# Genetic profiles of cervical tumors by high-throughput sequencing for personalized medical care

Etienne Muller, Baptiste B. Brault, Allyson M. Holmes, Angelina Legros, Emmanuelle Jeannot, Maura Campitelli, Antoine Rousselin, Nicolas Goardon, Thierry Frebourg, Sophie Krieger, et al.

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Etienne Muller, Baptiste B. Brault, Allyson M. Holmes, Angelina Legros, Emmanuelle Jeannot, et al.. Genetic profiles of cervical tumors by high-throughput sequencing for personalized medical care. Cancer Medicine, 2015, 4 (10), pp.1484-1493. 10.1002/cam4.492 . hal-01259401

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Distributed under a Creative Commons Attribution 4.0 International License
# Genetic profiles of cervical tumors by high-throughput sequencing for personalized medical care

Etienne Muller<fnref n="1" />,<fnref n="2" />, Baptiste Brault<fnref n="1" />, Allyson Holmes<fnref n="3" />, Angelina Legros<fnref n="1" />, Emmanuelle Jeannot<fnref n="4" />,
Maura Campitelli<fnref n="5" />, Antoine Rousselin<fnref n="1" />, Nicolas Goardon<fnref n="1" />, Thierry Frébourg<fnref n="2" />,<fnref n="6" />, Sophie Krieger<fnref n="1" />,<fnref n="2" />,<fnref n="7" />,
Hubert Crouet<fnref n="8" />, Alain Nicolas<fnref n="3" />, Xavier Sastre<fnref n="4" />, Dominique Vaur<fnref n="1" />,<fnref n="2" /> & Laurent Castéra<fnref n="1" />,<fnref n="2" />

**Keywords**

Cervix, diagnosis, NGS, panel, targeted
therapie

**Correspondence**

Laurent Castéra, Department of Cancer
Biology and Genetics, Centre François
Baclesse, 3 Avenue du Général Harris,
14076 Caen, France.

Tel: +33 231 455 154; Fax: +33 231 455 053;

E-mail: l.castera@baclesse.unicancer.fr

**Funding Information**

This work was conducted as a part of the
Integrated Actions for Research Program
(PAIR-HPV) and was supported by grants from
INCa (French Cancer National Institute),
Fondation ARC (Association pour la recherche
sur le cancer) and the Ligue Contre Le Cancer
and the French National League Against
Cancer (LNCC).

Received: 15 April 2015; Revised: 2 June

2015; Accepted: 4 June 2015

**Cancer Medicine 2015; 4(10):1484–1493**

doi: 10.1002/cam4.492

**Abstract**

Cancer treatment is facing major evolution since the advent of targeted therapies. Building genetic profiles could predict sensitivity or resistance to these therapies and highlight disease-specific abnormalities, supporting personalized patient care. In the context of biomedical research and clinical diagnosis, our laboratory has developed an oncogenic panel comprised of 226 genes and a dedicated bioinformatic pipeline to explore somatic mutations in cervical carcinomas, using high-throughput sequencing. Twenty-nine tumors were sequenced for exons within 226 genes. The automated pipeline used includes a database and a filtration system dedicated to identifying mutations of interest and excluding false positive and germline mutations. One-hundred and seventy-six total mutational events were found among the 29 tumors. Our cervical tumor mutational landscape shows that most mutations are found in *PIK3CA* (E545K, E542K) and *KRAS* (G12D, G13D) and others in *FBXW7* (R465C, R505G, R479Q). Mutations have also been found in *ALK* (V1149L, A1266T) and *EGFR* (T259M). These results showed that 48% of patients display at least one deleterious mutation in genes that have been already targeted by the Food and Drug Administration approved therapies. Considering deleterious mutations, 59% of patients could be eligible for clinical trials. Sequencing hundreds of genes in a clinical context has become feasible, in terms of time and cost. In the near future, such an analysis could be a part of a battery of examinations along the diagnosis and treatment of cancer, helping to detect sensitivity or resistance to targeted therapies and allow advancements towards personalized oncology.

**Introduction**

Cancer treatment faces a major evolution since the advent
of targeted therapies. It is now possible to specifically
treat tumors presenting with a clearly identified genetic
alteration(s). It is the case of nonsmall-cell lung cancers
associated with *EGFR* mutations that responds to specific
tyrosine kinase inhibitors [1]. This type of therapy can

only be effective in the case of a known molecular target.
It requires the characterization of various alterations (com-
monly called actionable mutations) that a tumor may
accumulate. Establishment of such genetic profiles would
allow sensitivity, resistance, and toxicity predictions for
such therapies [2]. Sequences from many human genomes
have shown a wide interindividual genetic heterogeneity
[3]. Variations among individuals are attributed to

<footnote n="1">¹Department of Cancer Biology and Genetics, CCC François Baclesse, Caen, France</footnote>

<footnote n="2">²Inserm U1079, Rouen, France</footnote>

<footnote n="3">³Recombination and Genetic Instability, UMR 3244, Institut Curie, Paris, France</footnote>

<footnote n="4">⁴Biopathology Department, Institut Curie, Paris, France</footnote>

<footnote n="5">⁵Department of Radiotherapy, Institut Curie, Paris, France</footnote>

<footnote n="6">⁶Department of Genetics, University Hospital, Rouen, France</footnote>

<footnote n="7">⁷Caen University, Caen, France</footnote>

<footnote n="8">⁸Gynecology Oncology Department, CCC François Baclesse, Caen, France</footnote>
“germline” mutations and constitute the individual’s in-
herited genetic characteristics. These mutations, however,
should be distinguished from other mutations that may
occur during the individual’s life, and also, might only
be observed in one organ or some tissue. These events
are called somatic mutations and have been shown to
provoke various oncogenic processes [2] (they are called
“driver” mutations).

Many research teams, brought together in international
consortia, are engaged in the characterization of
mutations causing tumorigenesis. The Catalog Of
Somatic Mutations In Cancer (COSMIC) Project [4],
for example, aims at creating a database gathering all
somatic mutations already described. The International
Cancer Genome Consortium plans to build a compre-
hensive catalog of somatic mutations in tumors of 50
different types/subtypes of cancer, with additional epi-
genomic and transcriptomic information, with the aim
of highlighting differences and common abnormalities
across tumor types [5]. These works on tumor genetic
profiling have demonstrated that tumors of different
types can share protumorigenic signaling pathways, de-
fined by common driver mutations which could also
be actionable [6]. Consequently, it makes sense to ex-
plore the presence or absence of mutations within these
driver genes, to identify biomarkers for sensibility or
resistance to treatments, and to indicate those patients
most likely to benefit from targeted therapies.

The number of genes to test, in order to establish a
cancer gene map, is huge [7]. This approach is made
possible by a new generation of sequencing devices (Next
Generation Sequencing, NGS), able to analyze the equiva-
lent of several entire genomes [8]. However, sequencing
a whole genome can be quite long, regardless of analysis
time for such an extensive quantity of data. Another ap-
proach consists of limiting the analysis to exomes or
several hundreds of genes, using targeted methods. Using
a rational approach, which targets a panel consisting of
a few hundred genes, considered to be “actionable” and/
or “driver”, it is possible to sequence and characterize a
tumor, depending on its major molecular characteristics.
The main advantages of this approach lie in the reduced
delay for reporting the results and limited costs, compat-
ible with a diagnostic use, while maintaining sufficient
sequencing quality to detect somatic variations [9].

The sensitivity of these methods depends on bioinfor-
matic tools specifically developed to highlight somatic
mutations in cancer, by comparing healthy tissue and
tumor DNA from the same individual (paired-mode).
These tools verify that a somatic mutation found in tumor
does not actually correspond to germline mutation or
systematic false-positive mutation, found in healthy tissues
[10]. In some cases, it is difficult to obtain the matched

healthy tissue in a clinical context, depending on legisla-
tive (local or national), ethical or logistical considerations;
a constraint that must be overcome. On the other hand,
the detection of mutations strongly depends on algorithms
and their adjusted parameters used. In addition, their
sensitivity depends on the type and rate of mutation pre-
sent in the tumor. To date, there is no global consensus
for the use of a particular algorithm or mode of analysis
[10].

We report on the genetic profiles of cervix uteri tumors which represents the fourth leading cause of death by cancer in females worldwide [11]. It is well-known that some human papillomavirus (HPV) types are the cause of this cancer and its development is linked to the stable insertion of the HPV genome into the tumor's DNA [12]. Cancer of cervix uteri can go undetectable for years and years, and is associated with a 5-year survival rate of 68% [13], depending on clinical stage at presentation: the 5-year relative survival rate is 91%, 58%, and 17% for patients with localized, regional, and metastatic disease, respectively (http://seer.cancer.gov/statfacts/html/cervix.html). Moreover, up to one-third of patients will develop recurrent tumors mostly within 2 years after initial treatments [14]. Most recurrences being detected by imaging or medical examination, it is essential that highly specific, sensitive and less invasive markers are identified to predict response to treatment, disease progression, and to develop personalized therapies.

In our laboratory, we have developed the sequencing
of a panel of genes associated with tumors of cervix uteri,
including exons within 226 genes chosen for their action-
able nature and their potential implication in cervical
tumorigenesis (Table 1). To overcome the lack of matched
healthy tissue, an average healthy tissue has been gener-
ated in silico from nonmatched healthy tissues at our
disposal. The aim of the study was to prove that a tumor
can be managed in a clinical context using NGS technol-
ogy and automated bioinformatic pipeline and to identify
potential actionable mutations that could have a direct
link with treatments.

**Material and Methods**

Tumor samples

Twenty-five squamous cell carcinoma and four adenocar-
cinoma tumor samples were obtained under approved
protocols from the Curie Institute in Paris, France
(Table 2). Seven healthy tissue samples were obtained
from cervical surgical specimen provided by the Anatomic
Pathology laboratory of cancer center François Baclesse
in Caen, France. All patients gave written informed consent
before entering the study.
<table><caption>**Table 1.** Genes selected for cervix uteri cancer panel.</caption><tr><td>ABL1</td><td>CDK8</td><td>FGFR3</td><td>LTK</td><td>NSD1</td><td>SMAD2</td></tr><tr><td>ABL2</td><td>CDKN1A</td><td>FGFR4</td><td>MAP2K1</td><td>NTRK1</td><td>SMAD3</td></tr><tr><td>AFF3</td><td>CDKN2A</td><td>FHIT</td><td>MAP2K2</td><td>NTRK2</td><td>SMAD4</td></tr><tr><td>AKT1</td><td>CEACAM5</td><td>FKBP9</td><td>MAP2K4</td><td>NTRK3</td><td>SMARCA4</td></tr><tr><td>AKT2</td><td>CEBPA</td><td>FLT1</td><td>MCL1</td><td>PAK3</td><td>SMARCB1</td></tr><tr><td>AKT3</td><td>CHEK1</td><td>FLT3</td><td>MDM2</td><td>PARP1</td><td>SMO</td></tr><tr><td>ALK</td><td>CHEK2</td><td>FLT4</td><td>MDM4</td><td>PAX5</td><td>SOCS1</td></tr><tr><td>APC</td><td>CREBBP</td><td>FOXP4</td><td>MED1</td><td>PDGFRA</td><td>SOX10</td></tr><tr><td>AR</td><td>CRKL</td><td>GATA1</td><td>MEN1</td><td>PDGFRB</td><td>SOX2</td></tr><tr><td>ARFRP1</td><td>CROCC</td><td>GLIS2</td><td>MET</td><td>PDPK1</td><td>SRC</td></tr><tr><td>ARID4A</td><td>CSF1R</td><td>GNA11</td><td>MGMT</td><td>PIK3CA</td><td>STK11</td></tr><tr><td>ATM</td><td>CSMD1</td><td>GNAQ</td><td>MITF</td><td>PIK3R1</td><td>SUFU</td></tr><tr><td>AURKA</td><td>CTLA4</td><td>GNAS</td><td>MLH1</td><td>PKHD1</td><td>TBX22</td></tr><tr><td>AURKB</td><td>CTNNB1</td><td>GPR124</td><td>MLL</td><td>PLCG1</td><td>TCF4</td></tr><tr><td>BCL2</td><td>CTTN</td><td>GUCY1A2</td><td>MLL2</td><td>PLEKHO2</td><td>TENM1</td></tr><tr><td>BCL2A1</td><td>DAPK1</td><td>HIC1</td><td>MLL3</td><td>PRDM9</td><td>TERT</td></tr><tr><td>BCL2L1</td><td>DDX3X</td><td>HNF1A</td><td>MPL</td><td>PRKDC</td><td>TET2</td></tr><tr><td>BCL2L2</td><td>E2F3</td><td>HOXA3</td><td>MRE11A</td><td>PTCH1</td><td>TGFBR2</td></tr><tr><td>BCL6</td><td>EGFR</td><td>HRAS</td><td>MSH2</td><td>PTEN</td><td>TIMP2</td></tr><tr><td>BRAF</td><td>EMSY</td><td>HSP90AA1</td><td>MSH6</td><td>PTK2</td><td>TIMP3</td></tr><tr><td>BRCA1</td><td>EPHA3</td><td>IDH1</td><td>MTOR</td><td>PTK2B</td><td>TNFAIP3</td></tr><tr><td>BRCA2</td><td>EPHA5</td><td>IDH2</td><td>MUC1</td><td>PTPN11</td><td>TNFRSF10C</td></tr><tr><td>CADM1</td><td>EPHA6</td><td>IGF1R</td><td>MYB</td><td>PTPRD</td><td>TNFRSF10D</td></tr><tr><td>CASP8</td><td>EPHA7</td><td>IGF2R</td><td>MYC</td><td>RAD51</td><td>TOP1</td></tr><tr><td>CASZ1</td><td>EPHB1</td><td>IKBKE</td><td>MYCL1</td><td>RAD51B</td><td>TP53</td></tr><tr><td>CAV1</td><td>EPHB4</td><td>IKZF1</td><td>MYCN</td><td>RAF1</td><td>TP63</td></tr><tr><td>CBL</td><td>EPHB6</td><td>INHBA</td><td>MYH15</td><td>RARA</td><td>TP73</td></tr><tr><td>CCND1</td><td>ERBB2</td><td>IRF6</td><td>NF1</td><td>RARB</td><td>TSC1</td></tr><tr><td>CCND2</td><td>ERBB3</td><td>IRS2</td><td>NF2</td><td>RASSF1</td><td>TSC2</td></tr><tr><td>CCNE1</td><td>ERBB4</td><td>JAK2</td><td>NFE2L2</td><td>RB1</td><td>TSHR</td></tr><tr><td>CCNL1</td><td>ESR1</td><td>JAK3</td><td>NFIB</td><td>REL</td><td>USP9X</td></tr><tr><td>CDC73</td><td>EZH2</td><td>KDR</td><td>NKX2-1</td><td>RET</td><td>VHL</td></tr><tr><td>CDH1</td><td>FAM123B</td><td>KEAP1</td><td>NOTCH1</td><td>RICTOR</td><td>WT1</td></tr><tr><td>CDH2</td><td>FANCC</td><td>KIAA0774</td><td>NOTCH2</td><td>ROS1</td><td>ZBTB7C</td></tr><tr><td>CDH20</td><td>FANCF</td><td>KIT</td><td>NOTCH3</td><td>RPS6KB1</td><td>ZNF668</td></tr><tr><td>CDH5</td><td>FBXW7</td><td>KLF5</td><td>NOTCH4</td><td>RPTOR</td><td>ZNF91</td></tr><tr><td>CDK4</td><td>FGFR1</td><td>KRAS</td><td>NPM1</td><td>RUNX1</td><td></td></tr><tr><td>CDK6</td><td>FGFR2</td><td>LRP1B</td><td>NRAS</td><td>RUNX1T1</td><td></td></tr></table>

## Sample preparation and next-generation sequencing

Sequencing methods were described previously [15], enabling analysis of two tumors and one healthy tissue sample in the same sequencing run. The SureSelect-targeted enrichment process (Agilent, Santa Clara, CA) was performed after combining the indexed samples, equimolarly. Libraries were then sequenced on a MiSeq (Illumina, San Diego, CA), using the paired-end 2 × 150 bp method (full protocol available on request).

## Bioinformatic analysis

The CASAVA suite v1.8 (Illumina) was used for demultiplexing and generating fastq files. These raw data were then analyzed by a home-made pipeline (BAPT, Bioinformatic

Analysis Pipeline Toolkit), to automate alignment, variant-calling, and annotation steps. Reads were first mapped to a reference genome (hg19) using BWA algorithm [16] and output files were reworked following GATK [17] (Genome Analysis ToolKit, Broad Institute) best practices, especially to calculate quality scores and undergo local realignments. Five variant-callers were used to call single-nucleotide substitutions and insertion-deletion (indels) events: HaplotypeCaller [18], UnifiedGenotyper [18], LofreqStar [19], Varscan2 [20], and MuTect [21]. The BAM files from seven healthy samples were used by randomly selecting reads from each file, to create an average healthy synthetic file (using SAMtools [22]). This “synthetic” BAM file was used in paired-mode during the variant-calling step with the programs LofreqStar, MuTect, and Varscan2. All variants found by the variant callers were annotated with Alamut-Batch (Interactive Biosoftware, Rouen, France) and integrated into a database called CanDiD
<table><caption>**Table 2.** Demographic, histological, and biological characteristics.</caption><thead><tr><th>Patient no.</th><th>Histological type</th><th>Tumor stage (FIGO)</th><th>Age at diagnosis</th><th>HPV type</th></tr></thead><tbody><tr><td>1</td><td>SCC</td><td>IIB</td><td>40</td><td>HPV 18</td></tr><tr><td>2</td><td>SCC</td><td>IB</td><td>33</td><td>HPV 18</td></tr><tr><td>3</td><td>Adenocarcinoma</td><td>II</td><td>45</td><td>HPV 18</td></tr><tr><td>4</td><td>SCC</td><td>IB</td><td>37</td><td>HPV 18</td></tr><tr><td>5</td><td>SCC</td><td>IB</td><td>34</td><td>HPV 16</td></tr><tr><td>6</td><td>SCC</td><td>IB2</td><td>47</td><td>HPV 16</td></tr><tr><td>7</td><td>Adenocarcinoma</td><td>IIB</td><td>49</td><td>HPV 16</td></tr><tr><td>8</td><td>SCC</td><td>IVB</td><td>57</td><td>HPV 16</td></tr><tr><td>9</td><td>SCC</td><td>IB1</td><td>33</td><td>HPV 16</td></tr><tr><td>10</td><td>SCC</td><td>NA</td><td>NA</td><td>HPV45</td></tr><tr><td>11</td><td>SCC</td><td>IIB</td><td>60</td><td>HPV 16</td></tr><tr><td>12</td><td>SCC</td><td>IIB</td><td>42</td><td>HPV 18</td></tr><tr><td>13</td><td>SCC</td><td>IIB</td><td>47</td><td>HPV 16, 18</td></tr><tr><td>14</td><td>SCC</td><td>IIB</td><td>34</td><td>HPV 16</td></tr><tr><td>15</td><td>SCC</td><td>IIB</td><td>68</td><td>HPV 16</td></tr><tr><td>16</td><td>SCC</td><td>IIB</td><td>43</td><td>HPV 18</td></tr><tr><td>17</td><td>SCC</td><td>IIB</td><td>44</td><td>HPV 51</td></tr><tr><td>18</td><td>SCC</td><td>IIB</td><td>65</td><td>HPV 33</td></tr><tr><td>19</td><td>SCC</td><td>IIB</td><td>45</td><td>HPV 73</td></tr><tr><td>20</td><td>SCC</td><td>IIIB</td><td>43</td><td>HPV 16</td></tr><tr><td>21</td><td>SCC</td><td>IIB</td><td>53</td><td>HPV 16</td></tr><tr><td>22</td><td>Adenocarcinoma</td><td>IB2</td><td>42</td><td>HPV 16</td></tr><tr><td>23</td><td>Adenocarcinoma</td><td>IIB</td><td>54</td><td>HPV-</td></tr><tr><td>24</td><td>SCC</td><td>IIB</td><td>54</td><td>HPV 18</td></tr><tr><td>25</td><td>SCC</td><td>III</td><td>33</td><td>HPV 16</td></tr><tr><td>26</td><td>SCC</td><td>IIB</td><td>55</td><td>HPV 16</td></tr><tr><td>27</td><td>SCC</td><td>IV + metastasis</td><td>44</td><td>HPV 73</td></tr><tr><td>28</td><td>SCC</td><td>IB1</td><td>25</td><td>HPV 18</td></tr><tr><td>29</td><td>SCC</td><td>IB2</td><td>31</td><td>HPV-</td></tr></tbody></table>

FIGO, International Federation of Gynecology; HPV, human papillomavirus; SCC, squamous cell carcinoma; NA, not available.

(Cancer Diagnostic Database, PostgreSQL) (Fig. S1). Variants were retrieved from CanDiD database according to five criteria: (1) the variant was found on a list of preferred transcripts; (2) the variant was in targeted zones; (3) the variant was within the coding sequence or ±10 bp within the intronic sequences; (4) the variant was in a canonical splicing mutation sites or when outside they induced a 15% decrease of MaxEntScan score and a 5% decrease of the SpliceSiteFinder score [23] (hereafter called splicing mutation); and (5) the variant was observed 10 times at most in the Exome Sequencing Project Database (ESP; http://evs.gs.washington.edu.EVS/). Identified variants passed through a home-made filtration system (scripted on Python programming language, available on request) which compares sequencing data from the five variant-callers and tumor samples versus healthy samples, in order to rule out false-positive and germline variants. Data from each variant were first compared to both tumor and healthy tissue samples, to evaluate whether the variant have a greater presence in the tumor than in the healthy samples. Variants were then

filtered based on their quality score (the main criteria being the PHRED score and depth of coverage). The main objective was to obtain, for each tumor, a list of variants with a high probability of being somatic. Only variants seen by at least 2 variant callers were further considered.

Results were annotated and interpreted in sequence with: (1) two mutation impact prediction systems SIFT [24], Polyphen [25], (2) InterPro Database [26], (3) Clinical Trials.gov Database [27], (4) dbSNP [28], (5) COSMIC [4], (6) TARGET [29].

In the present study, all genes that constitute a target for the US Food and Drug Administration-approved targeted therapies were called actionable genes, regardless of the type of cancer. Similarly, a missense mutation was classified as deleterious if scored as “deleterious” by SIFT algorithm or “Probably Damaging” by Polyphen algorithm. Inactivating mutations (PTC, premature codon termination and splicing mutations) were directly considered as deleterious.

## Results

Tumor samples from 29 patients with cervical cancer (stade I B to IV B), including 25 squamous cell carcinomas and 4 adenocarcinomas, have been sequenced (Table 2), as well as seven samples from healthy cervical biopsy (“control” samples). The time from DNA extraction to acquisition of most likely somatic mutations was about height working days (Fig. 1). The sequencing process produced an average of 8,078,023 reads per sample, with an average sequencing depth of 268× and >92% of nucleotides covered ≥50-fold. Consequently, only variants with an allele ratio of more than 5% were called. After data processing in the BAPT pipeline, 11,267 variants were included in the CanDiD database. After extraction from the database, 2746 missense mutations were collected from the various variant callers (Fig. 2A). After passing through the filtration system, only 220 mutations were retained (Fig. 2B; Table S2). Most of the mutations deleted by the filtration system were germline mutations detected by HaplotypeCaller or UnifiedGenotyper (from the control samples) or false-positive selected by Varscan due to its lack of specificity. Among all mutations found in the 29 tumor samples, 41% (91/220) were identified by all variant callers, assuring accuracy of the mutations detected. Only variants detected by two or more variant-callers were selected leading to 156 missense mutations selected, among which 29 are already referenced in the COSMIC database. Seven nonsense mutations, nine small insertion/deletions (indels) inducing PTC and four splicing mutations were also identified. Altogether, they represent an average of 6.1 mutations per tumor sample. For each patient, 75 percent of mutations were considered
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Sequencing workflow's major steps. After DNA extraction (Day 1), DNA molecules are sheared by sonification, and DNA fragments are ligated to adapters containing a patient-specific index (Day 2). DNA from 2 patients and 1 control are pooled equimolarly together during the multiplexing step (Day 3). Regions of interest (exons from 226 genes) are retrieved by a targeted enrichment system with biotinylated baits (Day 4–5). Then DNA is sequenced in an Illumina MiSeq (Day 6) and sequencing raw data are processed by the bioinformatic pipeline (Day 7–8), to extract the most likely somatic variations.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Representation of missense mutations found by each variant caller. (A) Mutations extracted directly from CANDID database (total: 2746). (B) mutations remaining after passing through filtration system (total: 221). (C) Proportion of mutations detected by at least 2 variant-callers classified as deleterious by SIFT or POLYPHEN.</figcaption></figure>

deleterious (by SIFT or POLYPHEN algorithm) (Fig. 2C, Table S2).

No somatic missense mutation was found in the tumor suppressor gene *TP53*, which is consistent with molecular characteristics of cervix uteri tumors [30]. Similarly, missense mutations were found in the oncogene *PIK3CA* (*n* = 8), the *KRAS* gene implicated into anti-EGFR therapies resistance (*n* = 4) and in the *FBXW7* gene, recently described in the cervix uteri tumors mutational landscape (*n* = 3) (Table S2). Other missense mutations were found in tumor suppressor genes, including the susceptibility gene for breast cancer *BRCA2* (*n* = 6) or the RAS family negative regulator *NF1* (*n* = 1). Twenty inactivating

mutations (PTC and splicing mutations) were also identified in tumor suppressor genes including *RB1* (*n* = 1), *NF1* (*n* = 3) or *MLH1* (*n* = 2) (Table 3).

Among other actionable genes, deleterious missense mutations were observed in *ALK* (*n* = 4), *AR* (*n* = 1), *EGFR* (*n* = 3), *ERBB2* (*n* = 1), *MET* (*n* = 1), *mTOR* (*n* = 2), *PDGFRA* (*n* = 1) and *RAF1* (*n* = 2) (Table 4). Considering only deleterious mutations, 48% (*n* = 14/29) had at least one mutation in an actionable gene (Fig. 3A and B).

Several clinical trials (www.clinicaltrials.gov database) are looking for mutations in specific genes to evaluate sensitivity or resistance to approved, or in clinical trials, targeted therapies, among which *PIK3CA*, *ALK*, *EGFR*,
<table><caption>**Table 3.** Inactivating mutations.</caption><thead><tr><th>Coding effect</th><th>Gene</th><th>Coding DNA sequence<sup>1</sup></th><th>Protein sequence<sup>1</sup></th><th>Transcript</th><th>Patients tumor sample no.</th></tr></thead><tbody><tr><td>Splicing mutation</td><td>CBL</td><td>c.1096-1G>A</td><td>p. ?</td><td>NM_005188</td><td>5</td></tr><tr><td>Potential splicing mutations</td><td>mTOR</td><td>c.2514+3G>C</td><td>p. ?</td><td>NM_004958</td><td>15</td></tr><tr><td rowspan="5">Nonsense mutations</td><td>MYH15</td><td>c.1993-14G>A</td><td>p. ?</td><td>NM_014981</td><td>14</td></tr><tr><td>NF1</td><td>c.1392+5G>T</td><td>p. ?</td><td>NM_001042492</td><td>21</td></tr><tr><td>C11orf30</td><td>c.3004C>T</td><td>p.Gln1002*</td><td>NM_020193</td><td>26</td></tr><tr><td>CEACAM5</td><td>c.646G>T</td><td>p.Glu216*</td><td>NM_004363</td><td>26</td></tr><tr><td>EPHA5</td><td>c.2458G>T</td><td>p.Gly820*</td><td>NM_004439</td><td>6</td></tr><tr><td rowspan="7">Frameshift mutations</td><td>FBXW7</td><td>c.1053G>A</td><td>p.Trp351*</td><td>NM_033632</td><td>26</td></tr><tr><td>MLH1</td><td>c.1630C>T</td><td>p.Gln544*</td><td>NM_000249</td><td>20</td></tr><tr><td></td><td>c.755C>T</td><td>p.Ser252*</td><td></td><td>5</td></tr><tr><td>RB1</td><td>c.1399C>T</td><td>p.Arg467*</td><td>NM_000321</td><td>5</td></tr><tr><td>CASP8</td><td>c.790dup</td><td>p.Val264Glyfs*13</td><td>NM_001080125</td><td>5</td></tr><tr><td>CHEK2</td><td>c.1229del</td><td>p.Thr410Metfs*15</td><td>NM_001005735</td><td>11</td></tr><tr><td>CREBBP</td><td>c.4477dup</td><td>p.Ile1493Asnfs*26</td><td>NM_004380</td><td>5</td></tr><tr><td>FGFR2</td><td>c.962dup</td><td>p.Asn321Lysfs*21</td><td>NM_022970</td><td>5</td></tr><tr><td>FGFR4</td><td>c.2396_2403del</td><td>p.Gly799Aspfs*12</td><td>NM_213647</td><td>7</td></tr><tr><td>NF1</td><td>c.5907_5908del</td><td>p.Arg1970Serfs*6</td><td>NM_001042492</td><td>5</td></tr><tr><td></td><td>c.2033del</td><td>p.Pro678Argfs*10</td><td></td><td></td></tr><tr><td>NF2</td><td>c.301del</td><td>p.Tyr101Ilefs*22</td><td>NM_181832</td><td>16</td></tr><tr><td>NOTCH2</td><td>c.6909del</td><td>p.Ile2304Leufs*2</td><td>NM_024408</td><td>5</td></tr></tbody></table>

Mutations are classified by mutation type.

<table><caption>**Table 4.** Deleterious mutations found in actionable genes.</caption><thead><tr><th>Gene</th><th>Drugs in relation with gene of interest</th><th>Mutation</th><th>Number of tumors</th><th>Transcript</th><th>Associated clinical trial</th></tr></thead><tbody><tr><td>ALK</td><td>Crizotinib, Ceritinib</td><td>A1266T<br>A1234V<br>V1149L<br>R1120Q</td><td>4</td><td>NM_004304</td><td>NCT01548144,<br>NCT01744652</td></tr><tr><td>AR</td><td>Entuzalamide, Abiraterone</td><td>K809N</td><td>1</td><td>NM_000044</td><td>–</td></tr><tr><td>EGFR</td><td>Cetuximab, Panitumumab, Erlotinib, Gefetinib, Afatinib, Vandetanib</td><td>S511Y<br>T259M<br>A611T</td><td>3</td><td>NM_005228</td><td>NCT00770263</td></tr><tr><td>ERBB2</td><td>Trastuzumab, Pertuzumab, Lapatinib</td><td>L696F</td><td>1</td><td>NM_001005862</td><td>NCT01953926</td></tr><tr><td>KRAS</td><td>Resistance to Cetuximab and others</td><td>G12D<br>G13D</td><td>4</td><td>NM_033360</td><td>–</td></tr><tr><td>MET</td><td>Cabozantinib</td><td>L342F</td><td>1</td><td>NM_001127500</td><td>–</td></tr><tr><td>mTOR</td><td>Temsirolimus, Everolimus</td><td>M813I</td><td>1</td><td>NM_004958</td><td>–</td></tr><tr><td>PDGFRA</td><td>Regorafenib</td><td>P441L</td><td>1</td><td>NM_006206</td><td>NCT02029001</td></tr><tr><td>RAF1</td><td>Regorafenib</td><td>C96F<br>Q255H</td><td>2</td><td>NM_002880</td><td>–</td></tr></tbody></table>

Genes are linked to targeted therapies already approved by Food and Drug Administration in at least one indication. All potential drug targets of each therapy are considered.

mTOR, PTCH1, SMO, and PIK3R1 are included (Table S2). Overall, 59% of patients (17/29) would be eligible for potential inclusion in a clinical trial (Fig. 3A and B), based on carrier gene mutations and tumor origin.

Discussion

The aim of the study was to demonstrate in a clinical context the feasibility of detecting somatic variations, in

cervical cancers, using a targeted sequencing approach. The use of a “synthetic healthy tissue sample” (see above Material and Methods section) from the sequencing data of several unpaired healthy tissue samples allows the use of somatic variant-callers (which compares healthy tissues from tumors to extract somatic mutations) to detect poorly represented mutations. This approach, however, probably tolerates a few germline mutations which cannot be differentiated from somatic mutations. In our study, some

<footnote n="1">¹Nomenclature according to HGVS guidelines (Human Genome Variation Society).</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Distribution of deleterious mutations among the 29 tumors. A gene is considered actionable if linked to targeted therapies approved by Food and Drug Administration. A mutation is classified as deleterious if considered as such by SIFT algorithm or POLYPHEN algorithm. (A) Proportion of tumors with a deleterious mutation on gene considered actionable (blue) and proportion of patients for whom their deleterious mutations could allow inclusion in a clinical trial (purple). (B) Representation of most mutated genes across tumors.</figcaption></figure>

mutations found in *BRCA2* (hereditary predisposition for breast and ovarian cancer) or in *MLH1* (hereditary predisposition to Lynch syndrome) genes through the genetic characterization of tumors could be germline (Table S2). The identification of germline mutations in genes in relation with mendelian diseases must be taken into account and the appropriate ethical issues should be considered, a subject still of debate. Tools and methods developed in our laboratory should not be considered as an automatic interpretation system; they should be considered as help to ease work discerning the most relevant mutations. For instance, with our data, the number of mutations to analyze drops from about 388 to 6 for one tumor, which represents a huge time-saving for biologists who interpret data in a clinical context. Moreover, analysis time (±8 working days) is consistent with clinical practice, and should improve patient healthcare in a comprehensive way.

No somatic alterations were detected in the tumor suppressor gene *TP53* which seems consistent throughout our model. In cervical carcinomas *TP53* inactivation is linked to virus integration, which results in the expression of the E6 oncoprotein, able to induce the *TP53* degradation via the ubiquitin pathway [30]. Previous studies have already identified genes frequently mutated

in cervical carcinomas, such as *PIK3CA* [31], *KRAS* [32] (the most mutated genes in cervix uteri cancer according to COSMIC data) or *FBXW7* [33]. *PIK3CA* E545K (*n* = 4) and E542K (*n* = 2) have strong oncogenic properties due to increased kinase activity [34], consequences in growth factor-independent cell proliferation, resistance to apoptosis, and increased invasion and cell migration. This gene represents a prime target for drug development, and even if there is no available approved treatment, several clinical trials are currently testing *PIK3CA* selective inhibitors (ClinicalTrials ID: NCT01708161; NCT01928459). Two major mutations were also found in *KRAS*, G12D (*n* = 1) and G13D (*n* = 3), well known in colorectal cancer to predict a resistance to *EGFR* antibodies [35]. *FBXW7* is a gene recently described as mutated in cervical carcinoma [33] with two missense mutations already described, R505G and R456C. There was also a novel mutation at a position already found mutated, R479Q. All of these point mutations are located in the WD repeats domain. This domain is used to form a complex in charge of degrading several products of proto-oncogenes (including *MYC*, *CCNE1*, *NOTCH1*, and *JUN*) [36]. We found a novel truncating mutation W351*; it occurs just upstream of the WD domain (uniprot ID: Q969H0). Therefore, pipeline implementation
seems consistent, since key elements form cervical tu-
morigenesis were found.

Considering all of the actionable genes as defined before
(Table 4), deleterious mutations could influence sensitivity
or resistance to treatments. ALK mutations V1149L and
A1266T are located next to previously described mutations
L1152R [37] and G1269A [38], which have been identi-
fied as resistance mutations to Crizotinib and other tyrosine
kinase inhibitors, and could then have the same impact
on treatment response. Another mutation (T259M) has
been observed in a specific subpart of extracellular domain
of EGFR, called the dimerization loop [39]. This loop is
essential for receptor dimerization and hence activation.
A previous study showed that mutations in this subpart
were able to increase receptor oncogenic activity [40].
MET had also a new mutation at residue L342, which
correspond to a specific region of SEMA domain, con-
sidered as MET ligand-binding domain [41] and useful
to receptor dimerization and activation [42]. Other muta-
tions classified as deleterious were detected in mTOR
(M813I, splice mutation c.2514+3G>C) and PDGFRA
(P441L) but their real impact on protein functionality or
the effectiveness of related targeted therapies is still
unknown.

Eighteen deleterious mutations identified from our cases
reside in actionable genes (Table 3), although their impact
is still unclear for many. Therefore, functional studies
should be performed to identify potential drug sensitivities
or resistances to these targets. Deleterious classification
(SIFT and POLYPHEN algorithm) should be used with
caution since, for instance, PIK3CA mutations E545K and
E542K are classified as tolerated by SIFT despite the fact
that they are known driver mutations.

Among driver genes investigated in our study, interest-
ing mutations were found on BRCA2, with 2 variants of
unknown signification, classified here as deleterious mis-
sense mutations (L2721R and A2770D) in the OB1 (oli-
gonucleotide/oligosaccharide-binding 1) domain, which
serves to bind single-strand DNA. Functional assays showed
that mutations in this domain can be disease-causing [43].
Another mutation (V2066I) was observed in BRC repeat
domains, essential for binding to RAD51 protein [44].
The new PARP inhibitor Olaparib, now available, could
potentially be of interest in these BRCA mutated tumors.
Unfortunately, for the moment this drug is mainly tested
in breast and ovarian cancers. Altogether, the real new
challenge for a tumor's genetic profiling is the correct
interpretation of mutation.

Sixty-nine percent of patients could have had their
treatment decision guided by the targeted sequencing of
226 genes, either by revealing sensitivity or resistance to
available drugs or by directing patients toward treatment
protocols or diagnostic procedures being tested (Fig. 3A).

Even though no therapeutic answer could be provided
by our "oncopanel" for certain patients, they were all
analyzed for HPV insertion status. The HPV insertion
site constitutes another biomarker in cis, which can provide
information for targeted therapies. HPV has therefore been
found inserted into MYC, KLF5, KLF12, as well as RB1,
and many other important and potentially actionable genes
(A. Holmes, unpubl. data). Such tests might participate
in the overall study of cancer biomarkers in both prog-
nostic (assessing tumor outcomes and deciding whether
or not to undergo treatment) and predictive (evaluating
sensitivity and resistance to treatments to choose the most
effective drug) fields [45]. The outcome of such studies
strives toward personalized oncology, which is to provide
the most appropriate treatment to the patient with the
correct dosage. The overall benefits first and foremost
concern patients, who receive the most effective and least
toxic treatments. Such an approach in patient care has
economic advantages too. The correct administration of
the adequate drug instead of using other ineffective and
expensive therapies appears to be cost-effective. It has
been calculated that US health-care system could save up
to $740 million dollars by only treating patients with
Cetuximab with a wild-type KRAS status [46]. Other stud-
ies suggest that pharmacogenomics can be a good way
to improve cost-effectiveness [47]. However, more studies
are needed in this field to assess the real economic impact
of these therapies [48].

Interpretation of somatic mutations remains challenging
because, except for a few mutations already described,
the impact of each mutation requires functional studies.
In some countries, the inability to obtain matched healthy
tissue in a daily clinical practice makes detection of so-
matic mutations more difficult. On the other hand, se-
quencing a limited number of genes selected for their
implication in tumor development is now feasible in a
clinical setting (in term of costs and time), and the "on-
copanel" approach allows a larger screening power for
diagnosis or treatment orientation by identification of
sensitivity or resistance to drugs.

Acknowledgments

We thank the biological resources center of Curie Institute,
the sequencing platform SéSAME (Séquençage pour la
Santé, l'Agronomie, la Mer et l'Environnement), CCC
François Baclesse, Caen, the Tumorothèque Caen Basse
Normandie (TCBN), and Michel Henry-Amar for critical
review of the manuscript.

**Conflict of Interest**

None declared.
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**Supporting Information**

Additional Supporting Information may be found in the online version of this article:

<caption>**Figure S1.** Somatic mutations analysis pipeline: BAPT.</caption>

**Table S2.** Somatic mutations found across the 29 patients.