Morphological and phenotypical features of ovarian metastases in breast cancer patients

Inge T A Peters
Merle A Van Der Steen
Bertine W Huisman
Carina G J M Hilders
Vincent T H B M Smit
Alexander L Vahrmeijer
F M Cornelis
Sier
J Baptist Trimbos
Peter J K Kuppen
Morphological and phenotypical features of ovarian metastases in breast cancer patients
10.1186/s12885-017-3191-yR E S E A R C H A R T I C L E Open Access
Background: Autotransplantation of frozen-thawed ovarian tissue is a method to preserve ovarian function and fertility in patients undergoing gonadotoxic therapy. In oncology patients, the safety cannot yet be guaranteed, since current tumor detection methods can only exclude the presence of malignant cells in ovarian fragments that are not transplanted. We determined the need for a novel detection method by studying the distribution of tumor cells in ovaries from patients with breast cancer. Furthermore, we examined which cell-surface proteins are suitable as a target for non-invasive tumor-specific imaging of ovarian metastases from invasive breast cancer. Methods: Using the nationwide database of the Dutch Pathology Registry (PALGA), we identified a cohort of 46 women with primary invasive breast cancer and ovarian metastases. The localization and morphology of ovarian metastases were determined on hematoxylin-and-eosin-stained sections. The following cell-surface markers were immunohistochemically analyzed: E-cadherin, epithelial membrane antigen (EMA), human epidermal growth receptor type 2 (Her2/neu), carcinoembryonic antigen (CEA), αvβ6 integrin and epithelial cell adhesion molecule (EpCAM). Results: The majority of ovarian metastases (71%) consisted of a solitary metastasis or multiple distinct nodules separated by uninvolved ovarian tissue, suggesting that ovarian metastases might be overlooked by the current detection approach. Combining the targets E-cadherin, EMA and Her2/neu resulted in nearly 100% detection of ductal ovarian metastases, whereas the combination of EMA, Her2/neu and EpCAM was most suitable to detect lobular ovarian metastases. Conclusions: Examination of the actual ovarian transplants is recommended. A combination of targets is most appropriate to detect ovarian metastases by tumor-specific imaging.

Background

Cryopreservation of ovarian tissue is the only option to preserve fertility and restore ovarian activity in prepubescent girls and women who cannot postpone the start of adjuvant chemotherapy [1]. Although autotransplantation of frozen-thawed cortical ovarian tissue has resulted in more than 86 live births worldwide [2], this method has not yet been endorsed by the American Society for Reproductive Medicine (ASRM) [3]. One of the reasons that the ASRM committee has put forward is that the safety of the procedure has not been substantiated in patients with cancer. Cortical ovarian tissue may contain malignant cells that could lead to reseeding of cancer upon autotransplantation. This risk of reintroducing malignant cells cannot be eliminated, since the current tumor detection methods (e.g. PCR, immunohistochemistry) jeopardize the ovarian tissue's viability [4]. These methods can therefore only be used to examine cortical ovarian strips that are not transplanted.

Hence, the presence of tumor cells in the actual ovarian autografts remains questionable.

Whether the current approach for tumor detection is accurate depends on the distribution of metastatic tumor cells in the ovarian tissue [5,6]. If tumor cells are diffusely dispersed throughout the ovary, examination of one or two cortical ovarian strips might be sufficient. By contrast, if tumor cells are confined to a specific area in the ovarian cortex, this approach is inadequate. Then, cortical ovarian strips that are examined may turn out to be devoid of tumor cells whereas ovarian fragments that harbor metastases may be transplanted, possibly resulting in cancer relapse.

The implementation of a detection method that allows examination of the cortical ovarian strips that will be transplanted, will significantly reduce the risk of transferring malignant cells. Near-infrared fluorescence (NIRF) imaging might be an appropriate approach, as this technique discriminates malignant cells from nonmalignant tissue in real time while leaving the tissues viable [7]. A NIRF probe consists of a fluorophore that emits light in the near-infrared spectrum (λ = 700-900 nm) and an antibody or peptide with high affinity for a protein expressed specifically at the cell surface of tumor cells [8,9].

In order to use tumor-specific imaging to exclude malignant cells in cortical ovarian autografts, tumor markers should be identified that are present at the cell surface of ovarian metastases. Since a substantial proportion of patients who undergo ovarian tissue cryopreservation is diagnosed with breast cancer [10][11][12], we tested a panel of cell-surface markers known to be expressed by breast cancer cells, including E-cadherin [13], epithelial membrane antigen (EMA, also known as MUC1) [14,15], human epidermal growth factor receptor type 2 (Her2/neu) [16,17], carcinoembryonic antigen (CEA) [18], αvβ6 integrin [19] and epithelial cell adhesion molecule (EpCAM) [20][21][22]. The markers cytokeratin CAM 5.2, gross cystic disease fluid protein-15 (GCDFP15), Wilms' tumor antigen-1 (WT1), mammaglobin 1, and cytokeratin 7 (CK-7), which were used by Sánchez-Serrano et al. [23] and Rosendahl et al. [6], were excluded, as they are not expressed at the cell surface and therefore not suitable as a target for tumorspecific imaging.

In this study, we assessed the distribution of breast tumor cells in ovarian tissues from patients with ovarian metastases and determined which cell-surface proteins are suitable as a target for tumor-specific imaging of ovarian metastases derived from invasive breast cancer. Because it is crucial to select a target prior to the administration of the NIRF probe, we also examined whether invasive breast cancer tissue can be used to predict the most suitable target for the detection of ovarian metastases in a particular patient.

Methods

Patient selection and tissue collection

Via a nationwide search performed by PALGA, the Dutch histopathology and cytopathology network that encompasses all pathology laboratories within the Netherlands [24], a source population was compiled. This source population consisted of all patients who were diagnosed with primary invasive breast cancer at age < 41 years in the period 2000-2010 and who subsequently underwent an oophorectomy for any reason. From this source population, all patients who had histologically confirmed ovarian metastases from primary invasive breast cancer, were selected. Following this, hematoxylin-and-eosin (H&E) stained tissue sections and formalin-fixed paraffin-embedded (FFPE) tissue samples from the primary invasive breast tumors and their corresponding ovarian metastases were requested from pathology laboratories. If patients had locally recurrent breast cancer or a second primary invasive breast tumor prior to oophorectomy, FFPE tissue samples from these tumors were also requested. Clinical data were extracted from the patient's files after approval by the medical ethical committee of the Leiden University Medical Center (protocol number P14.106) and the local medical ethical committees of the participating hospitals.

Distribution of breast cancer cells in the ovary

The distribution of breast cancer cells in ovarian tissues was evaluated using the original H&E-stained sections by assessment of their localization and morphological features. The localization of breast cancer cells was determined as confined to the ovarian cortex and/or medulla. With respect to morphology, breast cancer cells were classified as a solitary metastasis, multiple distinct nodules separated by uninvolved ovarian tissue, or diffuse seeding without any discernable pattern.

Immunohistochemistry

Immunohistochemistry was performed on 4-μm thick FFPE sections of primary invasive breast cancers, locally recurrent breast cancers (if applicable) and their corresponding ovarian metastases. The tissue sections were deparaffinized in xylene, dehydrated in a stepwise series of graded alcohol solutions, and rinsed in distilled water. After blocking endogenous peroxidase activity with 0.3% hydrogen peroxide for 20 min, heat-induced antigen retrieval was performed by placing the slides in EnVision Flex Target Retrieval Solution high pH (pH 9.0; Ecadherin, EMA) or in the same solution but low pH (pH 6.0; Her2/neu) in PT Link (Dako, Denmark). EpCAM and αvβ6 integrin epitopes were unmasked by 30-min incubation with 0.125% trypsin and 0.4% pepsin, respectively, at 37°C. For CEA, no antigen retrieval was required. The sections were incubated overnight in a humidified chamber at room temperature with primary antibodies against Her2/neu (ERBB2, rabbit polyclonal, Dako), E-cadherin (NCH38, mouse monoclonal, Dako), EpCAM (323/A3, mouse monoclonal, provided by the Department of Pathology, LUMC, the Netherlands), CEA (A0115, rabbit polyclonal, Dako), αvβ6 integrin (6.2A1, mouse monoclonal, Cell Essentials), or EMA (E29, mouse monoclonal, Dako); all primary antibodies were used at their predetermined optimal dilution. After incubation with primary antibodies, the sections were rinsed with PBS, incubated with secondary antibodies (anti-mouse or anti-rabbit EnVision; Dako) for 30 min, and visualized using liquid DAB+ substrate buffer (Dako). The sections were counterstained with Mayer's hematoxylin solution, dehydrated, and mounted with Pertex (Leica Microsystems, Germany). For each immunostain, tissues expressing the antigen of interest were included as a positive control. Tissue sections stained without application of the primary antibody were used as a negative control.

Immunofluorescent triple staining

For immunofluorescent triple staining, the three most highly expressed markers for ductal and lobular ovarian metastases were chosen. In brief, FFPE sections of these ovarian metastases were deparaffinized as described above. Antigen retrieval was performed by placing the slides in EnVision Flex Target Retrieval Solution high pH (pH 9.0; Dako). Primary antibodies for ductal ovarian metastases: E-cadherin, EMA and Her2/neu. Primary antibodies for lobular ovarian metastases: EMA, Her2/neu and EpCAM. Secondary antibodies were all isotype-specific antibodies with Alexa Fluorochromes (LifeTechnologies, USA): anti-mouse IgG1-AlexaFluor488 (E-cadherin and EpCAM; green), anti-mouse IgG2a-AlexaFluor647 (EMA; red) and anti-rabbit-AlexaFluor546 (Her2/neu; orange). Sections were mounted with Vectashield containing DAPI (Vector Laboratories, USA). Primary invasive breast tumor samples that showed positive expression for all markers in previous experiments were used as a positive control. Tissue sections stained without application of primary antibodies were used as a negative control.

Image capture and quantification of immunoreactivity

The immunohistochemically stained slides were digitized using an IntelliSite Pathology Ultra-Fast Scanner 1.6 RA (Philips, The Netherlands). The percentage of malignant cells with immunohistochemically positive stained membranes were scored by two independent observers (I.P. and M.S.). In case of discrepancy, the observers reached consensus regarding a final score. The tumor cell membranes were considered positive if they showed immunoreactivity of any intensity. The immunofluorescent stained slides were digitized using a Pannoramic MIDI digital slide scanner (3DHistech, Hungary). The percentage of malignant cells with immunofluorescent positive stained membranes were also scored by two independent observers (I.P. and B.H.).

Statistical analysis

Statistical analysis was performed using SPSS version 23.0 (IBM, Armonk, NY). Inter-observer agreement was calculated using the Pearson correlation coefficient. Scatter plots based on generalized estimating equations analysis were made to determine whether invasive breast cancer tissue can be used to predict the most suitable target for the detection of ovarian metastases in a particular patient.

Results

Patient selection and clinicopathological characteristics

According to the PALGA registry, 2648 patients were diagnosed with primary invasive breast cancer at age < 41 years in the period 2000-2010 in the Netherlands who subsequently underwent an oophorectomy (Fig. 1). Among these patients, 63 patients had ovarian metastases. Of these 63 patients, tumor tissue samples were available from 46 patients. These 46 patients were included in this study. The clinicopathological characteristics of the 46 patients are shown in Table 1. The median age at the time of diagnosis was 36.5 years (range 28-40 years). Thirty-six patients were diagnosed with invasive ductal breast cancer and five patients were diagnosed with invasive lobular breast cancer. The remaining five patients had invasive ductolobular breast cancer. Almost 15% of patients had distant metastases outside the ovary at the time of breast cancer diagnosis. The median time between this diagnosis and oophorectomy was 41.9 months (range 0.3-141.8 months). In the majority of cases, the oophorectomy was done prophylactically or therapeutically because of breast cancer. In only one fourth of cases, the ovaries were removed because they appeared abnormal on ultrasound. Further patient and tumor characteristics are presented in Table 1.

Localization and morphology of ovarian metastases

Of the 46 patients, 29 patients had metastases in both ovaries (Table 1). Therefore, the total number of ovaries that contained metastases was 75. The localization and morphology of these 75 ovarian metastases are shown in Table 2. In 14 ovaries (19%) the metastases seemed confined to the cortex, whereas in 53 ovaries (70%) both the cortex and medulla were involved (Table 2). In half of the ovaries multiple distinct nodules were seen, while in 20% a solitary metastasis was found. Diffuse seeding without any discernable pattern was observed in 29% of ovaries. Figure 2 shows examples of these morphological features.

Expression of cell-surface proteins

Immunohistochemistry was performed to determine which cell-surface proteins are suitable as a target for tumorspecific imaging of ovarian metastases from invasive breast cancer. A strong correlation was observed between the scoring results obtained by the two observers; the median R 2 was 0.846 (range: 0.640-0.960). Representative examples of the immunohistochemical stainings of the invasive breast tumor samples and their corresponding ovarian metastases are shown in Additional file 1: Figure S1. Table 3 shows the mean percentage of positive tumor cells for the investigated markers in primary and recurrent invasive breast tumors and their ovarian metastases. Since loss of expression of the cell-adhesion molecule Ecadherin frequently occurs in invasive lobular carcinomas [25], the expression of markers was examined by histological subtype. With respect to invasive ductal carcinomas, E-cadherin, EMA and Her2/neu were most suitable; these markers were present in 91, 84 and 81% of metastatic breast tumor cells in the ovaries, respectively. In invasive lobular carcinomas, the mean percentage of positively stained breast tumor cells in the ovaries was highest for EMA, Her2/neu and EpCAM; specifically, 64, 74 and 68%, respectively. In patients diagnosed with ductolobular breast cancer, targeting EMA would result in the detection of 99% of disseminated breast cancer cells in the ovaries.

Correlation between the expression of cell-surface proteins in invasive breast tumors and their corresponding ovarian metastases

In patients diagnosed with ductolobular breast cancer, the expression of EMA in the invasive breast tumors was in accordance with the expression in their corresponding ovarian metastases, showing small standard deviations (Table 3). Therefore, EMA would be the most suitable target to detect ductolobular ovarian metastases. By contrast, in patients diagnosed with ductal or lobular breast cancer large variations in expression among tumors were found. To understand whether in these patients invasive breast tumor tissues can be used to predict the most suitable target for the detection of ovarian metastases in an individual patient, scatter plots were made (Fig. 3). For each patient, the percentage of positive tumor cells in primary and locally recurrent breast tumors (if applicable) was set against the percentage of positive tumor cells in their corresponding ovarian metastases. No correlation between these expressions could be substantiated, showing that ductal and lobular breast tumor tissues cannot be used to predict the most pertinent marker for the detection of their corresponding ovarian metastases.    Hence, cells within ovarian tissues that show membranous positivity for any of the three markers mentioned will be deemed malignant. In the remaining five ductal and three lobular ovarian metastases, the mean percentage of undetected metastatic cells was 5% (no range) and 25% (range , respectively. Figure 4 shows a representative image of the immunofluorescent triple staining in a lobular ovarian metastasis, in which the combination of EpCAM, EMA and Her2/neu led to the detection of all metastatic breast cancer cells.

Detection of ovarian metastases by a combination of markers

Discussion

One of the purposes of the present study was to examine the histological features of ovarian metastases in breast cancer patients to evaluate the current tumor detection approach [4] in ovarian tissues considered for autotransplantation. We found that 71% of ovarian metastases consisted of a solitary metastasis or multiple distinct nodules separated by uninvolved ovarian tissue. These findings suggest that tumor cells might have been missed if the current tumor detection approach would have been used.

The patients included in this study however, underwent oophorectomy after a median time interval of 42 months. In patients undergoing ovarian tissue cryopreservation an oophorectomy is performed soon after cancer diagnosis. In these patients, disseminated tumor cells may not yet have outgrown into overt metastases and may appear as micrometastases in the ovarian tissues [23,26,27]. The chance that tumor cells will then be overlooked is presumably greater. We therefore recommend examination of the actual ovarian autografts on the presence of malignant cells prior to autotransplantation. Ovarian metastases (n = 10)

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD E-

SD = standard deviation

The mean percentages of immunohistochemically positive stained tumor cells are subdivided by histological subtype. Tumor cell membranes were considered positive if they showed immunoreactivity of any intensity. EMA, epithelial membrane antigen; Her2/neu, human epidermal growth receptor type 2; CEA, carcinoembryonic antigen; EpCAM, epithelial cell adhesion molecule

In our study, merely 63 out of 2648 patients (2.4%) who were diagnosed with primary invasive breast cancer at age < 41 years had ovarian metastases. For the determination of suitable targets for NIRF imaging, it might have been relevant to focus on malignancies with a higher risk of ovarian contamination in the actual patient population, for instance leukemia [28][29][30]. Nonetheless, breast cancer can be perfectly used as a starting point to investigate whether NIRF imaging is feasible for the detection of ovarian metastases.

Considering the expression of Her2/neu in primary invasive breast cancers and ovarian metastases a high percentage of Her2/neu positive tumor cells (67-88%) was found, as we considered tumor cell membranes positive if they showed immunoreactivity of any intensity. This is in contrast to the diagnostic setting, where Her2/neu overexpression is determined because of its potential prognostic value [17,31]. We applied a lower cut-off point, because for NIRF imaging the staining intensity is less important as long as a significant tumor-to-background-ratio can be achieved. In the NIR spectrum, non-specific fluorescence background signal is substantially decreased compared to wavelengths lower than NIR [8]. Hence, since ovarian stromal cells do not immunohistochemically express Her2/neu [32], Her2/neu-targeting NIRF probes will detect metastatic breast cancer cells within ovarian tissues if these cells show immunohistochemical reactivity.

In individual patients, no correlation was found between the expression of the investigated markers in breast tumors and their corresponding metastases in the ovary. This might be due to the fact that breast cancer is known as a heterogeneous disease [17] or be in line with the hypothesis that disseminated tumor cells autonomously evolve from the primary tumor [33]. For the clinical application of these markers there should not be an obstacle, since a combination of three markers enhances the ability to detect breast tumor cells in ductal and lobular ovarian metastases. Furthermore, only the histological subtype of the invasive breast tumor needs to be known to determine which combination of markers is pertinent for the detection of the corresponding ovarian metastases, making the selection of suitable NIRF probes simple and straightforward. For the non- invasive detection of metastases in the actual ovarian autografts by tumor-specific imaging, NIRF probes could be administered intravenously, after which the removed ovary is dissected into cortical ovarian strips. Subcellular detailed fluorescent images of tumor cells within ovarian autografts could then be obtained by multiphoton microscopy [34]. Beside breast cancer cells, inclusion cysts will likely also be illuminated by NIRF imaging, as we previously showed that in normal ovaries, all markers (except CEA) were expressed on epithelial cells in inclusion cysts [32]. Nevertheless, we additionally demonstrated that full-field optical coherence tomography (FF-OCT), which creates histology-like images without the need for tissue manipulation, can be perfectly used to differentiate between inclusion cysts and metastases in the ovary [35]. On a tomographic FF-OCT image, an inclusion cyst is characterized by a thin dark outer layer and lack of interior structure, whereas micrometastatic lesions from primary invasive ductal carcinomas present as 'web-like' structures in which tumor cells appear light gray. Metastatic lesions derived from primary invasive lobular carcinomas often show an Indian file pattern, defined as infiltrating single rows of cells [36]. Since ovarian inclusion cysts are separately identifiable within the ovarian parenchyma [32], a distinction between these structures can also be made. In addition, FF-OCT and NIRF imaging might be combined to enhance their sensitivity and specificity rates, as both methods are noninvasive. The a priori probability that other benign epithelial ovarian abnormalities will be detected by our panel of cell-surface markers is low, since ovaries that present as an adnexal mass on preoperative ultrasonography are generally not used for ovarian tissue cryopreservation. In case primary ovarian cancer cells are present, these cells will be detected as E-cadherin [37], EMA [38], and EpCAM [39] are virtually always expressed in ovarian cancer, and approximately 33% of primary ovarian carcinomas show Her2/neu amplification [40].

Conclusions

In conclusion, we showed that in young breast cancer patients with ovarian metastases, metastatic breast tumor cells may be confined to a specific area in the ovarian cortex. A non-invasive tumor detection technique by which cortical ovarian fragments that are transplanted can be examined, is recommended to minimize the risk of reintroducing metastatic tumor cells by ovarian tissue autotransplantation in breast cancer patients. NIRF imaging is a promising technique to discriminate malignant from benign tissues while leaving the examined tissues vital. Our research opens a new avenue for the development of tumor-specific NIRF probes that can be used for non-invasive detection of breast cancer metastases in ovarian tissues prior to autotransplantation.

Additional files

Additional file 1: Figure S1. Immunohistochemical expression of tumor markers in invasive breast tumors and their corresponding ovarian metastases. Arrows indicate tumor cells that show heterogeneous expression of markers. Scale bars represent 100 μm. EMA, epithelial membrane antigen; Her2/neu, human epidermal growth receptor type 2; CEA, carcinoembryonic antigen; EpCAM, epithelial cell adhesion molecule. (TIFF 5316 kb) Additional file 2: Table S2. List of participating hospitals. An overview is given of the treatment hospitals that approved the study design. (DOCX 14 kb)

Figure 3
3also shows that the use of one marker would not always be sufficient to detect all metastatic ductal or lobular breast cancer cells in the ovaries. The use of one marker (E-cadherin, EMA or Her2/neu) would result in the detection of 100% of tumor cells in 44 out of 58 ductal ovarian metastases (data not shown). With respect to the lobular subtype, EMA, Her2/neu or EpCAM was present in 100% of tumor cells in 4 out of 10 ovarian metastases. To investigate whether a combination of markers would enable the detection of 100% of tumor cells in all ductal and lobular ovarian metastases, an

Fig. 1
1Patient selection and composition of the study population. The source population was compiled by the Dutch histopathology and cytopathology network. The exclusion criteria are indicated in the dotted boxes

Fig. 2
2Localization of ovarian metastases derived from patients diagnosed with invasive breast cancer. Three examples are shown: (a) a solitary metastasis, (b) multiple distinct nodules separated by uninvolved ovarian tissue and (c) diffuse seeding without any discernable pattern. In order to clearly display the solitary metastasis in (a) and the multiple distinct nodules in (b), a green line is drawn that delineates the metastases in the ovary. Scale bars represent 5 mmTable 3Immunohistochemical expression of the investigated markers in invasive breast tumors and their corresponding

Fig. 3
3The correlation between tumor marker expression in breast tumors and ovarian metastases for individual patients. Upper panel (a) shows invasive ductal breast cancer and lower panel (b) represents invasive lobular breast cancer. For each patient, the percentage of positive tumor cells in primary and locally recurrent breast tumors (if applicable) was set against the percentage of positive tumor cells in their corresponding ovarian metastases. EMA, epithelial membrane antigen; Her2/neu, human epidermal growth receptor type 2; EpCAM, epithelial cell adhesion molecule

Fig. 4
4Detection of ovarian metastases by a combination of markers. Representative image of a lobular ovarian metastasis stained with DAPI counterstain and triple immunofluorescence for EpCAM (a), EMA (b), Her2/neu (c), and the three stainings combined (d). The solid arrow indicates tumor cells that are positive for EpCAM, but negative for EMA and Her2/neu. The dashed arrow indicates tumor cells that are positive for Her2/neu, but negative for EpCAM and EMA. Scale bars represent 100 μm. EpCAM, epithelial cell adhesion molecule; EMA, epithelial membrane antigen; Her2/neu, human epidermal growth receptor type 2

Table 1
1Clinicopathological characteristics of patients with primary invasive breast cancer and ovarian metastasesClinicopathological characteristics
N = 46
%

Age at diagnosis of breast cancer,
years -median (range)

36.5 (28-40)
-

BRCA gene mutation

No
8
17.4

Yes, BRCA 1
1
2.2

Yes, BRCA 2
0
0.0

Unknown
37
80.4

Breast tumor localization

Left
23
50.0

Right
21
45.7

Both
2
4.3

Most extensively performed breast surgery

Needle biopsy
4
8.7

Breast conserving surgery
15
32.6

Mastectomy
27
58.7

Breast tumor histological subtype

Ductal
36
78.2

Lobular
5
10.9

Ductolobular
5
10.9

Scarff-Bloom-Richardson grade

I
4
8.7

II
19
41.3

III
15
32.6

Unknown
8
17.4

Estrogen receptor

Negative
5
10.9

Positive
41
89.1

Progesterone receptor

Negative
8
17.4

Positive
38
82.6

Her2/neu receptor

Negative
38
82.6

Positive
8
17.4

Tumor stage

T1
11
23.9

T2
24
52.2

T3
7
15.2

T4
4
8.7

Nodal stage

N0
14
30.4

N1
12
26.1

N2
10
21.7

N3
10
21.7

Table 1 Clinicopathological characteristics of patients with
primary invasive breast cancer and ovarian metastases
(Continued)

Distant metastasis

cM0
39
84.8

cM1
7
15.2

Age at diagnosis of ovarian metastases,
years -median (range)

40.0 (31-51)
-

Time between breast cancer and ovarian
metastases, months -median (range)

41.9 (0.3-141.8)
-

Recurrent disease prior to oophorectomy

No
15
32.6

Yes, locoregional recurrence
12
26.1

Yes, distant recurrence
19
41.3

Type of ovarian surgery

Unilateral oophorectomy
0
0.0

Bilateral oophorectomy
46
100.0

Indication for oophorectomy

Prophylactic because of breast cancer
9
19.6

Therapeutic because of breast cancer
25
54.3

Abnormal ovaries on ultrasound
12
26.1

Localization of ovarian metastases

Left
4
8.7

Right
6
13.0

Both
29
63.0

Unknown
7
15.2

Table 2
2Localization and morphology of ovarian metastases derived from patients diagnosed with invasive breast cancerHistological features
Ovarian metastases

N = 75
%

Localization of ovarian metastases

Cortex
14
18.7

Medulla
8
10.7

Both
53
70.1

Morphology of ovarian metastases

Solitary metastasis
15
20.0

Multiple distinct nodules separated
by uninvolved ovarian tissue

38
50.7

Diffuse seeding without any discernable pattern 22
29.3

Fallopian tube involved

No
55
73.3

Yes
5
6.7

Unknown
15
20.0

Of the 46 patients who were diagnosed with invasive breast cancer and
ovarian metastases, 29 patients had metastases in both ovaries. The total
number of ovaries that contained metastases was 75

AcknowledgmentsThe authors gratefully acknowledge the Dutch Pathology Registry (PALGA), the pathology laboratories, and the treatment hospitals for their collaboration. The authors also thank Rob Keyzer, BSc, Ronald L.P. van Vlierberghe, BSc for practical help, and Erik W. van Zwet, PhD for assistance with statistical analysis. These contributors have no conflict of interest. This study was approved by the medical ethical committee of the Leiden University Medical Center (protocol number P14.106) and the local medical ethical committees of the participating hospitals (Additional file 2:Table S2). Written human subject consent was not necessary, as the processing of personal data was performed according with the Wbp (Personal Data Protection Act). We received permission from the Dutch Pathology Registry (PALGA) to access the PALGA dataset. All patient samples and clinical data were handled in accordance with the medical ethics guidelines described in the Code of Conduct for Proper Secondary Use of Human Tissue of the Dutch Federation of Biomedical Scientific Societies (FMWV)[41].Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.FundingThis work was supported by the project grant H2020-MSCA-RISE grant number 644373 -PRISAR, DSW Health Insurance and the Zabawas Foundation. These funding sources were not involved in any part of the study.Availability of data and materialThe datasets used during the current study are available from the corresponding author on reasonable request.Authors' contributions IP, CH, VS, AL, CS, JBT, PK designed the study. IP, MS, BH, CS, PK performed experiments and/or gave technical support. IP analyzed data and prepared figures. IP wrote the manuscript. All authors read and approved the final manuscript, and agreed to be accountable for all aspects of the work.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participate• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
Ovarian tissue freezing: current status. J Donnez, M M Dolmans, Curr Opin Obstet Gynecol. 273Donnez J, Dolmans MM. Ovarian tissue freezing: current status. Curr Opin Obstet Gynecol. 2015;27(3):222-30.

86 succesful births and 9 ongoing pregnancies worldwide in women transplanted with frozen-thawed ovarian tissue: focus on birth and perinatal outcome in 40 of these children. A K Jensen, K T Macklon, J Fedder, E Ernst, P Humaidan, C Y Andersen, 10.1007/s10815-016-0843-9J Assist Reprod Genet. Jensen AK, Macklon KT, Fedder J, Ernst E, Humaidan P, Andersen CY. 86 succesful births and 9 ongoing pregnancies worldwide in women transplanted with frozen-thawed ovarian tissue: focus on birth and perinatal outcome in 40 of these children. J Assist Reprod Genet. 2016; doi:10.1007/s10815-016-0843-9.

The Practice Committee of the American Society for Reproductive Medicine. Fertil Steril. 1015Ovarian tissue cryopreservation: a committee opinionThe Practice Committee of the American Society for Reproductive Medicine. Ovarian tissue cryopreservation: a committee opinion. Fertil Steril. 2014; 101(5):1237-43.

Cryopreservation and Autotransplantation of Ovarian Tissue in Cancer Patients: Is It Safe?. L Bastings, Ccm Beerendonk, J R Westphal, Ddm Braat, R Peek, J Adolesc Young Adult Oncol. 21Bastings L, Beerendonk CCM, Westphal JR, Braat DDM, Peek R. Cryopreservation and Autotransplantation of Ovarian Tissue in Cancer Patients: Is It Safe? J Adolesc Young Adult Oncol. 2013;2(1):31-4.

Detection of Hodgkin lymphoma within ovarian tissue. S E Bittinger, S P Nazaretian, D A Gook, C Parmar, R A Harrup, C J Stern, Fertil Steril. 952Bittinger SE, Nazaretian SP, Gook DA, Parmar C, Harrup RA, Stern CJ. Detection of Hodgkin lymphoma within ovarian tissue. Fertil Steril. 2011;95(2):803.e803-6.

Cryopreservation of ovarian tissue for fertility preservation: no evidence of malignant cell contamination in ovarian tissue from patients with breast cancer. M Rosendahl, Timmermans Wielenga, V Nedergaard, L , Fertil Steril. 956Rosendahl M, Timmermans Wielenga V, Nedergaard L, et al. Cryopreservation of ovarian tissue for fertility preservation: no evidence of malignant cell contamination in ovarian tissue from patients with breast cancer. Fertil Steril. 2011;95(6):2158-61.

Image-guided cancer surgery using near-infrared fluorescence. A L Vahrmeijer, M Hutteman, J R Van Der Vorst, Nat Rev. Clin Oncol. 109Vahrmeijer AL, Hutteman M, van der Vorst JR, et al. Image-guided cancer surgery using near-infrared fluorescence. Nat Rev. Clin Oncol. 2013;10(9):507-18.

Optical image-guided surgery-where do we stand?. S Keereweer, Jdf Kerrebijn, Pbaa Van Driel, Mol Imaging Biol. 132Keereweer S, Kerrebijn JDF, van Driel PBAA, et al. Optical image-guided surgery-where do we stand? Mol Imaging Biol. 2011;13(2):199-207.

The use of fluorescent dyes and probes in surgical oncology. Te Velde, E A Veerman, T Subramaniam, V Ruers, T , Eur J Surg Oncol. 361Te Velde EA, Veerman T, Subramaniam V, Ruers T. The use of fluorescent dyes and probes in surgical oncology. Eur J Surg Oncol. 2010;36(1):6-15.

Cryopreservation of ovarian tissue for a decade in Denmark: a view of the technique. M Rosendahl, K T Schmidt, E Ernst, Reprod BioMed Online. 222Rosendahl M, Schmidt KT, Ernst E, et al. Cryopreservation of ovarian tissue for a decade in Denmark: a view of the technique. Reprod BioMed Online. 2011;22(2):162-71.

A review of 15 years of ovarian tissue bank activities. M-M Dolmans, P Jadoul, S Gilliaux, J Assist Reprod Genet. 303Dolmans M-M, Jadoul P, Gilliaux S, et al. A review of 15 years of ovarian tissue bank activities. J Assist Reprod Genet. 2013;30(3):305-14.

Ovarian cryopreservation and transplantation for fertility preservation for medical indications: report of an ongoing experience. K Oktay, O Oktem, Fertil Steril. 933Oktay K, Oktem O. Ovarian cryopreservation and transplantation for fertility preservation for medical indications: report of an ongoing experience. Fertil Steril. 2010;93(3):762-8.

Cadherins and catenins in breast cancer. P Cowin, T M Rowlands, S J Hatsell, Curr Opin Cell Biol. 175Cowin P, Rowlands TM, Hatsell SJ. Cadherins and catenins in breast cancer. Curr Opin Cell Biol. 2005;17(5):499-508.

Expression of WT1, CA 125 and GCFFP-15 as useful markers in the Differential Diagnosis of Primary Ovarian Carcinomas Versus Metastatic Breast Cancer to the Ovary. C Tornos, R Soslow, S Chen, Am J Surg Pathol. 29Tornos C, Soslow R, Chen S, et al. Expression of WT1, CA 125 and GCFFP-15 as useful markers in the Differential Diagnosis of Primary Ovarian Carcinomas Versus Metastatic Breast Cancer to the Ovary. Am J Surg Pathol. 2005;29:1482-9.

Is transplantation of cryopreserved ovarian tissue from patients with advanced-stage breast cancer safe? A pilot study. V Luyckx, J F Durant, A Camboni, J Assist Reprod Genet. 3010Luyckx V, Durant JF, Camboni A, et al. Is transplantation of cryopreserved ovarian tissue from patients with advanced-stage breast cancer safe? A pilot study. J Assist Reprod Genet. 2013;30(10):1289-99.

Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. D J Slamon, G M Clark, S G Wong, W J Levin, A Ullrich, W L Mcguire, Science. 235Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 1987;235:177-82.

Breast cancer metastasis: markers and models. B Weigelt, J L Peterse, L J Van 't Veer, Nat Rev. Cancer. 58Weigelt B, Peterse JL. van 't Veer LJ. Breast cancer metastasis: markers and models. Nat Rev. Cancer. 2005;5(8):591-602.

The distribution of carcinoembryonic antigen in breast carcinoma. Diagnostic and prognostic implications. F P Kuhajda, L E Offutt, G Mendelsohn, Cancer. 527Kuhajda FP, Offutt LE, Mendelsohn G. The distribution of carcinoembryonic antigen in breast carcinoma. Diagnostic and prognostic implications. Cancer. 1983;52(7):1257-64.

Important role of integrins in the cancer biology. R Rathinam, S K Alahari, Cancer Metastasis Rev. 291Rathinam R, Alahari SK. Important role of integrins in the cancer biology. Cancer Metastasis Rev. 2010;29(1):223-37.

EpCAM: structure and function in health and disease. U Schnell, V Cirulli, Bng Giepmans, Biochim Biophys Acta. 18288Schnell U, Cirulli V, Giepmans BNG. EpCAM: structure and function in health and disease. Biochim Biophys Acta. 2013;1828(8):1989-2001.

EpCAM expression varies significantly and is differentially associated with prognosis in the luminal B HER2(+), basal-like, and HER2 intrinsic subtypes of breast cancer. S D Soysal, S Muenst, T Barbie, Br J Cancer. 1087Soysal SD, Muenst S, Barbie T, et al. EpCAM expression varies significantly and is differentially associated with prognosis in the luminal B HER2(+), basal-like, and HER2 intrinsic subtypes of breast cancer. Br J Cancer. 2013;108(7):1480-7.

EpCAM as multi-tumour target for near-infrared fluorescent guided surgery. P B Van Driel, M C Boonstra, H A Prevoo, BMC Cancer. 161884Van Driel PB, Boonstra MC, Prevoo HA, et al. EpCAM as multi-tumour target for near-infrared fluorescent guided surgery. BMC Cancer. 2016;16(1):884.

Malignant cells are not found in ovarian cortex from breast cancer patients undergoing ovarian cortex cryopreservation. M Sánchez-Serrano, E Novella-Maestre, E Roselló-Sastre, N Camarasa, J Teruel, A Pellicer, Hum Reprod. 249Sánchez-Serrano M, Novella-Maestre E, Roselló-Sastre E, Camarasa N, Teruel J, Pellicer A. Malignant cells are not found in ovarian cortex from breast cancer patients undergoing ovarian cortex cryopreservation. Hum Reprod. 2009;24(9):2238-43.

Pathology databanking and biobanking in The Netherlands, a central role for PALGA, the nationwide histopathology and cytopathology data network and archive. M Casparie, A T Tiebosch, G Burger, Cell Oncol. 291Casparie M, Tiebosch AT, Burger G, et al. Pathology databanking and biobanking in The Netherlands, a central role for PALGA, the nationwide histopathology and cytopathology data network and archive. Cell Oncol. 2007;29(1):19-24.

Wide metastatic spreading in infiltrating lobular carcinoma of the breast. S Ferlicot, A Vincent-Salomon, J Medioni, Eur J Cancer. 403Ferlicot S, Vincent-Salomon A, Medioni J, et al. Wide metastatic spreading in infiltrating lobular carcinoma of the breast. Eur J Cancer. 2004;40(3):336-41.

Searching for metastases in ovarian tissue before autotransplantation: a tailor-made approach. E J Hoekman, V T Smit, T P Fleming, L A Louwe, G J Fleuren, C G Hilders, Fertil Steril. 1032Hoekman EJ, Smit VT, Fleming TP, Louwe LA, Fleuren GJ, Hilders CG. Searching for metastases in ovarian tissue before autotransplantation: a tailor-made approach. Fertil Steril. 2014;103(2):469-77.

Evaluation of quantitative polymerase chain reaction markers for the detection of breast cancer cells in ovarian tissue stored for fertility preservation. L Bockstaele, S Boulenouar, G Van Den Steen, Fertil Steril. 1042e414Bockstaele L, Boulenouar S, Van Den Steen G, et al. Evaluation of quantitative polymerase chain reaction markers for the detection of breast cancer cells in ovarian tissue stored for fertility preservation. Fertil Steril. 2015;104(2):410-417.e414.

Reimplantation of cryopreserved ovarian tissue from patients with acute lymphoblastic leukemia is potentially unsafe. M-M Dolmans, C Marinescu, P Saussoy, A Van Langendonckt, C Amorim, J Donnez, Blood. 11616Dolmans M-M, Marinescu C, Saussoy P, Van Langendonckt A, Amorim C, Donnez J. Reimplantation of cryopreserved ovarian tissue from patients with acute lymphoblastic leukemia is potentially unsafe. Blood. 2010;116(16):2908-14.

Autotransplantation of cryopreserved ovarian tissue in cancer survivors and the risk of reintroducing malignancy: a systematic review. L Bastings, Ccm Beerendonk, J R Westphal, Hum Reprod Update. 195Bastings L, Beerendonk CCM, Westphal JR, et al. Autotransplantation of cryopreserved ovarian tissue in cancer survivors and the risk of reintroducing malignancy: a systematic review. Hum Reprod Update. 2013; 19(5):483-506.

Risk of transferring malignant cells with transplanted frozen-thawed ovarian tissue. M-M Dolmans, V Luyckx, J Donnez, C Y Andersen, T Greve, Fertil Steril. 996Dolmans M-M, Luyckx V, Donnez J, Andersen CY, Greve T. Risk of transferring malignant cells with transplanted frozen-thawed ovarian tissue. Fertil Steril. 2013;99(6):1514-22.

HER-2/neu testing in breast carcinoma: a combined immunohistochemical and fluorescence in situ hybridization approach. R L Ridolfi, M R Jamehdor, J M Arber, Mod Pathol. 138Ridolfi RL, Jamehdor MR, Arber JM. HER-2/neu testing in breast carcinoma: a combined immunohistochemical and fluorescence in situ hybridization approach. Mod Pathol. 2000;13(8):866-73.

Identification of cell-surface markers for detecting breast cancer cells in ovarian tissue. I T Peters, C G Hilders, C F Sier, Arch Gynecol Obstet. 2942Peters IT, Hilders CG, Sier CF, et al. Identification of cell-surface markers for detecting breast cancer cells in ovarian tissue. Arch Gynecol Obstet. 2016;294(2):385-93.

Dissecting the metastatic cascade. K Pantel, R H Brakenhoff, Nat Rev. Cancer. 46Pantel K, Brakenhoff RH. Dissecting the metastatic cascade. Nat Rev. Cancer. 2004;4(6):448-56.

Infrared multiphoton microscopy: subcellular-resolved deep tissue imaging. V Andresen, S Alexander, W-M Heupel, M Hirschberg, R M Hoffman, P Friedl, Curr Opin Biotechnol. 201Andresen V, Alexander S, Heupel W-M, Hirschberg M, Hoffman RM, Friedl P. Infrared multiphoton microscopy: subcellular-resolved deep tissue imaging. Curr Opin Biotechnol. 2009;20(1):54-62.

Non-invasive detection of metastases and follicle density in ovarian tissue using full-field optical coherence tomography. I T Peters, P L Stegehuis, R Peek, 10.1158/1078-0432Clin Cancer Res. Peters IT, Stegehuis PL, Peek R, et al. Non-invasive detection of metastases and follicle density in ovarian tissue using full-field optical coherence tomography. Clin Cancer Res. 2016; doi:10.1158/1078-0432.

Ovarain metastases in breast carcinoma. Ytb Gagnon, Cancer. 64Gagnon YTB. Ovarain metastases in breast carcinoma. Cancer. 1989;64:892-8.

E-cadherin expression in human epithelial ovarian cancer and normal ovary. K Sundfeldt, Y Pionkewitz, K Ivarsson, Int J Cancer. 74Sundfeldt K, Pionkewitz Y, Ivarsson K, et al. E-cadherin expression in human epithelial ovarian cancer and normal ovary. Int J Cancer. 1997;74:275-80.

The immunoperoxidase localization of tumour markers in ovarian cancer: the value of CEA, EMA, cytokeratin and DD9. R H Hammond, T D Bates, D G Clarke, BJOG. 98Hammond RH, Bates TD, Clarke DG, et al. The immunoperoxidase localization of tumour markers in ovarian cancer: the value of CEA, EMA, cytokeratin and DD9. BJOG. 1991;98:73-83.

Intraperitoneal immunotherapy with T cells stably and transiently expressing anti-EpCAM CAR in xenograft models of peritoneal carcinomatosis. W X Ang, Z Li, Z Chi, 10.18632/oncotarget.14592Oncotarget. Ang WX, Li Z, Chi Z, et al. Intraperitoneal immunotherapy with T cells stably and transiently expressing anti-EpCAM CAR in xenograft models of peritoneal carcinomatosis. Oncotarget 2017;doi:10.18632/oncotarget.14592.

The status of Her2 amplification and Kras mutations in mucinous ovarian carcinoma. K L Chang, M-Y Lee, W-R Chao, C-P Han, Hum Genomics. 10140Chang KL, Lee M-Y, Chao W-R, Han C-P. The status of Her2 amplification and Kras mutations in mucinous ovarian carcinoma. Hum Genomics. 2016;10(1):40.

Code for proper secondary use of human tissue in the Netherlands. Fmwv Federa, Federa FMWV. Code for proper secondary use of human tissue in the Netherlands. 2002; http://www.federa.org/codes-conduct.


Attribution of recent temperature behaviour reassessed by a neural- network method OPEN
Published: xx xx xxxx

Antonello Pasini
Institute of Atmospheric Pollution Research
National Research Council
RomeItaly

Paolo Racca
Department of Economics and Statistics
University of Turin
TorinoItaly

Stefano Amendola
Department of Mathematics and Physics
Roma Tre University
RomeItaly

Giorgio Cartocci
Department of Mathematics and Physics
Roma Tre University
RomeItaly

Claudio Cassardo
Department of Physics
University of Turin
TorinoItaly

Department of Atmospheric Science and Engineering
Ewha Womans University
SeoulKorea

Attribution of recent temperature behaviour reassessed by a neural- network method OPEN
Published: xx xx xxxx10.1038/s41598-017-18011-8Received: 21 August 2017 Accepted: 24 November 20171 SCIENTIfIC REPORTS | 7: 17681 | Correspondence and requests for materials should be addressed to A.P. (email: email@example.com)
Attribution studies on recent global warming by Global Climate Model (GCM) ensembles convergein showing the fundamental role of anthropogenic forcings as primary drivers of temperature in the last half century. However, despite their differences, all these models pertain to the same dynamical approach and come from a common ancestor, so that their very similar results in attribution studies are not surprising and cannot be considered as a clear proof of robustness of the results themselves. Thus, here we adopt a completely different, non-dynamical, data-driven and fully nonlinear approach to the attribution problem. By means of neural network (NN) modelling, and analysing the last 160 years, we perform attribution experiments and find that the strong increase in global temperature of the last half century may be attributed basically to anthropogenic forcings (with details on their specific contributions), while the Sun considerably influences the period 1910-1975. Furthermore, the role of sulphate aerosols and Atlantic Multidecadal Oscillation for better catching interannual to decadal temperature variability is clarified. Sensitivity analyses to forcing changes are also performed. The NN outcomes both corroborate our previous knowledge from GCMs and give new insight into the relative contributions of external forcings and internal variability to climate.

GCMs are the standard tools for catching the complexity of climate system and simulating its behaviour 1,2 . In particular, in the framework of this virtual laboratory we are able to perform attribution experiments (e.g., by setting some forcings to their preindustrial values) in order to understand which factors have mainly influenced the behaviour of some variable of climatic importance, such as the global temperature, in the recent past. The results of these experiments (through ensemble runs of different GCMs) clearly indicate that anthropogenic forcings (primarily greenhouse gas forcing) are the main responsible for the recent global warming 3 .

May we consider the convergence of attribution results from these models as a proof of robustness of the results themselves? This is a critical point, due to the fact that the political choices to contrast climate change are based on the claim that anthropogenic forcings are the fundamental causes of the recent global warming. This problem has been recently analysed and discussed 4,5 . In short, to achieve robustness we need to obtain a common result from independent means of investigation (models) and GCMs do not seem so independent from each other, because they are all historically linked together by a common origin and relations of mutual generation, and follow the same methodological dynamical scheme.

Thus, attribution results from different approaches could be compared with GCMs' ones for understanding if we have robust results. Previous studies using data-driven models, such as neural networks and Granger causality analyses (see bibliography in ref. 5 ), suggest a dominant role of anthropogenic forcings, but a comprehensive comparison (following the same rationale used in GCMs' experiments) is lacking.

In this framework, basing on the quite recent application of NNs in atmospheric and climate sciences 6,7 , a NN model, specifically developed for modelling relationships among variables in small datasets 8 and previously applied in a simplified form to global/regional attribution studies [9][10][11] , is adopted here for an attribution analysis of the recent global warming.

We focus on annual data since the middle of the 19 th century related to global temperature T (HadCRUT4 time series) and radiative forcings (RF) of: greenhouse gases (RFGHG), black carbon (RFBC), anthropogenic

Reconstruction of T and attribution via neural network modelling

Generally, previous studies about NN attribution 9,14,15 aimed at understanding how much variance of the global temperature record could be explained by anthropogenic or natural forcings separately, through the analysis of the reconstruction performance of NN models endowed with one of the two different kinds of forcing as input. However, due to the powerful capacity of NNs to fit the data, this does not assure reliable results and it is not possible to compare these outcomes with the attribution ones by GCMs.

Thus, we follow the rationale of dynamical modelling, where one chooses some validated models and simulates the behaviour of global T by supposing that certain forcings remain fixed at their preindustrial levels. Analogously, now we search for some NN models that are able to well reconstruct the temperature time series once considered all observed values of forcings as inputs, then apply their transfer functions (the validated NN models) to new inputs, in which we mimic the fact that some of their values show no trend since 1850, finally obtaining new outputs in terms of "simulated" T. In this case one can appreciate the roles of the real changes in different forcings on the behaviour of T.

Each model ensemble consists of 20 NNs that supply temperature reconstructions for each year considered as the test set in a generalized leave-one-out training-validation-test procedure (see Methods). These networks are endowed with 3 different inputs (the complete set of natural and anthropogenic forcings: RFANTH, RFSOLAR, RFVOL), 4 hidden neurons and one output (to be compared with the annual global T, i.e. the target): the number of hidden neurons has been empirically fixed and keep the networks small enough to avoid overfitting. Figure 1a shows the good performance of the NN models in reconstructing the global T (R = 0.913 and RMSE = 0.109 K for the ensemble mean). In particular, the ensemble mean well follows the trends of the various periods. Just few data are underestimated or overestimated, such as the relative maxima around 1878 and 1944, and the minimum around 1910. Interannual and decadal variability are partially caught, too. A high variability for ensemble runs is visible in 1884 and 1992, the years following the two most intense volcanic episodes, but this is understandable if one looks at the "spot-like" time series of RFVOL, because the reconstructions of these two years generally require big extrapolations from RFVOL values presented to the NNs in the training set.

Thus, we consider that these NN models are able to catch the nonlinear mixing of influences of the various forcings on T and supply us with an ensemble of validated models, so that the attribution activity may start. Note that, even if we mainly deal with the reconstruction performance of the ensemble mean, that tends to hide nonlinear influences, the NN results presented in this study are always better than those coming from multilinear regressions using the same data, so that nonlinearities play however a role.

Specific attribution results. The first attribution experiment is the standard one: what happens if the anthropogenic forcing is set to preindustrial levels? Here we start by taking the validated NN models (networks endowed with fixed connection weights), for each year we put the observed inputs of natural forcings related to that year and the values of RFANTH fixed at 1850 (the first year of our dataset), and finally we propagate the forcing signal from inputs to output through the NNs. The result is presented in Fig. 1b, where it is evident that the recent increase in temperature disappears, being replaced by a constant temperature trend since 1960. This picture shows a fine similarity with the well-known Fig. 10.1(b) of ref. 3 , in which the GCMs' approach to attribution is adopted. Now, with a completely different approach, essentially the same result is obtained: anthropogenic forcings had a fundamental role in causing the recent global warming.

Of course, the various anthropogenic forcings have different magnitudes in terms of RFs, also as influence signs on warming (e.g., sulphates have a cooling effect). Thus, it would be interesting to investigate their separated roles in determining the temperature curve. One could substitute the single RFANTH input with the three anthropogenic RFs (RFGHG, RFBC and RFSOX) as distinct inputs, and repeat the previous attribution exercise for each forcing separately. In doing so, however, we should build larger NNs and this could lead to overfitting problems, due to the short time series available 16 . Thus, a distinct approach is followed.

We consider the difference between the ensemble-mean time series of reconstructed T in the full reconstruction runs (Fig. 1a) and in the attribution experiment when RFANTH is kept constant at its value of 1850 (Fig. 1b). Then, we reconstruct this residual time series (which approximates the influence of the total anthropogenic RF on T) by NNs endowed with the three anthropogenic forcings as inputs, and an attribution exercise can be performed as above, by separately keeping each forcing constant at its 1850 value (see Supplementary Fig. S3). Once the results of this attribution experiment are added to the temperature time series reconstructed in Fig. 1b, we can explore the specific roles of warming and cooling factors in different periods (see Fig. 2).

Thus, we can appreciate in more details the great influence of GHGs (and, at a lesser extent, BC) on the temperature of the last half century. Note also that, in this period, the curves obtained supposing GHGs (and BC)  constant are lower than the "attribution curve" in Fig. 1b by about 0.1 K, because here a cooling factor (RFSOX) has been considered with its real pattern. Furthermore, without changes in RFSOX, the hiatus during the period 1945-1975 would not have appeared and the increasing trend in temperature would have been monotonic. The maximum influence of RFGHG on T is about 0.9 K during the last years of the period considered here, while the highest perturbation to T by RFSOX is about 0.3 K at the end of the cited hiatus.

In this framework, what is the importance of natural forcings? Certainly, the solar radiation (with its 11-year period) influenced the decadal variability. However, the time series of RFSOLAR also shows the transition from a low-power regime to a high-power one during the period 1910-1950 (see Supplementary Fig. S1b). Has this phenomenon influenced the temperature time series during the recent warming or in other periods? This is also investigable (for the first time, at our knowledge) by our NN models. In particular, a synthetic time series of RFSOLAR (which is "stationary" and does not show that transition) has been built in terms of a first-order Fourier series based on the first 65 years available (see RFSOLSTAT in Supplementary Fig. S1b).

Thus, once considered the validated NN models, the influence of Sun on T can be studied by putting the new yearly values of RFSOLSTAT in input, transferring them through the fixed connection weights and looking at the new outputs. The results are sketched in Fig. 3. Here, the increasing trend in T during the years 1910-1945 is not caught with a stationary power from Sun and during the 1945-1975 hiatus T is about 0.2 K lower than observed. The "stationary Sun" has no detectable influence on the value of the increasing T trend of the last period.

In general, the influence of a forcing factor on the temperature behaviour in a certain period is bigger when its attribution curve in Figs 2 and 3 is more distant from the real temperature curve. From a more quantitative point of view, on all the period of investigation, the calculation of Root Mean Square Error (RMSE) can help (see Supplementary Table S1).

Analysing the role of natural variability. Thus, in this NN framework we are able to explain about 83% of the variance (R 2 ) of the global T curve through external forcings (NNs' inputs). Can the residual variance be explained, at least in part, by natural variability? To explore this topic, we took the time series of the residuals of "observed T minus reconstructed T" (ensemble mean) in Fig. 1a and tried to reconstruct this time series by NNs endowed with three indices of natural variability as inputs: AMO, SOI and PDO. The results show that our NNs reconstruct the residuals in a satisfying manner (see Supplementary Fig. S4a). Once the ensemble mean reconstruction of residuals is added to the ensemble mean reconstruction of the real T curve obtained through external forcings, this latter curve is better "simulated": R = 0.944, RMSE = 0.089 K, so that the explained variance is now about 89%. In particular (see Fig. 4), the better performance in catching interannual and decadal variability of the temperature curve is evident along all the period of investigation, and in the 1945-1975 hiatus period, in particular. This also leads to mitigate the overemphasized role of the 11-year solar cycle on temperature variability in previous NN models fed with just external forcings.

A further investigation can be performed on the roles of the single circulation patterns. The attribution strategy can be adopted once more by considering the validated NN models on the residuals and by feeding them in input with real values for two indices and a constant value for the remaining third index. In doing so, one discovers the big influence of AMO in catching both interannual and decadal variability of the temperature signal. This is consistent with other results in literature [17][18][19] , attesting its role of major driver of natural variability on the planet. ENSO appears to contribute especially to interannual variability (when it is taken constant, decadal variability is however caught), while PDO seems to not contribute substantially to the signal of global T (see Supplementary Fig. S4b-d). Obviously, our approach disregards the possible influences and relationships among the changes in the external forcings and the behaviour of these circulation patterns. In our framework, this could be considered assuming forcings and indices as inputs of the same NNs, so putting them at the same level.

One could insert the three indices as inputs together with external forcings. In doing so, however, we should build sensibly larger NNs and this could lead to overfitting problems. Thus, due to the great influence of AMO on T, just discovered, we choose to furtherly investigate its role, by adding only AMO as input in our NN models, together with external forcings.

The reconstruction performance of the ensemble mean obviously benefits from the direct inclusion of AMO as input, substantially showing the same performance (R = 0.943 and RMSE = 0.089 K) reached by the previous "residual strategy" when all the three indices were considered. However, the most notable result is obtained from the attribution proof performed by keeping AMO constant at its mean value. In this case, multi-decadal variability has been caught less well: this is evident especially in the T increasing period 1910-1945 and in the following hiatus period 1945-1975 (see Fig. 5). In particular, this shows that probably AMO had a role in the cited hiatus period, a role that we can superimpose to that coming from variations in sulphates. We will analyse better this problem in the next Sections.

Sensitivity analyses

Uncertainties in forcing factors. In this paper, data from reliable sources are used as forcing factors.

However, notable uncertainties are still present in these data, especially for those forcings that act in both direct and indirect manner on climate, such as sulphates and black carbon. Thus, some sensitivity studies are performed to analyse if our main results about attribution are robust, that is still valid even when we explore a variability in these forcings that can represent our range of uncertainties.

To do so, the results of NN ensembles are considered when the networks are fed by two different time series for the anthropogenic RF. Namely, we refer to the anthropogenic forcing factors used in CMIP5 runs 20 (RFANTHCMIP5) and in a notable work by Hansen et al. 21 (RFANTHANSEN). We can consider the first time series as an upper limit for the RF of anthropogenic origin, while the second one represents a more similar estimation of anthropogenic forcings if compared with our original one. In any case, in both new time series the cooling role of sulphates is clearly less important, especially during the 1945-1975 hiatus period (see Supplementary  Fig. S5).

Two new NN ensemble runs are performed through networks with architecture 3-4-1, as in the first numerical experiment presented in this paper. In these cases, the inputs are RFANTHCMIP5, RFSOLAR, RFVOL and RFANTHANSEN, RFSOLAR, RFVOL, respectively. The results of temperature reconstruction and attribution are shown in Supplementary Figs S6, 7 for the two cases.

When RFANTHCMIP5 is considered, NNs are still able to reconstruct the temperature data, including the recent global warming, in a satisfying manner (R = 0.912, RMSE = 0.109), even if the ensemble mean shows a continuous increasing trend since the end of the 19 th century and the 1945-1975 hiatus is not entirely caught: just a slowdown of warming can be appreciated in this period (see Supplementary Fig. S6a). This is obviously due to the decreased influence of the sulphates in these runs. The correspondent attribution run ( Supplementary  Fig. S6b) shows the same constant trend for the recent decades already found in the original run (Fig. 1b), but with lower values of temperature anomalies. This is perfectly understandable, because now the model for temperature reconstruction has been trained with higher values of anthropogenic forcings in the last decades and the difference in RFANTHCMIP5 between 1850 and the more recent years is higher in these runs.

When RFANTHANSEN is considered, the reconstruction performance is still quite satisfying (R = 0.909, RMSE = 0.111) and, once more, the 1945-1975 hiatus is not entirely caught (see Supplementary Fig. S7a). The attribution run shows a constant trend for the recent decades, which is very similar to that found in the original run (Fig. 1b).

Thus, the use of our adaptive method can lead to some considerations. First of all, the main attribution result, which claims the fundamental role of anthropogenic forcings in driving the recent global warming, seems robust: in any case of the anthropogenic RFs considered here, the attribution runs show the constancy of trend for recent decades, even if with slightly different T values in the case of RFANTHCMIP5.

Furthermore, in this section we find that considering other RFANTHs prevents a complete reconstruction of the 1945-1975 hiatus. Then a question may arise: what can be the role of natural variability in this framework? This is easily investigable by adopting the "residual strategy" already used in the first section, when the indices of variability are used in input to NNs in order to reconstruct the residual time series (observed T minus estimated T), or, more simply, by considering AMO as input together with external forcings.

In short, the results of these new runs show that the indices (and AMO in particular) lead to well reconstruct the cited hiatus even when RFANTHCMIP5 and RFANTHANSEN are considered. In the NN adaptive models adopted here, AMO is able to contribute substantially to the hiatus reconstruction, as sulphates mainly did in the standard runs with RFANTH. This is clearly visible in Supplementary Figs S8, 9: in particular, note that the 1945-1975 hiatus is not caught at all when AMO is kept constant.

Thus, in our NN runs, the relative weight of sulphates and AMO in determining the hiatus period depends on the choice of the anthropogenic forcing (and the cooling part -sulphates -inside it), so that no final conclusion can be drawn on which driver (sulphates or AMO) is the most influential on the hiatus temperatures. In the final Section we will examine again this problem.

Nonlinear dependences on the forcings. The adoption of a fully nonlinear method in this research opens the possibility to investigate the nature of the dependence of temperature on forcings. In particular, it is interesting to explore how much the temperature response to high or low forcings is linear or nonlinear. The previous sensitivity study on forcing uncertainties can be therefore the framework for such kind of analysis.

We choose to limit our analysis to periods in which anthropogenic forcings and temperature are undoubtedly increasing, i.e. since 1976, and consider the reconstruction performance of both NN models (through their ensemble mean) and multilinear regressions when anthropogenic forcings are estimated as RFANTH, RFANTHCMIP5 and RFANTHANSEN. The results in terms of R are presented in Table 1.

The situation seems quite clear. The NNs are able to reconstruct T much better than the multilinear regression when the forcing is low, while the performance are very similar when the forcing is high. This suggests a quasi-linear dependence of T on forcings for strong forcings and a more nonlinear behaviour for weak ones. At the present stage of investigation, we can only suppose that these stronger nonlinearities for low forcings could be linked to feedbacks or buffering effects (for instance, of the ocean). Further research seems to be needed on this topic.

Possible time lags.

Until now, in the present paper, synchronous time series are always considered.

However, delayed time series could be analysed to investigate if some influence shows lags. It is well known that, at a monthly time resolution, many lags have been identified between forcing factors/natural variability and global T 22 . Vice versa, at annual time resolution, it has been shown that the indices of natural variability used in this paper do not present delayed influences on T 23 . Annual lags are usually considered just for RFANTH 22 . Thus, we have performed NN ensemble runs by considering a record (RFANTHLAG10) to be synchronized in our NN models with RFNAT, RFVOL and T related to ten years later.

The results obtained by this new set of inputs lead to a decrease in reconstruction performance and the analysis of the reconstructed time series shows a delay (estimated in about 6-8 years) in the starting point of the 1945-1975 hiatus and in the beginning of the recent global warming. In short, our method does not show an increase in performance when time lags are considered, in accordance with recent results that clearly show a time delay in the response of global temperature to emissions, but not to concentrations and the related radiative forcings 24 . Can sulphates drive AMO? In a previous Section we analysed the importance of sulphates and AMO as drivers of the temperatures during the 1945-1975 hiatus period, implicitly assuming that they represent independent influence factors on T. However, recently two papers investigated the role of sulphate aerosols in driving the North Atlantic climate variability 25,26 . In particular, the authors tried to answer this question: are sulphate aerosols a driver for this variability? The two papers achieved contrasting results. In this final section we can check how our approach could contribute to this debate.

To investigate the possible influence of RFSOX on AMO we built NN models (4-4-1 networks) in which we considered AMO as target and the following forcings as inputs: the natural forcings (RFSOLAR and RFVOL), the warming part of the anthropogenic forcing (RFWARM = RFGHG + RFBC) and its cooling part (RFSOX), so that the specific influence of sulphates in determining AMO can be investigated within the attribution runs.

The reconstruction performance of the AMO time series is quite successful (R = 0.677 and RMSE = 0.135 for or the ensemble mean): see Fig. 6a. In the framework of these NN validated models, the most notable result is obtained from the attribution runs performed by keeping RFSOX constant at its annual value of 1866. As shown in Fig. 6b, when RFSOX is constant, the shape of AMO is no more caught and a linear increase of the AMO index is expected by the ensemble mean of the models. This output is in agreement with the results by Booth et al. 25 , who found a similar shape when trying to reconstruct SSTs in the Atlantic region by keeping constant the aerosol forcing. This suggests that RFSOX can be an effective driver for AMO.

The results by Booth et al. have been criticized by Zhang et al. 26 inside the framework of dynamical modelling, especially by considerations about salinity and ocean heat content. Unfortunately, the time series available for these variables are too short to be analysed in the context of NN modelling. However, we can perform further runs adopting the other forcing factors already used in the previous sensitivity studies to see if our result is robust.

After having split RFANTHCMIP5 and RFANTHANSEN into their warming and cooling parts (RFWARMCMIP5, RFSOXCMIP5, RFWARMHANSEN, RFSOXHANSEN), we repeated the previous experiment of AMO reconstruction by means of 4-4-1 NNs supplied with these new forcing series. Even if the reconstructions are now slightly less accurate, the attribution results when the cooling RF is fixed constant show that the shape of AMO is not caught at all also in these cases: the reconstruction shows a less increasing trend than in the run with our original forcings, but still quasi-linear (see Supplementary Figs S10, 11).

Thus, our results suggest that sulphates can be drivers for AMO, even if, of course, disentangling their complex relationship (and their mutual influence on T) probably would require more research.

Conclusions

Our investigation by NNs shows that the attribution results by GCMs about the main role of the anthropic forcing are robust and reliable. This, added to our new results on the role of Sun (irrelevant for the trend of the last decades, and quite strong at the middle of the 20 th century) and the role of sulphates and natural variability, gives a more complete picture in attribution studies. The sensitivity analyses performed here furtherly show that the NN attribution method itself can be considered robust with reference to uncertainties in anthropogenic forcings. Moreover, strong forcings seem to act in a quasi-linear manner, while the relationship between weaker forcings and temperature appears to be more nonlinear. At our annual time resolution, no delayed influence of forcings on temperature is clearly evident. Finally, our work shows that sulphates and AMO both contribute in driving the temperature behaviour (especially in the 1945-1975 hiatus period), and the NN method adopted here permits also to establish a potential driving role of sulphates on AMO which deserves further investigation.

Incidentally, our work can be relevant also for the climate debate on the media, where the anthropogenic influence on the global warming is often denied, basing on the critics to GCMs and their attribution results.

Methods

NN tool. The NNs used in this paper are quite standard feedforward networks with backpropagation training and one hidden layer, which allows them to be "universal approximators" 27,28 . These NNs are endowed with hyperbolic-tangent transfer functions at the hidden level and a linear function at the output neuron.

A training-validation-test procedure must be applied for obtaining a nonlinear relationship between inputs and targets (to be approximated by outputs) which can be generally valid. In particular, the connection weights (the free parameters of the NNs) have to be fixed on the training set -by stopping the training phase when the error on the validation set begins to increase -and the generalization performance must be measured using a third set (unknown to the NNs), the test set. The tool 8 adopted in this paper allows us to maximize the members of the training set, while leaving good generalization performance. In practice, starting from the first year of the dataset, a single input-target pattern is extracted from the total available dataset and considered as test set. A validation set is then randomly chosen (it represents the 10% of patterns) and the remaining patterns constitute the training set. After the run of the NN model, the connection weights are fixed, a transfer function is found from inputs to output and an estimation for the first value of T is obtained.

However, this result of reconstruction can be influenced by the specific random choice of the initial weights and the members of the validation set. Thus, multiple runs can be performed by choosing other random values for initial weights and members for the validation set. We perform 20 runs in an ensemble approach and then calculate the average of the reconstruction outputs, with the same rationale adopted in ensemble runs of GCMs when one considers the ensemble mean.

After this reconstruction of the first annual value of T, we follow the same procedure for the other years (each of them becomes -sequentially -the test set), thus permitting the estimation of all output values (and ensemble means) at the end of the procedure.

The training-validation-test procedure is not the only mean to avoid overfitting problems. Here, we keep very low the number of inputs and hidden neurons, so that the free parameters of our NN models are an order of magnitude less than the number of patterns in the training set.

Code availability. MATLAB ® code for NN generalized leave-one-out modelling and attribution runs is included in the Supplementary information. Data availability. We consider annual data of global combined land and marine temperature anomalies since 1850 (HadCRUT4) 29 from the Met Office Hadley Centre, available at https://crudata.uea.ac.uk/cru/data/ temperature/.

As far as data about radiative forcings are concerned, we use the freely available dataset collected at http:// www.sterndavidi.com/datasite.html and referred to a paper by Stern & Kaufmann 30 , where the data on GHGs concentrations are taken from the NASA/GISS website and the related RF calculations are performed through standard formulas 31,32 .

As for sulphates, data about global estimates of their emissions 33,34 are available only till 2011. The calculation 30 of direct and indirect RFs are based on slight modifications of previous studies 35,36 . Data about RF of black carbon 37 come from the RCP8.5 scenario. Solar irradiance is approximated by an index previously assembled 38 and available at https://data.giss.nasa. gov/modelforce/solar.irradiance/. The conversion from solar irradiance to RFSOLAR is obtained in a standard way 32 .

Volcanic activity of dust emission is considered through the optical thickness data 39 from https://data.giss. nasa.gov/modelforce/strataer/ and RFVOL is −27 times the optical thickness 30 .

In this paper we consider three indices for representing patterns of natural variability, i.e.:

• AMO 40  Data about anthropogenic radiative forcings in the alternative scenarios used for the sensitivity analyses are available at https://data.giss.nasa.gov/modelforce/Fi_Miller_et_al14_upd.txt and https://data.giss.nasa.gov/ modelforce/Fe_H11_1880-2011.txt, for CMIP5 20 and Hansen et al. 21 , respectively. The first available year for the latter dataset is 1880, so that here it is extended backward to 1850 by filling the first 30 years with zero values for anthropogenic RF.

The dataset used in this paper is included in the Supplementary information.

Figure 1 .
1Reconstruction of global T by NN models. Black line = observed T, red lines = results of ensemble runs, blue line = ensemble mean. (a) With real values of RFANTH, RFSOLAR and RFVOL as inputs. (b) Attribution runs when RFANTH is fixed at its value of 1850.

Figure 2 .
2Warming and cooling roles of the distinct anthropogenic forcings by a NN attribution experiment. Black line = observed T, red line = reconstructed T by all inputs with actual values, blue, light blue and green lines = attribution runs with constant forcing(s).

Figure 3 .
3Attribution experiment for solar influence. Black line = observed T, red lines = results of ensemble runs, blue line = ensemble mean. We mimic a stationary solar forcing by putting RFSOLSTAT in input to validated NN models.SCIENTIfIC REPORTS | 7: 17681 | DOI:10.1038/s41598-017-18011-8

Figure 4 .
4Reconstruction of the global temperature using also data about circulation patterns. Black line = observed T, blue line = ensemble mean from NN models with external forcings as inputs, red line = ensemble mean when the performance of the NN models with indices of natural variability as inputs is added to the previous result. Note that the graph starts in 1866 because data about SOI are not available before that year.

Figure 5 .
5Reconstruction of global T by NN models with AMO as input. Black line = observed T, red lines = results of ensemble runs, blue line = ensemble mean. (a) With real values of RFANTH, RFSOLAR, RFVOL and AMO as inputs. (b) Attribution runs when AMO is kept fixed at its mean value.

Figure 6 .
6Reconstruction of AMO by NN models. Black line = observed AMO, red lines = results of ensemble runs, blue line = ensemble mean. (a) With real values of RFWARM, RFSOX, RFSOLAR and RFVOL as inputs. (b) Ensemble mean of attribution runs when RFSOX is fixed at its value of 1866. SCIENTIfIC REPORTS | 7: 17681 | DOI:10.1038/s41598-017-18011-8

since 1856: data available at www.esrl.noaa.gov/psd/data/timeseries/AMO; • PDO 41 since 1854: data available at https://www.ncdc.noaa.gov/teleconnections/pdo/; • SOI, related to ENSO 42-44 since 1866: data available at www.cru.uea.ac.uk/cru/data/soi/soi.dat.
SCIENTIfIC REPORTS | 7: 17681 | DOI:10.1038/s41598-017-18011-8
© The Author(s) 2017
Author ContributionsA.P., P.R. and C.C. jointly designed the initial attribution experiment. P.R., S.A. and G.C. performed the runs of the neural network model. All the authors contributed to the analyses of progresses and results. A.P. wrote the paper, with contributions of the other authors.Additional InformationSupplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-18011-8.Competing Interests:The authors declare that they have no competing interests.Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Evaluation of Climate Models. G Flato, The Physical Science Basis. Cambridge, United Kingdom; New York, NY, USACambridge University PressClimate ChangeFlato, G. et al. Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis (ed. Stocker, T. F. et al.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741-866 (2013).

From Observations to Simulations: A Conceptual Introduction to Weather and Climate Modelling. A Pasini, World Scientific PublishersSingaporePasini, A. From Observations to Simulations: A Conceptual Introduction to Weather and Climate Modelling (World Scientific Publishers, Singapore, 2005).

Detection and Attribution of Climate Change: from Global to Regional. N L Bindoff, Climate Change 2013: The Physical Science Basis. Cambridge, United Kingdom; New York, NY, USACambridge University PressBindoff, N. L. et al. Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis (ed. Stocker, T. F. et al.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 867-952 (2013).

A multi-approach strategy in climate attribution studies: Is it possible to apply a robustness framework?. A Pasini, F Mazzocchi, Environ. Sci. Pol. 50Pasini, A. & Mazzocchi, F. A multi-approach strategy in climate attribution studies: Is it possible to apply a robustness framework? Environ. Sci. Pol. 50, 191-199 (2015).

Climate model pluralism beyond dynamical ensembles. F Mazzocchi, A Pasini, WIREs Clim. Change. 8477Mazzocchi, F. & Pasini, A. Climate model pluralism beyond dynamical ensembles. WIREs Clim. Change 8, e477 (2017).

W W Hsieh, Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Cambridge, UKCambridge University PressHsieh, W. W. Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels (Cambridge University Press, Cambridge, UK, 2009).

S E Haupt, A Pasini, C Marzban, Artificial Intelligence Methods in the Environmental Sciences. New York, NY, USASpringerHaupt, S. E, Pasini, A. & Marzban, C. (ed.) Artificial Intelligence Methods in the Environmental Sciences (Springer, New York, NY, USA, 2009).

Artificial neural networks for small dataset analysis. A Pasini, J. Thorac. Dis. 7Pasini, A. Artificial neural networks for small dataset analysis. J. Thorac. Dis. 7, 953-960 (2015).

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system. A Pasini, M Lorè, F Ameli, Ecol. Modell. 191Pasini, A., Lorè, M. & Ameli, F. Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system. Ecol. Modell. 191, 58-67 (2006).

Influence of circulation patterns on temperature behavior at the regional scale: A case study investigated via neural network modeling. A Pasini, R Langone, J. Clim. 25Pasini, A. & Langone, R. Influence of circulation patterns on temperature behavior at the regional scale: A case study investigated via neural network modeling. J. Clim. 25, 2123-2128 (2012).

Climatic attribution at the regional scale: a case study on the role of circulation patterns and external forcings. A Pasini, G Modugno, Atmos. Sci. Lett. 14Pasini, A. & Modugno, G. Climatic attribution at the regional scale: a case study on the role of circulation patterns and external forcings. Atmos. Sci. Lett. 14, 301-305 (2013).

Introduction to the Theory of Neural Computation. J Hertz, A Krogh, R G Palmer, Addison-WesleyNew York, NY, USAHertz, J., Krogh, A. & Palmer, R. G. Introduction to the Theory of Neural Computation (Addison-Wesley, New York, NY, USA, 1991).

C M Bishop, Neural Networks for Pattern Recognition. Oxford, UKOxford University PressBishop, C. M. Neural Networks for Pattern Recognition (Oxford University Press, Oxford, UK, 1995).

. Reports Scientific, 10.1038/s41598-017-18011-8SCIENTIfIC REPORTS | 7: 17681 | DOI:10.1038/s41598-017-18011-8

Statistical assessments of anthropogenic and natural global climate forcing. An update. C.-D Schönwiese, A Walter, S Brinckmann, Meteorol. Z. 19Schönwiese, C.-D., Walter, A. & Brinckmann, S. Statistical assessments of anthropogenic and natural global climate forcing. An update, Meteorol. Z. 19, 3-10, 2010.

Global warming is driven by anthropogenic emissions: A time series analysis approach. P F Verdes, Phys. Rev. Lett. 9048501Verdes, P. F. Global warming is driven by anthropogenic emissions: A time series analysis approach. Phys. Rev. Lett. 90, 048501 (2007).

Applying Neural Networks: A Practical Guide. K Swingler, Academic PressLondonSwingler, K. Applying Neural Networks: A Practical Guide (Academic Press, London, 1996).

A significant component of unforced multidecadal variability in the recent acceleration of global warming. T Delsole, M K Tippett, J Shukla, J. Clim. 24DelSole, T., Tippett, M. K. & Shukla, J. A significant component of unforced multidecadal variability in the recent acceleration of global warming. J. Clim. 24, 909-926 (2011).

Application of the singular spectrum analysis technique to study the recent hiatus on the global surface temperature record. D Macias, A Stips, E Garcia-Gorriz, PLoS ONE. 9107222Macias, D., Stips, A. & Garcia-Gorriz, E. Application of the singular spectrum analysis technique to study the recent hiatus on the global surface temperature record. PLoS ONE 9, e107222 (2014).

Evidence for the role of the Atlantic multidecadal oscillation and the ocean heat uptake in hiatus prediction. A Pasini, U Triacca, A Attanasio, Theor. Appl. Clim. 129Pasini, A., Triacca, U. & Attanasio, A. Evidence for the role of the Atlantic multidecadal oscillation and the ocean heat uptake in hiatus prediction. Theor. Appl. Clim. 129, 873-880 (2017).

CMIP5 historical simulations (1850-2012) with GISS ModelE2. R L Miller, J. Adv. Model. Earth Syst. 6Miller, R. L. et al. CMIP5 historical simulations (1850-2012) with GISS ModelE2. J. Adv. Model. Earth Syst. 6, 441-477 (2014).

Earth's energy imbalance and implications. J Hansen, M Sato, P Kharecha, K Von Schuckmann, Atmos. Chem. Phys. 11Hansen, J., Sato, M., Kharecha, P. & von Schuckmann, K. Earth's energy imbalance and implications. Atmos. Chem. Phys. 11, 13421-13449 (2011).

How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to. J L Lean, D H Rind, Geophys. Res. Lett. 3518701Lean, J. L. & Rind, D. H. How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophys. Res. Lett. 35, L18701 (2008).

Has natural variability a lagged influence on global temperature? A multi-horizon Granger causality analysis. A Attanasio, A Pasini, U Triacca, Dynamics and Statistics of the Climate System. 1Attanasio, A., Pasini, A. & Triacca, U. Has natural variability a lagged influence on global temperature? A multi-horizon Granger causality analysis. Dynamics and Statistics of the Climate System 1, 1-16 (2016).

Maximum warming occurs about one decade after a carbon dioxide emission. K L Ricke, K Caldeira, Environ. Res. Lett. 9124002Ricke, K. L. & Caldeira, K. Maximum warming occurs about one decade after a carbon dioxide emission. Environ. Res. Lett. 9, 124002 (2014).

Aerosols implicated as a prime driver of twentiethcentury North Atlantic climate variability. B B B Booth, N J Dunstone, P R Halloran, T Andrews, N Bellouin, Nature. 484Booth., B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T. & Bellouin, N. Aerosols implicated as a prime driver of twentieth- century North Atlantic climate variability. Nature 484, 228-232 (2012).

Have Aerosols Caused the Observed Atlantic Multidecadal Variability?. R Zang, J. Atmos. Sci. 70Zang, R. et al. Have Aerosols Caused the Observed Atlantic Multidecadal Variability? J. Atmos. Sci. 70, 1135-1144 (2013).

Approximation by superposition of a sigmoidal function. G Cybenko, Math. Control Sign. Syst. 2Cybenko, G. Approximation by superposition of a sigmoidal function. Math. Control Sign. Syst. 2, 303-314 (1989).

Multilayer feedforward networks are universal approximators. K Hornik, M Stinchcombe, H White, Neural Networks. 2Hornik, K., Stinchcombe, M. & White, H. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366 (1989).

Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 dataset. C P Morice, J J Kennedy, N A Rayner, P D Jones, J. Geophys. Res. 1178101Morice, C. P., Kennedy, J. J., Rayner, N. A. & Jones, P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 dataset. J. Geophys. Res. 117, D08101 (2012).

Anthropogenic and natural causes of climate change. D I Stern, R K Kaufmann, Clim. Change. 122Stern, D. I. & Kaufmann, R. K. Anthropogenic and natural causes of climate change. Clim. Change 122, 257-269 (2014).

Radiative Forcing of Climate Change. V Ramaswamy, Climate Change. TheScientific Basis (ed. Houghton, J. T. et al.Cambridge, United Kingdom; New York, NY, USACambridge University PressRamaswamy, V. et al. Radiative Forcing of Climate Change. In: Climate Change 2001: TheScientific Basis (ed. Houghton, J. T. et al.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 349-416 (2001).

Climate models-projections of future climate. A Kattenberg, The Science ofClimate Change. Houghton, J. T. et al.CambridgeCambridge University PressClimate ChangeKattenberg, A. et al. Climate models-projections of future climate. In: Climate Change 1995: The Science ofClimate Change (ed Houghton, J. T. et al.), Cambridge University Press, Cambridge, 285-357 (1996).

Anthropogenic sulfur dioxide emissions. S J Smith, Atmosph. Chem. Phys. 11Smith, S. J. et al. Anthropogenic sulfur dioxide emissions: 1850-2005. Atmosph. Chem. Phys. 11, 1101-1116 (2011).

The last decade of global anthropogenic sulfur dioxide: 2000-2011 emissions. Z Klimont, S J Smith, J Cofala, Environ. Res. Lett. 814003Klimont, Z., Smith, S. J. & Cofala, J. The last decade of global anthropogenic sulfur dioxide: 2000-2011 emissions. Environ. Res. Lett. 8, 014003 (2013).

Implications for climate and sea level of revised IPCC emissions scenarios. T M L Wigley, S C B Raper, Nature. 357Wigley, T. M. L. & Raper, S. C. B. Implications for climate and sea level of revised IPCC emissions scenarios. Nature 357, 293-300 (1992).

History of sulfate aerosol radiative forcings. O Boucher, M Pham, Geophys. Res. Lett. 299Boucher, O. & Pham, M. History of sulfate aerosol radiative forcings. Geophys. Res. Lett. 29(9), 22-1-22-4 (2002).

The RCP GHG concentrations and their extension from 1765 to 2300. M Meinshausen, Clim. Change. 109Meinshausen, M. et al. The RCP GHG concentrations and their extension from 1765 to 2300. Clim. Change 109, 213-241 (2011).

Evolution of the sun's spectral irradiance since the Maunder Minimum. J Lean, Geophys. Res. Lett. 27Lean, J. Evolution of the sun's spectral irradiance since the Maunder Minimum. Geophys. Res. Lett. 27, 2425-2428 (2000).

Stratospheric aerosol optical depth. M Sato, J E Hansen, M P Mccormick, J B Pollack, J. Geophys. Res. 98Sato, M., Hansen, J. E., McCormick, M. P. & Pollack, J. B. Stratospheric aerosol optical depth, 1850-1990. J. Geophys. Res. 98, 22987-22994 (1993).

The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US. D B Enfield, A M Mestas-Nunez, P J Trimble, Geophys. Res. Lett. 28Enfield, D. B., Mestas-Nunez, A. M. & Trimble, P. J. The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US. Geophys. Res. Lett. 28, 2077-2080 (2001).

Improved extended reconstruction of SST (1854-1997). T M Smith, R W Reynolds, J. Clim. 17Smith, T. M. & Reynolds, R. W. Improved extended reconstruction of SST (1854-1997). J. Clim. 17, 2466-2077 (2004).

An extension of the Tahiti-Darwin southern oscillation index. C F Ropelewski, P D Jones, Mon. Weather Rev. 115Ropelewski, C. F. & Jones, P. D. An extension of the Tahiti-Darwin southern oscillation index. Mon. Weather Rev. 115, 2161-2165 (1987).

A further extension of the Tahiti-Darwin SOI, early SOI results and Darwin pressure. R J Allan, N Nicholls, P D Jones, I J Butterworth, J. Clim. 4Allan, R. J., Nicholls, N., Jones, P. D. & Butterworth, I. J. A further extension of the Tahiti-Darwin SOI, early SOI results and Darwin pressure. J. Clim. 4, 743-749 (1991).

Pre-1866 extensions of the southern oscillation index using early Indonesian and Tahitian meteorological readings. G P Können, P D Jones, M H Kaltofen, R J Allan, J. Clim. 11Können, G. P., Jones, P. D., Kaltofen, M. H. & Allan, R. J. Pre-1866 extensions of the southern oscillation index using early Indonesian and Tahitian meteorological readings. J. Clim. 11, 2325-2339 (1998).