# A transcriptomic signature predicting septic outcome in patients undergoing autologous stem cell transplantation

Yasmine Labiad, Geoffroy Venton, Farnault Laure, Céline Baier, Julien Colle, Cédric Mercier, Ivanov Vadim, Corinne Nicolino-Brunet, Loriod Béatrice, Nicolas Fernandez-Nunez, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Yasmine Labiad, Geoffroy Venton, Farnault Laure, Céline Baier, Julien Colle, et al. A transcriptomic signature predicting septic outcome in patients undergoing autologous stem cell transplantation. Experimental Hematology, 2018, 10.1016/j.exphem.2018.06.001. hal-01850637

HAL Id: hal-01850637

https://amu.hal.science/hal-01850637

Submitted on 15 Jan 2019

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<figure><img src="image_2.png" /></figure>
# A transcriptomic signature predicting septic outcome in patients undergoing autologous stem cell transplantation

Running title: Predictive transcriptomic signature of sepsis

Labiad Yasmine ¹*, Venton Geoffroy ¹,²*, Farnault Laure ², Baier Céline ¹, Colle Julien ¹,², Mercier Cedric

², Ivanov Vadim ², Nicolino Corinne ², Loriod Béatrice ¹, Fernandez-Nunez Nicolas ¹, Torres Magali ¹,

Mattei Jean-Camille ³, Rihet Pascal ², Nguyen Catherine ¹ and Costello Régis ¹,² #

¹ Aix Marseille Univ, INSERM, TAGC, Marseille, France, ² AP-HM, Service d'Hématologie et de
Thérapie Cellulaire de La Conception, Marseille, France, ³Aix Marseille Univ, INSERM, CRO2, Marseille,

France

Paper's word count: 3174

# Corresponding author :

Prof. Costello Régis (MD., PhD.), AP-HM Centre Hospitalier Universitaire La Conception, Service d'Hématologie et Thérapie Cellulaire, 147 Boulevard Baille, 13005 Marseille, France. Phone number: +33 4 91 38 41 50, Fax number: +33 4 91 36 41 54, E-mail address: regis costello@free.fr

<footnote>* Equal contribution</footnote>
Complete contact informations

**Labiad Yasmine PhD** : Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France Phone : +33 4 91 82 87 26 Mail : labiad.yasmine@gmail.com

**Venton Geoffroy MD-PhD**: Campus de Luminy, 163 Avenue de Luminy, case 928 Marseille, 13288. AP-HM Centre Hospitalier Universitaire La Conception, Service d'Hématologie et Thérapie Cellulaire, 147 Boulevard Baille, 13005 Marseille, France. Phone : + 33 4 91 38 41 50 Mail : geoffroy.venton@ap-hm.fr

**Farnault Laure MD**: AP-HM Centre Hospitalier Universitaire La Conception, Service d'Hématologie et Thérapie Cellulaire, 147 Boulevard Baille, 13005 Marseille, France. Phone : + 33 4 91 38 41 50 Mail : laure.farnault@ap-hm.fr

**Baier Céline PhD**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France Phone : +33 4 91 82 87 26 Mail : celine.baier@yahoo.fr

**Colle Julien MD**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288. AP-HM Centre Hospitalier Universitaire La Conception, Service d'Hématologie et Thérapie Cellulaire, 147 Boulevard Baille, 13005 Marseille, France. Phone : + 33 4 91 38 41 50 Mail : julien.colle@ap-hm.fr

**Mercier Cédric MD**: AP-HM Centre Hospitalier Universitaire La Conception, Service d'Hématologie et Thérapie Cellulaire, 147 Boulevard Baille, 13005 Marseille, France. Phone : + 33 4 91 38 41 50 Mail : cedric.mercier@ap-hm.fr

**Ivanov Vadim MD**: AP-HM Centre Hospitalier Universitaire La Conception, Service d'Hématologie et Thérapie Cellulaire, 147 Boulevard Baille, 13005 Marseille, France. Phone : + 33 4 91 38 41 50 Mail : vadim.ivanov@ap-hm.fr
**Nicolino Corinne MD**: AP-HM Centre Hospitalier Universitaire La Conception, Service d'Hématologie et Thérapie Cellulaire, 147 Boulevard Baille, 13005 Marseille, France. Phone : + 33 4 91 38 41 50
Mail : corinne.nicolino@ap-hm.fr

**Loriod Béatrice**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France Phone : +33 4 91 82 87 26 Mail : <u>beatrice.loriod@inserm.fr</u>

**Fernandez-Nunez Nicolas**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France Phone : +33 4 91 82 87 26. Mail : <u>nicolas.fernandez-nunez@inserm.fr</u>

**Torres Magali**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France Phone : +33 4 91 82 87 26. Mail : <u>magali.torres@univ-amu.fr</u>

**Mattei Jean-Camille**: Aix-Marseille Université - INSERM, UMR 911, CRO2, Faculté de Médecine - 27, Boulevard Jean Moulin, Marseille, 13385, France. Phone : +33 4 91 38 00 00 Mail : <u>jeancamille.mattei@gmail.com</u>

**Rihet Pascal Pr**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France Phone : +33 4 91 82 87 26. Mail : <u>pascal.rihet@univ-amu.fr</u>

**Nguyen Catherine PhD**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France Phone : +33 4 91 82 87 26. Mail : <u>catherine.nguyen@inserm.fr</u>

**Costello Régis Pr**: Aix-Marseille Université - INSERM, UMR1090, TAGC Campus de Luminy, 163 Avenue de Luminy, case 928, Marseille, 13288, France. Service d'hématologie et de thérapie cellulaire APHM, Hôpital de la Conception, Marseille, France. Phone : + 33 4 91 38 41 50 Mail: <u>regis costello@free.fr</u>
Highlights :

1) A transcriptomic signature predicts infection in neutropenic patients

2) Expression of eleven genes can be used routinely for sepsis prediction

3) Early anti-infectious treatment may improve sepsis outcome

4) Identification of a predictive transcriptomic signature is ongoing in leukemia
1 **Abstract**

2 Autologous hematopoietic stem cell transplantation (auto-HSCT) is a standard treatment in multiple

3 myeloma and relapsed or refractory lymphomas. After auto-HSCT, hematologic reconstitution and

4 infectious complications are the main two critical issues. Though many patients develop infectious

5 complications after therapeutic intensification, it remains impossible to predict infection for each

6 individual. The goal of this work was to determine and identify a predictive transcriptomic signature

7 of systemic inflammatory response syndrome (SIRS) and/or sepsis in patients receiving auto-HSCT.

8 High throughput transcriptomic and bioinformatics analysis were performed to analyze gene

9 expression modulation in peripheral blood mononuclear cells (PBMCs) in 21 patients undergoing

10 auto-HSCT for hematological malignancies (lymphoma or multiple myeloma [MM]).

11 Transcriptomic analysis of PBMCs samples collected just after conditioning regimen identified an

12 eleven genes signature (CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425,

13 MTRM8, C6orf192 and LOC10289230 and XLOC-005643) that was able to early predict (at least 2 to 7

14 days before its occurrence) the development of SIRS or sepsis.

15 The possibility of SIRS or sepsis occurrence early prediction (2-7 days before occurrence) opens up to

16 new therapeutic strategies based on pre-emptive antibiotic and/or antifungal prophylaxis adapted to

17 the specific risk profile of each patient.

18

19 **Key words:** Systemic Inflammatory Response Syndrome (SIRS); Sepsis; Autologous Hematopoietic

20 Stem Cell Transplantation (Auto-HSCT); Transcriptomic Analysis; Treatment related mortality (TRM)

21 **Abstract word count:** 195
22

**Introduction**

Auto-HSCT is based on the administration of myelosuppressive high-dose chemotherapy, followed by infusion of autologous hematopoietic stem cells to obtain hematologic reconstitution. Hematopoietic stem cells (HSCs) infusion reduces chemotherapy-induced myelosuppression period and procedure-related mortality rate below 3% [1–3]. With few exceptions (solid tumors, autoimmune diseases), auto-HSCT is essentially indicated **for selected hematological malignancies** and considered as a standard treatment in young patients with MM and for relapsed or refractory lymphoma.

Besides direct toxicity of conditioning regimens, deep (<0.5 G/L neutrophils) and prolonged (usually 7–12 days) neutropenia exposes patients to significant risks of infection. The saprophytic gram-negative bacilli (such as *Escherichia coli*) are the most common cause of septic shock [4] and chronic immunosuppression exposes to the risk of fungal infection. An anti-fungal prophylaxis is usually administered [5] but antimicrobial prophylaxis is less often given because its effectiveness is not clearly established and increases the risk of *Clostridium difficile* diarrhea [6]. Unfortunately, it is not possible to foresee which patients will develop a SIRS and/or sepsis. Therefore, it remains impossible to adjust the antibiotic or antifungal prophylaxis to the specific risk profile of each patient.

The main objective of this work was to determine and identify a predictive transcriptomic signature of the SIRS and/or sepsis in patients receiving auto-HSCT, leading to the possibility of a pre-emptive anti-infectious treatment.

40
41 **Patients, materials and methods**

42 **Patients**

43 The **prospective** study was approved by the institutional review board of the Assistance Publique des
44 Hôpitaux de Marseille (AP-HM – AORC2012 2012-08). Written informed consent was obtained from
45 each patient. Patients were admitted in the hematology department of the Conception university
46 hospital for undergoing auto-HSCT. All patients were under 65 years and were already followed in
47 the hematology department for MM or high-grade lymphoma. Before auto-HSCT, patients were in
48 complete remission (CR) or in partial remission (PR) after conventional chemotherapy. Inclusion
49 criteria were the same as required for being eligible to auto-HSCT. Twenty four patients were
50 included in this study protocol. Twenty one were analyzed. Blood samples were collected for each
51 patient at three moments: before the conditioning regimen (T1), after the conditioning regimen and
52 before the graft infusion (T2) and at the end of the neutropenic phase (T3).

53 All patients (after written informed consent) hospitalized in the Hematology and Cellular Therapy
54 Department of the university hospital of Marseille between August 2012 and December 2015 have
55 been included. Only the patients (21 in all) in whom the RNA extraction was a success (after
56 quantification and quality control) have been analyzed.

57

58 **SIRS and sepsis definitions**

59 SIRS and sepsis definition are based on the American College of Chest Physicians society of Critical
60 Care Medicine consensus. SIRS is defined as the systemic inflammatory response to a variety of
61 severe clinical symptoms with at least two of the following criteria: a) Temperature higher than 38 °C
62 or lower than 36 °C b) Heart rate higher than 90 beats/min c) Respiratory rate higher than 20
63 breaths/min or PaCO2 lower than 32 mmHg d) White blood cell counts higher than 12,000 cells/mm³
64 or lower than 4,000 cells/mm³, or the presence of more than 10% immature neutrophils. The last
65 criterion cannot be considered in auto-HSCT context because of the aplasia phase following HSC
66 infusion [7, 8]. Sepsis is defined as SIRS secondary to documented or suspected infection. Patients
67 with severe sepsis are patients with sepsis and at least one organ dysfunction. Septic choc is defined
68 by severe sepsis associated with refractory hypotension [8].

69

70 **Conditioning regimens**

71 Conditioning regimen for therapeutic intensification was high dose melphalan (200 mg/m²) for
72 patients with MM and BEAM for patients with lymphoma (carmustin 300 mg/m2 at day -6,
73 etoposide 150 mg/m2 from day -5 to -2 twice daily, cytarabin 200mg/m2 from day -5 to -2 twice daily
74 and melphalan 140mg/m2 day -1, with auto-HSCT on day 0).

75

76 **Hematopoietic stem cell collection**

77 HSCs mobilization required hematopoietic growth factors (G-CSF in most cases). Two procedures
78 have been used to mobilize HSCs. For steady-state collection, G-CSF was injected at the dose of
79 10µg/kg-a-day. Mobilization chemotherapy consisted in high doses cyclophosphamide (1.5 to 4g/m²)
80 followed by 5µg/kg-a-day of G-CSF. When HSCs collection failed with these usual procedures,
81 plerixafor (SDF-1/CXCR4 interaction inhibitor) was used at 240 µg/kg.

82

83 **PBMCs isolation**

84 Blood samples were centrifuged to separate plasma and other blood components. Concentrate blood
85 was diluted and PBMCs were collected by Ficoll-Hystopaque density-gradient centrifugation [9].

86

87 **RNA extraction, quantification quality controls**

88 Depending on cell number, two kits were used for RNA extraction, the RNeasy Mini Kit Qiagen ™
89 (Qiagen, Valencia, California), which accepts 10 million of cells and the RNeasy Midi Kit Qiagen ™,
90 which accepts up to 100 million cells. RNAs were extracted according to the Qiagen protocol. RNAs
91 were quantified by NanoDrop 1000 (Nano Drop Technologies, San Diego, CA). Optical density was 92 measured at 260 and 280 nm and the ratio 260/280 (> 1.8) indicates its purity. Extracted RNA quality 93 was checked with Agilent 2100 Bioanalyzer™ (Agilent Technologies, Santa Clara, California). A score 94 on a scale of 0 to 10 was automatically attributed to each sample and corresponded to RNA Integrity 95 Number (RIN). Sample with a RIN under 7 was discarded. 96

97 **Pangenomic gene expression assay**

98 One hundred nanograms of total RNA was labelled using One-Color Microarray-Based Gene 99 Expression Analysis: Low Input Quick Amp (LIQA) labelling protocol. 0.6 µg of the purified Cy3 100 labelled cRNA were hybridized for 17h at 65 °C, at 60 rpm, using the SurePrint G3 human GE 8x60K 101 V2 chips (Agilent Technologies, Santa Clara, California). Microarrays were composed of 62 928 102 features. Probes synthesized on chips had a size of 60 nucleotides. Microarrays were washed using 103 Gene Expression Wash Buffer Kit (Agilent Technologies) and scanned through the standard Agilent 104 protocol. Data were processed using Feature Extraction software. 105

106 **Differential gene expression analysis**

107 The library AgiND is implemented in R software in order to analyze and visualize data. AgiND was 108 developed on Bioconductor library model (tagc.univ-mrs.fr/ComputationalBiology/AgiND/) and is 109 used to diagnose data quality and data-microarray normalization. Quantile method was used to 110 normalize data; the objective was to homogenize distribution of microarray intensity [10]. A filter 111 was applied on row data to delete controls, then a second filter was applied to delete genes which 112 were expressed under the background in at least 80% of samples in each group (SIRS-, SIRS+, 113 Sepsis+). 114

114 To test co-factors effects (gender, treatment, infection) on gene expression, GeneANOVA software 115 was used to perform ANalysis Of Variance (ANOVA) on normalized data to determine an estimation
116 of the contribution of each factor (genes, gender, treatment, infection) in gene expression variation.
117 Global ANOVA model is given in the following formula: $Y = \mu + \beta_1 G + \beta_2 T + \beta_3 I + \varepsilon$, where Y is
118 explained variable, μ is global mean, β₁, β₂, β₃, are model coefficients, and G, T, I are the quantitative
119 variables; β₁ G, is the gene effects, β₂ T, is the treatment effects, β₃ I, the infection effects, and ε is
120 the error term [11]. Differential gene expression analysis was performed using linear Models for
121 Microarray Data (Limma). Limma is a multivariate analysis and takes into account co-factors-effect
122 tested by ANOVA analysis (treatment and gender) (Table 2). In order to determine infection impact
123 on gene expression and on the heat map, new gene expression was calculated after subtracting
124 gender and treatment co-factors effects. Unsupervised hierarchical clustering was applied on
125 adjusted gene expression median adjusted data to group genes and samples, according to their
126 expression using ‘TMeV’ (Tigr MultiExperiment Viewer) MeV: MultiExperiment Viewer | Part of the
127 TM4 Microarray Software Suite [http://www.tm4.org/mev/]. Pearson correlation was used, and
128 clusters were grouped on the basis of average linkage method.

129

130 **Support Vector Machines (SVMs)**

131 SVMs were applied to predict classification of patients according to the predictive signature. SVMs
132 use training set in which genes known to be related to each other by function or samples related to a
133 group are described as positive examples and genes or samples known not to be members of that
134 class are labelled as negative examples. In this study, samples were attributed to two groups: SIRS
135 and sepsis patients in the first group and patients with no temperature in the second. They were
136 combined into a set of training examples used by SVM to distinguish class members from non-
137 members on the basis of expression data. After learning the class expression features, the SVM can
138 be used to recognize and classify each sample on the basis of their expression [11, 12]. SVMs tool is
139 implemented in TMeV software MeV: MultiExperiment Viewer | Part of the TM4 Microarray
140 Software Suite. [http://www.tm4.org/mev/].
141

142

143

144 High-throughput quantitative PCR

145

146

147

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149

150

151

152

153

154

155

156

<table><thead><tr><td>Gene</td><td>R</td><td>F</td></tr></thead><tbody><tr><td>SLC18B1</td><td>5'-GCAGGACAGCTTTTCAGTATCAC-3'</td><td>5'-CCTGGCCTTAAACATCACCG-3'</td></tr><tr><td>CNN3</td><td>5'-GGCTGGCTCCTTTATTAGTGC-3'</td><td>5'-AAGCTGGCCAAAGTGTAATTG-3'</td></tr><tr><td>MTMR8</td><td>5'-ACTTAAGTGGGGAGAGGGGT-3'</td><td>5'-GAGGCAGACTACTCCAAGCA-3'</td></tr><tr><td>GPAT2</td><td>5'-GCCCAGAGAAGCCTACATCA-3'</td><td>5'-CATCAGAAGCTCCTGGGGGA-3'</td></tr><tr><td>PLCG1</td><td>5'-ACAGGAATCTTGGTGCTTCAGT-3'</td><td>5'-CAGGGAGGTACATGGCCAAT-3'</td></tr><tr><td>ANKRD42</td><td>5'-CGCCCATGAAAAGCTGCATA-3'</td><td>5'-GGAGCAAATCTGACAGCCCA-3'</td></tr><tr><td>GAPDH</td><td>5'-CCACCACCCTGTTGCTGTGA-3'</td><td>5'-CCCACTCCTCCACCTTTGAC-3'</td></tr><tr><td>EDAR</td><td>5'-ACATGAGCTGACACTGGCTG-3'</td><td>5'-TGAGTGTGCCATTCCAGGAT-3'</td></tr><tr><td>TRAV3-</td><td>5'-GCCTCGGTTGGGGTATTGAA-3'</td><td>5'-CTCAGCCGGAAGATCAGGTC-3'</td></tr></tbody></table>
<table><tr><td>CHAT</td><td>5'-CAGCAGAACATCTCCGTCGT-3'</td><td>5'-ATGGCCATTGACAACCACCT-3'</td></tr><tr><td>LOC100289230</td><td>5'-GTGCTTCCGGAAAACGTAAA-3'</td><td>5'-TCCTGTGCCCGTAATTTCTC-3'</td></tr></table>
158 **Results**

159 **Patient's analysis**

160 Twenty-four patients were included in the study, among these patients; 21 validated the molecular
161 criteria for transcriptomic analysis. Among these 21 patients, 6 patients did not develop a fever or
162 SIRS (28.6%), nine patients developed SIRS (42.6%), 5 a sepsis (23.8%) and 1 a severe sepsis (5%).
163 Patients' clinical data are summarized in the **Table 1**.

164

165 **Pangenomic gene expression**

166 The 21 samples were analyzed on Agilent microarrays 'SurePrint G3 GE 8x60K human.' After 17 hours
167 of hybridization, the chips were washed and scanned. Results passed microarrays quality controls.
168 Raw data were transformed into log2 and normalized with quantile method. 24 046 probes
169 expressing a higher signal than background (in at least one group) have been selected. Two methods
170 were used for statistical analysis: ANOVA analysis to measure the impact of each factor on gene
171 expression variation (infection, gender, treatment) and Limma analysis on three groups (SIRS -,
172 SIRS+, sepsis +) with the following co-factors: gender and treatment, to define differential gene
173 expression (Figure 1). ANOVA analysis was performed on the 24 046 probes to estimate impact of
174 each factor (infection, gender, treatment) in gene expression variation. P-value was calculated for
175 each factor. For all factors (genes, infections, gender, and treatment) p-values were <10<sup>-4</sup> (Table 2).
176 Infection F score was the highest, suggesting that 'infection' had the greatest impact on the gene
177 expression. Genes F score was of 115.45 (p-value <10<sup>-4</sup>) and explains genes fluctuations. Treatment
178 and gender had an impact on the variation of gene expression with a F score of respectively 176.21
179 and 156.07 (p-value <10<sup>-4</sup>). Unsupervised hierarchical clustering method was used to classify the
180 differently expressed genes identified by Limma analysis. Expression similarity profiles of the genes
181 were grouped on the horizontal axis and samples on the vertical axis. Gene expression profiles are
shown in heat map (Figure 2). With a FDR (False Discovery Rate) fixed to 5%, 11 genes differentially expressed were identified between the patients who did not have temperature (SIRS-) and the patients who developed SIRS and sepsis (taking account effect of gender and treatment). The eleven differentially expressed genes were: *CHAT*, *CNN3*, *ANKRD42*, *LOC100505725*, *EDAR*, *GPAT2*, *ENST00000390425*, *MTRM8*, *LOC10289230* and *XLOC-005643* (Table 3). Gene expression profiling distinguished two groups: patients who did not develop temperature, on the one hand, and patients with SIRS or sepsis, on the other hand. *CHAT*, *CNN3*, *C6orf192*, *ANKRD42*, *LOC100505725*, *EDAR*, *GPAT2*, *ENST00000390425*, *MTRM8*, *LOC10289230* were over expressed and *XLOC-005643* was under-expressed in patient samples with SIRS or Sepsis in contrast to patients who did not develop fever and SIRS.

192

193 SVM

SVM and leave-one-out cross-validation were used to classify patients according to their gene expression. 21 samples were separated into two groups, the first group was composed of 15 SIRS and septic patients and the second of the 6 patients who did not have temperature. SIRS and sepsis patients were considered as positive experiments, and the other as negative. All 15 patients of the positive experiments were classified as positive, staying in positive class (true positives=15) and none was transferred from negative class to positive (False negatives=0). On the 6 patients of the negative experiments, all were classified as negative, all retained in negative class (true negatives=15) and none was recruited into negative class from positives (false positives=0). **We performed a Fisher exact test on our results; the p-value = 1.84 E-5.**

203

204 RT-qPCR

In order to confirm the microarray gene expression results, RT-qPCR was performed on 9 genes that composed the transcriptomic signature. The *LOC 100289230* gene was differently expressed (p-value
207 = 0.003) in patients who developed SIRS or sepsis compared with patients who did not. TRAV3, EDAR, PLCG1-AS1-001, GPAT2, MTRM8, CNN3, and SLC18B1 were also differentially expressed with p-values of 0.004, 0.01, 0.01, 0.005, 0.009, 0.09 and 0.01, respectively (Figure 3).

210

211

212 **Discussion and conclusion**

213 This clinical study identified eleven genes significantly and differentially expressed in patients who
214 developed SIRS or sepsis after the conditioning regimen for auto-HSCT. Ten of them were up-
215 regulated (CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPATZ, ENST00000390425, MTRM8,
216 C6orf192 and LOC10289230) while only one was down-regulated (XLOC-005643). All patients with
217 this specific transcriptomic signature developed a SIRS or sepsis within 48 hours (range 48 hours-7
218 days) following conditioning regimen. All patients were classified in the right group according to their
219 gene expression and based on SVMs analysis. After a wide scientific literature review, genes
220 composing our predictive signature are not directly involved in sepsis or infection pathways. In this
221 cohort of 21 patients, 9 developed a SIRS and 5 a sepsis. Only one patient developed a severe sepsis,
222 and no patient had septic shock, thus impeding the possibility to identify a specific transcriptomic
223 signature predictive for these life-threatening conditions. In addition, since a SIRS/sepsis predictive
224 signature before the conditioning regimen was not identified, the transcriptomic signature was not
225 linked to the patient pre-auto HSCT status but depended on the conditioning regimen patient's
226 response.

227 We wondered about the definition of sepsis and SIRS and the clustering of sirs and sepsis;
228 first, white blood cells count criteria is not relevant in patients in deep aplasia. Furthermore, in the
229 same way, patients in deep aplasia present grade II-III anemia especially after BEAM conditioning
230 regimen. In patients with no cardiovascular and/or pulmonary comorbidity, hemoglobin until 8g/dl is
tolerated. However, that often results in an increase of the heart rate higher than 90 beats/min and/or respiratory rate higher than 20 breaths/min, especially in patients with fever.

Fever is neither sensitive or specific in conventional patients. But, in our severely immunocompromised patients fever is more sensitive and specific than in immunocompetent patients. Nevertheless, it is not unusual to start an antibiotic therapy in patients with no fever but with a microbiological documentation and/or a major increase of the C reactive protein only.

At last, we have been very drastic on the definition of our sepsis patients group. Only the patients with fever and a microbiological documentation have been considered in sepsis. Anyway, our immunocompromised patients are paucisymtpomatic and very few infections were clinically probable.

Our transcriptomic signature predicts SIRS/sepsis profiles and is more robust than the main confounding factors, such as conditioning regimen, type or gender. BEAM conditioning regimen was more myelosuppressive than melphalan alone and men have had a more significant risk to develop infectious complications than women [13]. Auto-HSCT patients affected by MM are conditioned by high dose of melphalan while patients with lymphoma by high dose BEAM chemotherapy, meaning that treatment and pathology are confounding factors. SAM and ANOVA analyses were performed on the data of patient samples before chemotherapy, and no differentially expressed genes were found between patients affected by lymphoma or MM.

This work proposes a transcriptomic approach of the sepsis issue during auto-HSCT neutropenic phase. Vanska et al. have shown that high pentraxin 3 level predicted septic shock and bacteremia at the onset of febrile neutropenia after intensive chemotherapy of hematologic patients [14]. Nonetheless, high pentraxin 3 level had only a predictive value for septic shock in patients who already had a febrile neutropenia. In contrast, our predictive transcriptomic signature identifies patients who have a major risk to develop SIRS and/or sepsis at least 48 hours (range 48 hours–7 days) before onset of fever. In order to confirm our results, our transcriptomic signature has been
validated on a 10 patient's prospective validation cohort (data not shown). Since total gene expression analysis by microarrays is not possible in clinical routine for time and cost-effective reasons, RT-qPCR analysis on a blood sample of the eleven deregulated genes will have to be developed in order to assess the possibility of a routine use.

260

Prophylactic anti-fungal and/or anti-microbial treatments have not proven a high efficiency regarding the outcome in patients undergoing highly myeloablative chemotherapy. For example, the use of ciprofloxacin induces a 20% reduction of infection rates but lead to 70% increase in the development of cifloxacin resistance [15]. Early identification of patients who will develop SIRS and/or sepsis could perhaps contribute to a better use of anti-infectious agents as preemptive treatment instead of a prophylactic treatment, although this is only an hypothesis by should be tested in a prospective clinical trial. A limitation of our study, directly related to the small number of patients and of infectious events, is the impossibility to identify a specific profile for microbial or fungal or viral infections, in order to precisely orientate the preemptive treatment. Finally, identification of high vs low infectious risk patients after myeloablative chemotherapy, could help to select patients with a safe septic profile and allows an early hospital discharge or even outpatient based blood stem cell transplantation, in order to develop a more rational utilization of hospital resources but still maintaining optimal safety conditions [16-19]. A validation of this transcriptomic signature in a prospective and larger cohort of patients is necessary to have a stronger clinical impact. Another strategy (work in progress) is to validate in RT-qPCR only the eleven genes of interest in a prospective and larger cohort.

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278 **Conflict of interest**

279 All authors declare no financial conflict of interest

280

281 **Funding**

282 This work was supported by grants from AP-HM, Assistance Publique – Hôpitaux de Marseille (AORC

283 APHM Junior), and Aix-Marseille Université, Advanced BioDesign. TGML platform supported by the

284 France Génomique

285

286 **Acknowledgments**

287 We would like to thank INSERM, Aix-Marseille Université and Advanced BioDesign (Parc

288 Technologique de Lyon Bâtiment Cèdre 1, 97 Allée Alexandre Borodine, 69 800 Saint-Priest – Lyon)

289 for grants.

290 We are grateful to the patients who gave their informed consent to the use of their samples for

291 research. We are grateful to the IBiSA Transcriptomics and Genomics Marseille-Luminy (TGML)

292 platform was supported by the France Génomique National infrastructure. We thank Laurence Borge

293 for assistance and the use of the cell culture platform facilities (CRCM U1068, Marseille) Plateforme

294 de Culture Cellulaire de Marseille-Luminy, bâtiment TPR2 163 Avenue de Luminy, 13009 Marseille.

295 We also thank Geneviève Victorero and Noushine Mossadegh-Keller for technical supports.

296

297
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351 ANOVA analysis was performed to test the co-factors impact (infection, treatment and gender) on
352 gene expression for samples taken at T2 (after chemotherapy). With p-values <0, 05 infection,
353 treatment and gender have a significant effect on the gene expression. DF: Degrees of Freedom

354 **Table 3: Differentially expressed gene characteristics**

355 Ch: Chromosome, +1: Forward strand, from 3' to 5', -1: Reverse strand, from 5' to 3'

356

357

359 **Figure 1: Differential gene expression flow chart**

360 Pangenomic array for the transcriptomic analysis have been used, data were filtered and the controls
361 were suppressed, cofactors effects were tested based on their implication on variation of gene
362 expression. A multivariate analysis – LIMMA – using the cofactors already tested was performed to
363 define the gene differentially expressed using Bioconductor library. The LIMMA model is given in the
364 following formula: Y = α + β₁. T + β₂. G + β₃. I + ε, where Y is the explained variable, α is the global
365 mean, β₁. T, β₂. G, β₃. I, are the model coefficients, T, G, I, are the quantitative variables, β₁ T, is the
366 treatment effect, β₂. G, is the gene effect, β₃. I is the infection effect and ε is the error term. Then, we
367 performed multi-testing correction by fixing the threshold to 5%. Finally, we adjusted the expression
368 data based on the co-factors before performing the hierarchical clustering *: Linear Models for
369 Microarray data.

370

371 **Figure 2: Gene expression profile adjusted on the effects of the cofactors**
372 Microarray analysis revealed 11 genes differentially expressed. This genes cluster distinguished SIRS
373 or sepsis patients from patients who didn't develop any fever or SIRS.
374
375 **Figure 3: Transcriptomic validation signature by RT-qPCR**
376 Relative gene expression composed the transcriptomic signature of patients who developed SIRS or
377 sepsis compared to patients who did not develop fever and SIRS
378 * = 0.05; ** = 0.01; *** ≤0.01, NS = not significant
379
<table><caption>Table 1 : Patients, pathologies and auto-HSCT characteristics</caption><tbody><tr><td>Baseline and Demographic Characteristics</td><td>n = 21</td></tr><tr><td>Patients age (median)</td><td>58 years<br>Range [32-68]</td></tr><tr><td>Patient gender</td><td></td></tr><tr><td>Male</td><td>14 (67%)</td></tr><tr><td>Female</td><td>7 (33%)</td></tr><tr><td>Hematologic malignancy</td><td></td></tr><tr><td>Lymphoma</td><td>2</td></tr><tr><td>Multiple Myeloma</td><td>19</td></tr><tr><td>Conditioning Regimen</td><td></td></tr><tr><td>BEAM</td><td>2</td></tr><tr><td>Melphalan high dose</td><td>19</td></tr><tr><td>Injected CD 34+ (x 10<sup>6</sup> / kg)</td><td>2.82<br>Range [0.751-3.64]</td></tr><tr><td>Mean duration of fever</td><td>3 days<br>SEM [-4.30 - 4.30]</td></tr><tr><td>Mean duration of neutropenia</td><td>9 days<br>SEM [-2.90 - 2.90]</td></tr><tr><td>Documented bacterial infections</td><td>6 (30%)</td></tr><tr><td>Gram-positive bacteria</td><td>75%</td></tr><tr><td>Staphylococcus hominis</td><td>1</td></tr><tr><td>Staphylococcus epidermidis</td><td>2</td></tr><tr><td>Staphylococcus aureus</td><td>1</td></tr><tr><td>Streptococcus mitis</td><td>1</td></tr><tr><td>Clostridium difficile</td><td>1</td></tr><tr><td>Gram-negative bacteria</td><td>25%</td></tr><tr><td>Escherichia coli</td><td>1</td></tr><tr><td>Klebsiella pneumonia</td><td>1</td></tr><tr><td>Documented fungal infections</td><td>0 (0%)</td></tr><tr><td>Patient classification</td><td></td></tr><tr><td>SIRS-</td><td>6 (29%)</td></tr><tr><td>SIRS+</td><td>9 (42%)</td></tr><tr><td>Sepsis+</td><td>5 (24%)</td></tr><tr><td>Severe sepsis+</td><td>1 (5%)</td></tr><tr><td>Septic shock+</td><td>0 (0%)</td></tr></tbody></table>
<caption>**Table 2: Co-factors effect on gene expression.**</caption>

ANOVA analysis was performed to test the co-factors impact (infection, treatment and gender) on genes expression for samples taken at T2 (after chemotherapy). With p-values < 0, 05 infection, treatment and gender have a significant effect on gene expression. DF: Degrees of Freedom

<table><thead><tr><td>Factors</td><td>Sum of squares</td><td>DF</td><td>Variance</td><td>F score</td><td>P-value</td></tr></thead><tbody><tr><td>Genes</td><td>3476905.49</td><td>28114</td><td>123.67</td><td>115.45</td><td>&lt; 10<sup>-4</sup></td></tr><tr><td>Infection</td><td>954.39</td><td>1</td><td>954.39</td><td>890.96</td><td>&lt; 10<sup>-4</sup></td></tr><tr><td>Treatment</td><td>188.75</td><td>1</td><td>188.75</td><td>176.21</td><td>&lt; 10<sup>-4</sup></td></tr><tr><td>Gender</td><td>167.18</td><td>1</td><td>167.18</td><td>156.07</td><td>&lt; 10<sup>-4</sup></td></tr><tr><td>Residual</td><td>602328.16</td><td>562297</td><td>1.07</td><td>-</td><td>-</td></tr><tr><td>Total</td><td>4080543.97</td><td>590414</td><td>6.91</td><td>-</td><td>-</td></tr></tbody></table>
<caption>Table 3: Differentially expressed gene characteristics.</caption>

Ch : Chromosome, +1 : Forward strand, from 3' to 5', -1 : Reverse strand, from 5' to 3'

<table><thead><tr><td>Gene</td><td>Complete name</td><td>Ensembl ID</td><td>Location</td><td>Sens</td></tr></thead><tbody><tr><td>SLC18B1 / C6ORF192</td><td>solute carrier family 18 member B1</td><td>ENSG00000146409</td><td>Chr 6: 132,769,370-132,798,553</td><td>-1</td></tr><tr><td>CNN3</td><td>calponin 3</td><td>ENSG00000117519</td><td>Chr 1: 94,896,949-94,927,278</td><td>-1</td></tr><tr><td>MTMR8</td><td>myotubularin related protein 8</td><td>ENSG00000102043</td><td>Chr X: 64,268,081-64,395,431</td><td>-1</td></tr><tr><td>LOC100505725 PLCG1-AS1-001 / TOP1-AS1 /</td><td>PLCG1 antisense RNA 1</td><td>ENSG00000226648</td><td>Chr 20: 41,098,329-41,138,003</td><td>-1</td></tr><tr><td>CHAT</td><td>choline O-acetyltransferase</td><td>ENSG00000070748</td><td>Chr 10: 49,609,095-49,665,104</td><td>1</td></tr><tr><td>EDAR</td><td>ectodysplasin A receptor</td><td>ENSG00000135960</td><td>Chr 2: 108,894,471-108,989,372</td><td>-1</td></tr><tr><td>ANKRD42</td><td>ankyrin repeat domain 42</td><td>ENSG00000137494</td><td>Chr 11: 83,193,739-83,260,694</td><td>1</td></tr><tr><td>GPAT2</td><td>glycerol-3-phosphate acyltransferase 2, mitochondrial</td><td>ENSG00000186281</td><td>Chr 2: 96,021,946-96,039,451</td><td>-1</td></tr><tr><td>ENST00000390425/ TRAV3</td><td>T cell receptor alpha variable 3 (gene/pseudogene)</td><td>ENSG00000211777</td><td>Chr 14: 21,723,713-21,724,321</td><td>1</td></tr><tr><td>LOC100289230</td><td>NS</td><td>NS</td><td>Chr 5: 98,929,134-98,931,009</td><td>1</td></tr><tr><td>XLOC_005643</td><td>Inc-CMAHP-1:1 / linc-FAM65B-1/RP3-425P12.2</td><td>ENSG00000230372</td><td>Chr 6: 25,061,853-25,063,735</td><td>-1</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 1: Differential genes expression flow-chart</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2: Gene expression profile adjusted on the effects of the cofactors</figcaption></figure>

Color should be used
<figure><img src="image_5.png" /><figcaption>Figure 3 : Transcriptomic validation signature by RT-qPCR</figcaption></figure>