Effect of cross-platform gene-expression, computational methods on breast cancer subtyping in PALOMA-2 and PALLET studies.

Intrinsic breast cancer molecular subtyping (IBCMS) provides significant prognostic information for patients with breast cancer and helps determine treatment. This study compared IBCMS methods on various gene-expression platforms in PALOMA-2 and PALLET trials. PALOMA-2 tumor samples were profiled using EdgeSeq and nanostring and subtyped with AIMS, PAM50, and research-use-only (ruo)Prosigna. PALLET tumor biopsies were profiled using mRNA sequencing and subtyped with AIMS and PAM50. In PALOMA-2 (n = 222), a 54% agreement was observed between results from AIMS and gold-standard ruoProsigna, with AIMS assigning 67% basal-like to HER2-enriched. In PALLET (n = 224), a 69% agreement was observed between results from PAM50 and AIMS. Different IBCMS methods may lead to different results and could misguide treatment selection; hence, a standardized clinical PAM50 assay and computational approach should be used.Trial number: NCT01740427.

A Femtosecond Electron-Based Versatile Microscopy for Visualizing Carrier Dynamics in Semiconductors Across Spatiotemporal and Energetic Domains.

Carrier dynamics detection in different dimensions (space, time, and energy) with high resolutions plays a pivotal role in the development of modern semiconductor devices, especially in low-dimensional, high-speed, and ultrasensitive devices. Here, a femtosecond electron-based versatile microscopy is reported that combines scanning ultrafast electron microscopy (SUEM) imaging and time-resolved cathodoluminescence (TRCL) detection, which allows for visualizing and decoupling different dynamic processes of carriers involved in surface and bulk in semiconductors with unprecedented spatiotemporal and energetic resolutions. The achieved spatial resolution is better than 10 nm, and the temporal resolutions for SUEM imaging and TRCL detection are ≈500 fs and ≈4.5 ps, respectively, representing state-of-the-art performance. To demonstrate its unique capability, the surface and bulk carrier dynamics involved in n-type gallium arsenide (GaAs) are directly tracked and distinguished. It is revealed, in real time and space, that hot carrier cooling, defect trapping, and interband-/defect-assisted radiative recombination in the energy domain result in ordinal super-diffusion, localization, and sub-diffusion of carriers at the surface, elucidating the crucial role of surface states on carrier dynamics. The study not only gives a comprehensive physical picture of carrier dynamics in GaAs, but also provides a powerful platform for exploring complex carrier dynamics in semiconductors for promoting their device performance.

Experimental design considerations and statistical analyses in preclinical tumor growth inhibition studies.

Animal models are used in cancer pre-clinical research to identify drug targets, select compound candidates for clinical trials, determine optimal drug dosages, identify biomarkers, and ensure compound safety. This tutorial aims to provide an overview of study design and data analysis from animal studies, focusing on tumor growth inhibition (TGI) studies used for prioritization of anticancer compounds. Some of the experimental design aspects discussed here include the selection of the appropriate biological models, the choice of endpoints to be used for the assessment of anticancer activity (tumor volumes, tumor growth rates, events, or categorical endpoints), considerations on measurement errors and potential biases related to this type of study, sample size estimation, and discussions on missing data handling. The tutorial also reviews the statistical analyses employed in TGI studies, considering both continuous endpoints collected at single time-point and continuous endpoints collected longitudinally over multiple time-points. Additionally, time-to-event analysis is discussed for studies focusing on event occurrences such as animal deaths or tumor size reaching a certain threshold. Furthermore, for TGI studies involving categorical endpoints, statistical methodology is outlined to compare outcomes among treatment groups effectively. Lastly, this tutorial also discusses analysis for assessing drug combination synergy in TGI studies, which involves combining treatments to enhance overall treatment efficacy. The tutorial also includes R sample scripts to help users to perform relevant data analysis of this topic.

Assessing clinical response in early oncology development with a predictive biomarker.

In early oncology clinical trials there is often limited data for biomarkers and their association with response to treatment. Thus, it is challenging to decide whether a biomarker should be used for patient selection and enrollment. Most evidence about any potential predictive biomarker comes from preclinical research and, sometimes, clinical observations. How to translate the preclinical predictive biomarker data to clinical study remains an active field of research. Here, we propose a method to incorporate existing knowledge about a predictive biomarker - its prevalence, association with response and the performance of the assay used to measure the biomarker - to estimate the response rate in a clinical study designed with or without using the predictive biomarker. Importantly, we quantify the uncertainty associated with the biomarker and its predictability in a probabilistic model. This model estimates the distribution of the clinical response when a predictive biomarker is used to select patients and compares it to unselected cohort. We applied this method to two real world cases of approved biomarker-guided therapies to demonstrate its utility and potential value. This approach helps to make a data-driven decision whether to select patients with a predictive biomarker in early oncology clinical development.

Application of statistical machine learning in biomarker selection.

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.

Longitudinal multi-omics study of palbociclib resistance in HR-positive/HER2-negative metastatic breast cancer.

BACKGROUND: Cyclin-dependent kinase 4/6 inhibitor (CDK4/6) therapy plus endocrine therapy (ET) is an effective treatment for patients with hormone receptor-positive/human epidermal receptor 2-negative metastatic breast cancer (HR+/HER2- MBC); however, resistance is common and poorly understood. A comprehensive genomic and transcriptomic analysis of pretreatment and post-treatment tumors from patients receiving palbociclib plus ET was performed to delineate molecular mechanisms of drug resistance. METHODS: Tissue was collected from 89 patients with HR+/HER2- MBC, including those with recurrent and/or metastatic disease, receiving palbociclib plus an aromatase inhibitor or fulvestrant at Samsung Medical Center and Seoul National University Hospital from 2017 to 2020. Tumor biopsy and blood samples obtained at pretreatment, on-treatment (6 weeks and/or 12 weeks), and post-progression underwent RNA sequencing and whole-exome sequencing. Cox regression analysis was performed to identify the clinical and genomic variables associated with progression-free survival. RESULTS: Novel markers associated with poor prognosis, including genomic scar features caused by homologous repair deficiency (HRD), estrogen response signatures, and four prognostic clusters with distinct molecular features were identified. Tumors with TP53 mutations co-occurring with a unique HRD-high cluster responded poorly to palbociclib plus ET. Comparisons of paired pre- and post-treatment samples revealed that tumors became enriched in APOBEC mutation signatures, and many switched to aggressive molecular subtypes with estrogen-independent characteristics. We identified frequent genomic alterations upon disease progression in RB1, ESR1, PTEN, and KMT2C. CONCLUSIONS: We identified novel molecular features associated with poor prognosis and molecular mechanisms that could be targeted to overcome resistance to CKD4/6 plus ET. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03401359. The trial was posted on 18 January 2018 and registered prospectively.

Analysis of ß-nerve growth factor and its precursor during human pregnancy by immunoaffinity-liquid chromatography tandem mass spectrometry.

ß-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.

ALKBH5 inhibits TNF-α-induced apoptosis of HUVECs through Bcl-2 pathway.

The dysfunction and apoptosis of vascular endothelial cells are the initiating links in the formation of atherosclerosis. N6-methyladenosine (m6A) is an extremely extensive RNA methylation modification and its abnormality leads to the occurrence of various human diseases. In this study, we explored the effects of demethylase α-ketoglutarate-dependent dioxygenase ALKB homolog 5 (ALKBH5) on TNF-α-induced apoptosis of human umbilical vein endothelial cells (HUVECs). In TNF-α-treated HUVECs, the expression of ALKBH5 was significantly decreased. ALKBH5 overexpression promoted the proliferation and inhibited the apoptosis in TNF-α-treated HUVECs, suggesting that ALKBH5 had a protective effect on cell damage induced by TNF-α. Importantly, ALKBH5 promoted the expression of Bcl-2 in HUVECs. Bcl2 overexpression reduced the expression of Gadd45, Bax, and p21, which are transcriptionally activated by p53. But the expression of p53 has not been significantly affected, indicating that Bcl2 might regulate the apoptosis by inhibiting p53 downstream targets. In addition, ALKBH5 overexpression significantly increased the level of pri-miR-7 and decreased the level of miR-7. In conclusion, ALKBH5 attenuated the TNF-α-induced cell injury via promoting Bcl2 expression. Our research expands the understanding of the progression mechanism of atherosclerosis and provides a potential strategy for the protection of vascular endothelial injury.

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images.

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.

Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

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.

Cardiovascular outcomes associated with Ultrathin bioresorbable polymer sirolimus eluting stents versus thin, durable polymer everolimus eluting stents following percutaneous coronary intervention in patients with type 2 diabetes mellitus: A meta-analysis of published studies.

BACKGROUND: Percutaneous coronary intervention with the new generation drug eluting stents (DES) is 1 among the revascularization procedures required to treat patients with coronary artery disease (CAD). Since late stent thrombosis and silent myocardial infarction are highly associated with type 2 diabetes mellitus (T2DM), an analysis comparing the newer generation DES in this specific subgroup of patients would be scientifically relevant.In this analysis, we aimed to systematically compare the cardiovascular outcomes observed with the ultrathin bioresorbable polymer sirolimus eluting stents (SES) versus thin, durable polymer everolimus eluting stents (EES) following percutaneous coronary intervention in patients with T2DM. METHODS: Through online databases, relevant studies comparing ultrathin bioresorbable polymer SES versus the durable polymer EES were carefully searched. The cardiovascular outcomes were assessed during a follow-up time period of 1 year and more than 1 year (1-5 years) respectively. This meta-analysis was carried out by the latest version of the RevMan software. Following analysis, the results were represented by odds ratios (OR) with 95% confidence intervals (CI). RESULTS: A total number of 1967 patients with T2DM were included in this analysis. During a 1 year follow-up time period, target lesion failure (TLF) (OR: 0.59, 95% CI: 0.34-1.02; P = .06, target vessel revascularization (TVR) (OR: 0.97, 95% CI: 0.55-1.70; P = .91) and target lesion revascularization (TLR) (OR: 0.91, 95% CI: 0.44-1.87; P = .79) were similarly observed with ultrathin bioresorbable polymer SES versus the thin, durable polymer EES in these patients with T2DM. Other cardiovascular outcomes including myocardial infarction (MI), major adverse cardiac events, all-cause mortality (OR: 0.72, 95% CI: 0.37-1.40; P = .34), cardiac death and stent thrombosis (OR: 0.85, 95% CI: 0.45-1.62; P = .63) were also similarly observed with these 2 types of new stents. During a follow-up time period above 1 year (1-5 years), still no significant difference was observed in TLF, TVR, TLR, major adverse cardiac events, MI, all-cause mortality, cardiac death and stent thrombosis (OR: 0.62, 95% CI: 0.33-1.16; P = .14). CONCLUSIONS: The ultrathin bioresorbable polymer SES were similar to the durable polymer EES in these patients with T2DM. These 2 types of new generation stents were comparable in terms of cardiovascular outcomes. Hence, they might be recommended in patients with T2DM. Upcoming trials should be able to confirm this hypothesis.

Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome.

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.

Anti-tumor immunity influences cancer cell reliance upon ATG7.

Macroautophagy (autophagy) is an essential cellular catabolic process required for survival under conditions of starvation. The role of autophagy in cancer is complex, context-dependent and at times contradictory, as it has been shown to inhibit, promote or be dispensable for tumor progression. In this study, we evaluated the contribution of the immune system to the reliance of tumors on autophagy by depleting autophagy-related 7 (ATG7) in murine tumor cells and grafting into immunocompetent versus immunodeficient hosts. Although loss of ATG7 did not affect tumor growth in vitro or in immunodeficient mice, our studies revealed that cancer cell reliance on autophagy was influenced by anti-tumor immune responses, including those mediated by CD8+ T cells. Furthermore, we provide insights into possible mechanisms by which autophagy disruption can enhance anti-tumor immune responses and suggest that autophagy disruption may further benefit patients with immunoreactive tumors.

miR-20b-5p attenuates hypoxia-induced apoptosis in cardiomyocytes via the HIF-1α/NF-κB pathway.

Chronic hypoxia is a common inducer of end-stage cardiovascular disease. In cells under hypoxia, the hypoxia-inducible factor-1 (HIF-1) plays a vital role in regulating downstream target genes. However, the mechanism of hypoxia in cardiomyocytes is still unclear. In this study, we aimed to identify novel downstream epigenetic targets of HIF-1α in cardiomyocytes under hypoxia. H9c2 cells were exposed to hypoxia condition, and quantitative real-time PCR analysis was performed to evaluate the expression of miR-20b-5p. The results indicated that the expression of miR-20b-5p was down-regulated in H9c2 cells under low oxygen condition. Meanwhile, HIF-1α overexpression further down-regulated the miR-20b-5p expression in H9c2 cells transfected with HIF-1α plasmids. In addition, Annexin-V-FITC/PI flow cytometry analysis suggested that overexpression of miR-20b-5p attenuated cell apoptosis under hypoxia condition in H9c2 cells. Western blot analysis showed that the hypoxia apparently increased Bax and cleaved-caspase-3, but decreased Bcl-2 expression in H9c2 cells, indicating that hypoxia-induced NF-κB signaling pathway activation is mediated by miR-20b-5p. Hypoxia-induced H9c2 cell apoptosis was reduced after HIF-1α knockdown as shown by the flow cytometry analysis. In conclusion, we identified that miR-20b-5p plays an important role in mediating cardiomyocytes apoptosis under hypoxia, which is mediated by the HIF-1/NF-κB signaling pathway.

First-in-Human Study of Utomilumab, a 4-1BB/CD137 Agonist, in Combination with Rituximab in Patients with Follicular and Other CD20+ Non-Hodgkin Lymphomas.

PURPOSE: In this phase I study (NCT01307267), we evaluated safety, pharmacokinetics, clinical activity, and pharmacodynamics of treatment with utomilumab plus rituximab in patients with relapsed/refractory follicular lymphoma (FL) and other CD20+ non-Hodgkin lymphomas (NHL). PATIENTS AND METHODS: Primary objectives were to assess treatment safety and tolerability for estimating the MTD, using a modified time-to-event continual reassessment method, and selecting the recommended phase II dose (RP2D). RESULTS: Sixty-seven patients received utomilumab (0.03-10.0 mg/kg every 4 weeks) and rituximab (375 mg/m2 weekly) in the dose-escalation groups or utomilumab (1.2 mg/kg every 4 weeks) plus rituximab in the dose-expansion cohort. No patient experienced dose-limiting toxicity. The MTD for utomilumab in combination with rituximab was not reached and estimated to be ≥10 mg/kg every 4 weeks. The majority of the utomilumab treatment-related adverse events (AE) were grade 1 to 2; the most common AE was fatigue (16.4%). The pharmacokinetics of utomilumab in combination with rituximab was linear in the 0.03 to 10 mg/kg dose range. A low incidence (1.5%) of treatment-induced antidrug antibodies against utomilumab was observed. The objective response rate was 21.2% (95% CI, 12.1%-33.0%) in all patients with NHL, including four complete and 10 partial responses. Analysis of paired biopsies from a relapsed/refractory FL patient with complete response showed increased T-cell infiltration and cytotoxic activity in tumors. Biomarker correlations with outcomes suggested that clinical benefit may be contingent on patient immune function. CONCLUSIONS: Utomilumab in combination with rituximab demonstrated clinical activity and a favorable safety profile in patients with CD20+ NHLs.

Inhibition of Ctsk modulates periodontitis with arthritis via downregulation of TLR9 and autophagy.

OBJECTIVES: The mechanisms underlying the effects of Toll-like receptor 9 (TLR9) and autophagy on rheumatoid arthritis (RA)-aggravated periodontitis are unclear. We aimed to explore a novel target, cathepsin K (Ctsk)-mediated TLR9-related autophagy, during the progress of periodontitis with RA. MATERIALS AND METHODS: DBA/J1 mouse model of periodontitis with RA was created by local colonization of Porphyromonas gingivalis (Pg) and injection of collagen. The expression of Ctsk was inhibited by adeno-associated virus (AAV). Micro-CT, immunohistochemistry (IHC), Western blot and quantitative real-time polymerase chain reaction (qRT-PCR) were used to detect the expression of TLR9-related autophagy in periodontitis with RA. Small interfering RNA (siRNA) and CpG oligodeoxynucleotides (CpG ODN) were applied in macrophages. Western blot, immunofluorescence (IF) and qRT-PCR were used to verify the in vivo results. RESULTS: RA can promote periodontitis bone destruction in the lesion area, while inhibiting Ctsk could effectively alleviate this effect. The infiltration of macrophages, TLR9, autophagy proteins (TFEB and LC3) and inflammatory cytokines increased in the periodontitis-with-RA group and was reduced by the inhibition of Ctsk in the periodontal region. Macrophage stimulation confirmed the in vivo results. With the activation of TLR9 by CpG ODN, inhibition of Ctsk could suppress both TLR9 downstream signalling proteins and autophagy-related proteins. CONCLUSIONS: This study advanced a novel role for Ctsk in TLR9 and autophagy to explain the interaction between periodontitis and RA.

Cystine-glutamate antiporter xCT deficiency suppresses tumor growth while preserving antitumor immunity.

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 antigen-induced 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.

Model to improve specificity for identification of clinically-relevant expanded T cells in peripheral blood.

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) ß-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.