- Meeting report
- Open Access
Report on the first SLFN11 monothematic workshop: from function to role as a biomarker in cancer
© The Author(s) 2017
Received: 17 July 2017
Accepted: 12 September 2017
Published: 2 October 2017
SLFN11 is a recently discovered protein with a putative DNA/RNA helicase function. First identified in association with the maturation of thymocytes, SLFN11 was later causally associated, by two independent groups, with the resistance to DNA damaging agents such as topoisomerase I and II inhibitors, platinum compounds, and other alkylators, making it an attractive molecule for biomarker development. Later, SLFN11 was linked to antiviral response in human cells and interferon production, establishing a potential bond between immunity and chemotherapy. Recently, we demonstrated the potential role of SLN11 as a biomarker to predict sensitivity to the carboplatin/taxol combination in ovarian cancer. The present manuscript reports on the first international monothematic workshop on SLFN11. Several researchers from around the world, directly and actively involved in the discovery, functional characterization, and study of SLFN11 for its biomarker and medicinal properties gathered to share their views on the current knowledge advances concerning SLFN11. The aim of the manuscript is to summarize the authors’ interventions and the main take-home messages resulting from the workshop.
Introduction: SLFN11 as a potential predictive biomarker to assess response to DNA damage inhibitors
Presented by Gabriele Zoppoli
Cell cycle inhibitory function of SLFN11 in the DNA damage response (DDR)
Presented by Elisabetta Leo
SLFN11 was recently identified as a novel DNA damage response (DDR) gene in cancer cell genomic analyses of the NCI60  and the cancer cell line encyclopedia (CCLE)  cancer cell models. In 2012 we reported the causative effect of SLFN11 as a determinant of cancer cell sensitivity to multiple DNA damaging agents in different human cell lines: upon downregulation of SLFN11 by siRNA, cells showed a dramatic increase in viability after short and long-term treatments with camptothecin (CPT) and other DNA damaging agents (DDA) when compared to SLFN11-proficient cell lines . After these initial observations, we worked to elucidate the mechanisms by which SLFN11 impinges on the DDR.
We found that, in SLFN11 proficient cells, SLFN11 protein levels are constant in all the phases of the cell cycle (after FACS-sorting as well as upon pharmacological synchronization) G1, S, G2 and mitosis. Subcellular fractionation studies revealed that SLFN11 is preferentially localized in the nuclear compartment, and binds tighter to the chromatin upon accumulation of DNA damage. SLFN11 in the nucleus forms foci that are in close proximity ahead of the replication foci. SLFN11 is preferentially present in the euchromatic regions (open chromatin, identified by H3K9Ac) and is clearly excluded from heterochromatin (H3K9Me3). We also examined cell cycle progression and DNA replication after CPT treatment in SLFN11-proficient versus SLFN11-downregulated cells. In non-treated cells there is no apparent phenotypic difference; however, during treatment with low doses of CPT, dramatic differences in cell cycle and in DDR are observed between SLFN11-proficient and -deficient cells; these differences are visible as early as 4 h after DDA-treatments . If cells are SLFN11 proficient, they undergo an enforced G1/S arrest, with tight cell cycle and replication block, which leads to cell death [8, 9]. On the contrary, if SLFN11 is absent, cells are capable to re-enter the cell cycle, slowly progress through S-phase and are less prone to die. This slow progression is associated to an hyper-activation of the DNA replication and damage checkpoint: indeed, very high and persistent phosphorylation of ATM, ATR, Chk1, an Chk2 are observed. When SLFN11-depleted cells are co-treated with CPT and either ATM, ATR or Chk1/2 inhibitors, they progress much faster through S-phase and they are ultimately re-sensitized to the damage.
We suggest that SLFN11 works as an additional cell cycle checkpoint, that possibly acts upstream of the classical replication and damage checkpoint, preventing the cells to progress and to survive when they accumulate DNA damage and replication stress . Based on these observations, we can conclude that SLFN11 has high potential relevance in the clinics as predictive biomarker for patient stratification. SLFN11-proficient tumors may be more likely to respond to a DDA-based chemotherapy, whereas SLFN11-deficient tumors might require more aggressive combination treatments, for example with ATM or ATR inhibitors, or different anticancer strategies.
SLFN11 induces lethal S-phase arrest in response to DNA damage—a novel mechanism of how cancer cells are killed by DNA damaging agents
Presented by Yves Pommier and Junko Murai
Predictive markers in ovarian cancer
Presented by Domenico Ferraioli
Ovarian cancer is the seventh most common cancer worldwide and the eighth cause of cancer death in women . Early stages are hard to detect, and several patients are diagnosed when the disease is already in an advanced stage . Standard recommendations for patients with advanced ovarian cancer (AOC) include primary debulking surgery (PDS) followed by platinum-based adjuvant chemotherapy  but, in some cases, the PDS is not feasible or is associated with unacceptable morbidity; therefore, neoadjuvant chemotherapy (NACT) followed by debulking surgery should be performed . Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and approximately 60% of patients with AOC will relapse after first-line chemotherapy . Nowadays, tools predicting the sensitivity or the resistance to chemotherapy and allowing treatment stratification are not available; nevertheless, different biomarker assays are in active development. These approaches include functional assays, identification of resistance gene markers, and micro RNA analysis. A systematic review of 42 studies concerning the prediction of chemotherapy response in AOC using gene expression was performed in 2015 by Lloyd et al. . The authors concluded that a clinically applicable gene signature cannot be identified, highlighting the presence of a severe heterogeneity concerning the histological type, the tissue preservation techniques applied, and the manners of obtaining the gene signature among the different studies. Chemoresponse tests, or other biomarker assays, are thus not recommended to choose a chemotherapy regimen. The majority of the available studies failed to demonstrate a survival benefit of chemotherapy regimens selected on chemoresponse assays compared to chemotherapy regimens selected using traditional clinical factors . To conclude, a validated predictive biomarker does not currently exist, and the international guidelines only suggest the use of CA 125 to monitor response to chemotherapy as part of a clinical trial . Well-designed randomized controlled trials are needed to develop a predictive model of response to chemotherapy.
SLFN11 assessment in ovarian cancer: phenotypic and histological distribution and association with TIL infiltration
Presented by Valerio Gaetano Vellone
Epithelial carcinoma of the ovary has always been clinically considered as one disease, but there is now a much greater realization that the various subtypes have a different natural behavior and prognosis . At present, adjuvant therapy is mainly dependent upon tumor stage and grade rather than type . However, it is of common observation how tumors with similar stage and histologic type can behave in radical different ways and finding potential molecular markers represents one of the challenges of modern surgical pathology. To date, DNA-damaging chemotherapeutic agents constitute the backbone of treatment for most solid and hematological tumors. High expression levels of SLFN11 seems to correlate with the sensitivity of human cancer cells to DNA-damaging agents . In this setting, it appears clear how immunohistochemistry (IHC) testing for SLFN11 may represent a powerful tool to predict the response and modulate the chemotherapy for high-grade serous ovarian carcinoma (HGSC). To date no commercial kit for SLFN11 IHC testing is available, so we adapted two kits originally commercialized for Western Blot (WB), and we tested a population of 75 cases of HGSC. As positive control, we used a commercial culture of ovarian carcinoma (SKOV-3) processed with agarose-embedded cell block technique (CCB); SKOV-3 cell culture is known to have high level of expression for SLFN11.
Immunohistochemistry appeared clean and specific, no aspecific bonds were observed in tumor and residual ovarian stroma (Fig. 3B), and a relevant positive internal control was represented by a subpopulation of tumor infiltrating lymphocytes (TIL), which stained intensively for SLFN11 (Fig. 3C). SLFN11 expression resulted extremely variable among cases, and even in different fields of the same tumor. However, a dominant pattern of intensity seems to exist in the same neoplasia. For each case we assessed both the intensity score (IS) and the distribution score (DS) evaluating at least 300 cells. Intensity score (IS) evaluates the main pattern of intensity of stain in positive cancer cells as follow: 0: no stain (Fig. 3C); 1 ±: weak stain (visible at high magnification) (Fig. 3D); 2 +: moderate stain (visible at scan magnification) (Fig. 3E); 3 +: intense stain (Fig. 3F). Distribution score (DS) evaluates the percentage of stained cancer cells as follow: 0: no stained cells; 1 +: < 10% of stained cells; 2 +: 10–40% of stained cells; 3 +: > 40% of stained cells. These scores were combined to obtain a final histological score (HS) as follow: HS = IS × DS. Study cases were grouped on the base of HS in the following categories: cases with HS = 0 were considered SFLN11 negative, cases with HS 1 and 2 were considered SFLN11 low, cases with HS 3 and 4 were considered SFLN11 intermediate, while cases with HS 6 and 9 were considered SFLN11 high. At the end of the evaluation, the SLFN11 expression in the studied case set was distributed with an elegant Gaussian-like fashion: 27 cases (39.13%) resulted SLFN11 negative, 11 cases (15.94%) resulted SLFN11 low, 23 cases (33.33%) resulted SLFN11 intermediate and 8 cases (11.59%) resulted SLFN11 high. Globally, SLFN11 appears to be poorly expressed in HGSC, with the larger subpopulation composed by cases with no sign of stain (SLFN11 negative). We hypothesize that, if SLFN11 negative cases mirror the large population of chemotherapy-resistant patients, SLFN11-high cases may identify a subpopulation of chemotherapy-responsive patients with a better prognosis. Of interest, only a subpopulation of TIL appears to express SLFN11. Their nature and biological role remain to be studied. In the future the presented IHC data will be matched with RNA expression data and clinical data such as overall survival and disease free interval, to better estimate the role of SLFN11 as a potential novel, pivotal prognostic marker in HGSC.
Molecular determinants of immune responsiveness in breast cancer and putative role of SLFN11
Presented by Davide Bedognetti
By exploiting the integrative data available from the cancer genome atlas, we assessed the determinants of immune response in breast cancer (BC) . In that work, we identified that a T helper cell phenotype upregulation is associated with a better prognosis, validating such observation in an independent data set . SLFN11 was discovered in association with thymocyte maturation , and appears as an interferon (IFN) regulated gene . To investigate the transcriptional landscape of SLFN11 in BC, we performed a gene expression microarray meta-analysis of more than 7000 cases from 35 publicly available data sets . By pan-transcriptional SLFN11 correlative analysis, we identified 537 transcripts in the top 95th percentile of Pearson’s coefficients with SLFN11. The terms “lymphocyte activation”, “immune response”, and “T cell activation” resulted as top gene ontology enriched processes . We leveraged the method of multiple corresponding analysis, a multivariate statistical process aimed at inferring mutual associations among categorical variables . Thus, we identified a patient cluster defined by elevated SLFN11 expression, ER lack of staining, basal-like PAM50 phenotype, increased CD3D, STAT1 signature , and younger age at diagnosis. By penalized maximum likelihood lasso regression , we observed a very strong association of SLFN11 with the previously described stroma 1 and stroma 2 signatures [28, 29]. These signatures usually appear upregulated in basal-like BC and in ER- tumors responding to chemotherapy. Finally, using Cox proportional hazard regression, we characterized SLFN11 high levels, high proliferation index, and ER negativity as independent parameters for longer disease-free interval in patients undergoing chemotherapy. Altogether, our data point toward a role for SLFN11 in BC, in likely connection with the immune system modulation in such disease entity.
SLFN11 and sensitivity to irinotecan in colon cancer
Presented by Sana Intidhar Labidi-Galy
Shared by all the co-authors
SLFN11 is a protein with a causal association with response to DDA in cancer cells.
SLFN11 is induced by IFN, but the current relationship between TILs and SLFN11 expression in cancer tissues is not known.
SLFN11 can be assessed in human cancer tissues by IHC, with wide range of expression.
Several preclinical and clinical models points toward SLFN11 as a predictive marker of response to DDA and PARP inhibitors.
SLFN11 expression may be related to mutational burden and MSI in colon cancer.
At present, the predictive role of SLFN11 expression in human tumors is unclear and needs further investigation.
At present, there is no consensus on the exact function of SLFN11 in health and disease, but all available evidence points toward its relevance in cancer.
All authors equally contributed to writing the present manuscript, revised it. All authors read and approved the final manuscript.
GZ wishes to thank Dr. Daniela Piras for her invaluable contribution to the realization of the meeting, Dr. P. Blandini for his invaluable scientific insights, and Dr. Massimo Ivaldi for the impeccable IT support. SILG and PT want to thank Pr Arnaud Roth for allowing access to the gene-expression profile database of the PETACC3 study.
The authors declare that they no competing interests.
Availability of data and materials
All slide sets detailing the present meeting report are available on the webpage http://www.unige/dimi/qualcosacheoranonso.html.
Consent for publication
All researchers and participants to the meeting gave their consent to the publication of the meeting picture.
Ethics approval and consent to participate
The meeting venue, technical and housing support, as well as the submission fees were provided by University of Genova, IT with an ad-hoc grant to GZ.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Schwarz DA, Katayama CD, Hedrick SM. Schlafen, a new family of growth regulatory genes that affect thymocyte development. Immunity. 1998;9:657–68.View ArticlePubMedGoogle Scholar
- Liu F, Zhou P, Wang Q, Zhang M, Li D. The Schlafen family: complex roles in different cell types and virus replication. Cell Biol Int. 2017. doi:10.1002/cbin.10778.Google Scholar
- Zoppoli G, Regairaz M, Leo E, Reinhold WC, Varma S, Ballestrero A, et al. Putative DNA/RNA helicase Schlafen-11 (SLFN11) sensitizes cancer cells to DNA-damaging agents. Proc Natl Acad Sci USA. 2012;109:15030–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Li M, Kao E, Gao X, Sandig H, Limmer K, Pavon-Eternod M, et al. Codon-usage-based inhibition of HIV protein synthesis by human schlafen 11. Nature. 2012;491:125–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Nogales V, Reinhold WC, Varma S, Martinez-Cardus A, Moutinho C, Moran S, et al. Epigenetic inactivation of the putative DNA/RNA helicase SLFN11 in human cancer confers resistance to platinum drugs. Oncotarget. 2016;7:3084–97.View ArticlePubMedGoogle Scholar
- Seiler JA, Conti C, Syed A, Aladjem MI, Pommier Y. The intra-S-phase checkpoint affects both DNA replication initiation and elongation: single-cell and -DNA fiber analyses. Mol Cell Biol. 2007;27:5806–18.View ArticlePubMedPubMed CentralGoogle Scholar
- Bartek J, Lukas J. Chk1 and Chk2 kinases in checkpoint control and cancer. Cancer Cell. 2003;3:421–9.View ArticlePubMedGoogle Scholar
- Ciccia A, Elledge SJ. The DNA damage response: making it safe to play with knives. Mol Cell. 2010;40:179–204.View ArticlePubMedPubMed CentralGoogle Scholar
- Murai J, Huang SN, Das BB, Renaud A, Zhang Y, Doroshow JH, et al. Trapping of PARP1 and PARP2 by clinical PARP inhibitors. Cancer Res. 2012;72:5588–99.View ArticlePubMedPubMed CentralGoogle Scholar
- Murai J, Huang S-YN, Renaud A, Zhang Y, Ji J, Takeda S, et al. Stereospecific PARP trapping by BMN 673 and comparison with olaparib and rucaparib. Mol Cancer Ther. 2014;13:433–43.View ArticlePubMedGoogle Scholar
- Murai J, Feng Y, Yu GK, Ru Y, Tang S-W, Shen Y, et al. Resistance to PARP inhibitors by SLFN11 inactivation can be overcome by ATR inhibition. Oncotarget. 2016;7:76534–50.PubMedPubMed CentralGoogle Scholar
- Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–86.View ArticlePubMedGoogle Scholar
- Munkarah AR, Coleman RL. Critical evaluation of secondary cytoreduction in recurrent ovarian cancer. Gynecol Oncol. 2004;95:273–80.View ArticlePubMedGoogle Scholar
- NCCN. Clinical practice guidelines in oncology. Ovarian cancer (including fallopian tube cancer and primary peritoneal cancer). https://www.nccn.org/professionals/physician_gls/f_guidelines.asp.
- Wright AA, Bohlke K, Armstrong DK, Bookman MA, Cliby WA, Coleman RL, et al. Neoadjuvant chemotherapy for newly diagnosed, advanced ovarian cancer: Society of Gynecologic Oncology and American Society of Clinical Oncology Clinical Practice Guideline. Gynecol Oncol. 2016;143:3–15.View ArticlePubMedPubMed CentralGoogle Scholar
- Bristow RE, Puri I, Chi DS. Cytoreductive surgery for recurrent ovarian cancer: a meta-analysis. Gynecol Oncol. 2009;112:265–74.View ArticlePubMedGoogle Scholar
- Lloyd KL, Cree IA, Savage RS. Prediction of resistance to chemotherapy in ovarian cancer: a systematic review. BMC Cancer. 2015;15:117.View ArticlePubMedPubMed CentralGoogle Scholar
- Duffy MJ, Bonfrer JM, Kulpa J, Rustin GJS, Soletormos G, Torre GC, et al. CA125 in ovarian cancer: European Group on tumor markers guidelines for clinical use. Int J Gynecol Cancer. 2005;15:679–91.View ArticlePubMedGoogle Scholar
- Kobel M, Kalloger SE, Boyd N, McKinney S, Mehl E, Palmer C, et al. Ovarian carcinoma subtypes are different diseases: implications for biomarker studies. PLoS Med. 2008;5:e232.View ArticlePubMedPubMed CentralGoogle Scholar
- Jeronimo C, Forget D, Bouchard A, Li Q, Chua G, Poitras C, et al. Systematic analysis of the protein interaction network for the human transcription machinery reveals the identity of the 7SK capping enzyme. Mol Cell. 2007;27:262–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Hendrickx W, Simeone I, Anjum S, Mokrab Y, Bertucci F, Finetti P, et al. Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis. Oncoimmunology. 2017;6:e1253654.View ArticlePubMedPubMed CentralGoogle Scholar
- Curtis C, Shah SP, Chin S-F, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486:346–52.PubMedPubMed CentralGoogle Scholar
- Zoppoli G, Brohee S, Desmedt C, Sotiriou C, Ballestrero A. Clinico-pathological and transcriptomic determinants of SLFN11 expression in invasive breast carcinoma. J Immunother Cancer. 2015;3:O3. doi:10.1186/2051-1426-3-S1-O3.View ArticlePubMed CentralGoogle Scholar
- Gendoo DMA, Ratanasirigulchai N, Schroder MS, Pare L, Parker JS, Prat A, et al. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics. 2016;32:1097–9.View ArticlePubMedGoogle Scholar
- Abdi H, Valentin D. Multiple correspondence analysis. In: Encyclopedia of measurement and statistics. Thousand Oaks: SAGE Publications, Inc.; 2007. p. 651–7. doi:10.4135/9781412952644. NV-3.
- Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.View ArticlePubMedPubMed CentralGoogle Scholar
- Farmer P, Bonnefoi H, Anderle P, Cameron D, Wirapati P, Becette V, et al. A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer. Nat Med. 2009;15(1):68–74. doi:10.1038/nm.1908.View ArticlePubMedGoogle Scholar
- Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008;14(5):518–27. doi:10.1038/nm1764.View ArticlePubMedGoogle Scholar
- Van Cutsem E, Labianca R, Bodoky G, Barone C, Aranda E, Nordlinger B, et al. Randomized phase III trial comparing biweekly infusional fluorouracil/leucovorin alone or with irinotecan in the adjuvant treatment of stage III colon cancer: PETACC-3. J Clin Oncol. 2009;27:3117–25.View ArticlePubMedGoogle Scholar
- Bosman FT, Yan P, Tejpar S, Fiocca R, Van Cutsem E, Kennedy RD, et al. Tissue biomarker development in a multicentre trial context: a feasibility study on the PETACC3 stage II and III colon cancer adjuvant treatment trial. Clin Cancer Res. 2009;15:5528–33.View ArticlePubMedGoogle Scholar
- Roth AD, Delorenzi M, Tejpar S, Yan P, Klingbiel D, Fiocca R, et al. Integrated analysis of molecular and clinical prognostic factors in stage II/III colon cancer. J Natl Cancer Inst. 2012;104:1635–46.View ArticlePubMedGoogle Scholar
- Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LAJ, Kinzler KW. Cancer genome landscapes. Science. 2013;339:1546–58.View ArticlePubMedPubMed CentralGoogle Scholar
- Deng Y, Cai Y, Huang Y, Yang Z, Bai Y, Liu Y, et al. High SLFN11 expression predicts better survival for patients with KRAS exon 2 wild type colorectal cancer after treated with adjuvant oxaliplatin-based treatment. BMC Cancer. 2015;15:833.View ArticlePubMedPubMed CentralGoogle Scholar
- Rosty C, Young JP, Walsh MD, Clendenning M, Walters RJ, Pearson S, et al. Colorectal carcinomas with KRAS mutation are associated with distinctive morphological and molecular features. Modern Pathol. 2013;26:825–34.View ArticleGoogle Scholar