Comprehensive genomic profiles of small cell lung cancer methods
Show snapshot details
On this page
This experiment, in seven questions
Jump straight to the part of the recipe you need. Data and provenance labels stay close to the action they support.
Shopping and prep list
What do I need before I start?
Biological model pending
Subject model for the experiment.
- Use
- confirm full cohort details in the source paper
Before you run
What should be confirmed before execution?
First confirmation
Species or subject information is missing.
Confirm before execution
Protocol does not list structured equipment.
Confirm before execution
This page shows passages quoted from the paper. Equipment, materials, times, and temperatures are hidden until the page is rebuilt from the full paper and checked by a reviewer. Check the paper before using this method.
Confirm before execution
Open the source paper before finalizing run-specific details.
Procurement checkpoint
Use source-stated vendors where present. Treat mapped products as sourcing options unless the page marks an exact source match.
Open quote workflowStep-by-step procedure
What do I do, in order?
EdU incorporation assay
Transfected cells were treated with 10 µM EdU (5-ethynyl-2′-deoxyuridine) (Life Technologies) for 3 h before trypsinization for FACS. 1 × 10 5 live, GFP + cells were sorted and labelled with EdU using the Click-iT EdU Pacific Blue flow cytometry assay kit (Life Technologies). Cells were then run through the BD FACSAria to analyse for per cent EdU incorporation.
DNA and RNA extractions
Nucleic acids were extracted from fresh-frozen tissue specimen which were processed to 15-30 sections each of 20 µm thickness at a cryostat maintaining a temperature of -20 °C (Leica). In the case of FFPE samples, 6-10 sections of 10 µm thickness were prepared.
Extended Data
a, Survival analysis of SCLC patients based on clinical stage and treatment options (surgery and/or chemotherapy). Statistical significance was determined by log-rank test. b, Analyses of clinical stage and smoking status and the respective effect on number and type of mutations, as well as mutational subclonality in tumours. Statistical significance was determined by Kruskal-Wallis analysis.
METHODS
Clinical correlation studies were performed with the study cohort of 110 SCLC patients considering age of diagnosis, gender, tumour stage, surgery, treatment with chemotherapeutics, smoking status, smoking history and overall survival ( and and ). The median follow-up time for this cohort of 110 SCLC patients was 69 months, and 31% of the patients were alive at the time of last follow-up ( and ). Smoking status was available for 88% ( n = 97) of the patients; 63% ( n = 69) reported a smoking history amounting to a median of 45 pack-years. Patients with a known smoking history were further subcategorized to heavy smokers (>30 pack-years), average smokers (10-30 pack-years) and light/never smokers (<10 pack-years).
Copy number analysis by Affymetrix SNP 6.0 arrays
Human DNA extracted from fresh-frozen tumour specimen was hybridized to Affymetrix Genome-Wide Human SNP array 6.0 following the manufacturer's instructions. The signal intensities were processed to analyse for chromosomal gene copy number data. Raw copy number signals and segmented copy number data were computed following the procedure described previously.
Copy number analysis by Affymetrix SNP 6.0 arrays
The raw, unsegmented copy number signals were used to analyse for significant copy number alterations applying the method CGARS. Significant amplifications were determined with the upper quantiles 0.25, 0.15, 0.1, and 0.05; deletions were computed in reference to the 0.25 lower quantile. The significance threshold was set at a q -value of 0.05 ( ).
Analysis of subclonal architecture
To determine the subclonal architecture from genome sequencing data, we first computed the cancer cell fraction (CCF; that is, the fraction of cancer cells carrying a particular mutation) of each called somatic point mutation. To this end, we first estimated the tumour purity, absolute copy numbers, and subclonal copy number changes using our previously described method and computed for each mutation the expected allelic fraction under clonality assumption. The quotient between the observed allelic fraction of a mutation with its corresponding expected allelic fraction then yields the CCF. To assess the clonal and subclonal populations we next identified distinct clusters in the CCF profile and assigned each mutation to the cluster of highest probability. In order to provide a measure for the subclonal architecture, we proposed the following score: Subclonality score = ∑ i = 1 n...
Analysis of subclonal architecture
As a low sequencing depth limits the robust identification of subclonal populations, we computed the genome-wide average contribution of a single mutated read to the CCF. For a given tumour purity p, average ploidy π, and mean coverage c, this measure is given by: Average increase of C C F per read = 2 ( 1 - p ) + p π p c The smaller the average increase of CCF per read, the more accurately the subclonality score can be determined since more subclonal mutations can be called from the sequencing data. In this study, the most limiting factor for assessing the subclonal diversity is the relatively low sequencing depth (35 × on average). We therefore used this measure to select the samples that are suitable for a reliable calculation of the subclonality score. To this end, we systematically scanned from the average increase of CCF per read form large to s...
Measurement outputs
What raw and processed outputs should exist?
Analysis plan
How should the outputs become interpretable results?
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
inferred from protocolPreprocessing / cleaning
Review raw outputs for quality, remove unusable captures, and organize the data into a comparison-ready table or image set.
inferred from protocolScoring or quantification
Quantify the primary endpoint described in the methods before moving into any statistical comparison.
inferred from protocolStatistical comparison
Statistical method not yet structured for this page.
inferred from protocolReporting output
Report representative outputs alongside the summary comparison used to support the conclusion.
inferred from protocolSource and audit
What supports the facts on this page?
Evidence quotes (8)
Transfected cells were treated with 10 µM EdU (5-ethynyl-2′-deoxyuridine) (Life Technologies) for 3 h before trypsinization for FACS. 1 × 10 5 live, GFP + cells were sorted and labelled with EdU using the Click-iT EdU Pacific Blue flow cytometry assay kit (Life Technologies). Cells were then run through the BD FACSAria to analyse for per cent EdU incorporation.
Nucleic acids were extracted from fresh-frozen tissue specimen which were processed to 15-30 sections each of 20 µm thickness at a cryostat maintaining a temperature of -20 °C (Leica). In the case of FFPE samples, 6-10 sections of 10 µm thickness were prepared.
a, Survival analysis of SCLC patients based on clinical stage and treatment options (surgery and/or chemotherapy). Statistical significance was determined by log-rank test. b, Analyses of clinical stage and smoking status and the respective effect on number and type of mutations, as well as mutational subclonality in tumours. Statistical significance was determined by Kruskal-Wallis analysis.
Clinical correlation studies were performed with the study cohort of 110 SCLC patients considering age of diagnosis, gender, tumour stage, surgery, treatment with chemotherapeutics, smoking status, smoking history and overall survival ( and and ). The median follow-up time for this cohort of 110 SCLC patients was 69 months, and 31% of the patients were alive at the time of last follow-up ( and ). Smoking status was available for 88% ( n = 97) of the patients; 63% ( n = 69) reported a smoking history amounting to a median of 45 pack-years. Patients with a known smoking history were further subcategorized to heavy smokers (>30 pack-years), average smokers (10-30 pack-years) and light/never smokers (<10 pack-years).
Human DNA extracted from fresh-frozen tumour specimen was hybridized to Affymetrix Genome-Wide Human SNP array 6.0 following the manufacturer's instructions. The signal intensities were processed to analyse for chromosomal gene copy number data. Raw copy number signals and segmented copy number data were computed following the procedure described previously.
The raw, unsegmented copy number signals were used to analyse for significant copy number alterations applying the method CGARS. Significant amplifications were determined with the upper quantiles 0.25, 0.15, 0.1, and 0.05; deletions were computed in reference to the 0.25 lower quantile. The significance threshold was set at a q -value of 0.05 ( ).
To determine the subclonal architecture from genome sequencing data, we first computed the cancer cell fraction (CCF; that is, the fraction of cancer cells carrying a particular mutation) of each called somatic point mutation. To this end, we first estimated the tumour purity, absolute copy numbers, and subclonal copy number changes using our previously described method and computed for each mutation the expected allelic fraction under clonality assumption. The quotient between the observed allelic fraction of a mutation with its corresponding expected allelic fraction then yields the CCF. To assess the clonal and subclonal populations we next identified distinct clusters in the CCF profile and assigned each mutation to the cluster of highest probability. In order to provide a measure for the subclonal architecture, we proposed the following score: Subclonality score = ∑ i = 1 n c ϕ i m i ∑ i = 0 n c ϕ i m i where i = 0 represents the clonal population, i = 1,…, nc the subclonal populations; φ i is the CCF of each population (thus, φ 0 ≈ 1), and m i is the number of mutations assigned to cluster i. This subclonality score can be inter...
As a low sequencing depth limits the robust identification of subclonal populations, we computed the genome-wide average contribution of a single mutated read to the CCF. For a given tumour purity p, average ploidy π, and mean coverage c, this measure is given by: Average increase of C C F per read = 2 ( 1 - p ) + p π p c The smaller the average increase of CCF per read, the more accurately the subclonality score can be determined since more subclonal mutations can be called from the sequencing data. In this study, the most limiting factor for assessing the subclonal diversity is the relatively low sequencing depth (35 × on average). We therefore used this measure to select the samples that are suitable for a reliable calculation of the subclonality score. To this end, we systematically scanned from the average increase of CCF per read form large to small values and detected the point of the most prominent change in the distribution of the subclonality score ( ).
Machine-readable layer
[
{
"@context": "https://schema.org",
"@type": "HowTo",
"name": "Comprehensive genomic profiles of small cell lung cancer methods",
"description": "Protocol for Comprehensive genomic profiles of small cell lung cancer methods",
"step": [
{
"@type": "HowToStep",
"position": 1,
"name": "EdU incorporation assay",
"text": "Transfected cells were treated with 10 µM EdU (5-ethynyl-2′-deoxyuridine) (Life Technologies) for 3 h before trypsinization for FACS. 1 × 10 5 live, GFP + cells were sorted and labelled with EdU using the Click-iT EdU Pacific Blue flow cytometry assay kit (Life Technologies). Cells were then run through the BD FACSAria to analyse for per cent EdU incorporation."
},
{
"@type": "HowToStep",
"position": 2,
"name": "DNA and RNA extractions",
"text": "Nucleic acids were extracted from fresh-frozen tissue specimen which were processed to 15-30 sections each of 20 µm thickness at a cryostat maintaining a temperature of -20 °C (Leica). In the case of FFPE samples, 6-10 sections of 10 µm thickness were prepared."
},
{
"@type": "HowToStep",
"position": 3,
"name": "Extended Data",
"text": "a, Survival analysis of SCLC patients based on clinical stage and treatment options (surgery and/or chemotherapy). Statistical significance was determined by log-rank test. b, Analyses of clinical stage and smoking status and the respective effect on number and type of mutations, as well as mutational subclonality in tumours. Statistical significance was determined by Kruskal-Wallis analysis."
},
{
"@type": "HowToStep",
"position": 4,
"name": "METHODS",
"text": "Clinical correlation studies were performed with the study cohort of 110 SCLC patients considering age of diagnosis, gender, tumour stage, surgery, treatment with chemotherapeutics, smoking status, smoking history and overall survival ( and and ). The median follow-up time for this cohort of 110 SCLC patients was 69 months, and 31% of the patients were alive at the time of last follow-up ( and ). Smoking status was available for 88% ( n = 97) of the patients; 63% ( n = 69) reported a smoking history amounting to a median of 45 pack-years. Patients with a known smoking history were further subcategorized to heavy smokers (>30 pack-years), average smokers (10-30 pack-years) and light/never smokers (<10 pack-years)."
},
{
"@type": "HowToStep",
"position": 5,
"name": "Copy number analysis by Affymetrix SNP 6.0 arrays",
"text": "Human DNA extracted from fresh-frozen tumour specimen was hybridized to Affymetrix Genome-Wide Human SNP array 6.0 following the manufacturer's instructions. The signal intensities were processed to analyse for chromosomal gene copy number data. Raw copy number signals and segmented copy number data were computed following the procedure described previously."
},
{
"@type": "HowToStep",
"position": 6,
"name": "Copy number analysis by Affymetrix SNP 6.0 arrays",
"text": "The raw, unsegmented copy number signals were used to analyse for significant copy number alterations applying the method CGARS. Significant amplifications were determined with the upper quantiles 0.25, 0.15, 0.1, and 0.05; deletions were computed in reference to the 0.25 lower quantile. The significance threshold was set at a q -value of 0.05 ( )."
},
{
"@type": "HowToStep",
"position": 7,
"name": "Analysis of subclonal architecture",
"text": "To determine the subclonal architecture from genome sequencing data, we first computed the cancer cell fraction (CCF; that is, the fraction of cancer cells carrying a particular mutation) of each called somatic point mutation. To this end, we first estimated the tumour purity, absolute copy numbers, and subclonal copy number changes using our previously described method and computed for each mutation the expected allelic fraction under clonality assumption. The quotient between the observed allelic fraction of a mutation with its corresponding expected allelic fraction then yields the CCF. To assess the clonal and subclonal populations we next identified distinct clusters in the CCF profile and assigned each mutation to the cluster of highest probability. In order to provide a measure for the subclonal architecture, we proposed the following score: Subclonality score = ∑ i = 1 n..."
},
{
"@type": "HowToStep",
"position": 8,
"name": "Analysis of subclonal architecture",
"text": "As a low sequencing depth limits the robust identification of subclonal populations, we computed the genome-wide average contribution of a single mutated read to the CCF. For a given tumour purity p, average ploidy π, and mean coverage c, this measure is given by: Average increase of C C F per read = 2 ( 1 - p ) + p π p c The smaller the average increase of CCF per read, the more accurately the subclonality score can be determined since more subclonal mutations can be called from the sequencing data. In this study, the most limiting factor for assessing the subclonal diversity is the relatively low sequencing depth (35 × on average). We therefore used this measure to select the samples that are suitable for a reliable calculation of the subclonality score. To this end, we systematically scanned from the average increase of CCF per read form large to s..."
}
],
"tool": [],
"supply": [],
"isBasedOn": {
"@type": "ScholarlyArticle",
"headline": "Comprehensive genomic profiles of small cell lung cancer",
"datePublished": "2015",
"author": [
{
"@type": "Person",
"name": "Julie George"
},
{
"@type": "Person",
"name": "Jing Shan Lim"
},
{
"@type": "Person",
"name": "Se Jin Jang"
},
{
"@type": "Person",
"name": "Yupeng Cun"
},
{
"@type": "Person",
"name": "Luka Ozretić"
},
{
"@type": "Person",
"name": "Gu Kong"
},
{
"@type": "Person",
"name": "Frauke Leenders"
},
{
"@type": "Person",
"name": "Xin Lu"
},
{
"@type": "Person",
"name": "Lynnette Fernández-Cuesta"
},
{
"@type": "Person",
"name": "Graziella Bosco"
},
{
"@type": "Person",
"name": "Christian Müller"
},
{
"@type": "Person",
"name": "Ilona Dahmen"
},
{
"@type": "Person",
"name": "Nadine S. Jahchan"
},
{
"@type": "Person",
"name": "Kwon-Sik Park"
},
{
"@type": "Person",
"name": "Dian Yang"
},
{
"@type": "Person",
"name": "Anthony N. Karnezis"
},
{
"@type": "Person",
"name": "Dedeepya Vaka"
},
{
"@type": "Person",
"name": "Angela Torres"
},
{
"@type": "Person",
"name": "Maia Segura Wang"
},
{
"@type": "Person",
"name": "Jan O. Korbel"
},
{
"@type": "Person",
"name": "Roopika Menon"
},
{
"@type": "Person",
"name": "Sung-Min Chun"
},
{
"@type": "Person",
"name": "Deokhoon Kim"
},
{
"@type": "Person",
"name": "Matt Wilkerson"
},
{
"@type": "Person",
"name": "Neil Hayes"
},
{
"@type": "Person",
"name": "David Engelmann"
},
{
"@type": "Person",
"name": "Brigitte Pützer"
},
{
"@type": "Person",
"name": "Marc Bos"
},
{
"@type": "Person",
"name": "Sebastian Michels"
},
{
"@type": "Person",
"name": "Ignacija Vlasic"
},
{
"@type": "Person",
"name": "Danila Seidel"
},
{
"@type": "Person",
"name": "Berit Pinther"
},
{
"@type": "Person",
"name": "Philipp Schaub"
},
{
"@type": "Person",
"name": "Christian Becker"
},
{
"@type": "Person",
"name": "Janine Altmüller"
},
{
"@type": "Person",
"name": "Jun Yokota"
},
{
"@type": "Person",
"name": "Takashi Kohno"
},
{
"@type": "Person",
"name": "Reika Iwakawa"
},
{
"@type": "Person",
"name": "Koji Tsuta"
},
{
"@type": "Person",
"name": "Masayuki Noguchi"
},
{
"@type": "Person",
"name": "Thomas Muley"
},
{
"@type": "Person",
"name": "Hans Hoffmann"
},
{
"@type": "Person",
"name": "Philipp A. Schnabel"
},
{
"@type": "Person",
"name": "Iver Petersen"
},
{
"@type": "Person",
"name": "Yuan Chen"
},
{
"@type": "Person",
"name": "Alex Soltermann"
},
{
"@type": "Person",
"name": "Verena Tischler"
},
{
"@type": "Person",
"name": "Chang-min Choi"
},
{
"@type": "Person",
"name": "Yong-Hee Kim"
},
{
"@type": "Person",
"name": "Pierre P. Massion"
},
{
"@type": "Person",
"name": "Yong Zou"
},
{
"@type": "Person",
"name": "Dragana Jovanovic"
},
{
"@type": "Person",
"name": "Milica Kontic"
},
{
"@type": "Person",
"name": "Gavin M. Wright"
},
{
"@type": "Person",
"name": "Prudence A. Russell"
},
{
"@type": "Person",
"name": "Benjamin Solomon"
},
{
"@type": "Person",
"name": "Ina Koch"
},
{
"@type": "Person",
"name": "Michael Lindner"
},
{
"@type": "Person",
"name": "Lucia A. Muscarella"
},
{
"@type": "Person",
"name": "Annamaria la Torre"
},
{
"@type": "Person",
"name": "John K. Field"
},
{
"@type": "Person",
"name": "Marko Jakopovic"
},
{
"@type": "Person",
"name": "Jelena Knezevic"
},
{
"@type": "Person",
"name": "Esmeralda Castaños-Vélez"
},
{
"@type": "Person",
"name": "Luca Roz"
},
{
"@type": "Person",
"name": "Ugo Pastorino"
},
{
"@type": "Person",
"name": "Odd-Terje Brustugun"
},
{
"@type": "Person",
"name": "Marius Lund-Iversen"
},
{
"@type": "Person",
"name": "Erik Thunnissen"
},
{
"@type": "Person",
"name": "Jens Köhler"
},
{
"@type": "Person",
"name": "Martin Schuler"
},
{
"@type": "Person",
"name": "Johan Botling"
},
{
"@type": "Person",
"name": "Martin Sandelin"
},
{
"@type": "Person",
"name": "Montserrat Sanchez-Cespedes"
},
{
"@type": "Person",
"name": "Helga B. Salvesen"
},
{
"@type": "Person",
"name": "Viktor Achter"
},
{
"@type": "Person",
"name": "Ulrich Lang"
},
{
"@type": "Person",
"name": "Magdalena Bogus"
},
{
"@type": "Person",
"name": "Peter M. Schneider"
},
{
"@type": "Person",
"name": "Thomas Zander"
},
{
"@type": "Person",
"name": "Sascha Ansén"
},
{
"@type": "Person",
"name": "Michael Hallek"
},
{
"@type": "Person",
"name": "Jürgen Wolf"
},
{
"@type": "Person",
"name": "Martin Vingron"
},
{
"@type": "Person",
"name": "Yasushi Yatabe"
},
{
"@type": "Person",
"name": "William D. Travis"
},
{
"@type": "Person",
"name": "Peter Nürnberg"
},
{
"@type": "Person",
"name": "Christian Reinhardt"
},
{
"@type": "Person",
"name": "Sven Perner"
},
{
"@type": "Person",
"name": "Lukas Heukamp"
},
{
"@type": "Person",
"name": "Reinhard Büttner"
},
{
"@type": "Person",
"name": "Stefan A. Haas"
},
{
"@type": "Person",
"name": "Elisabeth Brambilla"
},
{
"@type": "Person",
"name": "Martin Peifer"
},
{
"@type": "Person",
"name": "Julien Sage"
},
{
"@type": "Person",
"name": "Roman K. Thomas"
}
],
"identifier": "10.1038/nature14664"
}
},
{
"@context": "https://schema.org",
"@type": "BreadcrumbList",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Experiments",
"item": "https://replicatescience.com/experiments"
},
{
"@type": "ListItem",
"position": 2,
"name": "Comprehensive genomic profiles of small cell lung cancer methods",
"item": "https://replicatescience.com/experiments/comprehensive-genomic-profiles-of-small-cell-lung-cancer-methods-julie-george-pmc4861069/comprehensive-genomic-profiles-of-small-cell-lung-cancer-mlph0v0w"
}
]
}
]