RESPIRATION DEFECTS LIMIT SERINE SYNTHESIS REQUIRED FOR LUNG CANCER GROWTH AND SURVIVAL.

Mitochondrial function is important for both energetic and anabolic metabolism. Pathogenic mitochondrial DNA (mtDNA) mutations directly impact these functions, resulting in the detrimental consequences seen in human mitochondrial diseases. The role of pathogenic mtDNA mutations in human cancers is less clear; while pathogenic mtDNA mutations are observed in some cancer types, they are almost absent in others. We report here that the proofreading mutant DNA polymerase gamma ( PolG D256A ) induced a high mtDNA mutation burden in non-small-cell lung cancer (NSCLC), and promoted the accumulation of defective mitochondria, which is responsible for decreased tumor cell proliferation and viability and increased cancer survival. In NSCLC cells, pathogenic mtDNA mutations increased glycolysis and caused dependence on glucose. The glucose dependency sustained mitochondrial energetics but at the cost of a decreased NAD+/NADH ratio that inhibited de novo serine synthesis. Insufficient serine synthesis, in turn, impaired the downstream synthesis of GSH and nucleotides, leading to impaired tumor growth that increased cancer survival. Unlike tumors with intact mitochondrial function, NSCLC with pathogenic mtDNA mutations were sensitive to dietary serine and glycine deprivation. Thus, mitochondrial function in NSCLC is required specifically to sustain sufficient serine synthesis for nucleotide production and redox homeostasis to support tumor growth, explaining why these cancers preserve functional mtDNA. In brief: High mtDNA mutation burden in non-small-cell lung cancer (NSCLC) leads to the accumulation of respiration-defective mitochondria and dependency on glucose and glycolytic metabolism. Defective respiratory metabolism causes a massive accumulation of cytosolic nicotinamide adenine dinucleotide + hydrogen (NADH), which impedes serine synthesis and, thereby, glutathione (GSH) and nucleotide synthesis, leading to impaired tumor growth and increased survival. Highlights: Proofreading mutations in Polymerase gamma led to a high burden of mitochondrial DNA mutations, promoting the accumulation of mitochondria with respiratory defects in NSCLC.Defective respiration led to reduced proliferation and viability of NSCLC cells increasing survival to cancer.Defective respiration caused glucose dependency to fuel elevated glycolysis.Altered glucose metabolism is associated with high NADH that limits serine synthesis, leading to impaired GSH and nucleotide production.Mitochondrial respiration defects sensitize NSCLC to dietary serine/glycine starvation, further increasing survival.

Immune Checkpoint Blockade Delays Cancer and Extends Survival in Murine DNA Polymerase Mutator Syndromes.

Mutations in polymerases Pold1 and Pole exonuclease domains in humans are associated with increased cancer incidence, elevated tumor mutation burden (TMB) and response to immune checkpoint blockade (ICB). Although ICB is approved for treatment of several cancers, not all tumors with elevated TMB respond. Here we generated Pold1 and Pole proofreading mutator mice and show that ICB treatment of mice with high TMB tumors did not improve survival as only a subset of tumors responded. Similarly, introducing the mutator alleles into mice with Kras/p53 lung cancer did not improve survival, however, passaging mutator tumor cells in vitro without immune editing caused rejection in immune-competent hosts, demonstrating the efficiency by which cells with antigenic mutations are eliminated. Finally, ICB treatment of mutator mice earlier, before observable tumors delayed cancer onset, improved survival, and selected for tumors without aneuploidy, suggesting the use of ICB in individuals at high risk for cancer prevention. Highlights: Germline somatic and conditional Pold1 and Pole exonuclease domain mutations in mice produce a mutator phenotype. Spontaneous cancers arise in mutator mice that have genomic features comparable to human tumors with these mutations.ICB treatment of mutator mice with tumors did not improve survival as only a subset of tumors respond. Introduction of the mutator alleles into an autochthonous mouse lung cancer model also did not produce immunogenic tumors, whereas passaging mutator tumor cells in vitro caused immune rejection indicating efficient selection against antigenic mutations in vivo . Prophylactic ICB treatment delayed cancer onset, improved survival, and selected for tumors with no aneuploidy.

Integrated metabolic and genetic analysis reveals distinct features of human differentiated thyroid cancer.

BACKGROUND: Differentiated thyroid cancer (DTC) affects thousands of lives worldwide each year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) quality of life and might be unnecessary in indolent DTC cases. On the other hand, the lack of biomarkers indicating a potential metastatic thyroid cancer imposes an additional challenge to managing and treating patients with this disease. AIM: The presented clinical setting highlights the unmet need for a precise molecular diagnosis of DTC and potential metastatic disease, which should dictate appropriate therapy. MATERIALS AND METHODS: In this article, we present a differential multi-omics model approach, including metabolomics, genomics, and bioinformatic models, to distinguish normal glands from thyroid tumours. Additionally, we are proposing biomarkers that could indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC. RESULTS: Normal and tumour thyroid tissue from DTC patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumour cells. The consistency of the DTC metabolic profile allowed us to build a bioinformatic classification model capable of clearly distinguishing normal from tumor thyroid tissues, which might help diagnose thyroid cancer. Moreover, based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intra-tumour heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease. DISCUSSION: Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy. CONCLUSIONS: Well-designed, prospective translational clinical trials will ultimately show the value of this integrated multi-omics approach and early diagnosis of DTC and potential metastatic PTC.

Integrated metabolic and genetic analysis reveals distinct features of primary differentiated thyroid cancer and its metastatic potential in humans.

Differentiated thyroid cancer (DTC) affects thousands of lives worldwide every year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) the quality of life and might be unnecessary in indolent DTC cases. This clinical setting highlights the unmet need for a precise molecular diagnosis of DTC, which should dictate appropriate therapy. Here we propose a differential multi-omics model approach to distinguish normal gland from thyroid tumor and to indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC. Based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intratumor heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease. Specifically, normal and tumor thyroid tissues from these patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumor cells. Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy. Well-designed, prospective translational clinical trials will ultimately show the value of this targeted molecular approach. TRANSLATIONAL RELEVANCE: In this article, we propose a new integrated metabolic, genomic, and cytopathologic methods to diagnose Differentiated Thyroid Cancer when the conventional methods failed. Moreover, we suggest metabolic and genomic markers to help predict high-risk Papillary Thyroid Cancer. Both might be important tools to avoid unnecessary surgery and/or radioiodine therapy that can worsen the quality of life of the patients more than living with an indolent Thyroid nodule.

Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-Art.

Wireless Capsule Endoscopy (WCE) is a highly promising technology for gastrointestinal (GI) tract abnormality diagnosis. However, low image resolution and low frame rates are challenging issues in WCE. In addition, the relevant frames containing the features of interest for accurate diagnosis only constitute 1% of the complete video information. For these reasons, analyzing the WCE videos is still a time consuming and laborious examination for the gastroenterologists, which reduces WCE system usability. This leads to the emergent need to speed-up and automates the WCE video process for GI tract examinations. Consequently, the present work introduced the concept of WCE technology, including the structure of WCE systems, with a focus on the medical endoscopy video capturing process using image sensors. It discussed also the significant characteristics of the different GI tract for effective feature extraction. Furthermore, video approaches for bleeding and lesion detection in the WCE video were reported with computer-aided diagnosis systems in different applications to support the gastroenterologist in the WCE video analysis. In image enhancement, WCE video review time reduction is also discussed, while reporting the challenges and future perspectives, including the new trend to employ the deep learning models for feature Learning, polyp recognition, and classification, as a new opportunity for researchers to develop future WCE video analysis techniques.

RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields.

Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to noise, motion, and partial volume effects, automated segmentation of lesions from MRI is still a challenging task. In this paper, we propose a two-stage supervised learning framework for automatic brain lesion segmentation. Specifically, in the first stage, intensity-based statistical features, template-based asymmetric features, and GMM-based tissue probability maps are used to train the initial random forest classifier. Next, the dense conditional random field optimizes the probability maps from the initial random forest classifier and derives the whole tumor regions referred as the region of interest (ROI). In the second stage, the optimized probability maps are further intergraded with features from the intensity-based statistical features and template-based asymmetric features to train subsequent random forest, focusing on classifying voxels within the ROI. The output probability maps will be also optimized by the dense conditional random fields, and further used to iteratively train a cascade of random forests. Through hierarchical learning of the cascaded random forests and dense conditional random fields, the multimodal local and global appearance information is integrated with the contextual information, and the output probability maps are improved layer by layer to finally obtain optimal segmentation results. We evaluated the proposed method on the publicly available brain tumor datasets BRATS 2015 & BRATS 2018, as well as the ischemic stroke dataset ISLES 2015. The results have shown that our framework achieves competitive performance compared to the state-of-the-art brain lesion segmentation methods. In addition, contralateral difference and skewness were identified as the important features in the brain tumor and ischemic stroke segmentation tasks, which conforms to the knowledge and experience of medical experts, further reflecting the reliability and interpretability of our framework.

Gaussian mixture model for texture characterization with application to brain DTI images.

A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variability in the main brain tissues at a macroscopic scale rather than searching for tumours or affected areas. The asymmetries of the mixture distributions between the hemispheres could be used as a sensitive, faster tool for early diagnosis. The k-means algorithm optimizes the parameters of the mixture distributions and ensures that the global maxima of the likelihood functions are determined. This method has been illustrated using 18 sub-classes of DTI data grouped into six levels of diffusion weighting (b = 0; 250; 500; 750; 1000 and 1250 s/mm2) and three main brain tissues. These tissues belong to three subjects, i.e., healthy, multiple haemorrhage areas in the left temporal lobe and ischaemic stroke. The mixing probabilities or weights at the class level are estimated based on the sub-class-level mixing probability estimation. Furthermore, weighted Euclidean distance and multiple correlation analysis are applied to analyse the dissimilarity of mixing probabilities between hemispheres and subjects. The silhouette data evaluate the objective quality of the clustering. By using a GMM in the present study, we establish an important variability in the mixing probability associated with white matter and grey matter between the left and right hemispheres.