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T cell-invigorating cancer immunotherapies have near-curative potential. However, their clinical benefit is currently limited, as only a fraction of patients respond, suggesting that these regimens may benefit from combination with tumor-targeting treatments. As oncogenic progression is accompanied by alterations in metabolic pathways, tumors often become heavily reliant on antioxidant machinery and may be susceptible to increases in oxidative stress. The cystine–glutamate antiporter xCT is frequently overexpressed in cancer and fuels the production of the antioxidant glutathione; thus, tumors prone to redox stress may be selectively vulnerable to xCT disruption. However, systemic inhibition of xCT may compromise antitumor immunity, as xCT is implicated in supporting antigeninduced T cell proliferation. Therefore, we utilized immune-competent murine tumor models to investigate whether cancer cell expression of xCT was required for tumor growth in vivo and if deletion of host xCT impacted antitumor immune responses. Deletion of xCT in tumor cells led to defective cystine uptake, accumulation of reactive oxygen species, and impaired tumor growth, supporting a cancer cell-autonomous role for xCT. In contrast, we observed that, although T cell proliferation in culture was exquisitely dependent on xCT expression, xCT was dispensable for T cell proliferation in vivo and for the generation of primary and memory immune responses to tumors. These findings prompted the combination of tumor cell xCT deletion with the immunotherapeutic agent anti–CTLA-4, which dramatically increased the frequency and durability of antitumor responses. Together, these results identify a metabolic vulnerability specific to tumors and demonstrate that xCT disruption can expand the efficacy of anticancer immunotherapies.

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

Current methods to quantify T-cell clonal expansion only account for variance due to random sampling from a highly diverse repertoire space. We propose a beta-binomial model to incorporate time-dependent variance into the assessment of differentially abundant T-cell clones, identified by unique T Cell Receptor (TCR) β-chain rearrangements, and show that this model improves specificity for detecting clinically relevant clonal expansion. Using blood samples from ten healthy donors, we modeled the variance of T-cell clones within each subject over time and calibrated the dispersion parameters of the beta distribution to fit this variance. As a validation, we compared pre- versus post-treatment blood samples from urothelial cancer patients treated with atezolizumab, where clonal expansion (quantified by the earlier binomial model) was previously reported to correlate with benefit. The beta-binomial model significantly reduced the false-positive rate for detecting differentially abundant clones over time compared to the earlier binomial method. In the urothelial cancer cohort, the beta-binomial model enriched for tumor infiltrating lymphocytes among the clones detected as expanding in the peripheral blood in response to therapy compared to the binomial model and improved the overall correlation with clinical benefit. Incorporating time-dependent variance into the statistical framework for measuring differentially abundant T-cell clones improves the model's specificity for T-cells that correlate more strongly with the disease and treatment setting of-interest. Reducing background-level clonal expansion, therefore, improves the quality of clonal expansion as a biomarker for assessing the T cell immune response and correlations with clinical measures.

To elucidate the effects of neoadjuvant chemotherapy (NAC), we conduct whole transcriptome profiling coupled with histopathology analyses of a longitudinal breast cancer cohort of 146 patients including 110 pairs of serial tumor biopsies collected before treatment, after the first cycle of treatment and at the time of surgery. Here, we show that cytotoxic chemotherapies induce dynamic changes in the tumor immune microenvironment that vary by subtype and pathologic response. Just one cycle of treatment induces an immune stimulatory microenvironment harboring more tumor infiltrating lymphocytes (TILs) and up-regulation of inflammatory signatures predictive of response to anti-PD1 therapies while residual tumors are immune suppressed at end-of-treatment compared to the baseline. Increases in TILs and CD8+ T cell proportions in response to NAC are independently associated with pathologic complete response. Further, on-treatment immune response is more predictive of treatment outcome than immune features in paired baseline samples although these are strongly correlated. Neoadjuvant chemotherapy is a therapeutic option for the treatment of breast cancer. Here, the authors characterize changes in the gene expression profiles and immune microenvironment in serial breast cancer biopsies taken before, during and after neoadjuvant chemotherapy.

Breast cancer (BC) in the Asia Pacific regions is enriched in younger patients and rapidly rising in incidence yet its molecular bases remain poorly characterized. Here we analyze the whole exomes and transcriptomes of 187 primary tumors from a Korean BC cohort (SMC) enriched in pre-menopausal patients and perform systematic comparison with a primarily Caucasian and post-menopausal BC cohort (TCGA). SMC harbors higher proportions of HER2+ and Luminal B subtypes, lower proportion of Luminal A with decreased ESR1 expression compared to TCGA. We also observe increased mutation prevalence affecting BRCA1 , BRCA2 , and TP53 in SMC with an enrichment of a mutation signature linked to homologous recombination repair deficiency in TNBC. Finally, virtual microdissection and multivariate analyses reveal that Korean BC status is independently associated with increased TIL and decreased TGF-β signaling expression signatures, suggesting that younger Asian BCs harbor more immune-active microenvironment than western BCs. While breast cancer incidence in the Asia Pacific region is rising, the molecular basis remains poorly characterized. Here the authors perform genomic screening of 187 Korean breast cancer patients and find differences in molecular subtype distribution, mutation pattern and prevalence, and gene expression signature when compared to TCGA.

β-Nerve growth factor (NGF) is a neurotrophin that plays a critical role in fetal development during gestation. ProNGF is the precursor form of NGF with a distinct biological profile. In order to investigate the role of NGF and proNGF in pregnant human females, a sensitive and selective immunoaffinity liquid chromatography-tandem mass spectrometry assay was developed and qualified to simultaneously measure the levels of total NGF (tNGF; sum of mature and proNGF) and proNGF using full and relative quantification strategies, respectively. The assay was used to determine serum tNGF and proNGF levels in the three gestational trimesters of pregnancy and in non-pregnant female controls. Mean tNGF ± SD were 44.6 ± 12.3, 42.6 ± 9.3, 65.4 ± 17.6 and 77.0 ± 17.8 pg/mL for non-pregnant, first, second, and third trimesters, respectively, demonstrating no significant increase in circulating tNGF between the control and the first trimester, and a moderate yet significant 1.7-fold increase through gestation. proNGF levels during the first trimester were unchanged compared to control. In contrast to tNGF, however, proNGF levels during gestation remained stable without significant changes. The development of this sensitive, novel immunoaffinity duplexed assay for both tNGF and proNGF is expected to enable further elucidation of the roles these neurotrophins play in human pregnancy as well as other models.

In the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supportive care alone as first-line maintenance therapy following first-line platinum-based chemotherapy in patients with advanced urothelial cancer (aUC). Discovering biomarkers using genomic profiling to understand potential patient heterogeneity is essential to help improve patient care with precision medicine. For the JAVELIN Bladder 100 trial, it is unclear which variable selection methods can most reliably identify biomarkers to inform patient care because the dataset is characterized by high collinearity and low signal. The aim of this paper was to evaluate available selection methods and their ability to discover prognostic and predictive biomarkers in patients with aUC receiving first-line maintenance therapy. A simulation study evaluated the performance of popular variable selection approaches for high-dimensional data including penalized regression models, random survival forests, and Bayesian variable selection methods. For Bayesian variable selection methods, a modified Bayesian Information Criterion (BIC) thresholding rule was proposed in addition to the traditional BIC thresholding rule. These methods were applied to the JAVELIN Bladder 100 dataset to investigate potential biomarkers associated with survival benefit. Results from the simulations demonstrated the strengths and limitations of the different methods. The variable selection methods demonstrated low false discovery rates under different conditions. However, their performance declined in the presence of high collinearity. Using the JAVELIN Bladder 100 data, we identified some potentially significant biomarkers across multiple models. Several lasso-related methods were able to identify potentially biologically meaningful variables in the trial. Some variable selection methods (such as stochastic search variable selection and random survival forest) may not be well suited to this type of data due to the presence of extreme collinearity and low signal. Future research should explore novel variable selection methods that may be more suitable for identifying prognostic and predictive biomarkers in this population. Trial registration: ClinicalTrials.gov Identifier: NCT02603432.

Schizophrenia is characterized by affective, cognitive, neuromorphological, and molecular abnormalities that may have a neurodevelopmental origin. MicroRNAs (miRNAs) are small noncoding RNA sequences critical to neurodevelopment and adult neuronal processes by coordinating the activity of multiple genes within biological networks. We examined the expression of 854 miRNAs in prefrontal cortical tissue from 100 control, schizophrenic, and bipolar subjects. The cyclic AMP-responsive element binding- and NMDA-regulated microRNA miR-132 was significantly down-regulated in both the schizophrenic discovery cohort and a second, independent set of schizophrenic subjects. Analysis of miR-132 target gene expression in schizophrenia gene-expression microarrays identified 26 genes upregulated in schizophrenia subjects. Consistent with NMDA-mediated hypofunction observed in schizophrenic subjects, administration of an NMDA antagonist to adult mice results in miR-132 down-regulation in the prefrontal cortex. Furthermore, miR-132 expression in the murine prefrontal cortex exhibits significant developmental regulation and overlaps with critical neurodevelopmental processes during adolescence. Adult prefrontal expression of miR-132 can be down-regulated by pharmacologie inhibition of NMDA receptor signaling during a brief postnatal period. Several key genes, including DNMT3A, GATA2, and DPYSL3, are regulated by miR-132 and exhibited altered expression either during normal neurodevelopment or in tissue from adult schizophrenic subjects. Our data suggest miR-132 dysregulation and subsequent abnormal expression of miR-132 target genes contribute to the neurodevelopmental and neuromorphological pathologies present in schizophrenia.

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.