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Lipid Heterogeneity Resulting from Fatty Acid Processing in the Human Breast Cancer Microenvironment Identified by GCIB-ToF-SIMS Imaging Tina Bernadette Angerer, Ylva Magnusson, Goran Landberg, and John Stephen Fletcher Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b03884 • Publication Date (Web): 26 Oct 2016 Downloaded from http://pubs.acs.org on October 27, 2016

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Analytical Chemistry

Lipid Heterogeneity Resulting from Fatty Acid Processing in the Human Breast Cancer Microenvironment Identified by GCIB-ToF-SIMS Imaging †

‡

‡

Tina B. Angerer , Ylva Magnusson , Göran Landberg *, John S. Fletcher † ‡

†*

Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden. Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden

KEYWORDS: Imaging mass spectrometry, human breast cancer, tumor microenvironment, lipids, ToF-SIMS

ABSTRACT: Breast cancer is an umbrella term used to describe a collection of different diseases with broad inter- and intra-tumor heterogeneity. Understanding this variation is critical in order to develop, and precisely prescribe, new treatments. Changes in the lipid metabolism of cancerous cells can provide important indications as to the metabolic state of the cells but are difficult to investigate with conventional histological methods. Due to the introduction of new higher energy (40 kV) gas cluster ion beams (GCIBs) time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is now capable of providing information on the distribution of hundreds of molecular species simultaneously on a cellular to sub-cellular scale. GCIB-ToF-SIMS was used to elucidate changes in lipid composition in 9 breast cancer biopsy samples. Improved molecular signal generation by the GCIB produced location specific information that revealed elevated levels of essential lipids to be related to inflammatory cells in the stroma while cancerous areas are dominated by non-essential fatty acids and a variety of phosphatidylinositol species with further in-tumor variety arising from decreased desaturase activity. These changes in lipid composition due to different enzyme activity are seemingly independent of oxygen availability and can be linked to favorable cell membrane properties for either proliferation/invasion or drug resistance/survival

INTRODUCTION Breast cancer is an umbrella term used to describe a collection of different diseases with broad inter- and intra-tumor heterogeneity. Understanding this variation is critical in order to develop and precisely prescribe new treatments. Changes in the lipid metabolism of cancerous cells can provide important indications as to the metabolic state of the cells but are hard to investigate with conventional histological methods. Metabolic changes in cancerous cells have been recognized as early as the 1920s and have been described as the Warburg effect.1 Briefly, normal cells under aerobic conditions use glucose efficiently to produce adenosine triphosphate (aerobic glycolysis: 36 moles ATP per mole glucose). Cancerous cells favour the conversion of glucose to lactate which is less energy efficient but is compensated with an increased glucose uptake (anaerobic glycolysis: 2 moles of ATP per mole of glucose). This occurs independently to the abundance of oxygen. The benefits of this behaviour are still under investigation.2 More recently the altered fatty acid and lipid metabolism3,4 in cancer cells has become of increased interest where most studies have focused on the influence of dietary fatty acid (FA) intake and the effects on cancer (ω3 versus ω6 FAs 5) but the ultimate fate/involvement of those FAs is unknown. Structural and signalling lipids are usually synthesized by modification of dietary fatty acids but in cancer this is not necessarily the case. Instead of fatty acids provided from diet, many cancer cells rely on de novo synthesised fatty acids to generate enough biomass for cell propagation. This is possible due to

an upregulation of FA synthase (FASN) which has been shown to occur in many cancer types and is usually connected to a poor prognosis.6 FASN is the major enzyme responsible for de novo fatty acid synthesis in mammalian cells and is also active during lactation in mammary glands.7 In a healthy organism FASN is mainly activated to transform an overabundance of nutrients into FAs which are eventually bound in triacylglycerides (TAGs), so called storage fat. This fat can be used to generate energy via β-oxidation if needed (during starvation or exercise). Cancer cells are not known to produce storage fat. FASN is currently under investigation as a therapeutic target but tested inhibitors show severe side effects.8 Lipid composition changes can occur much faster than changes in proteins so may be the first indicator of phenotypic alterations in cells. In some cases cancer cells have been observed to trigger the release of fats from neighboring fat cells, adipocytes, so that released fatty acids can be taken up.9 These mechanisms are also not understood. The traditional approach to study lipids in cancer is the analysis of lipid extracts from homogenized tissue as they are difficult to stain for conventional histology. This can be problematic since the heterogeneous cancerous microenvironment contains cells from different origins which are fulfilling varying functions (as summarized in reference 10). This diversity is potentially displayed in their lipid profiles but the spatial information is lost during homogenization/extraction.

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Table 1. Classification of the cancer sections included in this study. Listed are the classifications for all cancer sections analyzed, diagnosis, hormone receptor and cancer marker status. Sample

#1

#2

#3

Cancer type

Invasive, ductal

Invasive, ductal

Invasive, ductal

ER -

ER -

ER 90%

ER 100%

ER -

PR -

PR -

PR 90%

PR 20-30%

Ki67 80-90%

Ki67 80-90%

Ki67 30-40%

Her2 -

Her2 -

Her2 -

Hormone Receptor

Protein Expression

#4

#5

#6

#7

#8

#9

Invasive, ductal, bifocal



ER 100%

ER -

ER -

ER 100%

PR-

PR 100%

PR-

PR -

PR 100%

Ki67 60-70%

Ki67 80-90%

Ki67 15%

Ki67 70-80%

Ki67 50-60%

Ki67 15-20%

Her2 -

Her2 -

Her2 -

Her2 (3+)

Her2 -

Her2 -

Invasive, Invasive, Invasive, ductal and ductal, ductal DCIS* bifocal grade 3

Imaging mass spectrometry provides a means of studying location specific chemical changes. Micro-focused beams, often either a laser or ion beam, are used to remove material from a discrete point on a sample that is then analyzed. Each point on the sample that is analyzed becomes a pixel in the mass spectral image with a full mass spectrum associated with it.11 A constant analytical challenge with imaging mass spectrometry is the ability to detect the widest range of biochemical species from a sample with sufficient spatial resolution. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a technique routinely applied for materials analysis and has also been used to good effect in a range of biomedical studies on human samples including prostate cancer,12 colon cancer,13 fatty liver,14 atherosclerotic plaque,15 adipose tissue 16, skeletal muscle17 and recently breast cancer18 Additionally lipids in breast cancer have been studied using other MS imaging approaches (e.g. matrix assisted lased desorption ionization mass spectrometry, MALDI,19 and desorption electrospray ionization, DESI.20 While each method has its advantages, due to either poor spatial resolution, low signal intensity or limited chemical coverage achieved, the connections to cancer lipidmetabolomics could not be made. It is therefore vital to develop and test new methods for precise in situ molecular imaging. Recently the introduction of gas cluster ion beams (GCIBs) 21 have produced significant improvements (30-50×) in signal for intact lipid species compared with the previous gold standard C60 ion beams used for organic analysis.22,23 This provides a means of performing precise in situ lipidomics of tissue sections allowing the chemical changes associated with the heterogeneous cancerous environment to be elucidated. Mass spectra can be collected in either positive or negative mode, as different species are ejected with different charges, and due to the energy involved in the ion formation process intact molecular species along with characteristic fragments of these molecules are detected simultaneously. In this paper a GCIB-ToF-SIMS, using a prototype (40 kV) ion gun, is tested for the imaging of the tumour microenvironment. The improvements in the quality of the mass spectral data using this beam provide new insights into the distribution of lipids in this important sample type. Both the biological and analytical significance of the results are discussed.

EXPERIMENTAL SECTION Tumor sample preparation. Breast tissue samples were obtained from patients, who had never received chemotherapy, undergoing breast cancer surgery and were collected at the Department of Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden. The samples were cut from the breast and directly wrapped in aluminum foil, plunge-frozen in liquid nitrogen and stored in a -80 ºC freezer until they were further cryo-sectioned at 12 µm thickness at -20 °C using a cryomicrotome (Leica CM1520) filled with dry argon gas. Sections were thaw-mounted on indium-tin-oxide (ITO) coated glass slides and dried under vacuum. Details of the classification of the different tumor samples used in this study are provided in Table 1. ToF-SIMS Imaging. ToF-SIMS analyses were performed using a J105 instrument (Ionoptika Ltd, UK). The J105 has been described in detail elsewhere.24,25 Unlike conventional ToF-SIMS instruments the J105 uses a quasi-continuous primary ion beam to produce secondary ions that are sampled by a linear buncher prior to ToF measurement. In this study a 40 keV gas cluster ion beam (GCIB) was used to bombard the sample with clusters of Corgon 8 (8% CO2 in Ar) gas (AGA, Sweden). The nominal selected cluster size was 4000, for simplicity this will be written as Ar4000 throughout the manuscript although the gas used to produce the beam contained 8% CO2 in all measurements. Spectra were recorded over a mass range of 80-950 Da. Large area images were generated by producing a series of individual image “tiles” that were stitched together automatically by the acquisition software. Each tile comprised 64 × 64 pixels and covers an area of 640 × 640 µm2. In total, images were generated covering areas of different sizes using different numbers of tiles but samples #1-7 were analysed with the same spatial resolution (10 µm/pixel) and with the same primary ion dose density of 3.2 × 1011 ions/cm2 in negative ion mode taking ca. 3.5 minutes/tile. Sections of #1 and #9 were analysed with a spatial resolution of 7.8 µm/pixel and a primary ion dose of 1.3 × 1013 ions/cm2 in negative ion mode as well as a with primary ion dose of 1 × 1013 ions/cm2 in positive ion mode with an analysis time of 27 minutes/tile . Additional analysis of section #9 was acquired with a


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Figure 1. MS imaging, H&E staining and multivariate analysis of human breast cancer tissue section. (A and B) Scores image generated using principal components analysis (PCA) of the MS data, positive scoring pixels are colored green and negative scoring pixels are colored red. These correspond to stroma and cancerous tissue respectively in PC4 (A) and viable and necrotic tissue in PC1 (B). (C) RGB overlay mass spectrometry image (7.8 µm/pixel) of cancer sample #1, red (PI(38:4), stroma) green (PI(38:2), cancerous tissue) blue (SM(d34:1), necrotic tissue), negative ion mode data, scale bar: 500 µm (D) H&E stained microscopy image of the tissue section post MS analysis, cancerous (purple) and stroma (bright) tissue and (E) necrotic tissue, image areas are indicated by the white squares in C. Exact masses and identities can be found in Table S1.

spatial resolution of 3.9 µm/pixel and a primary ion dose of 1.3 × 1013 ions/cm2 in negative and positive ion mode these were single 256 × 256 pixel images taking ca. 1 h each. The beam size was determined by measuring the signal drop in line-scans over the edge of a Faraday cup in ion beam induced secondary electron images and was below 4 µm. When the beam size was smaller than the pixel size the beam was dithered over each pixel to ensure an even primary ion dose density and hence improve reproducibility and remove any variation in sampling depth. Imaging PCA. Multivariate analysis (principal components analysis, PCA) on ToF-SIMS images was performed in MatLab (The MathWorks Inc.) in 2 steps. In the first step, PCA was performed on the whole image. To avoid contribution of inorganic species from the ITO-coated glass substrate in images where the tissue did not cover the analyzed area completely (e.g. the data in Figure 1), background subtraction based on the first principal component was performed eliminating all substrate containing pixels (an illustration of this approach is provided in Fig. S1) . Prior to the second step, the spectra contained in the remaining image pixels were treated as follows. The mass range 100-950 Da. was selected and the time resolution down sampled from 1 to 10 ns (corresponding to approximately 0.1 m/z interval bins at m/z 500) to reduce computer memory requirements. The square root of the MS signal intensity was used to reduce the dynamic range of the data set. Output scores images are displayed in a red-green colour scheme. Red areas correspond to negative scoring pixels, green areas to positive scoring pixels on each principal component with intense red/green showing a high scoring region/great variance for this principal component while darker tones and black signify low or no variance in this region. Different principal components capture different variations in the MS data sets. For the loadings (sup. Info.) the same colour scheme was adapted.

Spectral PCA from pre-selected tissue regions. For each tissue slice, spectra from the different cancerous regions, the stroma region and, if present, the necrotic region were extracted based on identified lipid biomarkers discussed below. Each spectrum comprised of summed up spectra over an area of 150 pixels (15000 µm2). PCA of ToF-SIMS spectra was performed using SIMCA (Umetrix, Sweden). To reduce data size and the possible influence of artefacts (e.g. slight calibration differences), spectra were down binned from the original 1 ns spectra to uniform 0.1 Da bins and normalized to their total intensity to compensate for slight variations in the size of the selected area and any potential drift in instrument performance. These binned and normalized spectra were imported into SIMCA where PCA-X with Pareto scaling was performed. Pareto scaling scales data by dividing each variable by the square root of the standard deviation and reduces the dynamic range of the data. The SIMCA software can generate “contribution plots” where individual or groups of similarly scoring samples can be selected and the contribution of variables (m/z values) in the various principal components that separate those samples from the average data are determined. Line Scans. A line scan displays the changes in intensity of a specific species along a chosen path. Different species can be selected and compared along the same path. For the line scans in this report the data points are approximately 10 µm apart and each represents the average intensity of the selected species over 3 × 3 pixels (900 µm2, approx. 9 cells) to improve signal to noise while still maintaining useful resolution. The species selected for line scans were FA(20:4), FA(20:3), FA(20:2) and FA(20:1). To more clearly display trends all line scans were scaled to their local maximum (=100%). Original relative intensities are indicated by the numbers in the boxes above the local maximum, displaying the ratio MAX(species)/MAX(FA(20:3)).



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Figure 2. Combination of techniques reveals heterogeneity in cancerous tissue areas. H&E stained microscopy image of tissue sections #2 (A) and #3 (E) showing cancerous (purple) and stroma (bright) tissue; (B, F) overlay mass spectrometry image (10 µm/pixel, negative ion mode), of tissue sections consecutive to the H&E stained sections, red (FA(20:4), m/z 303.2, stroma) green (FA(18:1), m/z 281.3, cancerous tissue). PCA scores image generated by principal components analysis of MS image data, (C) displaying PC4 for section #2 and (G) PC3 for section #3, showing further heterogeneity within the cancerous part of the tissue, loadings can be found in Fig S3. (D, H) overlay mass spectrometry image based on molecular species identified in PCA analysis red (FA(20:4), m/z 303.2, stroma), green (FA(20:3), m/z 305.3, cancerous tissue C1), blue (FA(20:2), m/z 307.3, cancerous tissue C2), both scales bar: 500 µm.

RESULTS AND DISCUSSION PCA generates lipid profiles that correlate to histology. Consecutive sections from sample #1 were analysed in positive and negative ion mode as well at two different image resolutions (Fig. S2). Without previous knowledge of the tumor anatomy principal components analysis (PCA) of the data from the MS analysis of the whole tissue section in negative ion mode revealed 3 chemically distinct areas cancerous areas shown in the red, negatively scoring (red) pixels of PC 4 in Fig. 1 A, stroma shown in the positive scoring (green) pixels of PC 4 in Fig. 1 A and necrotic tissue shown by the negatively scoring (red) pixels of PC 1 in Fig. 1 B. The increased signal levels for intact lipids afforded by the use of the new GCIB resulted in PCA loadings containing detailed lipid profiles associated with different regions of the tissue. Some lipids were higher or lower in intensity in different areas but always present to some degree while others were only detected in a specific area. Ion intensity maps of three of these area specific lipids were combined to produce the RGB overlay image in Fig. 1, C. Subsequent hematoxylin and eosin (H&E) staining of the analyzed section (Fig. 1 D/E) confirmed that the chemically different areas identified by PCA represented cancerous (green), stroma (red) and necrotic (blue) regions within the tissue sample. Analysis in positive ion mode revealed an intermediate area between the stroma and necrotic tissue that showed increased signal from acylcarnitines that have been reported as potential indicators of hypoxia.26 In negative ion mode no, or only small changes in peak intensity appeared to be specific to this area. In both ion modes the spectrum from the necrotic area was dominated by sphingomyelin (SM) signals. Sphingomyelins and phosphatidyleth-

anolamine (PE)-ceramides have the same chemical formula so cannot be distinguished in the mass spectrum although the presence of the SM head group in positive ion mode (m/z 184.1) combined with the absence of signals from the PE-head group (m/z 142.0 or m/z 140.0 in positive or negative ion mode respectively) and phosphatidylcholine (PC) fragment peak (m/z 224.1) supports this SM assignment. Additionally, the molecular ions and fragment peaks observed have been reported as SM signals in previous studies.27 In positive ion mode cancer and stroma were mainly distinguished by one specific PC species each (cancerous area PC(34:1), stroma PC(32:0)) which agrees with previous reports of monounsaturated PC species being highly abundant in cancer.28 In negative ion mode the information provided by the analysis was more extensive. Detection of heterogeneity within cancerous areas. All of the biopsy tissue sections in this study showed very similar characteristic signals in the different areas. Assignment as cancerous and non-cancerous tissue was confirmed by comparison of H&E staining of consecutive sections. Fig. 2 shows 2 examples of biopsy tissue samples, (sample #2 and #3). Fig. 2 A/E show micrographs of H&E stained tissue where the cancerous areas shows a darker purple tone while the brighter areas are stroma. As with the data in Figure 1 individual ion species from the ToF-SIMS image were used to visualize the cancerous cells and stroma (Fig. 2. B/F). PCA analysis was performed on all the SIMS data sets and, in the absence of a necrotic region, the first principal component (capturing the highest variance in the data) always discriminated cancerous versus stroma regions as shown in Fig. S3 A/E. Loadings assigned to the different regions show similar lipid profiles for the different areas in the analyzed cancer sections (Fig. S3


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B/F). It was observed that further principal components revealed additional, more discrete, patterns within the cancerous areas themselves (Fig 2 and S3, C/G) that did not correlate with any obvious features in the H&E stained images. The loadings associated with those patterns (Fig. S3 D/H) showed that the same species are responsible for the separation in this principal component for all tissue sections. Inspection of the distribution of the individual ions identified in the loadings for this principal component confirmed that there was a heterogeneous distribution of lipid species within the cancerous area and that, although the heterogeneity resulted from variations of the same species in each case, their abundance varied in different tumor sections. An example is shown in Fig. 2 D/H. Both, samples (#2 and #3) show different distributions of FA(20:3) and FA(20:2) within the cancerous area but while sample #3 is dominated by FA(20:3), sample #2 contains mainly FA(20:2). Phosphatidylinositol (PI) lipid species PI(38:3) and PI(38:2) show the same heterogeneous distribution in the cancerous tissue as FA(20:3) and FA(20:2) but are slightly less clearly defined (Fig. S4 B-D/F-H). This slight loss in clarity is attributed to interferences in the mass spectrum mainly from the naturally occurring 13C isotope peaks (Fig. S4 A/E). The 13C isotope peak of PI(38:4) at m/z 887.549 cannot be resolved from the main peak for PI(38:3) at m/z 887.565 and, assuming an equal abundance of the 2 lipids, would contribute to approximately 16% of this peaks intensity. This effect is less pronounced with the FA species (only a 2% contribution). Since there are no free fatty acids expected in cancerous cells we can assume that all FA species in our spectra are lipid fragments and that the FA(20:3) peak is directly related to the PI(38:3) peak but with less interference from the 4× unsaturated FA equivalent. MALDI-MS studies have also confirmed that PI(38:3) in breast cancer samples contains FA(20:3) and FA(18:0).19 Table 2. Summary of Lipid species detected in different areas of cancerous tissue. Main fatty acid and lipid species found in necrotic areas (NC), stroma (ST), cancerous area 1 (C1) and 2 (C2). A complete list of masses and molecular formulas can be found in Table S1. NC

ST

C1

C2

FA(24:0)

FA(20:4)

FA(20:3)

SM(d34:1)

PE(38:4)

SM(d36:1)

PS(38:4)

PI(36:3)

PI(36:2)

PI(36:1)

SM(d40:1)

PI(38:4)

PI(38:3)

PI(38:2)

PI(38:1)

FA(20:2)

FA(20:1) PI(34:1)

The cancerous areas dominated by FA(20:3) will be referred to henceforth as C1 and those dominated by FA(20:2) as C2. In order to identify further chemical changes distinguishing C1 and C2, PCA was performed on spectra extracted from C1, C2, stroma and necrotic regions from the different samples. The resulting scores plot (Fig. S5 A) clearly distinguished necrotic, stroma and cancerous tissue using the first 2 principal components. For cancer area C1 versus C2 the separation was not perfect. The overlap between these two is attributed to 1) area C1 and C2 exhibit a gradient rather than a clear border and 2) the biopsies originate from different patients and show a wide range of structural variation. Contributions plots generated from this PCA (Fig. S4. B-E) identified the main chemical species that were different in each type of tissue region. A

short summary of those species is displayed in Table 2 while the identities of all species, chemical formulas and detected masses can be found in Table S1. The trend from these observations was that cancerous area C1 mainly contained FAs and lipids with 3 unsaturated bonds while C2 contained double or monounsaturated species. FAs with an even greater number of unsaturated double bonds (4-6) could only be found in the stroma regions. Image data reveals gradual fatty acid saturation changes. Based on the importance of the FA(20:n) saturation identified by the spectral PCA, line scans of the intensity changes of the differently saturated FA(20:n) across the cancerous regions in the tissue samples were generated from the MS-image data and compared with microscope images of the H&E stained consecutive tissue slices (Fig. 3 and Fig. S6).

Figure 3. Line-scans showing different trends for fatty acid distributions in cancerous tissue. (A-C) overlay mass spectrometric images from different regions in tissue sample #4, red (FA(20:3), m/z 303.23, stroma), green (FA(20:2), m/z 305.25, cancerous tissue C1), blue (FA(20:2), m/z 307.25, cancerous tissue C2), scale bars: 100 µm. White arrows indicate the position and direction of the line-scans. (D-F) H&E stained microscopy images of consecutive tissue sections showing cancerous (purple) and stroma (bright) tissue. The black boxes indicate the approximate positions of the line-scans in the MS images. (G-I) Linescans of FA species FA(20:4), FA(20:3), FA(20:2) and FA(20:1) showing intensity variations in different areas of the tissue. All line-scans are scaled to their maximum intensity (relative intensity 0 – 100%). Numbers displayed in the boxes indicate the ratio of maximum intensity for each FA relative to the FA(20:3) signal.

The different origins of these species (e.g. essential/dietary or de novo synthesis) are relatively well documented.29 FA(20:4) is not known to be synthesised de novo in mammalian cells without FA species from dietary intake. FA(20:3) and FA(20:2) are non-essential which means they can be synthesized in mammalian cells with FA(20:3) being the end point of the synthesis pathway (FA(22:3) is also possible but abundance is low). Due to the lack of certain enzymes mammals do not have the ability to introduce more than 3 double bonds. The synthesis pathway from essential linoleic (FA(18:2)) and α-linoleic (FA(18:3)) acids to arachidonic/juniperonic acid ((FA(20:4)) can produce FA species (20:3/2) with double bonds in positions different from the positions in non-essential



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FA(20:3/2). While FA(20:3) represents and end point in the non-essential FA synthesis pathway, FA(20:3/2) are intermediate species in the essential FA pathway and are usually processed quickly without accumulating. Hence the detection of increased FA(20:3) would indicate a preference for de novo lipid synthesis. FA(20:1) is not part of the essential fatty acid synthesis pathway and has been shown to increase in aging brains.30

Figure 4. Dual ion mode comparison of lipid and fatty acid localizations at increased resolution. MS single ion images of tissue sections #9, resolution 3.9 µm/pixel, showing intensity and distribution of (A) [M-H]- ions of PI(38:4), (B) PI(38:3), (C) PI(38:2), (D) FA(20:4), (E) FA(20:3), (F) FA(20:2) imaged in negative ion mode and (G) [PC(32:0)+Na]+ and (H) [PC(34:1)+K]+ in positive ion mode, scale bar: 100 µm. (I) H&E stained microscopy image of consecutive tissue section #9 showing cancerous (purple) and stroma (bright) tissue.

It has been shown recently that while cancer cells do utilize the accessible storage fat, the FA composition in adipocytes surrounded by healthy or cancerous cells is unchanged with respect to FA(16:0, 16:1, 18:0, 18:1, 18:1 and 18:2) stored in TAGs. No preferential uptake of specific FA species was detected. This supports the hypothesis that the increased FA requirements for each of those species can be met by de novo synthesis.31 It is therefore assumed that all FA(20:4) signals are derived from essential FA species while FA(20:3-1) are non-essential. Other fatty acid species can originate from too many sources to draw conclusions (e.g. FA(18:n/ 16:n)) or were detected with very low intensities (FA(22:n)). One trend observed was that the FA(20:2/1) species were highest in cancerous areas further away from the stroma (C2), although FA(20:3) was still present. C1 areas contained only small amounts of FA(20:2/1) and were dominated by FA(20:3), but most importantly, in some cases also contained low amounts of FA(20:4) (e.g. Fig. S6 H). Little or no FA(20:3/2/1) were detected in the stroma regions. Occasional spikes of FA(20:3) in the line-scans over a stroma area correlated with small cancerous domains in the stroma. Separate from this trend were areas that did not show any gradient but an overall dominance of one or the other FA species (as shown

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in Fig. 3, B). It was observed that FA(18:2) was more abundant in C1 and FA(18:1), FA(16:0) and FA(16:1) in C2, however due to the multiple possible origins of those FAs we cannot draw conclusion about cancer FA metabolism solely based on this observation. Higher resolution (ca. 4 µm per pixel) imaging of the tumor border region in biopsy #9 allowed the changes to be examined on a truly cellular scale. The use of the GCIB provided complete spectra from the individual pixels even at this size with differences in the intact lipid signals between different cell types apparent (Fig. S7). The FA and lipids described above as being dominant or even confined to the cancerous region (Fig. 4 B, C, E, F) and the FA(20:4) and PI(38:4) to be associated with inflammatory cells (Fig. 4 A, D). Regions containing neither cancerous nor inflammatory cells contained very little signal in negative ion mode. Positive ion mode analysis in the same area confirmed the earlier described species (Table S1) to be associated with the cancerous or the stroma/inflammatory region but intensity differences between one region and the other were less pronounced compared to species detected in negative ion mode (Fig. 4 G, H).

Figure 5. Fatty acid C20 comparison for different cell types (A) H&E stained microscopy image of consecutive tissue section of sample #9 showing cancerous (purple) and stroma (bright) tissue. A normal milk duct is indicated by the black box, the abnormal (hyperplasia) duct with an arrow. The box and arrow are also included on the MS images. (B) Ratios of FA(20:3), FA(20:2) and FA(20:1) to FA(20:4) for inflammatory cells (Inf.), normal duct cells (NorD.), abnormal duct (AbnD.), cancer cells from cancerous area C1 (C1) and C2 (C2); MS single ion images of tissue sample #9, resolution 7.8 µm/pixel, showing intensity and distribution of (C) FA(20:3+2) and (D) FA(20:4), scale bar: 500 µm; Detailed H&E images of (E) inflammatory cells, (F) normal duct, (G) hyperplasia duct and (H) cancerous area with corresponding zoomed MS images next to them, the MS image of FA(20:3+2) is displayed on top and FA(20:4) below.

Comparison of cancerous and normal epithelium. Sample #9 (Fig. 5) contained several non-cancerous milk ducts along with invasive tumors. The H&E stained image of sample #9 (Fig. 5 A) revealed that this tissue contained both normal ducts and ducts exhibiting hyperplasia along with the cancerous areas. The mass spectrometric images show that neither the healthy or hyperplastic ducts produce the same characteristic FA signals as the cancerous cells. Fig. 5 C shows a combined FA(20:3+2) MS image; the 2 FA species that have been found to be particularly elevated in cancerous tissue. A spectral


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Scheme 1. Fatty acid synthesis pathways. This scheme shows parts of the synthesis pathways and products derived from A) de novo fatty acid synthesis initiated by FAS and B) linoleic acid (18:2) from the diet. Arrows indicate desaturation or elongation steps with the responsible enzyme above them. The areas in the samples where specific FAs are most abundant are stated in brackets next to the species with C1 and C2 indicating the different cancerous regions detected in ToF-SIMS images and ST indicating the stroma.

↗ 18: 2 20: 2 20: 3 ) → 16: 0 18: 0 18: 1 ($9 &'()*+) " ↘ 20: 1

,) -)./ → 18: 2 18: 3 20: 3 20: 41 22: 41 ($6 &'()*+) comparison between the cancerous, inflammatory, normal duct and abnormal duct cells (Fig. 5 B) shows that the C20 FA composition in a normal duct is closer to that of the inflammatory cells than the cancer cells. Signals from the normal duct and inflammatory cells are expected to represent the natural abundance of FAs acquired through diet while cancer cells accumulate FA(20:3/2) containing lipids as end products of the de novo fatty synthesis pathway. Interestingly the hyperplastic duct contains cells that show elevated levels of FA(20:3). For a clearer illustration Fig. 5 E-H contains images of the different cell types within tumor sample #9 with zoomed-in MS images for FA(20:3+2) top and FA(20:4) bottom. This confirms the detected lipid profiles to be associated with metabolic reprogramming in cancer rather than being specific to epithelial cells. Implications of observed changes in lipids and fatty acids. The overabundance of PI-lipid species in cancerous samples has been observed previously (e.g. using MALDI 19) and can be explained due to PI being the precursor to the lipid messenger phosphatidylinositol-3,4,5-trisphosphate [PI(3,4,5)P3], a molecule that is formed by the action of phosphatidylinositol3-kinase and activates protein kinase B/Akt to stimulate cell proliferation and survival.32 Akt in return upregulates FASN activity in breast cancer which leads to a positive feedback loop.33 PC species present in the cancer were most likely present as structural components of the cell membrane. Overall elevation of the PC content in cancerous relative to normal mammary epithelial cells has been reported previously.34 The necrotic areas of the samples, containing mainly sphingolipids (such as sphingomyelin), were detected in tumors with FA(20:1) hotspots, while being barely present in FA(20:3) dominated tumor tissue. Sphingomyelin is involved in the tumor necrosis factor α (TNF-α) pathway which induces apoptosis so the detection of SM-species in necrotic tissue areas was not surprising.35 The same SM-species as described here have been previously found to be elevated in human breast cancer tissue but due to the non-location specific approach (HPLC on whole tissue extracts) the connection to necrosis could not be made.36 Intense signals from the stroma mainly corresponded to the abundance of inflammatory cells that surround, but do not seem to penetrate, the tumor. Although the presence of the inflammatory cells is in response to the tumor, they are not being produced by the tumor, therefore their FA content is not related to tumor metabolism. This provides an explanation as to why only those cells contain essential fatty acids while the signals from these species are much lower in the tumor regions. Studies on homogenized tumor tissue versus healthy

tissue (the healthy tissue should not contain inflammatory cells) would lead to the conclusion that FA(20:4) is upregulated in tumors however the lipid imaging shows that this is clearly not the case. Enhanced levels of arachidonic acid (FA(20:4), ω6) have reportedly been associated with both apoptosis and tumor promotion37 whereas it has been suggested that ω3 FAs suppress tumor growth38 therefore incorporating these species could be disadvantageous for the cancer. The mechanism behind this behavior could be the susceptibility of ω3 FAs to degradation.39 Excerpts of the pathways leading to the formation of the FA species observed in the ToF-SIMS image analysis are displayed in Scheme 1, with the cancerous region where each FA species shows a maximum signal indicated in brackets. For a detailed description of all known pathways and enzymes involved in fatty acid synthesis see reference 29. Correlating the fatty acid synthesis pathways in mammals, of which some are dependent (ω3 and ω6 fatty acids) and some are independent (ω7, ω9 and ω10 fatty acids) of dietary fatty acid intake, with the imaging MS data suggests that all of the fatty acids detected in the cancerous cells are generated by de novo FA synthesis while the incorporation of exogenous fatty acids occurs almost exclusively in the outer regions of the tumor, not in the core regions. Conversely, studies on cell lines in media have reported that under hypoxic conditions cancerous cells lower their de novo FA synthesis in favor of incorporating fatty acids in the form of lyso-phospholipids (LPC).40 The absence of signals from essential FA species in the cancerous areas (or only small amounts in C1) indicates that the behavior in real tumors differs from cancer cell lines grown in media. A possible explanation could be that the cell lines had LPC readily available in the cell media whereas access to growing tumors is blocked by the surrounding tissue. It has also been previously reported that the behavior of cell lines can differ from tissues when it comes to observations about lipid uptake.41 An alternative explanation for the absence of FA species that can only be generated from dietary FA precursors (e.g. 20:4, 22:4, 22:5, and 22:6 from FA18:2) in the tumor is that while essential fatty acids are imported into the tumor the fatty acid desaturases (FADS) are not functional due to a lack of oxygen. Assuming that the FA species detected in the cancerous areas are non-essential, the main difference between cancer areas C1 and C2 can be related to the activity of the enzymes FADS1 and FADS2. FA(20:3) is a highly abundant end product of mammalian FA-synthesis. Active FADS2 and inactive FADS1 would lead to an accumulation of FA(20:2), inactive FADS2 to an accumulation of FA(20:1) and FA(18:1).



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We propose that de novo production of FAs in combination with anaerobic glycolysis may act as a form of double protection against cell death. Bypassing mitochondrial processes avoids the production of reactive oxygen species, and essential ω3 fatty acids are prone to degradation by free radical species and their products cause cytotoxicity and apoptosis.39 Therefore the lack of these essential FAs could be beneficial to tumor cell survival. Lipids containing fatty acid species with a high degree of unsaturation are localized to the periphery of the tumor which would increase cell membrane fluidity, necessary for cell propagation, proliferation and invasion. The tumor core contains mainly di- and mono-unsaturated as well as fully saturated lipid species that are associated with less fluid and flexible membranes and reduced membrane permeability to drugs.42 Variation in oxygen ability in the tumor will no doubt also play a role in determining the degree of unsaturation in the fatty acid as oxygen is required for the desaturation although oxygen variation would affect both FADS1 and 2 and so alone cannot explain the changes in the localized C1/C2 character in the different tumors measured here.

CONCLUSIONS GCIB-ToF-SIMS provides clear improvements for the imaging of lipid species in breast cancer tumours, with potential applicability in the study a wide range of other cancer and disease types also. The GCIB/J105 system was able to put previous findings into a spatial and histologically relevant context and provide new insights into cancer metabolism. Cancerous cells can have varying lipidomic profiles within one tumorous growth which could explain treatment difficulties even in pre-hypoxic conditions. Essential fatty acids, often ambiguously linked with cancer risk or disease severity, were depleted in the cancerous cells compared with epithelial cells in normal, non-cancerous, milk ducts. The highest signal levels for these dietary FA species originated from inflammatory cells in the stroma. Intermediate lipid profiles were observed in hyperplastic duct cells. Sub-groups of cancer cells in the tumor with lipid profiles indicative of a reduction in fatty acid desaturase (FADS1) activity were observed. Such a reduction in activity may occur due to limited oxygen availability in the hypoxic center of the tumor however in some cases this sub-population of cells was observed near the edge of the tumor suggesting a secondary purpose for this change. Cancer cells are known to show metabolism changes independent from oxygen availability (e.g. the Warberg effect), therefore this altered membrane composition could be part of the cancer cell reprogramming to improve cell survival, with these cells expected to have decreased membrane fluidity and potential drug permeability.

ASSOCIATED CONTENT Supporting information contains further positive and negative MS images, additional PCA results and loadings, additional line scans, a complete list of observed chemical species in the different tumor samples and spectral PCA scores and contribution plots. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION Corresponding Authors Pathology: [email protected] Imaging mass spectrometry: [email protected]

Author Contributions All authors have given approval to the final version of the manuscript.

Funding Sources This work was supported in part by the Swedish Research Council (VR) and the University of Gothenburg.

ACKNOWLEDGMENTS The MS analysis was performed at Go:IMS in Gothenburg, Sweden, part of the GU/Chalmers Bio-analytical Centre.

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164x56mm (150 x 150 DPI)


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Lipid Heterogeneity Resulting from Fatty Acid ... - ACS Publications

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