The pipeline for this project mimics the steps taken by Genovese et al. to identify putative somatic mutations in the ClinSeq cohort of 1001 individuals. Binomial testing of allelic fractions, false discovery rate correction, and filtering mutations observed more than twice or with allelic fractions under 10% were performed. Investigation of mutation profiles uncovered issues with one capture kit prone to errors. After filtering based on strand bias, putative driver mutations were identified, including in DNMT3A and TET2. Future work will validate mutations and obtain follow-up samples.
Evolutionary theories are critical for understanding cancer development at the level of species as well as at the level of cells and tissues, and for developing effective therapies.
Clinical diagnosis of chronic myeloid leukemia by real time polymerase chain ...Teboho Mooko
Oncology study i did in my third year (2014). the study was basically about monitoring Chronic Myeloid leukemia (CML) using Real-Time PCR techniques to check how patients from Universitas Hospital responded to the treatment of Gleevec drug.
Analytical performance of a novel next generation sequencing assay for Myeloi...Thermo Fisher Scientific
To support clinical and translational research into precision oncology strategies for myeloid cancers, a next-generation sequencing (NGS) assay was developed to detect common and relevant somatic alterations. To define gene targets that were recurrently altered in myeloid cancers and relevant for clinical and translational research, an extensive survey of investigators at hematology oncology research labs was performed.
DNA Amplification is a Ubiquitous Mechanism of Oncogene Activation in Lung an...Shryli Shreekar
Chromosomal translocation is the best-characterized
genetic mechanism for oncogene activation. However, there
are documented examples of activation by alternate
mechanisms, for example gene dosage increase, though
its prevalence is unclear. Here, we answered the fundamental question of the contribution of DNA amplification
as a molecular mechanism driving oncogenesis. Comparing
104 cancer lines representing diverse tissue origins
identified genes residing in amplification ‘hotspots’ and
discovered an unexpected frequency of genes activated by
this mechanism. The 3431 amplicons identified represent
B10 per hematological and B36 per epithelial cancer
genome. Many recurrently amplified oncogenes were
previously known to be activated only by disease-specific
translocations. The 135 hotspots identified contain 538
unique genes and are enriched for proliferation, apoptosis
and linage-dependency genes, reflecting functions advantageous to tumor growth. Integrating gene dosage with
expression data validated the downstream impact of the
novel amplification events in both cell lines and clinical
samples. For example, multiple downstream components of
the EGFR-family-signaling pathway, including CDK5,
AKT1 and SHC1, are overexpressed as a direct result of
gene amplification in lung cancer. Our findings suggest that
amplification is far more common a mechanism of
oncogene activation than previously believed and that
specific regions of the genome are hotspots of amplification.
Transplant in pediatrics in Acute lymphoblastic Luekemia in CR1Dr. Liza Bulsara
to transplant or not to transplant pediatric luekemia in CR1 Has also been a controversial topic . here we give clear recommendation to transplant in difeerent biology group
Evolutionary theories are critical for understanding cancer development at the level of species as well as at the level of cells and tissues, and for developing effective therapies.
Clinical diagnosis of chronic myeloid leukemia by real time polymerase chain ...Teboho Mooko
Oncology study i did in my third year (2014). the study was basically about monitoring Chronic Myeloid leukemia (CML) using Real-Time PCR techniques to check how patients from Universitas Hospital responded to the treatment of Gleevec drug.
Analytical performance of a novel next generation sequencing assay for Myeloi...Thermo Fisher Scientific
To support clinical and translational research into precision oncology strategies for myeloid cancers, a next-generation sequencing (NGS) assay was developed to detect common and relevant somatic alterations. To define gene targets that were recurrently altered in myeloid cancers and relevant for clinical and translational research, an extensive survey of investigators at hematology oncology research labs was performed.
DNA Amplification is a Ubiquitous Mechanism of Oncogene Activation in Lung an...Shryli Shreekar
Chromosomal translocation is the best-characterized
genetic mechanism for oncogene activation. However, there
are documented examples of activation by alternate
mechanisms, for example gene dosage increase, though
its prevalence is unclear. Here, we answered the fundamental question of the contribution of DNA amplification
as a molecular mechanism driving oncogenesis. Comparing
104 cancer lines representing diverse tissue origins
identified genes residing in amplification ‘hotspots’ and
discovered an unexpected frequency of genes activated by
this mechanism. The 3431 amplicons identified represent
B10 per hematological and B36 per epithelial cancer
genome. Many recurrently amplified oncogenes were
previously known to be activated only by disease-specific
translocations. The 135 hotspots identified contain 538
unique genes and are enriched for proliferation, apoptosis
and linage-dependency genes, reflecting functions advantageous to tumor growth. Integrating gene dosage with
expression data validated the downstream impact of the
novel amplification events in both cell lines and clinical
samples. For example, multiple downstream components of
the EGFR-family-signaling pathway, including CDK5,
AKT1 and SHC1, are overexpressed as a direct result of
gene amplification in lung cancer. Our findings suggest that
amplification is far more common a mechanism of
oncogene activation than previously believed and that
specific regions of the genome are hotspots of amplification.
Transplant in pediatrics in Acute lymphoblastic Luekemia in CR1Dr. Liza Bulsara
to transplant or not to transplant pediatric luekemia in CR1 Has also been a controversial topic . here we give clear recommendation to transplant in difeerent biology group
How to transform genomic big data into valuable clinical informationJoaquin Dopazo
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The impact of genomics in translational medicine: present view
13th October 2014, Vall d’Hebron Institute of Research (VHIR), Barcelona, Spain
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Aneuploidy is a feature of most cancer cells that is often accompanied by an elevated rate of chromosome mis-segregation termed chromosome instability (CIN). While CIN can act as a driver of cancer genome evolution and tumor progression, recent findings point to the existence of a threshold level beyond which CIN becomes a barrier to tumor growth and therefore can be exploited therapeutically. Drugs known to increase CIN beyond the therapeutic threshold are currently few in number, and the clinical promise of targeting the
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anal squamous cell carcinoma (ASCC) is a relatively rare malignancy ac-counting for about 2-3% of all the gastrointestinal tumors. The standard of treatment for localized disease is chemoradiotherapy. Several studies reported a sex disparity
in ASCC prognosis showing a better survival for female compared
to men. Methods: we examined 1,380 patients with ASCC who received comprehensive genomic profiling as part of routine clinical
care and present key
Developing a framework for for detection of low frequency somatic genetic alt...Ronak Shah
Cancer is a complex, heterogeneous disease of the genome. Most cancers result
from an accumulation of multiple genetic alterations that lead to dysfunction of cancer-associated
genes and pathways. Recent advances in sequencing technology have enabled comprehensive
profiling of genetic alterations in cancer. We have established a targeted sequencing platform
(IMPACT: Integrated Mutation Profiling of Actionable Cancer Targets) using hybridization capture and
next-generation sequencing (NGS) technology, which can reveal mutations, indels and copy number
alterations involving 340 cancer related genes.
Tumor Mutational Load assessment of FFPE samples using an NGS based assayThermo Fisher Scientific
Understanding the molecular determinants of response to immune checkpoint blockade inhibitors is a critical unmet need for translational oncology research. Research tools to characterize the mutational landscape of cancers may potentially help identify predictive biomarkers for immuno-therapy that can be tested in future studies. Herein, we describe a targeted Ion AmpliSeq assay to determine the mutational load and signature of cancer research samples.
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
TEMPUS xT, Biópsia sólida vs TEMPUS xF, Biópsia LíquidaComunicaoIberlab
Para amostras correspondentes, a concordância do teste Tempus xF em comparação com o Tempus xT para alvos clinicamente acionáveis foi de 74,31%.
65% dos pacientes descobriram pelo menos uma variante patogénica extra quando a biópsia líquida e o tumor sólido foram solicitados simultaneamente, em comparação apenas com o tumor sólido.
1. Abstract
Comparative Genomics Analysis Unit, Cancer Genetics and Comparative Genomics Branch, NHGRI
Dahlia Shvets, James C. Mullikin, Leslie Biesecker, and Nancy F. Hansen
Investigation of Clonal Hematopoiesis in Whole-Exome Sequencing of ClinSeq Individuals
National Human Genome Research Institute
Comparative Genomics Analysis Unit
Cancer is thought to arise from the gradual
accumulation of specific genetic mutations,
sometimes years before the presence of clinical
symptoms. Early mutations can result in clonal
expansion of mutated stem or progenitor cells.
Clonal expansion subsequently increases the
likelihood of cooperating mutations occurring in
cells already harboring initiating mutations.
Clonal hematopoiesis--the clonal expansion of
hematopoietic stem cells--may signal the onset
of many hematologic cancers. In previous work,
clonal hematopoiesis has been shown to occur
with higher incidence in the elderly and is a risk
factor for later hematopoietic cancers.
Two recent studies published in the New
England Journal of Medicine, by Genovese, et
al. and Jaiswal, et al., performed large-scale
analyses searching for recurrent somatic
mutations in whole-exome sequencing of DNA
isolated from blood. These studies conclude that
clonal hematopoiesis with somatic mutations can
be detected via DNA sequencing, that it
increases in prevalence with age, and is
associated with an increased risk of hematologic
cancer. We aim to replicate their work using
whole-exome sequences from 1,001 individuals
in the ClinSeq cohort. We examine blood
derived DNA sequence data from ClinSeq
individuals to identify the genes and their
mutations that may drive clonal expansion.
Prior to this work, the ClinSeq data had already
been aligned with NovoAlign to the GRCh37
human reference sequence. We used the
program LoFreq to call low-frequency variants
using these alignments. Following Genovese et
al., we attempted to remove unreliable data from
our analysis by excluding genomic regions of
low complexity, excess coverage, segmental
duplications, known large insertions, and sites
failing Hardy Weinberg equilibrium tests. Then,
to separate germline from somatic variants,
binomial tests of the null hypothesis that the true
allelic fraction is 50% were implemented, along
with a false discovery rate correction using the
Benjamini-Hochberg method. In addition, any
variants occurring with an allele fraction of less
than 10% or occurring more than three times in
the cohort were removed.
In the process of searching for drivers of clonal
hematopoiesis, we uncovered an underlying
issue related to sequencing capture kits. The
mutation profile of our discovered somatic
mutations failed to mimic the expected mutation
profile seen by Genovese et al. A large number
of A to C (and T to G) base changes were
reported in our results, which upon visual
examination, displayed extremely high strand
biases. This pointed towards the need to further
filter the data based on strand bias. After
extensive filtering, our final gene lists of putative
drivers in the ClinSeq whole exome data
included almost all of the candidate driver genes
noted by Genovese et al. and Jaiswal et al.
However, we also observed numerous other
genes with an even larger number of somatic
mutations that were not previously noted
candidate drivers.
1. Workflow
2. ClinSeq Cohort
3. Mutation Profiles
The ClinSeq cohort includes males and females
primarily between the ages of 45 and 65, a somewhat
narrower range than that of the Genovese cohort.
The ClinSeq cohort consisting of 1001 individuals
provides the opportunity to obtain follow up samples
in the future from any individuals that may exhibit
somatic mutations in genes previously known to
cause clonal expansion.
The pipeline for this project mimics the steps taken by Genovese
et al. to identify putative somatic mutations. Binomial testing was
performed on the null hypothesis that the allelic fraction is 50%,
Benjamini Hochberg method was implemented for multi-test
correction, and samples appearing more than twice in the cohort
or with an allelic fraction of less than 10% were removed.
Figure S5 from Supplementary Data of Genovese et al.
“Clonal Hematopoiesis and Blood-Cancer Risk Inferred from
Blood DNA Sequence”. New England Journal of Medicine.
26 Nov. 2014; 371:2477-87.
Specific tissues are known to
exhibit certain mutation profiles.
Our original mutation profile, seen
above left, for both driver genes,
and total genes was most similar to
the inclusive somatic Wave1 seen
by Genovese et al. which is not
expected given our data. Wave1
was excluded from the Genovese
analysis due to sequencing error.
After investigating the cause of
potential error in our data, and after
subsequent strand bias filtering of
less than 15, the final mutation
profile seen above matches what is
expected for somatic mutations in
DNA from the blood.
The number of total somatic mutations
in most samples is very low. The
graph on the left excludes 6 samples
that have greater than 50 somatic
mutations. All final somatic mutations
were calculated at a false discovery
rate cut off of 0.05 and a strand bias
cut off of less than 15. On the right are
the top genes that had the greatest
number of samples containing at least
one mutation in the given gene, along
with the driver genes seen by
Genovese, et al. We found a total of
4,600 genes exhibiting at least one
somatic mutation. The remaining five
of the 14 putative drivers discovered
by Genovese et al. were not present in
the ClinSeq samples.
4. Capture Kit
Analysis
The skewed mutation profile lead to
further investigation of the effects of the
different capture kits used on the ClinSeq
cohort. Three different capture kit types
were used in sequencing the 1001
samples: ICGC and Index, Exon, Truseq
V1 and V2. Each base change for each
capture kit was plotted against the
number of times that it occurred in the
cohort. It became evident that one of the
capture kits was prone to error because
so many of the A to C and T to G base
changes were appearing only on one
strand. For the other capture kits, and
other base changes, the majority of
strand bias values are extremely low.
Due to this finding, all variants with a
strand bias higher than 15 were removed
from the final analysis. These graphs
exclude any strand bias values higher
than 50.
5. Total Somatic Mutations
Future Directions
6. Mutations in Driver Genes
Next generation read data showing a somatic
DNMT3A p.R882H mutation. This mutation was
covered by 136 reads, 27.9% of which displayed the
mutant allele. DNMT3A p.R882H mutations are found
frequently in acute myeloid leukemia (AML) and are
associated with shorter overall survival [Ley et al.,
NEJM, "DNMT3A Mutations in Acute Myeloid
Leukemia", 2010].
Read data showing a somatic frameshift insertion
in the TET2 gene. This mutation causing a
frameshift at p.C262, was covered by 63 reads,
and had 30.2% of reads displaying the altered
allele. Truncating mutations in TET2 have been
found in roughly 15% of a variety of malignant
myeloid disorders [Delhommeau et al., NEJM,
"Mutation in TET2 in Myeloid Cancers", 2009].
Future work will include ensuring that genes with a large number of somatic mutations aren’t subject to
copy number variation, and searching in known driver regions for additional low-level variants that may
have been missed by our Lofreq analysis. Additional work will involve following up with ClinSeq individuals
who have somatic mutations in the putative driver genes, obtaining new DNA samples if possible, and
analyzing them for the presence of any newly acquired mutations.
SAMPLE
ANNOTATION
BINOMIAL
HYPOTHESIS
TESTING
FALSE
DISCOVERY RATE
CORRECTION
FILTER
MUTATIONS
OBSERVED 3 OR
MORE TIMES IN
COHORT
LOFREQ VARIANT
CALLING
ERROR PRONE
REGION
FILTERING
PUTATIVE
CLONAL
HEMATOPOIESIS
DRIVERS
ALIGNED
CLINSEQ BAM
FILES
ALLELE FRACTION
FILTERING
0
10
20
30
40
40 50 60 70
Age
NumberofPatients
Gender
Male
Female
Clinseq Cohort Age & Gender
0
50
100
150
0 10 20 30 40 50
Number of Somatic Mutations
NumberofSamples
0
50
100
count
Total Somatic Mutations per ClinSeq Sample
0
5
10
15
20
25
TTN SYNEI DNMT3A LYST MUC16 // TET2 ATM ASXL1 CBL JAK2 TP53 SF3B1 MYD88
Gene Names
TotalGeneCounts
0
5
10
15
20
25
Mutation.Count
Top Genes with Somatic Mutations
0
2000
4000
6000
8000
0 10 20 30 40 50
Strand Bias
Count
Base Change
A > C−
T > G−
ICGC and Index Capture Kit
0
5000
10000
15000
0 10 20 30 40
Strand Bias
Count
Base Change
A > C−
T > G−
Exon Capture Kit
0
2000
4000
6000
0 10 20 30 40 50
Strand Bias
Count
Base Change
A > C−
T > G−
Truseq V1 and V2 Capture Kit
0
20000
40000
60000
0 10 20 30 40 50
Strand Bias
Count
Base Change
C > T−
G > A−
ICGC and Index Capture Kit
0e+00
5e+04
1e+05
0 10 20 30 40
Strand Bias
Count
Base Change
C > T−
G > A−
Exon Capture Kit
0
20000
40000
60000
0 10 20 30 40 50
Strand Bias
Count
Base Change
C > T−
G > A−
Truseq V1 and V2 Capture Kit