Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers.

OBJECTIVES: To compare computed tomography (CT)-based radiomics for preoperatively differentiating type I and II epithelial ovarian cancers (EOCs) using different machine learning classifiers and to construct and validate the best diagnostic model. METHODS: A total of 470 patients with EOCs were included retrospectively. Patients were divided into a training dataset (N = 329) and a test dataset (N = 141). A total of 1316 radiomics features were extracted from the portal venous phase of contrast-enhanced CT images for each patient, followed by dimension reduction of the features. The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), naïve Bayes (NB), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) classifiers were trained to obtain the radiomics signatures. The performance of each radiomics signature was evaluated and compared by the area under the receiver operating characteristic curve (AUC) and relative standard deviation (RSD). The best radiomics signature was selected and combined with clinical and radiological features to establish a combined model. The diagnostic value of the combined model was assessed. RESULTS: The LR-based radiomics signature performed well in the test dataset, with an AUC of 0.879 and an accuracy of 0.773. The combined model performed best in both the training and test datasets, with AUCs of 0.900 and 0.934 and accuracies of 0.848 and 0.823, respectively. CONCLUSION: The combined model showed the best diagnostic performance for distinguishing between type I and II EOCs preoperatively. Therefore, it can be a useful tool for clinical individualized EOC classification. KEY POINTS: • Radiomics features extracted from computed tomography (CT) could be used to differentiate type I and II epithelial ovarian cancers (EOCs). • Machine learning can improve the performance of differentiating type I and II EOCs. • The combined model exhibited the best diagnostic capability over the other models in both the training and test datasets.

MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma.

BACKGROUND: Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients. PURPOSE: To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC. STUDY TYPE: Retrospective. POPULATION: An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts. FIELD STRENGTH/SEQUENCES: Contrast-enhanced T1 -weight MRI was performed with a 1.5T scanner. ASSESSMENT: A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling. STATISTICAL TESTS: The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness. RESULTS: The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively. DATA CONCLUSION: The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. J. MAGN. RESON. IMAGING 2020;52:1679-1687.

SRGN Promotes Colorectal Cancer Metastasis as a Critical Downstream Target of HIF-1α.

BACKGROUND/AIMS: The chondroitin sulfate proteoglycan serglycin (SRGN), a hematopoietic cell granule proteoglycan, has been implicated in promoting tumor metastasis; however, the underlying mechanisms remain to be elucidated. The present study aimed to investigate the SRGN gene expression and its regulation as downstream signaling of hypoxia-inducible transcription factor 1 alpha (HIF-1α) in colorectal cancer (CRC) cells and tissues. METHODS: The expression of SRGN was analyzed in CRC specimens for its correlation with progression and metastasis. Using chromatin-immunoprecipitation (ChIP), quantitative real-time PCR, Western blot, and transwell assay, the functional role and underlying mechanism of SRGN in CRC metastasis were elucidated. Thus, this study provides evidence of a critical role of SRGN in metastatic progression of CRC. RESULTS: Our results indicated that SRGN overexpression was significantly associated with poor prognosis in CRC specimens. SRGN overexpression promoted CRC cell migration and invasion in vitro; however, SRGN depletion exhibited contrasting effects. Mechanistic investigations revealed that HIF-1α regulated SRGN transcription via physically binding to a hypoxia response element in its promoter region. CONCLUSIONS: In conclusion, we demonstrated that dysregulated HIF-1α/SRGN signaling promotes CRC progression and metastasis. SRGN may serve as a potential candidate therapeutic target for metastatic CRC.

Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT.

OBJECTIVE: To identify the differences among preinvasive lesions, minimally invasive adenocarcinomas (MIAs) and invasive pulmonary adenocarcinomas (IPAs) based on radiomic feature analysis with computed tomography (CT). METHODS: A total of 109 patients with ground-glass opacity lesions (GGOs) in the lungs determined by CT examinations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation and segmentation of the GGOs as regions of interest (ROIs), the patients were subdivided into three groups based on pathologic analyses: the preinvasive lesions (including atypical adenomatous hyperplasia and adenocarcinoma in situ) subgroup, the MIA subgroup and the IPA subgroup. Next, we obtained the texture features of the GGOs. The data analysis was aimed at finding both the differences between each pair of the groups and predictors to distinguish any two pathologic subtypes using logistic regression. Finally, a receiver operating characteristic (ROC) curve was applied to accurately evaluate the performances of the regression models. RESULTS: We found that the voxel count feature (P<0.001) could be used as a predictor for distinguishing IPAs from preinvasive lesions. However, the surface area feature (P=0.040) and the extruded surface area feature (P=0.013) could be predictors of IPAs compared with MIAs. In addition, the correlation feature (P=0.046) could distinguish preinvasive lesions from MIAs better. CONCLUSIONS: Preinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CT images, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseases are very important for clinical surgery.

MicroRNA-494 Activation Suppresses Bone Marrow Stromal Cell-Mediated Drug Resistance in Acute Myeloid Leukemia Cells.

Acute myeloid leukemia (AML) is not sensitive to chemotherapy partially because of the protection of AML cells by mesenchymal stromal cells (MSCs). Our previous studies found that MSCs protected AML cells from apoptosis through the c-Myc-dependent pathway. However, the mechanism by which MSCs regulate c-Myc in AML cells is still unknown. To elucidate the mechanism, we performed microRNA array analysis of AML cell lines and validated by TaqMan realtime PCR. The results showed that the expression of microRNA-494 (miR-494) in AML cells after coculture with MSCs was downregulated. Reporter gene analysis confirmed miR-494 as one of the regulators of c-Myc. In the coculture system, activation of miR-494 in AML cells suppressed proliferation and induced apoptosis of AML cells in vitro. After addition of mitoxantrone to the coculture system, the proliferation of AML cells with miR-494 activation was suppressed more than that of control cells. After subcutaneous injection of AML cell lines in combination with MSC, tumor growth was suppressed in mice injected with miR-494-overexpressing AML cells. The rate of tumor formation was even lower after mitoxantrone treatment in the miR-494 overexpressing group. Moreover, miR-494 activation resulted in a decrease of leukemic cell counts in peripheral blood (PB) and bone marrow, and prolonged survival in mice injected with miR-494-overexpressing AML cellls and MSCs compared to the control mice. Our results indicate that miR-494 suppresses drug resistance in AML cells by downregulating c-Myc through interaction with MSCs and that miR-494 therefore is a potential therapeutic target. J. Cell. Physiol. 232: 1387-1395, 2017. © 2016 Wiley Periodicals, Inc.

Elevated THBS2, COL1A2, and SPP1 Expression Levels as Predictors of Gastric Cancer Prognosis.

BACKGROUND/AIMS: Gastric cancer (GC) is an important health problem. Classification based on molecular subtypes may help to determine the prognosis of patients with GC. Tumor invasion and metastasis are important factors affecting the prognosis of cancer. We aimed to identify genes related to tumor invasion and metastasis, which may serve as indicators of good GC prognosis. METHODS: Tumor tissues and adjacent normal tissues were collected from 105 patients with primary GC who were treated by undergoing radical surgery. Samples were used for tissue microarray analysis. Identified genes with altered expression were further analyzed using the Gene Ontology (Go) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The expression levels of THBS2, COL1A2 and SPP1 were analyzed by RT-PCR, western blot and immunohistochemistry. The overall survival curves of patients with high and low expression of each gene of interest were plotted and compared. RESULTS: Forty-three genes were identified. THBS2, COL1A2 and SPP1 were selected for further analysis. Altered expression levels of THBS2, COL1A2 and SPP1 in tumor tissues were confirmed. Patients with low THBS2 expression had a better prognosis; the expression of COL1A2 and SPP1 might not affect the prognosis of patients with GC. CONCLUSION: THBS2, but not COL1A2 and SPP1, may serve as an indicator of GC prognosis.

Survey of MapReduce frame operation in bioinformatics.

Bioinformatics is challenged by the fact that traditional analysis tools have difficulty in processing large-scale data from high-throughput sequencing. The open source Apache Hadoop project, which adopts the MapReduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services. In this article, we present MapReduce frame-based applications that can be employed in the next-generation sequencing and other biological domains. In addition, we discuss the challenges faced by this field as well as the future works on parallel computing in bioinformatics.

Correlations between the minimum and mean apparent diffusion coefficient values of hepatocellular carcinoma and tumor grade.

PURPOSE: To investigate the correlations between the minimum and mean apparent diffusion coefficient (ADC) values of hepatocellular carcinoma (HCC) and pathological grade. MATERIALS AND METHODS: Preoperative magnetic resonance imaging (MRI) images of 241 patients with HCC confirmed by pathology were retrospectively analyzed. All patients underwent preoperative diffusion-weighted imaging (DWI) on a 1.5T MRI scanner. The mean and minimum ADC values of the tumors were measured. The ADC values were compared in tumors with different grades and the correlations between ADC values and pathological grade were analyzed. Receiver operating characteristic (ROC) curves of ADC values were obtained and compared to distinguish poorly and nonpoorly differentiated HCCs. Interobserver agreements were assessed by intraclass correlation coefficient (ICC). RESULTS: The mean and minimum ADC values of poorly differentiated HCCs were lower than those of nonpoorly differentiated HCCs (P = 0.000, 0.000, respectively). The mean and minimum ADC values were negatively correlated with pathological grade (rs = -0.180 and -0.202, respectively) (P = 0.005, 0.002, respectively). For the differentiation between poorly and nonpoorly differentiated HCCs, the mean ADC value provided a sensitivity of 69.57% and a specificity of 73.39% with a cutoff value of 0.96 × 10-3 mm2 /s while the minimum ADC value showed a sensitivity of 78.26% and a specificity of 61.47% with a cutoff value of 0.90 × 10-3 mm2 /s. No significant difference existed between both ROC curves (P = 0.64). The ICC for the measurements of the mean and minimum ADC values was 0.92 (95% confidence interval [CI] 0.90-0.93) and 0.91 (95% CI 0.89-0.93), respectively. CONCLUSION: DWI of HCC could preoperatively provide quantitative parameters for predicting tumor histological grade. J. Magn. Reson. Imaging 2016;44:1442-1447.

Clear cell carcinoma of the ovary: multi-slice computed tomography findings.

BACKGROUND: The aim of the study was to describe the multi-slice computed tomography (MSCT) features of clear cell carcinoma (CCC) of the ovary. METHODS: Twenty patients with histology-confirmed CCC of the ovary were retrospectively reviewed. All patients underwent preoperative plain and contrast-enhanced MSCT examinations. Imaging studies were evaluated for the following: (a) location; (b) maximal transverse diameter; (c) shape (round, oval, lobular, or irregular); (d) margin (well defined or ill defined); (e) solid, solid with cystic regions, or cystic; (f) attenuation of the cystic portion; (g) enhancement pattern of the solid portions of the tumor; and (h) the secondary manifestations. RESULTS: The mean age of the patients was 63 years (range, 44 to 77 years). Tumors were unilateral in 19 patients and bilateral on 1 patient. The maximal transverse diameter of the tumors was relatively larger with a mean diameter of 105.7 mm (range, 45.5 to 260.6 mm). CCCs demonstrated cystic masses with solid regions in 20 lesions. Most lesions were ovoid (15/21), unilocular (16/21), and well-defined (17/21). The CT value of cystic or necrotic portion ranged from 12 to 28 HU (average, 18 HU) on plain image. The solid protrusions of the cystic masses were both few and round with obviously heterogeneous enhancements after contrast. CONCLUSIONS: The ovarian CCCs typically present as a large, well-defined, unilocular cystic mass with solid protrusion and relatively high attenuated cystic or necrotic portions. The solid protrusions are usually both round and few in number with obviously heterogeneous enhancement.

Magnetic resonance imaging features of alveolar soft part sarcoma: report of 14 cases.

BACKGROUND: The purpose of this study was to assess the magnetic resonance imaging findings of alveolar soft part sarcoma. METHODS: Magnetic resonance images of pathologically proven alveolar soft part sarcoma in 14 patients were retrospectively reviewed, including lesion location, size and shape, border definition, signals on T1-weighted and T2-weighted images, presence or absence of peritumoral and intratumoral flow voids, and enhancement pattern. RESULTS: Patients included five women and nine men, ranging in age from 27 to 54 years, with a mean age of 36 years. A slow-growing mass without pain was the chief complaint. Eight patients had pulmonary metastases at presentation. Ten lesions arose from the extremities, two were located in the gluteal regions, one affected the presacral space and one occurred in the back. The mean maximal size of the lesions was 9.8 cm, ranging from 6.2 to 16 cm. All lesions appeared as a round (n = 2), ovoid (n = 8) or irregular (n = 4) shape with ill-defined margins. The lesions mainly demonstrated isointense or mildly hyperintense compared to muscle on T1-weighted images, and heterogeneous high signal intensity on T2-weighted images. Peritumoral edemas were observed in six patients. Ten lesions showed intense inhomogeneous enhancement after contrast. Intra- and peritumoral tubular flow voids representing tortuous dilated vessels with rapid blood flow were present in all cases. CONCLUSIONS: Alveolar soft part sarcoma has some distinctive magnetic resonance imaging features including a slow-growing, large mass in the soft tissue of the extremities in young adults, with numerous signal voids on T1-weighted and T2-weighted images, and strong enhancement after contrast.

Mining and exploration of rehabilitation nursing targets for colorectal cancer.

BACKGROUND: There are often subtle early symptoms of colorectal cancer, a common malignancy of the intestinal tract. However, it is not yet clear how MYC and NCAPG2 are involved in colorectal cancer. METHOD: We obtained colorectal cancer datasets GSE32323 and GSE113513 from the Gene Expression Omnibus (GEO). After downloading, we identified differentially expressed genes (DEGs) and performed Weighted Gene Co-expression Network Analysis (WGCNA). We then undertook functional enrichment assay, gene set enrichment assay (GSEA) and immune infiltration assay. Protein-protein interaction (PPI) network construction and analysis were undertaken. Survival analysis and Comparative Toxicogenomics Database (CTD) analysis were conducted. A gene expression heat map was generated. We used TargetScan to identify miRNAs that are regulators of DEGs. RESULTS: 1117 DEGs were identified. Their predominant enrichment in activities like the cellular phase of the cell cycle, in cell proliferation, in nuclear and cytoplasmic localisation and in binding to protein-containing complexes was revealed by Gene Ontology (GO). When the enrichment data from GSE32323 and GSE113513 colon cancer datasets were merged, the primary enriched DEGs were linked to the cell cycle, protein complex, cell cycle control, calcium signalling and P53 signalling pathways. In particular, MYC, MAD2L1, CENPF, UBE2C, NUF2 and NCAPG2 were identified as highly expressed in colorectal cancer samples. Comparative Toxicogenomics Database (CTD) demonstrated that the core genes were implicated in the following processes: colorectal neoplasia, tumour cell transformation, inflammation and necrosis. CONCLUSIONS: High MYC and NCAPG2 expression has been observed in colorectal cancer, and increased MYC and NCAPG2 expression correlates with worse prognosis.

A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches.

Background: Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Methods: Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution. Results: We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade. Conclusion: The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.

SMARCA4 is a haploinsufficient B cell lymphoma tumor suppressor that fine-tunes centrocyte cell fate decisions.

SMARCA4 encodes one of two mutually exclusive ATPase subunits in the BRG/BRM associated factor (BAF) complex that is recruited by transcription factors (TFs) to drive chromatin accessibility and transcriptional activation. SMARCA4 is among the most recurrently mutated genes in human cancer, including ∼30% of germinal center (GC)-derived Burkitt lymphomas. In mice, GC-specific Smarca4 haploinsufficiency cooperated with MYC over-expression to drive lymphomagenesis. Furthermore, monoallelic Smarca4 deletion drove GC hyperplasia with centroblast polarization via significantly increased rates of centrocyte recycling to the dark zone. Mechanistically, Smarca4 loss reduced the activity of TFs that are activated in centrocytes to drive GC-exit, including SPI1 (PU.1), IRF family, and NF-κB. Loss of activity for these factors phenocopied aberrant BCL6 activity within murine centrocytes and human Burkitt lymphoma cells. SMARCA4 therefore facilitates chromatin accessibility for TFs that shape centrocyte trajectories, and loss of fine-control of these programs biases toward centroblast cell-fate, GC hyperplasia and lymphoma.

Morphological distribution and internal enhancement architecture of contrast-enhanced magnetic resonance imaging in the diagnosis of non-mass-like breast lesions: a meta-analysis.

The aim of this study is to assess the diagnostic performance of contrast-enhanced magnetic resonance (MR) imaging in patients with non-mass-like breast lesions in a meta-analysis. Literature study was performed on PubMed data base on the diagnostic performance of MR imaging in patients with non-mass-like breast lesions. Details of the relevant studies were reviewed and a meta-analysis was performed to estimate the overall sensitivity and specificity of contrast-enhanced MR imaging of non-mass-like breast lesions. A summary receiver operating characteristic curve (sROC) was developed to explore the threshold effect by ROC space. Spearman correlation coefficient was calculated using Meta-Disc version 1.4 to analyze the heterogeneity between studies. A total of 858 non-mass-like lesions from 15 studies were included in the meta-analysis (sample size range: 27-131). Pooled weighted estimates of sensitivity and specificity were 50% (95% CI: 46%, 53%) and 80% (95% CI: 77%, 83%), respectively. The heterogeneity among studies was caused by other factors other than threshold effect. The findings were influenced by cancer prevalence (p = 0.0359). Subgroup analyses indicated that the sensitivity and specificity in studies with combined diagnostic criterion was higher than that in studies with single diagnostic criterion. In evaluation of non-mass-like breast lesions, contrast-enhanced MRI has high specificity and relatively lower sensitivity.

[Feasibility of volume perfusion CT (VPCT) imaging in antiangiogenic treatment of rabbit VX2 soft-tissue tumor].

OBJECTIVE: To explore the feasibility of volume perfusion CT imaging to dynamically monitor and evaluate the response of rabbit VX2 soft-tissue tumor to antiangiogenic treatment. METHODS: To establish an experimental animal model of VX2 soft tissue tumor on 20 New Zealand white rabbits. Twenty rabbits were randomly divided into 2 groups. The therapy group was treated with recombinant human endostatin (3 mg·kg⁻¹·d⁻¹) for 7 days, and the control group received saline in the same dose only. Four times of CT volume perfusion scan were performed before treatment and on the second, forth, seventh days of treatment, respectively. The value of blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability (PMB) in the VX2 tumors were measured after scanning. The microvessel density (MVD) and expression of vascular endothelial growth factor (VEGF) in the tumors were determined using immunohistochemical staining. RESULTS: The tumor volume of the therapy group was (1.36 ± 0.73) cm³ on the forth day of treatment and (1.69 ± 0.68) cm³ on the seventh day of the treatment. The tumor volume of the control group was (2.35 ± 0.62) cm³ on the fourth day of treatment and (3.87 ± 0.93) cm³ on the seventh day of the treatment (P < 0.05). On the seventh day of treatment, tumor necrosis ratio of the therapy group and the control group was (25.58 ± 5.51)% and (42.93 ± 4.34)%, respectively (P < 0.05). Comparing the perfusion parameters between the two groups on the same day, and the second, forth, seventh days of treatment, the value of PMB of the therapy group was (70.36 ± 23.46) ml·100 ml⁻¹·min⁻¹, (79.64 ± 13.68) ml·100 ml⁻¹·min⁻¹ and (84.76 ± 3.55) ml·100 ml⁻¹·min⁻¹, respectively, and that in the control group was (26.61 ± 6.47) ml·100 ml⁻¹·min⁻¹, (33.74 ± 16.47) ml·100 ml⁻¹·min⁻¹ and (30.47 ± 10.64) ml·100 ml⁻¹·min⁻¹, respectively (P < 0.05). The value of BF in the therapy group and control group was (71.19 ± 12.21) ml·100 ml⁻¹·min⁻¹ and (43.56 ± 12.21) ml·100 ml⁻¹·min⁻¹, respectively, on the seventh day of treatment (P < 0.05). The parameters on different days in the same group were compared. In the control group, the value of BF on the seventh day of treatment was significantly lower than that before and on the second and forth days of treatment (P < 0.05). However, in the therapy group, the value of PMB on the second, forth, and seventh days of treatment was significantly higher than that before treatment (P < 0.05). MVD of tumor in the control group was increased gradually, whereas increased on the first day and then decreased more in the therapy group. The VEGF expressions did not differ significantly between the experimental and control groups. CONCLUSIONS: Volume perfusion CT is helpful to quantify the tumor perfusion and evaluate the functional changes of tumor vasculature, and then evaluate the early therapeutic effect of antiangiogenic treatment.

Recurrent composite markers of cell types and states.

Determining concise sets of genomic markers that identify cell types and states within tissue ecosystems remains challenging. To address this challenge, we developed Recurrent Composite Markers for Biological Identities with Neighborhood Enrichment (RECOMBINE). Validations of RECOMBINE with simulation and transcriptomics data in bulk, single-cell and spatial resolutions demonstrated the method's ability for unbiased selection of composite markers that characterize biological subpopulations. RECOMBINE captured markers of mouse visual cortex from single-cell RNA sequencing data and provided a gene panel for targeted spatial transcriptomics profiling. RECOMBINE identified composite markers of CD8 T cell states including GZMK + HAVCR2 - effector memory cells associated with anti-PD1 therapy response. The method outperformed differential gene expression analysis in characterizing a rare cell subpopulation within mouse intestine. Using RECOMBINE, we uncovered hierarchical gene programs of inter- and intra-tumoral heterogeneity in breast and skin tumors. In conclusion, RECOMBINE offers a data-driven approach for unbiased selection of composite markers, resulting in improved interpretation, discovery, and validation of cell types and states.

A prognostic matrix code defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.

A single-cell atlas of CD19 chimeric antigen receptor T cells.

Li et al. present a resource of single-cell RNA sequencing (scRNA-seq) data from the infusion products of relapsed or refractory large B cell lymphoma (rrLBCL) patients treated with standard-of-care axicabtagene ciloleucel and identify features that are significantly different between products from responders and non-responders at 3-month followup by PET/CT, an important landmark for long-term outcomes.

A prognostic matrix code defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.

Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. Methods: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.