Cell Capture and Profiling

Enrichment and Molecular Profiling of Tumor Cells
for Lung Cancer Biomarker Discovery

Srikanth Perike, Yoon-Tae Kang, Cissy Jiang, Nathan Knapp, Angelica Olcott, Linda Lingelbach, Elizabeth Dreskin
Bio-Rad Laboratories, Inc., 1000 Alfred Nobel Drive, Hercules, CA 94547
Bio-Rad Laboratories, Inc., 100 Phoenix Drive, Suites 321 and W180, Ann Arbor, MI 48108

Abstract

Liquid biopsies, which involve analyzing circulating tumor cells (CTCs) or cell-free nucleic acids in blood,
hold immense promise for noninvasive cancer diagnostics. This study presents a comprehensive
workflow combining rare cell enrichment, RNA-Seq, and reverse transcription quantitative PCR (RT-qPCR)
to identify unique molecular biomarkers associated with a lung cancer cell line. Our approach enables
the enrichment of tumor cells from whole-blood samples, followed by transcriptomic profiling to pinpoint
differentially expressed genes specific to lung cancer.
Introduction
Identifying reliable molecular biomarkers is critical for early cancer
detection, prognosis, and treatment stratification. Traditional
tissue biopsies are invasive and may not always capture theheterogeneity of tumors. Liquid biopsies offer an alternative by
allowing the analysis of tumor-derived material present in peripheral
blood. However, it can be challenging to capture and enrich raretumor cells from a background of normal blood cells and identify
biomarkers that enable the detection of tumor cells across a
broad range.
Circulating tumor cells are shed into the bloodstream during
intermediate stages of metastasis (Ortiz-Otero 2020). Because
tumor cells are often present in very low numbers within the
bloodstream, researchers must enrich them to isolate them fromthe multitude of other cell types found in blood (red blood cells,
white blood cells, platelets). Furthermore, tumor cell numbers canfluctuate over time, making consistent enrichment difficult (Lone2022). Commonly used techniques for cell isolation, such as
microfluidics or magnetic beads, are limited in terms of sensitivity,
specificity, reproducibility, and efficiency. Furthermore, due to thefluctuations in cell numbers, the technology used to profile themolecular fingerprint of enriched cells must accommodate a broad
range of input RNA, ensuring the detection of biomarkers acrossboth low and high tumor–burden samples.

Bulletin 3686

Here, we describe a novel workflow that combines the Genesis
Cell Isolation System with Celselect Slides™ to enrich tumor cells
directly from whole-blood samples, then profile the transcriptomeof the enriched tumor cells using RNA-Seq to identify a list of
potential biomarkers. Using RT-qPCR, we further refined our list
to those gene targets that are uniquely differentially expressedin tumor cells (Figure 1). Our integrated approach bridges thegap between cell isolation techniques, high-throughput RNA
sequencing, and targeted gene validation. The gene expression
analysis procedure utilizing RNA-Seq followed by RT-qPCR is
described in Figure 2. The resulting unique molecular biomarkers
identified through gene expression analysis hold promise for
improving lung cancer diagnosis, the monitoring of treatment
response, and personalized therapies.

Enrichment and Molecular Profiling of Tumor Cells for Lung Cancer Biomarker Discovery

RNA-Seq

Lungcancercells
RT-qPCR

Contrived
samples createdGenesis System —rare tumor cell enrichment

Rare cells captured on
Celselect Slides

Recovery of rare cells
and RNA extraction

Genomic
applications

Fig. 1. A novel cell enrichment and biomarker discovery workflow. Lung cancer cells spiked into whole blood at dilutions of 50, 100, 500, and 1,000 cells/ml to mimicliquid biopsy were enriched using the Genesis Cell Isolation System with Celselect Slides. Total RNA was purified to construct RNA-Seq libraries using the SEQuoia™ Complete
Stranded RNA Library Prep Kit and SEQuoia RiboDepletion Kit. Differential gene expression of select tumor-specific gene targets identified during the RNA-Seq step was
validated using RT-qPCR on a CFX Opus Real-Time PCR System with a custom PrimePCR Assay panel plate.
2.5 ngRNA-Seq

50 pg
RT-qPCR

Extracted RNA

cDNA synthesis

Preamplification

RT-qPCR

Analyze data

50 pg RNA

Custom PrimePCR Panel

Fig. 2. Biomarker discovery using low RNA input for gene expression analysis with both RNA-Seq and RT-qPCR. RNA-Seq and RT-qPCR workflows were assessed
for biomarker discovery using low RNA input, focusing on detection of genetic markers of rare cells at low concentration in whole blood samples. RNA-Seq used 2.5 ng ofRNA for SEQuoia Complete library prep followed by SEQuoia Ribodepletion before Illumina® sequencing to identify target genes to select for further analysis by RT-qPCR.
RT-qPCR used 50 pg of RNA, reverse transcribed with Bio-Rad’s iScript Explore One-Step RT and PreAmp Kit for unbiased cDNA synthesis. A 1:10 dilution of cDNA provided
a 500 µl stock for 250 PCR reactions. Custom PrimePCR Plates with RNA-Seq–targeted genes and SsoAdvanced Universal SYBR Green Supermix were used. Runs utilized
Bio-Rad’s PTC Tempo Thermal Cycler, CFX Opus Real-Time PCR System, and CFX Maestro Software for gene expression data analysis.

Materials and Methods

Modified Eagle Medium (Thermo Fisher Scientific Inc., catalog#11995065) supplemented with 10% fetal bovine serum (Hyclone,
GE Life Sciences, #SH30071.03) and 1% Penicillin/Streptomycin
Solution (Thermo Fisher Scientific, #15140122). To harvest, cells
were washed with phosphate-buffered saline (PBS), trypsinized,
centrifuged, and resuspended in complete cell culture media.
Cell counts were performed using a hemocytometer and cell stock
suspensions prepared by diluting 1,000–10,000 cells/ml in PBS.
The desired concentration of cells was then diluted into 200 µl of
PBS and spiked into 4 ml of donor blood.

Contrived Sample Creation

Contrived samples that allowed us to study tumor burden–
associated biomarkers were created by spiking non–small-celllung cancer (A549) cells (American Type Culture Collection,
CRM-CCL-185) into whole blood at 0, 50, 100, 500, or 1,000 cells/ml
concentrations. At these concentrations, the contrived samples
mimic what might be present in patients with a range of tumor
burdens, from low tumor–burden (50 cells/ml) to high tumor–
burden (1,000 cells/ml). Briefly, blood samples from healthy donors
served as matrices for spiking and as negative controls. A549
cells were cultured to 80% confluence in nutrient-rich Dulbecco’s© 2024 Bio-Rad Laboratories, Inc.

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Enrichment and Molecular Profiling of Tumor Cells for Lung Cancer Biomarker Discovery

Tumor Cell Enrichment

Sequencing and NGS Data Analysis

The Genesis Cell Isolation System with Celselect Slides (Bio-Rad
Laboratories, Inc., #CEL20101) captured and enriched the tumor
cells from whole blood based on cell size, following the CelselectSlides Enrichment Kit User Manual (Bio-Rad bulletin 3337).

Individual libraries were diluted to 2 nM using 10 mM Tris
(pH 8.0) and 0.1 mM EDTA, then pooled and denatured following
the manufacturer’s instructions for NextSeq 1000/2000 P2
Reagent Cartridge (Illumina, #20044468). Denatured libraries were
further diluted to 750 pM with resuspension buffer containing
Tween 20 and mixed with 1.5 μl of 10 nM PhiX Sequencing Control
v3 (Illumina, #FC-110-3001). Sequencing was performed on an
Illumina NextSeq 1000 Sequencing System (Illumina, #20038898)
using paired-end reads with the following cycle lengths: read 1, 75;
read 2, 8; read 3, 8; read 4, 8.

Briefly, 4 ml contrived and control blood samples were diluted
with an equal volume of PBS before loading into the sample inletof the Celselect Slides Enrichment Kit (Bio-Rad, #CEL80110). The
enrichment workflow automatically captures cells of a specific sizein the microfluidic wells, allowing other cell types to pass through.
Enriched cells were recovered using Celselect Slides Dilution
Buffer (included in kit), transferred to a 15 ml conical tube, andcentrifuged at 400 RCF for 7 min. The resulting cell pellet wasthen immediately resuspended in 700 µl of QIAzol Lysis Reagent(QIAGEN N.V., #79306) and then processed using the RNA
extraction methods described below.

FASTQ files generated via BaseSpace Sequence Hub (Illumina)
were analyzed using the SEQuoia Complete Analysis Toolkit(bio-rad.com/seqsense) on a Linux system. The quality of the
raw RNA-Seq reads was assessed with FastQC (Babraham
Institute), then the poly(A) tails, primer, and adapter sequencesfrom the sequencing contigs were trimmed using Cutadapt(National Bioinformatics Infrastructure Sweden). Alignment against
the human transcriptome from ENSEMBL Genome Reference
Consortium Human Build 38 was performed using the Spliced
Transcripts Alignment to an open-source Reference (STAR)
Aligner Tool. Transcript estimates were summarized at the genelevel in final reports. A549 cell-specific transcripts were identified
by subtracting control sample transcripts, with only transcripts
featuring 5 or more reads considered in the analysis.

RNA Extraction and Quantification

Total RNA was isolated from enriched cell samples and controls
using the miRNeasy Micro Kit (QIAGEN N.V., #217084) following
the manufacturer’s instructions. Enriched cells collected in dilution
buffer were centrifuged at 300 RCF for 7 min. The supernatant
was discarded and the cell pellet was lysed with 700 µl of QIAzol
Lysis Reagent. RNA was separated from the aqueous fraction
using chloroform. Total RNA and miRNA were copurified on a spincolumn and then eluted with elution buffer. RNA integrity by RNA
integrity number (RIN) score and quantity were assessed using theAgilent RNA 6000 Pico Kit (Agilent Technologies, #5067-1513) per
the manufacturer’s instructions.

RT-qPCR and Differential Gene Expression Analysis

The iScript Explore One-Step RT and PreAmp Kit (Bio-Rad,#12004856) was utilized to eliminate genomic DNA and synthesize
cDNA from 50 pg of total RNA from contrived and control samples.
Preamplification was conducted for 14 cycles using pooled
PrimePCR Preamp Assays (bio-rad.com/primepcr) targeting
38 mRNA targets. For RT-qPCR, a custom 384-well PrimePCR
Plate with predispensed primers was used. Preamplified cDNA
was diluted tenfold with TE buffer, and 2 μl of cDNA was combinedwith SsoAdvanced Universal SYBR® Green Supermix (Bio-Rad,#1725270) and nuclease-free water in a 10 μl reaction. RT-qPCR
was performed in triplicate on a CFX Opus 384 Real-Time PCR
System (Bio-Rad, #12011452) using the PrimePCR cycling protocol(95°C for 2 min; 40 cycles: 95°C for 5 sec, 60°C for 30 sec;
1 cycle melt curve: 65–95°C (0.5°C increments) for 5 sec/step.
Data analysis was conducted using CFX Maestro Software(Bio-Rad), including a Student t-test (p < 0.05) to compare target
levels between contrived and control samples.

RNA-Seq Library Preparation

An input of 2.5 ng total RNA was fragmented at 94°C for 3 minusing the SEQuoia Complete Stranded RNA Library Prep Kit(Bio-Rad, #17005726) per manufacturer’s recommendations.
Libraries were then amplified with a SEQuoia Dual Indexed Primers
Plate (Bio-Rad, #12011930) using 12 PCR cycles. To remove cDNA
constructs from ribosomal RNA (rRNA), the cDNA libraries werethen depleted using the SEQuoia RiboDepletion Kit (Bio-Rad,#17006487) with 0.5 ng library input. All incubation and thermal
cycling steps were carried out in a PTC Tempo Deepwell Thermal
Cycler (Bio-Rad, #12015392), using 0.2 ml Flat PCR Tube 8-Cap
Strips, optical, ultraclear (#TCS0803).

© 2024 Bio-Rad Laboratories, Inc.

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Enrichment and Molecular Profiling of Tumor Cells for Lung Cancer Biomarker Discovery

Results

RNA yield and quality varied based on the number of cells spiked
into whole blood (Figure 3). Total RNA extracted ranged from4 ng for the control and the 50 cells/ml sample to 21 ng forthe 1,000 cells/ml sample. The RNA integrity number (RIN),
indicated by the ratio of the 28s to the 18s rRNA peak in anelectropherogram, was >8 for the 500 and 1,000 cells/ml samples
but very low for the control and 50 cells/ml samples (Figure 3).
These findings suggest effective capture of cancer cells by the
Genesis System and Celselect Slides workflow, even in samples
with minute numbers of cells reflecting low tumor burden. However,further analysis is needed to ascertain significant differences
between low tumor–burden and control samples.

Effective Enrichment of Cancer Cells from Whole Blood

Cancer cells diluted in whole blood were efficiently enriched
and recovered using the Celselect Slides Rare Cell Enrichment
workflow. Contrived liquid biopsy samples were created by spiking
A549 cells into whole blood at concentrations of 50, 100, 500, or
1,000 cells/ml, representing low, medium, or high tumor burden.
Whole blood without spiked cells run alongside each sample toaccount for inherent biological variability served as a negative
control.

A. Contrived samples

B. Whole-blood control
25

20

20
FU

50 cells/ml

FU

15
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200 500

1,000

2,000
nt

4,000

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200 5005

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2,000

4,000

15
10

5

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0

0
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200 500

1,000

2,000
nt

4,000

25

4,000

Fig. 3. Evidence of tumor cell enrichment from contrived samples. Although the vast majority of cells captured on the Genesis System are WBC, evidence of the spiked-incell line is detectable. A, electropherogram profile of total RNA extracted from contrived samples consisting of A549 cells spiked into whole blood at a concentration of50, 100, 500, or 1,000 cells/ml is markedly different from B, electropherogram profile of the control samples containing whole blood only. FU, fluorescence units;
nt, nucleotide size.

© 2024 Bio-Rad Laboratories, Inc.

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Enrichment and Molecular Profiling of Tumor Cells for Lung Cancer Biomarker Discovery

snoRNA, snRNA, miRNA, and scRNA) (Figure 4C). Ribosomal RNA(rRNA) reads were negligible in each sample, a significant finding
considering the propensity for post-library ribodepletion workflows
to retain more rRNA reads relative to pre-library rRNA depletion
workflows (Herbert et al. 2018, Perike et al. 2022).

Unique Molecular Signature Discovered Using RNA-Seq

The SEQuoia Complete Stranded RNA Library Prep Kit captures
both long RNA (for example, messenger RNA [mRNA] and longnoncoding RNA [lncRNA]) and short RNA (for example, micro RNA[miRNA], small conditional RNA [scRNA], small nucleolar RNA[snoRNA], transfer RNA [tRNA], and small nuclear RNA [snRNA]) ina single library. It uses proprietary chemistry enhanced by postlibrary ribodepletion to remove rRNA-derived cDNA fragments.
This novel approach results in a comprehensive transcriptome
dataset and broadens the pool of potential molecular biomarkers.

Next, we compared gene lists from each contrived sample toidentify shared transcripts. Due to fewer unique gene transcripts
in the 50 cells/ml sample, we performed this process in stages.
Initially, we compared lists from the 100, 500, and 1,000 cells/ml
samples, resulting in 2,768 commonly shared transcripts (Figure 5A).
We then refined this list by cross-referencing it with the 50 cells/ml
sample, resulting in 759 common genes (Figure 5B). Thesetranscripts serve as potential biomarkers unique to A549 cells,
detectable in enriched cells from whole blood regardless oftumor burden.

To identify cancer cell–specific molecular profiles, we filteredtranscripts with a transcripts-per-kilobase million (TPM) valueof 5 or higher from contrived samples, excluding those found incontrol samples. Between 3,000–8,000 transcripts with five or
more reads were uniquely detected in enriched contrived samples
and absent from control samples (Figure 4A) encompassing
mRNA and lncRNA (Figure 4B) and small RNA biotypes (tRNA,

A

B

C

8,000
6,000
4,000
2,000
0

400
Overall small RNA biotypes

8,000
Overall long RNA biotypes

6,000
4,000
2,000

300
200
100
0

A

scRNA

miRNA

snRNA

snoRNArRNA

Protein-coding/mRNA

100 500 1,000
Cells/ml of blood

tRNA

0
50

lncRNA

Unique genes >5 reads

10,000Fig. 4. SEQuoia Complete workflow captures theentire transcriptome, including both long and short RNA
biotypes. A, a significant number of transcripts detected at≥5 reads per million reads are unique to the contrived samples.
When focusing on unique genes present in 100 cells/ml ofblood, B, diverse RNA biotypes such as long RNA (>200 bp)
C, and small RNA (<200 bp) were present.

B

1,539
1,000 cells/mlof blood ( )1,812

50 cells/mlof blood ( )500 cells/mlof blood ( )

735
2,009

2,768

2,431

1,089

537

100 cells/mlof blood ( )

759

3,442

© 2024 Bio-Rad Laboratories, Inc.

Common genes detected in
1,000, 500, and 100 cells/ml of blood ( )5Fig. 5. Numerous potential biomarkers unique to A549 cells
were identified across various concentrations of spiked
input. 2,768 genes unique to A549 cells were identified insamples with concentrations of 100, 500, and 1,000 cells/ml
which were absent in control samples. Among these 2,768
unique genes, 759 were also present in the low-concentration
sample (50 cells/ml) representing a pool of potential uniquebiomarkers for cancer cells.

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Confirmation Molecular Biomarkers Are Differentially Expressed Using RT-qPCR

A. 50 cancer cells/ml of blood

To augment our potential biomarker list, we compared our
759 transcripts with a frequently studied gene list in lung cancer
research using the Lung cancer (SAB Target List) H384 Panel(Bio-Rad, #10034527). This panel includes predesigned, wet-lab
validated primer assays targeting a subset of the highest-ranked
genes relevant for gene expression analysis in the field (Bureeva2012). Cross-referencing our RNA-Seq–derived targets withthose on the PrimePCR panel led to the identification of 34 genecandidates for further investigation (Table 1).

50 A549 cells spiked per ml of blood(normalized expression)

Enrichment and Molecular Profiling of Tumor Cells for Lung Cancer Biomarker Discovery

27
25
23
21
2–1
2–3
2–5
2–7
2–9
2–11
2–13
2–13 2–11 2–9 2–7 2–5 2–3 2–1 21 23 25
Whole blood (normalized expression)

Table 1. Frequently investigated genes that were identified as potential
biomarkers. Cross-referencing the 759 genes identified as potential biomarkers
specific to A549 cells against a list of mRNA targets frequently investigated inlung cancer research reduced the target list to 34 genes. Assays targeting these34 genes and four reference genes were chosen for differential expression analysis
using RT-qPCR.

27

27
25
23
21

Target Genes

Target Genes

Reference Genes

AGER

HMMR

ACTB

AGR2

KRT5

B2M

AKT1

MET

RPLP0

ANXA5

MKI67

GUSB

APC

MMD

BCL2

MTHFR

BIRC5

NF1

BRAF

PAX5

C. 500 cancer cells/ml of blood

CDKN2APTGS2

CDKN2BRASSF1CEACAM6

SPP1

CLIC5

TERT

CYP1B1

TNF

EGFR

TOP2A

ERBB2

TP53

FHIT

VEGFA

GREM1

GAPDH

500 A549 cells spiked per ml of blood(normalized expression)

100 A549 cells spiked per ml of blood(normalized expression)

B. 100 cancer cells/ml of blood

2–1
2–3
2–5
2–7
2–9
2–11
2–13
2–13 2–11 2–9 2–7 2–5 2–3 2–1 21 23 25
Whole blood (normalized expression)
27
25
23
21
2–1
2–3
2–5
2–7
2–9
2–11
2–13
2–13 2–11 2–9 2–7 2–5 2–3 2–1 21 23 25
Whole blood (normalized expression)

We compared the gene expression of 34 gene targets in contrived
samples versus whole-blood control samples and identified avarying number of genes that showed at least a twofold difference
and were significantly differentially expressed (Figure 6).

2727

1,000 A549 cells spiked per ml of blood(normalized expression)

D. 1,000 cancer cells/ml of blood
27
25
23
21
2–1
2–3
2–5
2–7
2–9
2–11
2–13
2–13 2–11 2–9 2–7 2–5 2–3 2–1 21 23 25
Whole blood (normalized expression)

27

Fig. 6. Each contrived sample showed numerous A594-specific genes thatwere differentially expressed. Differentially expressed genes were identified in the
different dilutions of A549 cells. A, 50 cells/ml; B, 100 cells/ml; C, 500 cells/ml;
D, 1,000 cells/ml spiked into whole blood. Upregulated genes are denoted by red
dots, downregulated genes by green dots, and equivalently expressed genes by
black dots, using a 2-fold cutoff. The same set of reference genes was employed forthe normalization of variation of input RNA across contrived and control samples.
+ 2-fold ( ); – 2-fold ( ); not significant, p < 0.05 ( ).

© 2024 Bio-Rad Laboratories, Inc.

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Enrichment and Molecular Profiling of Tumor Cells for Lung Cancer Biomarker Discovery

Discussion and Conclusion
Liquid biopsies and other cell suspensions are complex andheterogeneous, posing challenges in identifying and characterizingrare tumor cells amid various cell types and biomolecules. This
study demonstrates a workflow approach to overcome thesechallenges, enabling the potential use of CTCs to reveal themolecular characteristics of tumors.

For the samples with 100, 500, and 1,000 cells/ml, six genes
exhibited at least twofold higher or lower transcript expression
compared to control samples (Figure 7). Among these, MET and
TOP2A were also differentially expressed in the 50 cells/ml sample(Figure 8), highlighting their significance as consistent molecularbiomarkers for lung cancer across different cell concentrations.
A

The Genesis Cell Isolation System with Celselect Slides uses
56,400 microchambers with pores sized to capture cells in the
8–30 µm range selectively. This method ensures gentle, sizebased capture of cells, maintaining their integrity and viability
and achieving high capture rates crucial for detecting rare cells
like circulating tumor cells in liquid biopsies. Unlike traditional
methods requiring sample preprocessing, our method captures
cells directly, simplifying workflows, ensuring compatibility acrossdiverse samples, and enhancing downstream applications anddata quality (Bio-Rad bulletin 3599).

1

1,000 cells/mlof blood ( )500 cells/mlof blood ( )

1
6
2

2

100 cells/mlof blood ( )4Relative normalized
expression

B
29
28
27
26
25
24
23
22
21
20
2–1
2–2
2–3
ANXA5

GAPDH

MET

NF1

SPP1

In this experiment, we successfully enriched contrived samples
with a range of tumor cells spiked into whole blood, evenmimicking minimal tumor burden (50 cells/ml). The Genesis
System effectively enriched tumor cells in these low-concentration
samples. We showed that extracted RNA from these enrichedcells can be used for RNA-Seq and RT-qPCR downstream, which
promises advancements in diagnostic and prognostic clinical tests
by enabling direct enrichment of rare cells, such as tumor cells,
from whole blood.

TOP2A

TargetFig. 7. Differentially expressed genes specific to A549 cells that are sharedacross various concentrations of spiked input. A, out of the differentiallyexpressed genes identified for each contrived sample, six genes were identified.
B, ANXA5, GAPDH, MET, NFI, SPP1, and TOP2A are identified in the blood sample
spiked with 100 cells/ml. * p < 0.05. Blood ( ), contrived samples ( ).

The SEQuoia Complete workflow captures both long (for example,
mRNA, lncRNA) and short RNA biotypes (for example, miRNA,
snoRNA, tRNA, snRNA) from diverse RNA inputs. This is achieved
through the SEQuoia Complete Stranded RNA Library Prep Kit’s
unique chemistry, combined with conducting ribodepletion afterconverting RNA to stable cDNA. Together, these steps result ina richer dataset more representative of the entire transcriptome.
Focusing on mRNA targets, we identified 759 potential lung cancer
gene targets. Other RNA biotypes can be similarly examined for
differential expression and biomarker potential.

A

4

2

3

50 cells/mlof blood ( )Common genes detected in
1,000, 500, and 100 cells/ml of blood ( )After defining our potential biomarker list, we transitioned to
RT-qPCR for differential expression assessment because it has
a simpler workflow compared to RNA-Seq, wide availability
in molecular laboratories, lower cost per sample, and quicker
turnaround time, all of which are crucial for clinical laboratory
settings. Leveraging custom PrimePCR panels with prevalidated
primers that targeted our genes of interest streamlined theworkflow and saved significant time. Our analysis highlighted two
promising lung cancer biomarkers in liquid biopsy samples: MET,
a receptor tyrosine kinase linked to tumor progression, and
TOP2A, an enzyme overexpressed in lung cancer.

Relative normalized
expression

B
29
28
27
26
25
24
23
22
21
20
2–1
2–2
2–3
MET

TOP2A
TargetFig. 8. Two prominent biomarkers for lung cancer were detected across all of thecontrived samples. A, comparing the list of differentially expressed genes identified
in the 100–1,000 cells/ml samples to the list of genes from the sample with the fewest
cancer cells (50 cell/ml); B, MET and TOP2A were identified in all samples. These twogenes could serve as robust biomarker candidates for detecting lung cancer cells in
liquid biopsy samples irrespective of tumor burden. * p < 0.05. Blood ( ), contrived
samples ( ).
© 2024 Bio-Rad Laboratories, Inc.

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Enrichment and Molecular Profiling of Tumor Cells for Lung Cancer Biomarker Discovery

The Genesis Cell Isolation System with Celselect Slides simplifies
rare cell enrichment, enabling efficient biomarker discovery anddisease monitoring sensitive enough to detect biomarkers in as
few as 50 cells/ml of whole blood. Next, we plan to assess longnoncoding RNA targets and small RNA biotypes available in thePrimePCR portfolio to identify those uniquely expressed in tumor
cells. Additionally, we aim to further explore the lower limits ofdetection for our workflow.
References
Bureeva S et al. (2012). PrimePCR pathway analysis: Pathway curation and real-time
PCR panel design strategy. Bio-Rad Bulletin 6263.
Herbert ZT et al. (2018) Cross-site comparison of ribosomal depletion kits for Illumina
RNAseq library construction. BMC Genomics 19, 199.
Lone SN et al. (2022) Liquid biopsy: A step closer to transform diagnosis, prognosis
and future of cancer treatments. Mol Cancer 21, 79.
Ortiz-Otero N et al. (2020) Chemotherapy-induced release of circulating-tumor cells
into the bloodstream in collective migration units with cancer-associated fibroblasts
in metastatic cancer patients. BMC Cancer 20, 873.
Perike S et al. (2022). Comprehensive capture of FFPE RNA for RNA-Seq using
continuous synthesis chemistry and post-library ribodepletion. Bio-Rad Bulletin 3421.

Visit bio-rad.com/genesis-system for more information.
BIO-RAD, CELSELECT SLIDES, and SEQUOIA are trademarks of Bio-Rad Laboratories,
Inc. in certain jurisdictions. All trademarks used herein are the property of their respective
owner. © 2024 Bio-Rad Laboratories, Inc.
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