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Legumain has been identified as a target for diagnosis and treatment of associated cancers. Therefore, real-time imaging of legumain activity in vivo is helpful in diagnosing and evaluating therapeutic efficacy of associated cancers. Fluorescent/photoacoustic (FL/PA) dual-modal imaging developed rapidly because of its good sensitivity and spatial resolution. As far as we know, a tumor-targeted probe for FL/PA imaging of legumain activity in vivo has not been reported. Hence, we intended to develop a tumor-targeted hemicyanine (HCy) probe ( HCy-AAN-Bio ) for FL/PA imaging of legumain in vivo. The control probe HCy-AAN does not have tumor-targeting ability. Legumain can specifically cleave HCy-AAN-Bio or HCy-AAN with the generation of FL/PA signal while more HCy-AAN-Bio could be recognized by legumain than HCy-AAN with higher sensitivity in vitro. Due to the tumor-targeting ability, HCy-AAN-Bio could image 4T1 cells with an additional 1.3-fold FL enhancement and 1.9-fold PA enhancement than HCy-AAN . In addition, HCy-AAN-Bio could image legumain activity in vivo with an additional 1.5-fold FL enhancement and 1.9-fold PA enhancement than HCy-AAN . We expected that HCy-AAN-Bio will be a powerful tool for early diagnosis of associated cancer.

Here, we present a protocol for exploring the effects of PPP1R15A inhibitor, Sephin1, on antitumor immunity of B16F1 subcutaneous tumor in mice. We describe steps for constructing single-cell transcriptome and TCR libraries, sequencing, and using sequencing data for the integration of expression and TCR data. We then detail procedures for gene differentiation, regulon and cell-cell communication analysis, and validation of single-cell analysis results. For complete details on the use and execution of this protocol, please refer to Wang et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

The activable NIR-based phototheranostic nanoplatform (NP) is considered an efficient and reliable tumor treatment due to its strong targeting ability, flexible controllability, minimal side effects, and ideal therapeutic effect. This work describes the rational design of a second near-infrared (NIR-II) fluorescence imaging-guided organic phototheranostic NP (FTEP-TBFc NP). The molecular-engineered phototheranostic NP has a sensitive response to glutathione (GSH), generating hydrogen sulfide (H 2 S) gas, and delivering ferrocene molecules in the tumor microenvironment (TME). Under 808 nm irradiation, FTEP-TBFc could not only simultaneously generate fluorescence, heat, and singlet oxygen but also greatly enhance the generation of reactive oxygen species to improve chemodynamic therapy (CDT) and photodynamic therapy (PDT) at a biosafe laser power of 0.33 W/cm 2 . H 2 S inhibits the activity of catalase and cytochrome c oxidase (COX IV) to cause the enhancement of CDT and hypothermal photothermal therapy (HPTT). Moreover, the decreased intracellular GSH concentration further increases CDT's efficacy and downregulates glutathione peroxidase 4 (GPX4) for the accumulation of lipid hydroperoxides, thus causing the ferroptosis process. Collectively, FTEP-TBFc NPs show great potential as a versatile and efficient NP for specific tumor imaging-guided multimodal cancer therapy. This unique strategy provides new perspectives and methods for designing and applying activable biomedical phototheranostics.

β-Galactosidase (β-gal) is the gold standard marker of cellular senescence, which is linked with various age-related diseases. Therefore, it is essential to develop more excellent probes that can real-time monitor β-gal activity in cellular senescence in vivo . Fluorescent/photoacoustic (FL/PA) dual-modal imaging possesses excellent sensitivity and spatial resolution. To our knowledge, there has been no tumor-targeted FL/PA probe to image cellular senescence by monitoring the activity of β-gal in vivo . Therefore, we developed a tumor-targeted FL/PA probe ( Gal-HCy-Biotin ) for β-gal-activatable imaging of tumor senescence. Gal-HCy without tumor-targeted biotin is used as a control probe. Gal-HCy-Biotin is superior to Gal-HCy due to the higher kinetic parameter of Gal-HCy-Biotin than Gal-HCy in vitro . Moreover, biotin could help Gal-HCy-Biotin enter and accumulate in tumor cells with higher FL/PA signal. In detail, Gal-HCy-Biotin or Gal-HCy could image senescent tumor cells with 4.6-fold or 3.5-fold FL enhancement and 4.1-fold or 3.3-fold PA enhancement. Gal-HCy-Biotin or Gal-HCy could image tumor senescence with 2.9-fold or 1.7-fold FL enhancement and 3.8-fold or 1.3-fold PA enhancement. We envision that Gal-HCy-Biotin will be applied for FL/PA imaging of tumor senescence in clinic.

Caspase-3 is an essential executor in apoptosis, and its activation has been regarded as a biomarker of cell apoptosis. The development of Caspase-3-responsive multimodal probes is a promising research prospect. Fluorescent/photoacoustic (FL/PA) imaging has attracted considerable attention due to the high sensitivity of FL as well as the high spatial resolution and penetration depth of PA. To our knowledge, there has been no tumor-targeted FL/PA probe to monitor the activity of Caspase-3 in vivo . Therefore, we developed a tumor-targeted FL/PA probe ( Bio-DEVD-HCy ) for Caspase-3-responsive imaging of tumor apoptosis. Ac-DEVD-HCy without tumor-targeted biotin is used as a control probe. In vitro experiments indicated that Bio-DEVD-HCy is superior to Ac-DEVD-HCy because of the higher kinetic parameter of Bio-DEVD-HCy in comparison to Ac-DEVD-HCy . Cell and tumor imaging results suggested that Bio-DEVD-HCy could enter and accumulate in tumor cells with higher FL/PA signal with the help of tumor-targeted biotin. In detail, Bio-DEVD-HCy or Ac-DEVD-HCy could image apoptotic tumor cells with 4.3-fold or 3.5-fold FL enhancement and 3.4-fold or 1.5-fold PA enhancement. Bio-DEVD-HCy or Ac-DEVD-HCy could image tumor apoptosis with 2.5-fold or 1.6-fold FL enhancement and 4.1-fold or 1.9-fold PA enhancement. We envision that Bio-DEVD-HCy will be applied for FL/PA imaging of tumor apoptosis in clinical settings.

Effectively interfering energy metabolism in tumor cells and simultaneously activating the in vivo immune system to perform immune attacks are meaningful for tumor treatment. However, precisely targeted therapy is still a huge challenge. Herein, a mitochondrial-targeting phototheranostic system, FE-T nanoparticles (FE-T NPs) are developed to damage mitochondria in tumor cells and change the tumor immunosuppressive microenvironment. FE-T NPs are engineered by encapsulating the near-infrared (NIR) absorbed photosensitizer IR-FE-TPP within amphiphilic copolymer DSPE-SS-PEG-COOH for high-performing with simultaneous mitochondrial-targeting, near-infrared II (NIR-II) fluorescence imaging, and synchronous photothermal therapy (PTT) /photodynamic therapy (PDT) /immune therapy (IMT). In tumor treatment, the disulfide in the copolymer can be cleaved by excess intracellular glutathione (GSH) to release IR-FE-TPP and accumulate in mitochondria. After 808 nm irradiation, the mitochondrial localization of FE-T NPs generated reactive oxygen species (ROS), and hyperthermia, leading to mitochondrial dysfunction, photoinductive apoptosis, and immunogenic cell death (ICD). Notably, in situ enhanced PDT/PTT in vivo via mitochondrial-targeting with FE-T NPs boosts highly efficient ICD toward excellent antitumor immune response. FE-T NPs provide an effective mitochondrial-targeting phototheranostic nanoplatform for imaging-guided tumor therapy., (© 2023 Wiley-VCH GmbH.)

Alkaline phosphatase (ALP) is a tumor marker for early diagnosis and treatment. Tumor targeting can recognize and fight tumor cells more accurately from healthy cells. Glycyrrhetinic acid (GA) is a targeting ligand of liver tumors. Photoacoustic imaging (PAI) and photothermal therapy (PTT) are promising techniques for tumor diagnosis and treatment. The outstanding characteristics of Hemicyanine (HCy) dye make it suitable for tumor diagnosis and treatment. However, using HCy nanoparticle (HCy NP) for liver tumor-targeting PAI and PTT has not been reported. Herein, Probe-1 is developed to enhance PAI and PTT of liver tumors due to GA targeting and intracellular ALP-instructed self-assembly of HCy NP. Compared to Probe-2 without self-assembly ability, Probe-1 displays a 4.6-fold higher PAI signal or 1.7-fold lower half inhibitory concentrations in HepG2 cells. Moreover, Probe-1 shows extended retention time (10 vs 6 h) and 2.1-fold higher PAI signal than Probe-2 in HepG2 tumors. The HepG2 tumors in Group Probe-1 obviously increase 18 °C (T max : 55 °C) with a 3.3-fold decreased volume while that in Group Probe-2 mildly increase 9.8 °C (T max : 46.8 °C) with a 4.3-fold increased volume. It is envisioned that this smart self-assembly strategy can be easily adjusted for PAI and PTT of more tumors., (© 2023 Wiley-VCH GmbH.)

Naringin, a natural product, can be used as a therapeutic agent due to its low systemic toxicity and negligible adverse effect. However, due to its hydrophobic nature and thereby low solubility, high-dose treatment is required when used for human therapy. Herein, we demonstrate the employment of a metal-organic framework (MOF) as a nontoxic loading carrier to encapsulate naringin, and the afforded nairngin@MOF composite can serve as a multifunctional bioplatform capable of treating Gram-positive bacteria and certain cancers by slowly and progressively releasing the encapsulated naringin as well as improving and modulating immune system functions through synergy between naringin and the MOF.

Motivation: Accurately predicting cancer survival is crucial for helping clinicians to plan appropriate treatments, which largely improves the life quality of cancer patients and spares the related medical costs. Recent advances in survival prediction methods suggest that integrating complementary information from different modalities, e.g. histopathological images and genomic data, plays a key role in enhancing predictive performance. Despite promising results obtained by existing multimodal methods, the disparate and heterogeneous characteristics of multimodal data cause the so-called modality gap problem, which brings in dramatically diverse modality representations in feature space. Consequently, detrimental modality gaps make it difficult for comprehensive integration of multimodal information via representation learning and therefore pose a great challenge to further improvements of cancer survival prediction., Results: To solve the above problems, we propose a novel method called cross-aligned multimodal representation learning (CAMR), which generates both modality-invariant and -specific representations for more accurate cancer survival prediction. Specifically, a cross-modality representation alignment learning network is introduced to reduce modality gaps by effectively learning modality-invariant representations in a common subspace, which is achieved by aligning the distributions of different modality representations through adversarial training. Besides, we adopt a cross-modality fusion module to fuse modality-invariant representations into a unified cross-modality representation for each patient. Meanwhile, CAMR learns modality-specific representations which complement modality-invariant representations and therefore provides a holistic view of the multimodal data for cancer survival prediction. Comprehensive experiment results demonstrate that CAMR can successfully narrow modality gaps and consistently yields better performance than other survival prediction methods using multimodal data., Availability and Implementation: CAMR is freely available at https://github.com/wxq-ustc/CAMR., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2023. Published by Oxford University Press.)

A tumor-targeting therapy strategy is urgently needed to increase the accumulation of drugs in tumors and reduce the side effects in normal tissues. Herein, we developed an esterase-activatable curcumin prodrug Cur-RGD for tumor-targeting therapy. Armed with the tumor-targeting RGD peptide and in situ esterase-triggered drug release, this prodrug Cur-RGD can efficiently improve the therapeutic effect of curcumin in tumors.

Phototherapy is a conducive and non-invasive strategy for cancer therapy under light irradiation. Inspiringly, fluorescence imaging in the second near-infrared window (NIR-II, 1000-1700 nm) holds a great promise for imaging-guided phototherapy with deep penetration and high spatiotemporal resolution. However, most phototherapeutics still face great challenges, including complicated synthesis of agents, potential biotoxicity and unsatisfied therapeutic outcomes. Herein, a near-infrared laser triggered molecular photosensitizer FEPT, modified with triphenylphosphine PEGylation (PEG2000-TPP), is developed for NIR-II imaging-guided mitochondria-targeting synergistic photothermal therapy (PTT)/photodynamic therapy (PDT)/immune therapy (IMT). The mitochondria-targeting photosensitizer FEPT can produce reactive oxygen species (ROS) and hyperpyrexia upon 808 nm laser irradiation, resulting in mitochondrial dysfunction and photo-induced apoptosis via caspase-3 pathway. Phototherapy-induced hyperthermia or ROS triggers the release of immunogenic intracellular substrates from dying tumor cells, thereby promoting the activation of antitumor immunity. Herein, this work provides a practicable strategy to develop a molecular phototheranostic platform for imaging-guided cancer therapy via mitochondria-targeting., (© 2022. The Author(s).)

Extending the benefits of tumor molecular profiling for all cancer patients requires a comprehensive analysis of tumor genomes across distinct patient populations worldwide. In this study, we perform deep next-generation DNA sequencing (NGS) from tumor tissues and matched blood specimens from over 10,000 patients in China by using a 450-gene comprehensive assay, developed and implemented under international clinical regulations. We perform a comprehensive comparison of somatically altered genes, the distribution of tumor mutational burden (TMB), gene fusion patterns, and the spectrum of various somatic alterations between Chinese and American patient populations. Here, we show 64% of cancers from Chinese patients in this study have clinically actionable genomic alterations, which may affect clinical decisions related to targeted therapy or immunotherapy. These findings describe the similarities and differences between tumors from Chinese and American patients, providing valuable information for personalized medicine., (© 2022. The Author(s).)

Introduction: Next-generation sequencing (NGS) enables simultaneous detection of actionable somatic variants and estimation of genomic signatures such as tumor mutational burden (TMB) or microsatellite instability (MSI) status, which empowers therapeutic decisions in clinical oncology., Objective: Our retrospective study investigated the clinical performance of somatic variant detection in paired tissue and blood samples using a large targeted gene panel, the OncoScreen Plus, which interrogates 520 cancer-related genes., Methods: We analyzed sequencing data derived from paired tissue and blood samples of 3005 patients spanning 20 solid tumor types, including lung (n = 1971), gastrointestinal (n = 625), breast (n = 120) and gynecological (n = 110), genitourinary (n = 38), and other cancers (n = 141)., Results: Across tumor types, the OncoScreen Plus panel achieved a high tissue detection rate, with an average of 97.9%. The average plasma detection rate was 72.2%, with an average tissue concordance rate of 36.6%. Considering all variant types, the plasma assay yielded an average sensitivity/true positive rate of 45.7%, with a positive predictive value of 64.7% relative to tissue assay. Pearson correlation analysis revealed a strong correlation in TMB estimated from blood and tissue samples (correlation coefficient 0.845, R 2 = 0.756). MSI-high status was identified in five tumor types, including endometrial cancer (28.6%), colorectal cancer (2.5%), ovarian cancer (2.0%), gastric cancer (1.5%), and lung adenocarcinoma (0.2%)., Conclusion: Paired tumor and blood samples from a large cohort of patients spanning 20 tumor types demonstrated that the OncoScreen Plus is a reliable pan-cancer panel for the accurate detection of somatic variants and genomic signatures that could guide individualized treatment strategies to improve the care of patients with advanced cancer., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Motivation: Cancer survival prediction can greatly assist clinicians in planning patient treatments and improving their life quality. Recent evidence suggests the fusion of multimodal data, such as genomic data and pathological images, is crucial for understanding cancer heterogeneity and enhancing survival prediction. As a powerful multimodal fusion technique, Kronecker product has shown its superiority in predicting survival. However, this technique introduces a large number of parameters that may lead to high computational cost and a risk of overfitting, thus limiting its applicability and improvement in performance. Another limitation of existing approaches using Kronecker product is that they only mine relations for one single time to learn multimodal representation and therefore face significant challenges in deeply mining rich information from multimodal data for accurate survival prediction., Results: To address the above limitations, we present a novel hierarchical multimodal fusion approach named HFBSurv by employing factorized bilinear model to fuse genomic and image features step by step. Specifically, with a multiple fusion strategy HFBSurv decomposes the fusion problem into different levels and each of them integrates and passes information progressively from the low level to the high level, thus leading to the more specialized fusion procedure and expressive multimodal representation. In this hierarchical framework, both modality-specific and cross-modality attentional factorized bilinear modules are designed to not only capture and quantify complex relations from multimodal data, but also dramatically reduce computational complexity. Extensive experiments demonstrate that our method performs an effective hierarchical fusion of multimodal data and achieves consistently better performance than other methods for survival prediction., Availability and Implementation: HFBSurv is freely available at https://github.com/Liruiqing-ustc/HFBSurv., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Cancer imposes a severe threat to people's health and lives, thus pressing a huge medical and economic burden on individuals and communities. Therefore, early diagnosis of cancer is indispensable in the timely prevention and effective treatment for patients. Exosome has recently become an attractive cancer biomarker in noninvasive early diagnosis because of the unique physiology and pathology functions, which reflects remarkable information regarding the cancer microenvironment, and plays an important role in the occurrence and evolution of cancer. Meanwhile, biosensors have gained great attention for the detection of exosomes due to their superior properties, such as convenient operation, real-time readout, high sensitivity, and remarkable specificity, suggesting promising biomedical applications in the early diagnosis of cancer. In this review, the latest advances of biosensors regarding the assay of exosomes were summarized, and the superiorities of exosomes as markers for the early diagnosis of cancer were evaluated. Moreover, the recent challenges and further opportunities of developing effective biosensors for the early diagnosis of cancer were discussed., (© 2021. The Author(s).)

Motivation: Detecting driver genes from gene mutation data is a fundamental task for tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various subgroups, subgroup-specific driver genes are the key factors in the development of precision medicine for heterogeneous cancer. However, the existing driver gene detection methods are not designed to identify subgroup specificities of their detected driver genes, and therefore cannot indicate which group of patients is associated with the detected driver genes, which is difficult to provide specifically clinical guidance for individual patients., Results: By incorporating the subspace learning framework, we propose a novel bioinformatics method called DriverSub, which can efficiently predict subgroup-specific driver genes in the situation where the subgroup annotations are not available. When evaluated by simulation datasets with known ground truth and compared with existing methods, DriverSub yields the best prediction of driver genes and the inference of their related subgroups. When we apply DriverSub on the mutation data of real heterogeneous cancers, we can observe that the predicted results of DriverSub are highly enriched for experimentally validated known driver genes. Moreover, the subgroups inferred by DriverSub are significantly associated with the annotated molecular subgroups, indicating its capability of predicting subgroup-specific driver genes., Availability and Implementation: The source code is publicly available at https://github.com/JianingXi/DriverSub., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

At present, with the in-depth study of gene expression data, the significant role of tumor classification in clinical medicine has become more apparent. In particular, the sparse characteristics of gene expression data within and between groups. Therefore, this paper focuses on the study of tumor classification based on the sparsity characteristics of genes. On this basis, we propose a new method of tumor classification-Sparse Group Lasso (least absolute shrinkage and selection operator) and Support Vector Machine (SGL-SVM). Firstly, the primary selection of feature genes is performed on the normalized tumor datasets using the Kruskal-Wallis rank sum test. Secondly, using a sparse group Lasso for further selection, and finally, the support vector machine serves as a classifier for classification. We validate proposed method on microarray and NGS datasets respectively. Formerly, on three two-class and five multi-class microarray datasets it is tested by 10-fold cross-validation and compared with other three classifiers. SGL-SVM is then applied on BRCA and GBM datasets and tested by 5-fold cross-validation. Satisfactory accuracy is obtained by above experiments and compared with other proposed methods. The experimental results show that the proposed method achieves a higher classification accuracy and selects fewer feature genes, which can be widely applied in classification for high-dimensional and small-sample tumor datasets. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/SGL-SVM/., (Copyright © 2019. Published by Elsevier Ltd.)

Motivation: Prediction of cancer patient's response to therapeutic agent is important for personalized treatment. Because experimental verification of reactions between large cohort of patients and drugs is time-intensive, expensive and impractical, preclinical prediction model based on large-scale pharmacogenomic of cancer cell line is highly expected. However, most of the existing computational studies are primarily based on genomic profiles of cancer cell lines while ignoring relationships among genes and failing to capture functional similarity of cell lines., Results: In this study, we present a novel approach named NRL2DRP, which integrates protein-protein interactions and captures similarity of cell lines' functional contexts, to predict drug responses. Through integrating genomic aberrations and drug responses information with protein-protein interactions, we construct a large response-related network, where the neighborhood structure of cell line provides a functional context to its therapeutic responses. Representation vectors of cell lines are extracted through network representation learning method, which could preserve vertices' neighborhood similarity and serve as features to build predictor for drug responses. The predictive performance of NRL2DRP is verified by cross-validation on GDSC dataset and methods comparison, where NRL2DRP achieves AUC > 79% for half drugs and outperforms previous methods. The validity of NRL2DRP is also supported by its effectiveness on uncovering accurate novel relationships between cell lines and drugs. Lots of newly predicted drug responses are confirmed by reported experimental evidences., Availability and Implementation: The code and documentation are available on https://github.com/USTC-HIlab/NRL2DRP., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Carcinoma-associated fibroblasts (CAFs) are abundant and heterogeneous stromal cells in tumor microenvironment that are critically involved in cancer progression. Here, we demonstrate that two cell-surface molecules, CD10 and GPR77, specifically define a CAF subset correlated with chemoresistance and poor survival in multiple cohorts of breast and lung cancer patients. CD10 + GPR77 + CAFs promote tumor formation and chemoresistance by providing a survival niche for cancer stem cells (CSCs). Mechanistically, CD10 + GPR77 + CAFs are driven by persistent NF-κB activation via p65 phosphorylation and acetylation, which is maintained by complement signaling via GPR77, a C5a receptor. Furthermore, CD10 + GPR77 + CAFs promote successful engraftment of patient-derived xenografts (PDXs), and targeting these CAFs with a neutralizing anti-GPR77 antibody abolishes tumor formation and restores tumor chemosensitivity. Our study reveals a functional CAF subset that can be defined and isolated by specific cell-surface markers and suggests that targeting the CD10 + GPR77 + CAF subset could be an effective therapeutic strategy against CSC-driven solid tumors., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Inter-patient heterogeneity is a major challenge for mutated cancer genes detection which is crucial to advance cancer diagnostics and therapeutics. To detect mutated cancer genes in heterogeneous tumour samples, a prominent strategy is to determine whether the genes are recurrently mutated in their interaction network context. However, recent studies show that some cancer genes in different perturbed pathways are mutated in different subsets of samples. Subsequently, these genes may not display significant mutational recurrence and thus remain undiscovered even in consideration of network information. We develop a novel method called mCGfinder to efficiently detect mutated cancer genes in tumour samples with inter-patient heterogeneity. Based on matrix decomposition framework incorporated with gene interaction network information, mCGfinder can successfully measure the significance of mutational recurrence of genes in a subset of samples. When applying mCGfinder on TCGA somatic mutation datasets of five types of cancers, we find that the genes detected by mCGfinder are significantly enriched for known cancer genes, and yield substantially smaller p-values than other existing methods. All the results demonstrate that mCGfinder is an efficient method in detecting mutated cancer genes.

Background: Copy number alterations (CNA) and loss of heterozygosity (LOH) represent a large proportion of genetic structural variations of cancer genomes. These aberrations are continuously accumulated during the procedure of clonal evolution and patterned by phylogenetic branching. This invariably results in the emergence of multiple cell populations with distinct complement of mutational landscapes in tumor sample. With the advent of next-generation sequencing technology, inference of subclonal populations has become one of the focused interests in cancer-associated studies, and is usually based on the assessment of combinations of somatic single-nucleotide variations (SNV), CNA and LOH. However, cancer samples often have several inherent issues, such as contamination of normal stroma, tumor aneuploidy and intra-tumor heterogeneity. Addressing these critical issues is imperative for accurate profiling of clonal architecture., Methods: We present CLImAT-HET, a computational method designed for capturing clonal diversity in the CNA/LOH dimensions by taking into account the intra-tumor heterogeneity issue, in the case where a reference or matched normal sample is absent. The algorithm quantitatively represents the clonal identification problem using a factorial hidden Markov model, and takes an integrated analysis of read counts and allele frequency data. It is able to infer subclonal CNA and LOH events as well as the fraction of cells harboring each event., Results: The results on simulated datasets indicate that CLImAT-HET has high power to identify CNA/LOH segments, it achieves an average accuracy of 0.87. It can also accurately infer proportion of each clonal population with an overall Pearson correlation coefficient of 0.99 and a mean absolute error of 0.02. CLImAT-HET shows significant advantages when compared with other existing methods. Application of CLImAT-HET to 5 primary triple negative breast cancer samples demonstrates its ability to capture clonal diversity in the CAN/LOH dimensions. It detects two clonal populations in one sample, and three clonal populations in one other sample., Conclusions: CLImAT-HET, a novel algorithm is introduced to infer CNA/LOH segments from heterogeneous tumor samples. We demonstrate CLImAT-HET's ability to accurately recover clonal compositions using tumor WGS data without a match normal sample.

Background: Copy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression. Accurate detection of copy number alterations is necessary for discovering cancer-causing genes. Whole-exome sequencing has become a widely used technology in the last decade for detecting various types of genomic aberrations in cancer genomes. However, there are several major issues encountered in these detection problems, including normal cell contamination, tumor aneuploidy, and intra-tumor heterogeneity. Especially, deciphering the intra-tumor heterogeneity is imperative for identifying clonal and subclonal copy number alterations., Results: We introduce CloneCNA, a novel bioinformatics tool for efficiently addressing these issues and automatically detecting clonal and subclonal somatic copy number alterations from heterogeneous tumor samples. CloneCNA fully explores the log ratio of read counts between paired tumor-normal samples and tumor B allele frequency of germline heterozygous SNP positions, further employs efficient statistical models to quantitatively represent copy number status of tumor sample containing multiple clones. We examine CloneCNA on simulated heterogeneous and real tumor samples, and the results demonstrate that CloneCNA has higher power to detect copy number alterations than existing methods., Conclusions: CloneCNA, a novel algorithm is developed to efficiently and accurately identify somatic copy number alterations from heterogeneous tumor samples. We demonstrate the statistical framework of CloneCNA represents a remarkable advance for tumor whole-exome sequencing data. We expect that CloneCNA will promote cancer-focused studies for investigating the role of clonal evolution and elucidating critical events benefiting tumor tumourigenesis and progression.

Background: Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification., Results: We propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate., Conclusions: TAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.

Tumor samples are usually heterogeneous, containing admixture of more than one kind of tumor subclones. Studies of genomic aberrations from heterogeneous tumor data are hindered by the mixed signal of tumor subclone cells. Most of the existing algorithms cannot distinguish contributions of different subclones from the measured single nucleotide polymorphism (SNP) array signals, which may cause erroneous estimation of genomic aberrations. Here, we have introduced a computational method, Cancer Heterogeneity Analysis from SNP-array Experiments (CHASE), to automatically detect subclone proportions and genomic aberrations from heterogeneous tumor samples. Our method is based on HMM, and incorporates EM algorithm to build a statistical model for modeling mixed signal of multiple tumor subclones. We tested the proposed approach on simulated datasets and two real datasets, and the results show that the proposed method can efficiently estimate tumor subclone proportions and recovery the genomic aberrations.

Aspirin is arguably the synthesized drug that has been used most commonly in human history. Aspirin was originally developed and marketed for the treatment of inflammatory disorders at the end of the 19th century, but its mechanism of action remained unknown until the second half of the 20th century. Since the latter part of the 20th century aspirin also has been used for the primary and secondary prevention of cardiovascular diseases given its anti-thrombotic properties. An association between intake of aspirin and decreased cancer risk was identified in the past decades. Whether aspirin can be used as an anticancer agent in patients with a diagnosis of cancer was unknown until recently. Recent studies suggest that aspirin might provide therapeutic benefit in the adjuvant treatment of certain forms of cancer. This review provides a critical update on this topic, which has potential implications for oncologists and their patients., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The authors describe an integrated method for analysing cancer driver aberrations and disrupted pathways by using tumour single nucleotide polymorphism (SNP) arrays. The authors new method adopts a novel statistical model to explicitly quantify the SNP signals, and therefore infers the genomic aberrations, including copy number alteration and loss of heterozygosity. Examination on the dilution series dataset shows that this method can correctly identify the genomic aberrations even with the existence of severe normal cell contamination in tumour sample. Furthermore, with the results of the aberration identification obtained from multiple tumour samples, a permutation-based approach is proposed for identifying the statistically significant driver aberrations, which are further incorporated with the known signalling pathways for pathway enrichment analysis. By applying the approach to 286 hepatocellular tumour samples, they successfully uncover numerous driver aberration regions across the cancer genome, for example, chromosomes 4p and 5q, which harbour many known hepatocellular cancer related genes such as alpha-fetoprotein (AFP) and ectodermal-neural cortex (ENC1). In addition, they identify nine disrupted pathways that are highly enriched by the driver aberrations, including the systemic lupus erythematosus pathway, the vascular endothelial growth factor (VEGF) signalling pathway and so on. These results support the feasibility and the utility of the proposed method on the characterisation of the cancer genome and the downstream analysis of the driver aberrations and the disrupted signalling pathways.

Transforming growth factor beta (TGF-β) is a potent inhibitor of cell growth. TGFBR1 6A is a polymorphism consisting of a 9-base pair in-frame deletion within exon 1 of the type I TGF-β receptor (TGFBR1), which results in a receptor with decreased TGF-β signaling capability. The discovery of an association between TGFBR1*6A and cancer susceptibility led to the hypothesis that hypomorphic variants of the TGF-β signaling pathway may predispose to the development of cancer. This hypothesis was tested in vivo with the development of a mouse model of Tgfbr1 haploinsufficiency. Tgfbr1 (+/-) mice developed twice as many intestinal tumors as Tgfbr1 (+/+). Tgfbr1 haploinsufficiency was also associated with early onset adenocarcinoma and increased tumor cell proliferation. A case control study identified two haplotypes associated with constitutively decreased TGFBR1 and substantially increased colorectal cancer risk indicating that TGFBR1 may act as a potent modifier of cancer risk.

Background: As one of the most common types of co-regulatory motifs, feed-forward loops (FFLs) control many cell functions and play an important role in human cancers. Therefore, it is crucial to reconstruct and analyze cancer-related FFLs that are controlled by transcription factor (TF) and microRNA (miRNA) simultaneously, in order to find out how miRNAs and TFs cooperate with each other in cancer cells and how they contribute to carcinogenesis. Current FFL studies rely on predicted regulation information and therefore suffer the false positive issue in prediction results. More critically, FFLs generated by existing approaches cannot represent the dynamic and conditional regulation relationship under different experimental conditions., Methodology/principal Findings: In this study, we proposed a novel filter-wrapper feature selection method to accurately identify co-regulatory mechanism by incorporating prior information from predicted regulatory interactions with parallel miRNA/mRNA expression datasets. By applying this method, we reconstructed 208 and 110 TF-miRNA co-regulatory FFLs from human pan-cancer and prostate datasets, respectively. Further analysis of these cancer-related FFLs showed that the top-ranking TF STAT3 and miRNA hsa-let-7e are key regulators implicated in human cancers, which have regulated targets significantly enriched in cellular process regulations and signaling pathways that are involved in carcinogenesis., Conclusions/significance: In this study, we introduced an efficient computational approach to reconstruct co-regulatory FFLs by accurately identifying gene co-regulatory interactions. The strength of the proposed feature selection method lies in the fact it can precisely filter out false positives in predicted regulatory interactions by quantitatively modeling the complex co-regulation of target genes mediated by TFs and miRNAs simultaneously. Moreover, the proposed feature selection method can be generally applied to other gene regulation studies using parallel expression data with respect to different biological contexts.

It has been demonstrated that survivin, a novel member of the inhibitor of apoptosis (IAP) protein family, is expressed in human cancers but is undetectable in normal differentiated tissues. We employed a recombinant adenoviral vector (reAdGL3BSurvivin) in which a tumor-specific survivin promoter and a luciferase reporter gene were inserted into the E1-deleted region of adenovirus vector. Luciferase activity was measured in both multiple tumor cell lines and two primary melanoma cells infected with reAdGL3BSurvivin. Human fibroblast and mammary epithelial cell lines were used as negative controls. A reAdGL3CMV, containing the CMV promoter and luciferase gene, was used as a positive control to normalize the luciferase activity generated by the survivin promoter. Our data revealed that the survivin promoter showed high activity in both established tumor cell lines and the primary melanoma cells. In contrast, the in vivo studies indicated that the activities of survivin promoter were extremely low in the major mouse organs. The survivin promoter appears to be a promising tumor-specific promoter exhibiting a "tumor on" and "liver off" profile, and therefore, it may prove to be a good candidate for transcriptional targeting of cancer gene therapy in a wide variety of tumors.

Oncolytic adenoviruses, which selectively replicate in and subsequently kill cancer cells, have emerged as a promising approach for treatment of tumors resistant to other modalities. Although preclinical results have been exciting, single-agent clinical efficacy has been less impressive heretofore. The immunogenicity of adenoviruses, and consequent premature abrogation of replication, may have been a partial reason. Improving the oncolytic potency of agents has been hampered by the inability to study host–vector interactions in immune-competent systems, since human serotype adenoviruses do not productively replicate in animal tissues. Therefore, approaches such as immunomodulation, which could result in sustained replication and subsequently increased oncolysis, have not been studied. Utilizing the osteocalcin promoter for restricting the replication of a canine adenovirus to dog osteosarcoma cells, we generated and tested the first nonhuman oncolytic adenovirus. This virus effectively killed canine osteosarcoma cells in vitro and yielded a therapeutic benefit in vivo. Canine osteosarcoma is the most frequent malignant disease in large dogs, with over 8000 cases in the United States annually, and there is no curative treatment. Therefore, immunomodulation for increased oncolytic potency could be studied with clinical trials in this population. This could eventually translate into human trials. [Copyright &y& Elsevier]