scholarly journals Translating Genomics to the Clinic: Implications of Cancer Heterogeneity

2013 ◽  
Vol 59 (1) ◽  
pp. 127-137 ◽  
Author(s):  
Nardin Samuel ◽  
Thomas J Hudson
Keyword(s):  
Genome Sequencing ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Cancer Genomics ◽  
Clinical Decision ◽  
Sequencing Analysis ◽  
Breast Cancers ◽  
Sequencing Data ◽  
Cancer Heterogeneity ◽  
Cancer Genomes

BACKGROUND Sequencing of cancer genomes has become a pivotal method for uncovering and understanding the deregulated cellular processes driving tumor initiation and progression. Whole-genome sequencing is evolving toward becoming less costly and more feasible on a large scale; consequently, thousands of tumors are being analyzed with these technologies. Interpreting these data in the context of tumor complexity poses a challenge for cancer genomics. CONTENT The sequencing of large numbers of tumors has revealed novel insights into oncogenic mechanisms. In particular, we highlight the remarkable insight into the pathogenesis of breast cancers that has been gained through comprehensive and integrated sequencing analysis. The analysis and interpretation of sequencing data, however, must be considered in the context of heterogeneity within and among tumor samples. Only by adequately accounting for the underlying complexity of cancer genomes will the potential of genome sequencing be understood and subsequently translated into improved management of patients. SUMMARY The paradigm of personalized medicine holds promise if patient tumors are thoroughly studied as unique and heterogeneous entities and clinical decisions are made accordingly. Associated challenges will be ameliorated by continued collaborative efforts among research centers that coordinate the sharing of mutation, intervention, and outcomes data to assist in the interpretation of genomic data and to support clinical decision-making.

scholarly journals A Comprehensive Assessment of Somatic Mutation Calling in Cancer Genomes

2014 ◽  
Author(s):  
Tyler S Alioto ◽  
Sophia Derdak ◽  
Timothy A Beck ◽  
Paul C Boutros ◽  
Lawrence Bower ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Somatic Mutation ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cancer Genome ◽  
Whole Genome Sequence ◽  
Cancer Genomes ◽  
Genome Data Analysis ◽  
Somatic Mutation Calling

The emergence of next generation DNA sequencing technology is enabling high-resolution cancer genome analysis. Large-scale projects like the International Cancer Genome Consortium (ICGC) are systematically scanning cancer genomes to identify recurrent somatic mutations. Second generation DNA sequencing, however, is still an evolving technology and procedures, both experimental and analytical, are constantly changing. Thus the research community is still defining a set of best practices for cancer genome data analysis, with no single protocol emerging to fulfil this role. Here we describe an extensive benchmark exercise to identify and resolve issues of somatic mutation calling. Whole genome sequence datasets comprising tumor-normal pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, were shared within the ICGC and submissions of somatic mutation calls were compared to verified mutations and to each other. Varying strategies to call mutations, incomplete awareness of sources of artefacts, and even lack of agreement on what constitutes an artefact or real mutation manifested in widely varying mutation call rates and somewhat low concordance among submissions. We conclude that somatic mutation calling remains an unsolved problem. However, we have identified many issues that are easy to remedy that are presented here. Our study highlights critical issues that need to be addressed before this valuable technology can be routinely used to inform clinical decision-making.

Whole-genome sequencing to improve sarcoma diagnosis and patient care.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11540-11540
Author(s):  
Luuk J. Schipper ◽  
Kim Monkhorst ◽  
Kris Samsom ◽  
Petur Snaebjornsson ◽  
Hester Van Boven ◽  
...  
Keyword(s):  
Decision Making ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Therapeutic Potential ◽  
Clinical Care ◽  
Unmet Need ◽  
Clinical Decision ◽  
Whole Genome

11540 Background: With more than 70 different histological subtypes, accurate classification sarcomas is challenging. Although pathognomonic genetic events aid accurate classification, large-scale molecular profiling is generally not incorporated in regular diagnostic workflows for sarcoma patients. We hypothesized that whole genome sequencing (WGS) optimizes clinical care of sarcoma patients by detection of pathognomonic and actionable variants, and of underlying hereditary conditions. Methods: WGS of tumor and germline DNA was incorporated in the diagnostic work-up of 83 patients with a (presumed) sarcoma as part of the WIDE (Whole genome sequencing Implementation in standard Diagnostics for Every cancer patient) study in a tertiary referral center. WGS results were reported back to the pathologist and treating clinician. Clinical follow-up data were collected prospectively to assess impact of WGS on clinical decision making. Results: WGS analysis had impact on multiple levels. First, in 14% of cases (12/83 patients), the genomic profile led to a revision of the diagnosis (table). All patients had undergone multiple diagnostic procedures (mean number: 4) and pathologist assessments (mean: 6) before WGS analysis was performed. Secondly, actionable biomarkers with therapeutic potential were detected for 36/83 patients and finally, 8 pathogenic germline variants were present. Taken together, WGS had implications for clinical decision making in 52% of patients with (presumed) sarcomas. Conclusions: WGS is an important extension of the diagnostic arsenal of pathologists and has contributed to change of care in 52% of patients with sarcomas. Given the diagnostic complexity and high unmet need for new treatment opportunities in sarcomas we advocate the use of WGS for sarcoma patients early in the disease course. Clinical trial information: NL68609.031.18. [Table: see text]

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

The Johns Hopkins Molecular Tumor Board Precision Oncology elective for Medical Oncology fellows.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11035-11035
Author(s):  
Kristen Marrone ◽  
Jessica Tao ◽  
Jenna VanLiere Canzoniero ◽  
Paola Ghanem ◽  
Emily Nizialek ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Cancer Genomics ◽  
Medical Oncology ◽  
Clinical Decision ◽  
Tumor Board ◽  
Precision Oncology ◽  
Adaptive Expertise ◽  
Molecular Tumor Board

11035 Background: The accelerated impact of next generation sequencing (NGS) in clinical decision making requires the integration of cancer genomics and precision oncology focused training into medical oncology education. The Johns Hopkins Molecular Tumor Board (JH MTB) is a multi-disciplinary effort focused on integration of NGS findings with critical evidence interpretation to generate personalized recommendations tailored to the genetic footprint of individual patients. Methods: The JH MTB and the Medical Oncology Fellowship Program have developed a 3-month precision oncology elective for fellows in their research years. Commencing fall of 2020, the goals of this elective are to enhance the understanding of NGS platforms and findings, advance the interpretation and characterization of molecular assay outputs by use of mutation annotators and knowledgebases and ultimately master the art of matching NGS findings with available therapies. Fellow integration into the MTB focuses on mentored case-based learning in mutation characterization and ranking by levels of evidence for actionability, with culmination in form of verbal presentations and written summary reports of final MTB recommendations. A mixed methods questionnaire was administered to evaluate progress since elective initiation. Results: Three learners who have participated as of February 2021 were included. Of the two who had completed the MTB elective, each have presented at least 10 cases, with at least 1 scholarly publication planned. All indicated strong agreement that MTB elective had increased their comfort with interpreting clinical NGS reports as well as the use of knowledgebases and variant annotators. Exposure to experts in the field of molecular precision oncology, identification of resources necessary to interpret clinical NGS reports, development of ability to critically assess various NGS platforms, and gained familiarity with computational analyses relevant to clinical decision making were noted as strengths of the MTB elective. Areas of improvement included ongoing initiatives that involve streamlining variant annotation and transcription of information for written reports. Conclusions: A longitudinal elective in the JHU MTB has been found to be preliminarily effective in promoting knowledge mastery and creating academic opportunities related to the clinical application of precision medicine. Future directions will include leveraging of the MTB infrastructure for research projects, learner integration into computational laboratory meetings, and expansion of the MTB curriculum to include different levels of learners from multiple medical education programs. Continued elective participation will be key to understanding how best to facilitate adaptive expertise in assigning clinical relevance to genomic findings, ultimately improving precision medicine delivery in patient care and trial development.

Pathogenesis of Peritoneal Sclerosis

2005 ◽  
Vol 28 (2) ◽  
pp. 90-96 ◽  
Author(s):  
C. Pollock
Keyword(s):  
Peritoneal Dialysis ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Inflammatory Cells ◽  
Clinical Decision ◽  
Cumulative Exposure ◽  
Peritoneal Membrane ◽  
Peritoneal Fibrosis ◽  
Histological Data ◽  
The Individual

Peritoneal sclerosis is an almost invariable consequence of peritoneal dialysis. In most circumstances it is “simple” sclerosis, manifesting clinically with an increasing peritoneal transport rate and loss of ultrafiltration capacity. In contrast, encapsulating peritoneal sclerosis is a life threatening and usually irreversible condition, associated with bowel obstruction, malnutrition and death. It is unknown whether common etiological factors underlie the development of these 2 clinically and pathologically distinct forms of peritoneal sclerosis. The majority of studies to date have investigated factors that contribute to “simple” sclerosis, although it remains possible that similar mechanisms are amplified in patients who develop encapsulated peritoneal sclerosis. The cellular elements that promote peritoneal sclerosis include the mesothelial cells, peritoneal fibroblasts and inflammatory cells. Factors that stimulate these cells to promote peritoneal fibrosis and neoangiogenesis, both inherent in the development of peritoneal sclerosis, include cytokines that are induced by exposure of the peritoneal membrane to high concentrations of glucose, advanced glycation of the peritoneal membrane and oxidative stress. The cumulative exposure to bioincompatible dialysate is likely to have an etiological role as the duration of dialysis correlates with the likelihood of developing peritoneal sclerosis. Indeed peritoneal dialysis using more biocompatible fluids has been shown to reduce the development of peritoneal sclerosis. The individual contribution of the factors implicated in the development of peritoneal sclerosis will only be determined by large scale peritoneal biopsy registries, which will be able to prospectively incorporate clinical and histological data and support clinical decision making.

scholarly journals A164 UPTAKE OF FECAL CALPROTECTIN IN PRACTICE: PATTERNS IN A TERTIARY GI REFERRAL CENTRE

2020 ◽  
Vol 3 (Supplement_1) ◽  
pp. 28-30
Author(s):  
A Kundra ◽  
T Ritchie ◽  
M Ropeleski
Keyword(s):  
Medical Therapy ◽  
Bowel Disease ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Practice Patterns ◽  
Clinical Decision ◽  
Fecal Calprotectin ◽  
Time Interval ◽  
Chart Audit ◽  
Frequency Pattern

Abstract Background Fecal Calprotectin (FC) is helpful in distinguishing functional from organic bowel disease. Also, it has proven useful in monitoring disease activity in inflammatory bowel disease (IBD). The uptake of its use in clinical practice has increased considerably, though access varies significantly. Studies exploring current practice patterns among GI specialists and how to optimize its use are limited. In 2017, Kingston Health Sciences Centre (KHSC) began funding FC testing at no cost to patients. Aims We aimed to better understand practice patterns of gastroenterologists in IBD patients where there is in house access to FC assays, and to generate hypotheses regarding its optimal use in IBD monitoring. We hypothesize that FC is not being used in a regular manner for monitoring of IBD patients. Methods A retrospective chart audit study was done on all KHSC patients who had FC testing completed from 2017–2018. Qualitative data was gathered from dictated reports using rigorous set definitions regarding indication for the test, change in clinical decision making, and frequency patterns of testing. Specifically, change in use for colonoscopy or in medical therapy was coded only if the dictated note was clear that a decision hinged largely on the FC result. Frequency of testing was based on test order date. Reactive testing was coded as tests ordered to confirm a clinical flare. Variable testing was coded where monitoring tests that varied in intervals greater than 3 months and crossed over the other set frequency codes. Quantitative data regarding FC test values, and dates were also collected. This data was then analyzed using descriptive statistics. Results Of the 834 patients in our study, 7 were under 18 years old and excluded. 562(67.34%) of these patients had a pre-existing diagnosis of IBD; 193 (34%) with Ulcerative Colitis (UC), 369 (66%) with Crohn’s Disease (CD). FC testing changed the clinician’s decision for medical therapy in 12.82% of cases and use for colonoscopy 13.06% of the time for all comers. Of the FC tests, 79.8% were sent in a variable frequency pattern and 2.68% with reactive intent. The remaining 17.5% were monitored with a regular pattern, with 8.57% patients having their FC monitored at regular intervals greater than 6 months, 7.68% every 6 months, and 1.25% less than 6 months. The average FC level of patients with UC was 356.2ug/ml and 330.6 ug/ml for CD. The mean time interval from 1st to 2nd test was 189.6 days. Conclusions FC testing changed clinical decisions regarding medical therapy and use for colonoscopy about 13% of the time. FC testing was done variably 79.8% of the time, where as 17.5% of patients had a regular FC monitoring schedule. An optimal monitoring interval for IBD flares using FC for maximal clinical benefit has yet to be determined. Large scale studies will be required to answer this question. Funding Agencies None

Variant calling and quality control of large-scale human genome sequencing data

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul
Keyword(s):  
Quality Control ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Sequencing Errors ◽  
Human Genome Sequencing ◽  
Number Of Individuals

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.

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