scholarly journals Mutational interactions define novel cancer subgroups

2017 ◽  
Author(s):  
Jack Kuipers ◽  
Thomas Thurnherr ◽  
Giusi Moffa ◽  
Polina Suter ◽  
Jonas Behr ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Cancer Progression ◽  
Large Scale ◽  
De Novo ◽  
Drug Repositioning ◽  
Tissue Type ◽  
Survival Prediction ◽  
Cancer Type ◽  
Cancer Data ◽  
Cancer Types

Large-scale genomic data can help to uncover the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues of origin highlights differences and similarities which can guide drug repositioning as well as the design of targeted and precise treatments. Here, we developed an improved Bayesian network model for tumour mutational profiles and applied it to 8,198 patient samples across 22 cancer types from TCGA. For each cancer type, we identified the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we found genes which interact both within and across cancer types. To detach cancer classification from the tissue type we performed de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We found 22 novel clusters which significantly improved survival prediction beyond clinical and histopathological information. The models highlight key gene interactions for each cluster that can be used for genomic stratification in clinical trials and for identifying drug targets within strata.

Inmunotherapy related hepatitis: A retrospective study of Chilean patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15144-e15144
Author(s):  
Carlos Salinas ◽  
Suraj Rajesh Samtani ◽  
Alex Renner ◽  
Osvaldo Rudy Aren ◽  
Carlos Rojas ◽  
...  
Keyword(s):  
Renal Cancer ◽  
De Novo ◽  
Checkpoint Inhibitors ◽  
Early Recognition ◽  
Single Agent ◽  
Solid Cancer ◽  
Cancer Type ◽  
Cancer Data ◽  
Number Of Cycles ◽  
Chilean Population

e15144 Background: Immune checkpoint inhibitors (ICI) have shown dramatic efficacy in many trials of advanced solid cancer. Data of toxicity for these agents setting in Chilean patients is lacking and is of importance to confirm the tolerance of immunotherapy in different population. Immunotherapy-related hepatitis (liver irAEs) represents a diagnostic and management challenge, and its frequency varies according to various factors. We aim to report the incidence, features and treatments used to manage liver irAEs in Chilean population. Methods: A retrospective review of 139 patients with diagnosis of NSCLC, Renal Cancer, Urothelial, Melanoma, treated with ICI from April 2013 until November 2019. Baseline clinical factors (age, ECOG score, cancer type, stage, Type and number of cycles of immunotherapy, comorbidities), biochemistries and treatment received were recorded for all patients with liver irAEs. Checkpoint-inhibitor-associated liver disease was graded according to the Common Terminology Criteria for Adverse Events (CTCAE) V.4 Treatment outcomes were analyzed by measuring progression free (PFS) and overall survival (OS). Results: 32 (23%) patients developed liver irAEs. Median age at diagnosis was 62 years (range 37-86). 62% patients were male, 59% ECOG 1, 96% had de novo metastatic disease. The most frequent primary sites of cancer were Renal Cancer 71% and NSCLC 9%. 78% of patients received treatment with both anti-PD-1/PD-L1 and CTLA-4 drugs. Liver irAEs G2 and G3 were 43.7% and 56.2% respectively. 31,2% and 12,5% of patients treated with single agent immunotherapy developed G2 and G3 Liver irAEs respectively. The total averages of cycles administered were 12, 4 cycles. 28% of patients developed liver irAEs after the first cycle of treatment and 12.5% after the second cycle. 6.2% patients required hospitalization and 78.1% required treatment with steroids. Conclusions: Liver irAEs was more frequent in Chilean population than data reported on previous clinical trials with a high incidence caused by ICI monotherapy and further reinforced by combination therapy. Local registries play an important role in recognizing different patterns of toxicity according to different population. Early recognition and management of liver irAEs should be of vital importance in clinical practice and as prescribing physicians we must maintain strict vigilance of liver irAES in Chilean population.

scholarly journals Repurposing RNA sequencing for discovery of RNA modifications in clinical cohorts

2021 ◽  
Vol 7 (32) ◽  
pp. eabd2605
Author(s):  
Kar-Tong Tan ◽  
Ling-Wen Ding ◽  
Chan-Shuo Wu ◽  
Daniel G. Tenen ◽  
Henry Yang
Keyword(s):  
Rna Sequencing ◽  
Cancer Progression ◽  
De Novo ◽  
Rna Modification ◽  
Rna Modifications ◽  
Multiple Cancer ◽  
Patients With Cancer ◽  
Statistical Framework ◽  
Cancer Types ◽  
Nucleotide Resolution

The study of RNA modifications in large clinical cohorts can reveal relationships between the epitranscriptome and human diseases, although this is especially challenging. We developed ModTect (https://github.com/ktan8/ModTect), a statistical framework to identify RNA modifications de novo by standard RNA-sequencing with deletion and mis-incorporation signals. We show that ModTect can identify both known (N1-methyladenosine) and previously unknown types of mRNA modifications (N2,N2-dimethylguanosine) at nucleotide-resolution. Applying ModTect to 11,371 patient samples and 934 cell lines across 33 cancer types, we show that the epitranscriptome was dysregulated in patients across multiple cancer types and was additionally associated with cancer progression and survival outcomes. Some types of RNA modification were also more disrupted than others in patients with cancer. Moreover, RNA modifications contribute to multiple types of RNA-DNA sequence differences, which unexpectedly escape detection by Sanger sequencing. ModTect can thus be used to discover associations between RNA modifications and clinical outcomes in patient cohorts.

scholarly journals Recessive effects in cancer predisposition exposed by genome-wide and proteome-wide association studies

2020 ◽  
Author(s):  
Nadav Brandes ◽  
Nathan Linial ◽  
Michal Linial
Keyword(s):  
Cancer Risk ◽  
Large Scale ◽  
Association Studies ◽  
Cancer Predisposition ◽  
Small Scale ◽  
Cancer Type ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Cancer Types ◽  
Genomic Regions

AbstractThe characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Current attempts to detect cancer predisposition genomic regions are typically based on small-scale familial studies or genome-wide association studies (GWAS) over dedicated case-control cohorts. In this study, we utilized the UK Biobank as a large-scale prospective cohort to conduct a comprehensive analysis of cancer predisposition using both GWAS and proteome-wide association study (PWAS), a method that highlights genetic associations mediated by functional alterations to protein-coding genes. We discovered 137 unique genomic loci implicated with cancer risk in the white British population across nine cancer types and pan-cancer. While most of these genomic regions are supported by external evidence, our results highlight novel loci as well. We performed a comparative analysis of cancer predisposition between cancer types, finding that most of the implicated regions are cancer-type specific. We further analyzed the role of recessive genetic effects in cancer predisposition. We found that 30 of the 137 cancer regions were recovered only by a recessive model, highlighting the importance of recessive inheritance outside of familial studies. Finally, we show that many of the cancer associations exert substantial cancer risk in the studied cohort, suggesting their clinical relevance.

NIMG-39. RADIOMIC ANALYSIS FOR NON-INVASIVE IN VIVO PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS: A MULTI-INSTITUTIONAL EVALUATION FOR GENERALIZABILITY IN THE RESPOND CONSORTIUM

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi137-vi137
Author(s):  
Hamed Akbari* ◽  
Spyridon Bakas* ◽  
Chiharu Sako* ◽  
Anahita Fathi Kazerooni ◽  
Jose A Garcia ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Characteristic Curve ◽  
Tumor Resection ◽  
Structural Mri ◽  
Survival Prediction ◽  
Non Invasive ◽  
Signal Abnormality ◽  
Prognostic Stratification

Abstract PURPOSE Multi-parametric MRI based radiomic signatures have highlighted the promise of artificial intelligence (AI) in neuro-oncology. However, inter-institution heterogeneity hinders generalization to data from unseen clinical institutions. To this end, we formulated the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium for glioblastoma. Here, we seek non-invasive generalizable radiomic signatures from routine clinically-acquired MRI for prognostic stratification of glioblastoma patients. METHODS We identified a retrospective cohort of 606 patients with near/gross total tumor resection ( >90%), from 13 geographically-diverse institutions. All pre-operative structural MRI scans (T1,T1-Gd,T2,T2-FLAIR) were aligned to a common anatomical atlas. An automatic algorithm segmented the whole tumors (WTs) into 3 sub-compartments, i.e., enhancing (ET), necrotic core (NC), and peritumoral T2-FLAIR signal abnormality (ED). The combination of ET+NC defines the tumor core (TC). Quantitative radiomic features were extracted to generate our AI model to stratify patients into short- (< 14mts) and long-survivors ( >14mts). The model trained on 276 patients from a single institution was independently validated on 330 unseen patients from 12 left-out institutions, using the area-under-the-receiver-operating-characteristic-curve (AUC). RESULTS Each feature individually offered certain (limited but reproducible) value for identifying short-survivors: 1) TC closer to lateral ventricles (AUC=0.62); 2) larger ET/brain (AUC=0.61); 3) larger TC/brain (AUC=0.59); 4) larger WT/brain (AUC=0.55); 5) larger ET/WT (AUC=0.59); 6) smaller ED/WT (AUC=0.57); 7) larger ventricle deformations (AUC=0.6). Integrating all features and age, through a multivariate AI model, resulted in higher accuracy (AUC=0.7; 95% C.I.,0.64-0.77). CONCLUSION Prognostic stratification using basic radiomic features is highly reproducible across diverse institutions and patient populations. Multivariate integration yields relatively more accurate and generalizable radiomic signatures, across institutions. Our results offer promise for generalizable non-invasive in vivo signatures of survival prediction in patients with glioblastoma. Extracted features from clinically-acquired imaging, renders these signatures easier for clinical translation. Large-scale evaluation could contribute to improving patient management and treatment planning. *Indicates equal authorship.

scholarly journals Complement System: Promoter or Suppressor of Cancer Progression?

Antibodies ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 57
Author(s):  
Margot Revel ◽  
Marie V. Daugan ◽  
Catherine Sautés-Fridman ◽  
Wolf H. Fridman ◽  
Lubka T. Roumenina
Keyword(s):  
Cancer Progression ◽  
Complement System ◽  
Current Knowledge ◽  
Prognostic Impact ◽  
Cancer Type ◽  
Human Cancers ◽  
Cancer Types ◽  
The Complement System

Constituent of innate immunity, complement is present in the tumor microenvironment. The functions of complement include clearance of pathogens and maintenance of homeostasis, and as such could contribute to an anti-tumoral role in the context of certain cancers. However, multiple lines of evidence show that in many cancers, complement has pro-tumoral actions. The large number of complement molecules (over 30), the diversity of their functions (related or not to the complement cascade), and the variety of cancer types make the complement-cancer topic a very complex matter that has just started to be unraveled. With this review we highlight the context-dependent role of complement in cancer. Recent studies revealed that depending of the cancer type, complement can be pro or anti-tumoral and, even for the same type of cancer, different models presented opposite effects. We aim to clarify the current knowledge of the role of complement in human cancers and the insights from mouse models. Using our classification of human cancers based on the prognostic impact of the overexpression of complement genes, we emphasize the strong potential for therapeutic targeting the complement system in selected subgroups of cancer patients.

scholarly journals Proteomic signatures of 16 major types of human cancer reveal universal and cancer-type-specific proteins for the identification of potential therapeutic targets

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yangying Zhou ◽  
T. Mamie Lih ◽  
Jianbo Pan ◽  
Naseruddin Höti ◽  
Mingming Dong ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Cancer ◽  
Therapeutic Targets ◽  
Small Cell ◽  
Tissue Type ◽  
Cancer Type ◽  
Proteomic Profiling ◽  
Human Cancers ◽  
Proteomic Approach ◽  
Specific Proteins

Abstract Background Proteomic characterization of cancers is essential for a comprehensive understanding of key molecular aberrations. However, proteomic profiling of a large cohort of cancer tissues is often limited by the conventional approaches. Methods We present a proteomic landscape of 16 major types of human cancer, based on the analysis of 126 treatment-naïve primary tumor tissues, 94 tumor-matched normal adjacent tissues, and 12 normal tissues, using mass spectrometry-based data-independent acquisition approach. Results In our study, a total of 8527 proteins were mapped to brain, head and neck, breast, lung (both small cell and non-small cell lung cancers), esophagus, stomach, pancreas, liver, colon, kidney, bladder, prostate, uterus and ovary cancers, including 2458 tissue-enriched proteins. Our DIA-based proteomic approach has characterized major human cancers and identified universally expressed proteins as well as tissue-type-specific and cancer-type-specific proteins. In addition, 1139 therapeutic targetable proteins and 21 cancer/testis (CT) antigens were observed. Conclusions Our discoveries not only advance our understanding of human cancers, but also have implications for the design of future large-scale cancer proteomic studies to assist the development of diagnostic and/or therapeutic targets in multiple cancers.

scholarly journals Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types

2009 ◽  
Vol 4 ◽  
pp. BMI.S930 ◽  
Author(s):  
George C. Tseng ◽  
Chunrong Cheng ◽  
Yan Ping Yu ◽  
Joel Nelson ◽  
George Michalopoulos ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Meta Analysis ◽  
Organ Donor ◽  
Cancer Biomarkers ◽  
Data Sets ◽  
Cancer Type ◽  
Whole Genome ◽  
Multiple Cancer ◽  
Cancer Data ◽  
Cancer Types

Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adjacent to tumor and tumor tissues from liver, prostate, lung and bladder samples. We identified 99 distinct multi-cancer biomarkers in the comparison of all three tissues in liver and prostate and 44 in the comparison of normal versus tumor in liver, prostate and lung. The bladder samples appeared to have a different list of biomarkers from the other three cancer types. The identified multi-cancer biomarkers achieved high accuracy similar to using whole genome in the within-cancer-type prediction. They also performed superior than the one using whole genome in inter-cancer-type prediction. To test the validity of the multi-cancer biomarkers, 23 independent prostate cancer samples were evaluated and 96% accuracy was achieved in inter-study prediction from the original prostate, liver and lung cancer data sets respectively. The result suggests that the compact lists of multi-cancer biomarkers are important in cancer development and represent the common signatures of malignancies of multiple cancer types. Pathway analysis revealed important tumorogenesis functional categories.

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Results We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. Conclusions sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals Fitting the Erlang distribution to cancer incidence by age may predict key carcinogenic events

2016 ◽  
Author(s):  
Aleksey V. Belikov
Keyword(s):  
Cancer Progression ◽  
Cancer Incidence ◽  
Age Of Onset ◽  
Erlang Distribution ◽  
Time Interval ◽  
Progression Rate ◽  
Cancer Type ◽  
Growth Equations ◽  
Cancer Types ◽  
Multiple Hit

AbstractCancer is the second-leading cause of death worldwide, after cardiovascular diseases. Cancers arise from various cells and organs at different ages and develop at different rates. However, the reasons for this variation in the cancer progression rate and the age of onset are poorly understood. Especially puzzling is the late-life decrease in cancer incidence, which cannot be explained by previously proposed power law or exponential growth equations. By using the latest publicly available USA cancer incidence statistics, comprised of 20 million cancer cases documented over 14 years, I show that cancer incidence by age closely follows the Erlang probability distribution (R2=0.9543-0.9999), which is a special case of the gamma distribution. The Erlang distribution describes the probability y of k independent random events occurring by the time x, but not earlier or later, with each event happening on average every b time intervals. This fits well with the multiple-hit hypothesis, and potentially allows to predict the number k of key carcinogenic events and the average time interval b between them, for each cancer type. Moreover, the amplitude parameter A likely predicts maximal populational susceptibility to a given type of cancer. These parameters are estimated for 20 most common cancer types, and provide clues for further research on cancer development.

scholarly journals Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

2017 ◽  
Author(s):  
Joana Carlevaro-Fita ◽  
Andrés Lanzós ◽  
Lars Feuerbach ◽  
Chen Hong ◽  
David Mas-Ponte ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Cancer Genomics ◽  
De Novo ◽  
Cancer Type ◽  
Cancer Genes ◽  
Driver Genes ◽  
Protein Coding ◽  
Evidence Type ◽  
Non Coding Rnas ◽  
Functional Screens

AbstractLong non-coding RNAs (lncRNAs) that drive tumorigenesis are a growing focus of cancer genomics studies. To facilitate further discovery, we have created the “Cancer LncRNA Census” (CLC), a manually-curated and strictly-defined compilation of lncRNAs with causative roles in cancer. CLC has two principle applications: first, as a resource for training and benchmarking de novo identification methods; and second, as a dataset for studying the fundamental properties of these genes.CLC Version 1 comprises 122 lncRNAs implicated in 29 distinct cancers. LncRNAs are included based on functional or genetic evidence for causative roles in cancer progression. All belong to the GENCODE reference annotation, to enable integration across projects and datasets. For each entry, the evidence type, biological activity (oncogene or tumour suppressor), source reference and cancer type are recorded. Supporting its usefulness, CLC genes are significantly enriched amongst de novo predicted driver genes from PCAWG. CLC genes are distinguished from other lncRNAs by a series of features consistent with biological function, including gene length, high expression and sequence conservation of both exons and promoters. We identify a trend for CLC genes to be co-localised with known protein-coding cancer genes along the human genome. Finally, by integrating data from transposon-mutagenesis functional screens, we show that mouse orthologues of CLC genes tend also to be cancer genes.Thus CLC represents a valuable resource for research into long non-coding RNAs in cancer. Their evolutionary and genomic properties have implications for understanding disease mechanisms and point to conserved functions across ~80 million years of evolution.

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