that was used to study a diet with anti-inflammatory properties. Finally we discuss open issues and give recommendation for further research. [ABSTRACT FROM AUTHOR]

Relations among hormone serum concentrations are complex and depend on various factors, including gender, age, body mass index, diurnal rhythms and secretion stochastics. Therefore, endocrine deviations from healthy homeostasis are not easily detected or understood. A generic method is presented for detecting regulatory relations between hormones. This is demonstrated with a cohort of obese women, who underwent blood sampling at 10 minute intervals for 24-hours. The cohort was treated with bromocriptine in an attempt to clarify how hormone relations change by treatment. The detected regulatory relations are summarized in a network graph and treatment-induced changes in the relations are determined. The proposed method identifies many relations, including well-known ones. Ultimately, the method provides ways to improve the description and understanding of normal hormonal relations and deviations caused by disease or treatment. [ABSTRACT FROM AUTHOR]

The regulatory mechanisms underlying pulsatile secretion are complex, especially as it is partly controlled by other hormones and the combined action of multiple agents. Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods. Time delayed associations between hormone concentrations and/or extracted secretion pulse profiles reveal the information on regulatory mechanisms. The above mentioned regulatory mechanisms are illustrated with simulated data. Additionally, data from a lean cohort of healthy control subjects is used to illustrate activation (ACTH and cortisol) and circadian synchronicity (ACTH and TSH) in real data. The simulation and the real data both consist of 145 equidistant samples per individual, matching a 24-hr time span with 10 minute intervals. The results of the simulation and the real data are in concordance. [ABSTRACT FROM AUTHOR]

The detection of hormone secretion episodes is important for understanding normal and abnormal endocrine functioning, but pulse identification from hormones measured with short interval sampling is challenging. Furthermore, to obtain useable results, the model underlying hormone secretion and clearance must be augmented with restrictions based on biologically acceptable assumptions. Here, using the assumption that there are only a few time points at which a hormone is secreted, we used a modern penalized nonlinear least-squares setup to select the number of secretion events. We did not assume a particular shape or frequency distribution for the secretion pulses. Our pulse identification method, VisPulse, worked well with luteinizing hormone (LH), cortisol, growth hormone, or testosterone. In particular, applying our modeling strategy to previous LH data revealed a good correlation between the modeled and measured LH hormone concentrations, the estimated secretion pattern was sparse, and the small and structureless residuals indicated a proper model with a good fit. We benchmarked our method to AutoDecon, a commonly used hormone secretion model, and performed releasing hormone infusion experiments. The results of these experiments confirmed that our method is accurate and outperforms AutoDecon, especially for detecting silent periods and small secretion events, suggesting a high-secretion event resolution. Method validation using (releasing hormone) infusion data revealed sensitivities and selectivities of 0.88 and 0.95 and of 0.69 and 0.91 for VisPulse and AutoDecon, respectively. [ABSTRACT FROM AUTHOR]

Background: Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method; it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist. Methods: A permutation approach is used to validate megavariate effects observed with ASCA. By permuting the class labels of the underlying experimental design, a distribution of no-effect is calculated. If the observed effect is clearly different from this distribution the effect is deemed significant Results: The permutation approach is studied using simulated data which gave successful results. It was then used on real-life metabolomics data set dealing with bromobenzene-dosed rats. In this metabolomics experiment the dosage and time-interaction effect were validated, both effects are significant. Histological screening of the treated rats' liver agrees with this finding. Conclusion: The suggested procedure gives approximate p-values for testing effects underlying metabolomics data sets. Therefore, performing model validation is possible using the proposed procedure. [ABSTRACT FROM AUTHOR]

Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts. Integrative analyses that link molecular data to treatment sensitivity are essential for precision medicine. Here the authors introduce WON-PARAFAC to integrate multiple genomics data to identify sparse and interpretable factors. [ABSTRACT FROM AUTHOR]

An abstract of the article "Diagnostic Utility of the Physical Examination for Moderate and Severe Pulmonary Hypertension" by Daniel Vis and colleagues is presented.