This presentation talkes about the history of next generation sequencing and how it is used in drug discovery.

1. Revolutionizing Drug Discovery:
Applications of Next Generation
Sequencing (NGS)
Callen Rogers and Vanshika Jain

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Seqwell, Sequencing Services, Next day plasmid sequencing service with de novo assembly
(seqwell.com), 2023 (Accessed October 31, 2023)

5. 
MilliporeSigma, Sanger Sequencing Steps and Method, Next day plasmid sequencing service with de novo assembly (seqwell.com), 2023 (Accessed October 31, 2023)

6. Next-Generation
Sequencing shares many
of the principles of
Sanger sequencing, but
on a much larger scale
 Rather than single DNA template,
NGS can being with an array of
multiple template strands mapped
to well plates/channels
 Sequences still extended and
detected, but many are detected
at once
Price,
Kristin
&
Svenson,
Ashley
&
King,
Elisabeth
&
Ready,
Kaylene
&
Lazarin,
Gabriel.
(2018).
Inherited
Cancer
in
the
Age
of
Next-Generation
Sequencing.
Biological
research
for
nursing.
20.
1099800417750746.

7. Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, Thakare RP, Banday S, Mishra AK, Das G, Malonia SK. Next-
Generation Sequencing Technology: Current Trends and Advancements. Biology (Basel). 2023 Jul 13;12(7):997. doi:
10.3390/biology12070997. PMID: 37508427; PMCID: PMC10376292.
• Sequencing by
synthesis considered
the most widely used
approach

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9. • Isolation of DNA and
ligation to tech-
compatible adapter
sequences
• PCR-based
expansion
• Database
created of
amplified/m
easured
sequences
• Specialized
software used to
probe sequence
database
ThermoFisher, What is Next-Generation Sequencing (NGS)?, https://www.thermofisher.com/us/en/home/life-science/sequencing/sequencing-learning-
center/next-generation-sequencing-information/ngs-basics/what-is-next-generation-sequencing.html, 2023. (Accessed October 31, 2023)

10. DataAcquisition:
• Prepare DNA libraries.
• Sequence samples using NGS platforms.
QualityAssessment:
• Assess the quality of NGS reads.
• Use tools like FastQC to identify errors, base-calling issues, and poor-quality reads.
ReadAlignment:
• Address the issue of ambiguity in mapping short reads.
Variant Identification:
• Identify mutations supported by several reads.
• Divided into germline callers, somatic callers,CNV identification, and structural variants (SV) identification.
identification.
VariantAnnotation:
• Provide biological significance by linking to databases like dbSNP.
• Tools likeANNOVAR with SIFT, PolyPhen2, and Provean annotations for functional significance evaluation.
evaluation.
DataVisualization:
• Visualize annotated variants using tools and genome browsers.
• Gain insights into mapping quality, aligned reads, and annotation information.
NGSToolsSelectionCriteria:
• For variant identification, choose tools accepting BAM or SAM formats and offeringVCF output.
Rucha
M.
Wadapurkar,
Renu
Vyas,,
Computational
analysis
of
next
generation
sequencing
data
and
its
applications
in
clinical
oncology,,
nformatics
in
Medicine
Unlocked,,
Volume
11,2018,Pages
75-82,SSN
2352-9148,
https://doi.org/10.1016/j.imu.2018.05.003.
(https://www.sciencedirect.com/science/article/pii/S2352914818300790)
Fig: NGS Data Analysis Workflow

11. Data Analysis in NGS : ctDNA-Based Monitoring in
Advanced NSCLC Patients on Pembrolizumab
Therapy
 Blood samples from 67 No Small Cell Lung Cancer (NSCLC)
patients collected at T0 (before infusion) and T1(9 weeks after
infusion).
 Cell-free DNA isolation and DNA sequencing was conducted.
 32 patients analysed with Guardant360 assay, and 35 patients with
GuardantOMNI assay.
 Molecular response calculated from baseline plasma samples.
 Kaplan-Meier curves for PFS and OS based on a molecular
response of 50%.
 All statistical analyses were two-sided and performed using
GraphPad Prism, STATA, and R.
 Molecular response assessed by plasma sequencing correlates with
clinical response and outcomes.
 Complete clearance of ctDNA at 9 weeks is associated with
significantly improved survival.

12. • 93% had at least one somatic variant in pretreatment plasma.
• Detected 201 nonsynonymous variants and indels in 43 genes (mean 3.4
mutations per patient).
• Most mutated genes at baseline: TP53 (n=41), KRAS (n=29).
• STK11 mutations (associated with inferior outcomes) found in eight patients.
• Swimmer plots for Visual representation of patient treatment duration.
• Patients with molecular responses in blue, without in red; pembrolizumab
monotherapy marked in gold, and pembrolizumab plus chemotherapy in purple.
Fig: Kaplan-Meier Survival association of Molecular Response molecular response with survival outcomes
• Using a molecular response cutoff of 50% to stratify patients.
• Patients showed molecular response indicating positive response to
the drug.
• PFS was significantly longer with a median of 14.1 months.
• Median of 4.4. months for non-responders.
• Median for OS was longer than non-responders (12 months).
• 17 out of 32 patients had completely cleared circulating tumor DNA
(ctDNA) from their bloodstream at 9 weeks.
Fig: Association of Molecular Response and the pembrolizumab-based therapy
Thompson,
J.
C.,
Carpent,
E.
L.,
Silva,
B.
A.,
Rosenstein,
J.,
Chien,
A.
L.,
Quinn,
K.,
...
&
Aggarwal,
C.
(2021).
Serial
monitoring
of
circulating
tumor
DNA
by
next-generation
gene
sequencing
as
a
biomarker
of
response
and
survival
in
patients
with
advanced
NSCLC
receiving
pembrolizumab-
based
therapy.
JCO
Precision
Oncology,
5,
510-524.
Thompson, J. C., Carpent, E. L., Silva, B. A., Rosenstein, J., Chien, A. L., Quinn, K., ... & Aggarwal, C. (2021). Serial monitoring
of circulating tumor DNA by next-generation gene sequencing as a biomarker of response and survival in patients with advanced NSCLC
receiving pembrolizumab-based therapy. JCO Precision Oncology, 5, 510-524.

13. Target Identification
Biomarker Optimization
Preclinical/Clinical Studies

14. Genetic Info in Drug Discovery:
Vital for target identification,
validating hypotheses, and
compound safety assessment.
NGS Surpassing GWAS: Prominent
in identifying genetic disease
mutations and target genes.
DiscovEHR Study: Loss of Function
(LoF) variants was confirmed in
drug targets for mutations in
NPC1L1 and PCSK9 genes that
were related to LDL-C levels.
Clinical Relevance of WGS &
EHRs: Identifying pathogenic
germline mutations in cancer-
predisposing genes, improving
healthcare delivery.

15. Leveraging Extreme Phenotypes: Involves rare
individuals or families with unique traits and investigating
these phonotypes can yield in novel insights to genetics
of rare diseases. (e.g., LRP5 mutations inspiring
osteoporosis therapies).
Rapid analysis of well-phenotyped populations to identify
novel drug targets.
Gene Therapy Milestones (2017): FDA approval of CAR-T
cell therapy (Kymriah & Yescarta) for B-cell acute
lymphoblastic leukemia, to target and eliminate tumor
cells, achieving high remission rates.
LUXTURNA Approval: First gene therapy for inherited
retinal dystrophy, emphasizing genetic modulation's
importance.
 NGS can expedite patient identification for gene therapy
clinical trials and enable finding suitable candidates for
treatment.
Torshizi, Abolfazl Doostparast, and Kai Wang. "Next-generation sequencing in drug development: target
identification and genetically stratified clinical trials." Drug Discovery Today 23.10 (2018): 1776-1783.
Fig: Target identification with NGS
a: NGS coupled with EHR to identify genes associated with a variety of phenotypic traits
b: NGS analysis on extreme phenotypes of therapeutic implications is also a growing opportunity to discover underlying drivers
of such phenotypes

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17. NGS for Biomarker
Optimization – IHC
Antibody Analysis
for Gastric Cancer
 Missense mutations in TP53 gene are frequently associated
with gastric cancer
 Earlier detection is believed to lead to improved patient
outcomes
 Can be hard to distinguish between missense mutations and elevated
wild-type TP53 levels due to cancer-related external stressors
 Immunohistochemistry (IHC) is commonly used to screen for
the disease
 NGS was used to study sensitivity of 4 antibodies commonly used
for IHC analysis of TP53 for gastric cancer detection

18. NGS for Biomarker Optimization – IHC
Antibody Analysis for Gastric Cancer
 Tumor tissue samples taken from 43 patients, DNA
extracted and QCd by spectrophotometry
 DNA hybridized to GeneseeqOne™ pancancer gene panel
for target enrichment
 Template of 425 cancer-associated genes
 Sequenced by Illumina HiSeq4000 Platform
 Evaluated relationships between sequenced mutations of
target genes and IHC detection of observed antibodies
Twist
Bioscience,
Capturing
The
Basics
of
NGS
Target
Enrichment,
Basics
of
NGS
|
Target
Capture
|
Twist
Bioscience,
2023.
(Accessed
October
30,
2023).

19. NGS for Biomarker Optimization
– IHC Antibody Analysis for
Gastric Cancer
Yu
R,
Sun
T,
Zhang
X,
Li
Z,
Xu
Y,
Liu
K,
Shi
Y,
Wu
X,
Shao
Y,
Kong
L.
TP53
Co-Mutational
Features
and
NGS-Calibrated
Immunohistochemistry
Threshold
in
Gastric
Cancer.
Onco
Targets
Ther.
2021;14:4967-4978,
https://doi.org/10.2147/OTT.S321949
 All antibodies could detect TP53
mutations with >93% accuracy with
optimized staining thresholds
Yu
R,
Sun
T,
Zhang
X,
Li
Z,
Xu
Y,
Liu
K,
Shi
Y,
Wu
X,
Shao
Y,
Kong
L.
TP53
Co-
Mutational
Features
and
NGS-Calibrated
Immunohistochemistry
Threshold
in
Gastric
Cancer.
Onco
Targets
Ther.
2021;14:4967-4978,
https://doi.org/10.2147/OTT.S321949
 SP5 antibody detected TP53 missense mutations
with highest accuracy and specificity

20. FDA approved MSK-IMPACT in Nov 2017: NGS-based tumor-profiling
panel.
Capable of detecting mutations, copy number, structural
rearrangements in cancer-associated genes.
Evaluated in a large study involving over 10,000+ cancer patients
leading to 11% enrollment in genomically matched clinical trials.
Basket Trials: MSK-IMPACT enables therapy for patients with specific
mutations, irrespective of cancer type or location.
Keytruda (May 2017): Marks precision oncology milestone, treating
patients based on genetic signatures rather than the location of cancer.
Ongoing oncology trials target specific genetic mutations.

21. Basket trial approach may be applicable with shared genetic causes with
shared molecular etiologies.
Necessitates a deep understanding of the genetic basis of disease and how it
is impacts on a genetic level and defining an appropriate clinical endpoint.
Stratified Clinical Trials: NGS assists in genetically stratified clinical trial
design, enhancing patient recruitment based on specific mutations.
DIAN trial for Alzheimer's disease uses NGS to select patients for early
intervention with limited sample sizes.
Generation Program for Alzheimer's Disease: Two Phase II/III studies
targeting a cognitively unimpaired population at risk for Alzheimer's disease
(AD) based on APOE e4 risk allele status.
Generation Study 1 and Generation Study 2 target individuals at elevated risk
for Alzheimer's disease, enrolling those with two APOE e4 risk alleles or one
APOE e4 allele with elevated brain amyloid levels to evaluate the
effectiveness of anti-amyloid treatments in delaying AD symptoms.
Torshizi, Abolfazl Doostparast, and Kai Wang. "Next-generation sequencing in drug development: target
identification and genetically stratified clinical trials." Drug Discovery Today 23.10 (2018): 1776-1783.
Fig: Clinical Trials using NGS
a: Autosomal dependent Alzheimer’s disease (ADAD) individuals carrying a dominant mutation in one of three genes, amyloid
precursor protein (APP), presenilin 2 (PSEN2) or presenilin 1 (PSEN1), are predicted to develop AD later in life
b: DIAN study: A genetically stratified clinical trial is the Generation Program for Alzheimer’s disease

22. Genomic Profiling: Sequencing entire genomes for disease-related
mutations and structural changes.
Target Identification: Comparing genetic profiles to pinpoint genes
with mutations as potential targets.
Biomarker Discovery: Identifying markers for disease susceptibility
and pharmacological targets.
Pharmacogenomics: Recognizing genetic variations impacting drug
responses.
Epigenomics: Studying DNA methylation and histone modifications
as potential drug targets.
Metagenomics: Analyzing human microbiome for novel treatments.
High-Throughput Screening: Accelerating drug discovery by
efficient compound testing.
Data Integration & Analysis: Enhancing interpretation, candidate
identification, and efficacy prediction.
Clinical Trial Design: Stratifying patient populations for faster,
successful trials.
https://www.europeanpharmaceuticalreview.com/article/10409/dna-sequencing-
technologies-and-emerging-applications-in-drug-discovery/
Fig: NGS and emerging applications in Drug discovery

23. NGS Challenges
 Genome-Wide Association Studies need samples
from diversified populations
 High upfront costs make adaptation to NGS difficult
for smaller orgs
 NGS still more error-prone than Sanger
sequencing
 Re-assembly of sequence reads could result in
low-frequency mutations from systemic error

24. Multi-Omics Integration: Combine genomics, transcriptomics, proteomics, and metabolomics data to gain a holistic
view of disease mechanisms and drug responses.
Single-Cell Profiling: Extend single-cell RNA sequencing to capture individual cell responses to drug treatments,
enabling precise target identification and validation.
AI-Driven Drug Design: Implement artificial intelligence and machine learning for predictive modeling of drug-target
interactions, accelerating drug development.
Drug Repurposing Networks: Develop comprehensive networks of drug-disease associations using NGS data,
facilitating drug repurposing efforts.
Long-Read Sequencing: Utilize long-read sequencing technologies to resolve complex genomic regions, structural
variants, and gene isoforms for more accurate target identification.
Patient-Derived Models: Utilize patient-derived organoids and 3D culture models combined with NGS to assess drug
responses in a more clinically relevant context.
Multi-Cohort Analysis: Integrate data from diverse patient cohorts and clinical trials to enhance the statistical power of
NGS-driven drug discovery efforts.

25. NGS refers to a broad range of technologies
involving amplification and parallel
sequencing of construct libraries
Massively increased sequencing throughput
offered by NGS has revolutionized the use of
–omics based approaches in drug discovery
and development
As NGS costs decrease and ease-of-use
increases, exponential increases in informatic
potential of the technology will still be
possible

26.  Yu R, Sun T, Zhang X, Li Z, Xu Y, Liu K, Shi Y, Wu X, Shao Y, Kong L. TP53 Co-Mutational Features and NGS-Calibrated Immunohistochemistry Threshold in Gastric
Cancer. Onco Targets Ther. 2021;14:4967-4978
https://doi.org/10.2147/OTT.S321949
 Nagahashi M, Shimada Y, Ichikawa H, Kameyama H, Takabe K, Okuda S, Wakai T. Next generation sequencing-based gene panel tests for the management of solid tumors.
Cancer Sci. 2019 Jan;110(1):6-15. doi: 10.1111/cas.13837. Epub 2018 Nov 27. PMID: 30338623; PMCID: PMC6317963.
 Twist Bioscience, Capturing The Basics of NGS Target Enrichment, Basics of NGS | Target Capture | Twist Bioscience, 2023. (Accessed October 30, 2023).
 Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, Thakare RP, Banday S, Mishra AK, Das G, Malonia SK. Next-Generation Sequencing Technology: Current
Trends and Advancements. Biology (Basel). 2023 Jul 13;12(7):997. doi: 10.3390/biology12070997. PMID: 37508427; PMCID: PMC10376292.
 Price, Kristin & Svenson, Ashley & King, Elisabeth & Ready, Kaylene & Lazarin, Gabriel. (2018). Inherited Cancer in the Age of Next-Generation Sequencing. Biological research
for nursing. 20. 1099800417750746.
 MilliporeSigma, Sanger Sequencing Steps and Method, Next day plasmid sequencing service with de novo assembly (seqwell.com), 2023 (Accessed October 31, 2023)
 Seqwell, Sequencing Services, Next day plasmid sequencing service with de novo assembly (seqwell.com), 2023 (Accessed October 31, 2023)
 Yadav NK, Shukla P, Omer A, Pareek S, Srivastava AK, Bansode FW, Singh RK. Next generation sequencing: potential and application in drug discovery.
ScientificWorldJournal. 2014 Feb 5;2014:802437. doi: 10.1155/2014/802437. Retraction in: ScientificWorldJournal. 2020 Aug 25;2020:1757504. Erratum in:
ScientificWorldJournal. 2014;2014:621354. Srivastava, A K [added]; Bansode, F W [added]. PMID: 24688432; PMCID: PMC3933208.
 Peter M. Woollard, Nalini A.L. Mehta, Jessica J. Vamathevan, Stephanie Van Horn, Bhushan K. Bonde, David J. Dow, The application of next-generation sequencing
technologies to drug discovery and development, Drug Discovery Today, Volume 16, Issues 11–12, 2011, Pages 512-519, ISSN 1359-6446,
https://doi.org/10.1016/j.drudis.2011.03.006.

27.  Dotolo, S.; Esposito Abate, R.; Roma, C.; Guido, D.; Preziosi, A.; Tropea, B.; Palluzzi, F.; Giacò, L.; Normanno, N. Bioinformatics: From NGS Data to Biological Complexity in
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 Goodwin, S., McPherson, J. & McCombie, W. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17, 333–351 (2016). https://doi-
org.ezproxy.library.wisc.edu/10.1038/nrg.2016.49
 Nagahashi M, Shimada Y, Ichikawa H, Kameyama H, Takabe K, Okuda S, Wakai T. Next generation sequencing-based gene panel tests for the management of solid tumors.
Cancer Sci. 2019 Jan;110(1):6-15. doi: 10.1111/cas.13837. Epub 2018 Nov 27. PMID: 30338623; PMCID: PMC6317963.
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 https://www.thermofisher.com/us/en/home/life-science/sequencing/sequencing-learning-center/next-generation-
sequencing-information/ngs-basics/what-is-next-generation-sequencing.html

https://doi.org/10.1016/j.imu.2018.05.003 https://www.sciencedirect.com/science/article/pii/S2352914818300790

 Torshizi, Abolfazl Doostparast, and Kai Wang. "Next-generation sequencing in drug development: target identification and genetically stratified clinical trials." Drug Discovery
Today 23.10 (2018): 1776-1783.
 https://www.europeanpharmaceuticalreview.com/article/10409/dna-sequencing-technologies-and-emerging-applications-in-drug-discovery/