Identifying the effectiveness of 3D culture systems to recapitulate breast tumor tissue in situ

Tumor subtypes were based on clinical assessment and a pathologist independently evaluated the tissue used in this study (Fig. S1 and Table S1). The levels of ERα and EGF receptor (HER1), as appropriate for the tumor type, were also evaluated as a complement to the H&E sections. The data set contained tumors from nineteen different breast cancer patients and seven samples from breast surgery reduction (normal). To identify a method that will simplify the visualization and quantitation of intra- and inter-tumor heterogeneity we used immunofluorescence (IF) and initially, examined the distribution of K8 and K14 within the starting tissue (ST). K8 and K14 are expressed in the normal breast epithelium but alterations in the expression pattern of these markers occurs during malignant transformation in the breast and impacts patient survival [38]. Therefore, we initially analyzed how the expression of K8 and K14 correlated with normal breast tissue, ER + breast cancer or TNBC.

Samples were taken from various random locations within the normal breast epithelium or breast cancer epithelium tissue to minimize sample bias. Formalin-fixed paraffin-embedded (FFPE) samples were prepared, and the sections analyzed for K8 and K14 by IF. The extent of K8 and K14 per area for each section was determined. The ratio of K8 + area to K14 + area (K₈/K₁₄) allowed us to compare two variables at the same time. Multiple sections were obtained for a patient’s starting tissue (ST), resulting in a total data set of 2,532 images obtained from the 26 starting tissues. The log₂(K₈/K₁₄) was performed to facilitate data visualization. The distribution of TNBC skewed to the left of the normal reflecting the high K14 + content whereas the ER + breast cancer skewed to the right indicative of the high K8 + content (Fig. 1A). To further visualize heterogeneity, we plotted the K₈/K₁₄ ratio using violin plots to clearly illustrate the phenotype frequency distribution and highlight the heterogeneity of the individual samples (Fig. 1B). Normal breast tissue shows heterogeneity but histologically normal breast tissue in the ST or organoids is readily distinguished from tumor tissue by its evident apical-basal polarity. Taken together, these data illustrate the difficulties in evaluating whether the ST has been successfully recapitulated in the organoid culture. We argue that it is important to capture the diverse cellular phenotypes within the tumor because different cell types respond differently to therapies and may be a source of resistance [24, 39‐41].

To simplify the analysis of heterogeneity we generated four bins based on the quartile distribution of the log₂(K₈/K₁₄) obtained in analysis of the normal tissue (Fig. 1A and inset). For each ST subtype the images were assigned to the bins based on the log₂(K₈/K₁₄) and the section distributions were plotted as heatmaps. The number of organoids analyzed was limited by the amount of ST and/or the success of culturing. To ensure that we were accurately represent the underlying heterogeneity of the samples we calculated the minimal number of sections necessary to analyze. This calculation was based on the bin with the lowest frequency of sections for both the ER + and TNBC ST in our data set. The average frequency of the least represented bin for both distributions was ~ 0.08 (Table S2). Therefore, assuming that we sample the underlying population N-times, the probability of not obtaining a section in that cluster is (0.92)^N. Thus, to obtain at least one section from that bin with 0.85 confidence a minimum of 23 sections needs to be analyzed, based on the formula 1-(0.92)^N = 0.85. This analysis was relevant to both ST and organoids (Tables S2 and S3). In most cases we exceeded the minimum sample size necessary to evaluate heterogeneity.

The aggregate heat map for the ER + ST shows an enrichment in K8 + cells whereas TNBC ST has more K14 + cells and by definition the normal distribution is present equally in all four bins (Figs. 1A, 1C). A heat map with the distribution for each starting tissue clearly illustrates the intra- tumor heterogeneity for patients with the same diagnosis (Fig. 1D). Representative images from each bin are shown for normal, TNBC and ER + tumor tissue (Fig. 1E). For example, patient samples 628, 679 and 711 have fewer sections in bin 1 than the other ER + breast tumor samples and patient sample 272 has fewer sections in bin 4 than the other TNBC samples. Representative images of sections through a single patient tumor clearly supports the variation captured in the heat maps (Fig. 1F). Collectively this straightforward analysis of K8 and K14 staining illustrates the intra- and inter-tumor heterogeneity that needs to be recapitulated within the organoid population if meaningful information on drug responses, which could translate to the patient, are to be obtained.

To generate a quantitative description of the intrinsic tumor heterogeneity we used the Jensen-Shannon divergence (JSD) method to compare the probability density distributions, as defined by the bins, within the ST and the organoids (ORG) generated from the ST based on the following formula:in which M = 0.5(P + Q), P = probability distribution of the ST sections from an individual patient X (X = patient identifier), Q = probability distribution of the ORG sections generated from the patient. Inherently JSD is asymptotically bound by zero and one, with one defining no similarity between distributions P and Q and 0 defining identical distributions. The general application of JSD assumes no specific limitation on the interrogated data. However, in case of our data, there are three major limitations to consider: (i) we are comparing to real data sets, ST and ORG, as opposed to real and simulated; (ii) the data does not assume infinite distribution possibilities as all of the data are generated from the breast and therefore, some distributions are not possible; and (iii) distributions sum to one as the data is represented by the percentage of the total. Due to these limitations, the boundaries of the JSD indexes obtained from our comparisons are narrowed. Therefore, to better represent and interpret JSD values resulting from our comparisons, we determined the most disparate JSD value that we could obtain from our data set, which was achieved by comparing ER + and TNBC ST (Tables S4-S9). By performing this comparison a JSD of 0.56 was obtained indicating that there are inherent similarities between these distinct tumor subtypes most likely because all the samples originate from the breast. To account for these underlying similarities the data were normalized such that 0.56 represents zero similarity. In these comparisons the higher the normalized JSD score the more similar samples are to each other. The normalized JSD score indicates that samples from patients diagnosed with the same clinical diagnosis are more similar to each other than patients with a different diagnosis (Table 1).

To demonstrate that the JSD is sensitive to treatment-induced changes in K8 + and K14 + distribution organoids were generated from normal breast tissue [13] and cultured in AREG/FGF7, EGF/FGF7, or a system based on intestinal organoid culture, referred to as the R-spondin (R-s) culture method [42]. AREG/FGF7 was chosen because previously we identified that this cocktail effectively recapitulated the normal ductal architecture in situ compared to EGF/FGF7, which caused abnormal expansion of the K14 + population [13]. The R-s culture method comprises a complex mixture that includes EGF. Therefore, based on our previous observations using normal breast tissue we replaced EGF with AREG. The distribution and representative images for each bin generated for the various culture conditions are shown (Figs. 2A-C). The JSD values were calculated similar to the equation above except that Q was defined as the probability distribution of organoid sections generated from a particular culture condition. For four out of five individuals the normalized JSD value for normal organoids cultured with AREG/FGF7 was ~ 80 compared to EGF/FGF7 and R-s, with a value of ~ 53 and ~ 29, respectively (Tables 2 and S3). The heat map illustrates that both the R-s and EGF culture conditions resulted in an enrichment of K14 + cells. The R-s culture method did not contain EGF, and therefore, other components of the R-s cocktail are responsible for the increase in K14 + cells. Taken together, these data highlight the importance of having a quantitative approach to analyzing whether the organoids have recapitulated the tissue in situ.

We also evaluated whether markers in addition to K8 and K14 would aid in characterizing tumor heterogeneity. For TNBC ST we analyzed cluster of differentiation 10 (CD10), smooth muscle actin (SMA) and p63 in combination with K14. As an aggregate of all samples analyzed the distribution of CD10, SMA and p63 compared to that of K14 shows a strong positive linear correlation, which was significant and indicates that these markers provided similar information to K14 (Fig. 3A). We next investigated the use of the markers K18 and ERα in ER + breast cancer in combination with K8 for the aggregate of all samples analyzed. The distribution of K18 and ERα generated a very strong positive correlation, which was significant with K8, demonstrating that K18 and ERα are redundant with K8 (Fig. 3B). This analysis shows that the distribution of K8 + and K14 + cells are able to assess tumor heterogeneity without increasing the complexity of the analysis by addition of other markers.

In base media both normal and tumor organoids fail to expand, indicating that the epithelial tissue relies substantially on the microenvironment for its growth as opposed to autocrine signaling (Fig. 4A). To empirically identify optimal culture conditions, we performed growth factor and cytokine analysis from isolated normal and cancer-associated (CAF) stromal fibroblasts. All fibroblasts secreted FGF-7 and AREG, which we previously found necessary to generate proper ductal structure in organoids derived from the normal breast (Fig. 4B). The levels of EGF were ~ 500 fold below its K_d for HER1 and therefore, were not considered physiologically relevant. Furthermore, addition of EGF to TNBC, which has a high percentage of K14 + cells, resulted in an even greater increase in the K14 + population, which also argues against the use of EGF in organoid cultures (Fig. S2). The stromal fibroblasts secreted numerous cytokines with a wide variation between individual samples (Fig. S3). The cytokines, CXCL1, CXCL5, and leptin trended towards being higher in the majority of CAFs tested (Fig. 4C), which has also been observed in other studies [43‐45]. CXCL1, CXCL5 and leptin resulted in basal cell enrichment in the normal tissue compared to the base with CXCL5 producing the largest change in the normalized JSD score (Figs. 4D-4F and Table S10). However, CXCL1, CXCL5 and leptin did not induce proliferation (Fig. 4G). Therefore, in our studies with tumor tissue the 3D culture was supplemented only with AREG and FGF7.

Using the JSD analysis, we next evaluated how effectively the heterogeneity of TNBC tissue could be recapitulated in our in vitro system and whether differences in individual patient response to chemotherapy could be observed. Epithelial fragments were cultured in 3D. Tissues from 6 different patients were obtained; of these one had undergone prior chemotherapy (patient 272), one had been treated with both radiation and chemotherapy (patient 287) and the remaining were therapy naïve. The normalized JSD values obtained from comparing the ST with the organoids ranged from ~ 81 to ~ 33 (Figs. 5A-C and Table 3). The lowest normalized JSD was obtained from patient 275 (Table 3). The reason for the lack of recapitulation of the ST distribution is not obvious as patient 323 had a similar ST distribution to 275 but the organoid culture conditions were able for 323 to capture the phenotypes within the ST as shown by the higher normalized JSD score (Fig. 5B and Table 3). As a further measure of the organoid system to recapitulate the ST, HER1 levels were analyzed and found to be higher in TNBC ST than in normal tissue, consistent with the literature and this difference was maintained in the organoids (Fig. 5D) [46]. Interestingly, addition of EGF instead of AREG would have reduced intra-tumor heterogeneity and generated worse JSD scores (Fig. S2 and Table 2). Additionally, we confirmed that the gene expression differences between TNBC ST and their organoids were similar and differed from normal breast ST (Figs. 5E and S4). Taken together, these data demonstrate that using cellular phenotypes can be used to complement other approaches to assessing whether the organoids recapitulate the ST.

A primary goal of recapitulating tumor heterogeneity in vitro is to develop a tractable system for the analysis of drug responses, as tumor recurrence is likely to arise from cells that are unresponsive to the therapy. The JSD method summarizes the effects of proliferation, apoptosis and cell type in a single value that should highlight the development of a treatment -resistant cell population. Therefore, to test the ability of the JSD method to detect resistance, TNBC organoids were treated with paclitaxel, which is frequently used as an initial therapy for patients with TNBC [47]. The dosing and scheduling for paclitaxel was based on the pharmacokinetics observed in patients (Methods). A normalized JSD score of ~ 81 and ~ 74 was obtained with the organoids generated from patients 272 and 396, respectively demonstrating that the culture conditions were able to recapitulate the ST heterogeneity (Table 3). This concordance provides support for the physiological relevance of the drug responses obtained with the organoids. The probability density analysis for patient 272 skews to the right in response to paclitaxel indicating an increase in K8 + cells (Figs. 5F, 5G). The normalized JSD value decreased from ~ 81 with no drug to ~ 20 with a dose of 1 μM paclitaxel (Table 4). To identify a mechanism for changes to the JSD score with paclitaxel, the extent of proliferation and apoptosis was determined. The tumor organoids from patient 272 demonstrated an ~ three-fold decrease in total proliferation, as measured by Ki67, and an ~ eight-fold increase in apoptosis – as measured by cleaved caspase-3 staining (Fig. 5J). Interestingly, the decrease in proliferation was confined to the K14 + population. It was not possible to identify the phenotype of cells that had undergone apoptosis. In agreement with the proliferation and apoptosis data the total organoid area decreased by three-fold (Fig. 5K). Together, these data indicate that the growth of the tumor tissue is sensitive to paclitaxel, which is primarily due to the decreased proliferation of the K14 + population. Importantly, the change in the JSD score readily highlights the development of a therapy-resistant K8 + cell population.

In contrast to organoids from patient 272 the probability density for organoids from patient 396 was altered by 5 μM paclitaxel and correspondingly the normalized JSD score decreased by 20% (Figs. 5H, 5I and Table 4). Significant changes in proliferation were not detected but an ~ three-fold increase in apoptosis was observed (Fig. 5J). Consistent with these observations a decrease in organoid size occurred in response to paclitaxel (Fig. 5K). These data show that the growth of the tumor tissues is sensitive to paclitaxel as a result of increased apoptosis. However, the change in the normalized JSD score due to the enrichment in the K8 + population suggests that therapy-resistance may develop.

Tissue from seven different ER + breast cancers were obtained and epithelial fragments from these tumors were cultured in 3D. The normalized JSD values ranged from ~ 28 to ~ 71 (Figs. 6A-C and Table 3). The probability density distributions indicate that we were least successful in generating organoids that are highly enriched for K8 + cells in comparison to the ST. Breast cancers are classified as ER + when > 1% of the cells within the tumor tissue stains for ERα [48]. In analyzing the starting ER + breast tumor tissue in our cohort we found that ~ 20% of the total cells expressed ERα as compared to ~ 5% in normal tissue, which was similar to the distribution obtained in the organoid culture (Fig. 6D). The number of K8 + cells is highly linearly correlated with the number of ERα + cells (Figs. 3B, S5) and therefore, we conclude that organoids with a high percentage of K8 + cells also have a high percentage of ERα + cells. The problem of recapitulating organoids enriched for ERα + cells is consistent with the literature as maintaining ERα expression in culture is known to very difficult [49].

To determine whether we could identify therapy-induced resistance in luminal A/B either anti-estrogens or the CDK4/6 inhibitor were evaluated. In vivo the selective estrogen receptor modulator, tamoxifen, is converted to its active form, 4-hydroxy tamoxifen (4-OHT), and therefore, 4-OHT was used in the in vitro culture. The dosing and scheduling of all drugs was based on the pharmacokinetics observed in patients (Methods).

Organoids were generated from patients 461, 549 and 687. The normalized JSD scores ranged from ~ 28, 49, and 71, respectively (Table 3). Based on limiting amounts of material available from patient 461 only 4-OHT treatments were performed. The heat map shows that 4-OHT resulted in an increase in the K14 + population relative to the ST and the untreated organoid culture, suggesting the potential of developing a treatment-resistant population (Fig. S6 and Tables 3 and 5). The culture conditions were more effective for generating organoids from patient 549 and as a result more extensive analyses could be performed. Palbociclib increased the relative proportion of K8 + cells to more closely resemble the ST (Figs. 6E, 6F and Tables 3 and 5). The increase in the proportion of K8 + cells in the organoids in response to Palbociclib can be explained by the observed decrease in the proliferation of the K14 + population (Fig. 6I). Similar results were observed with 4-OHT with an increase in apoptosis also observed. Thus for patients 461 and 549 the organoid culture conditions promoted the growth of the K14 + population. Interestingly, although 4-OHT and Palbociclib decreased the K14 + population, their effects on the K8 + population were minimal as the proportion of K8 + to K14 + increased with the treatments. These results suggest that the K8 + population in patients 461 and 549 are relatively unaffected by 4-OHT and Palbociclib and may show treatment resistance.

In contrast to patients 461 and 549 the culture conditions for patient 687 were able to recapitulate the ST to a much greater degree as shown by the normalized JSD score (Figs. 6G, 6H and Table 3). These results are surprising given that 687 is also enriched in K8 + cells as are 461 and 549. These results demonstrate the difficulties in recapitulating the ST as individual patient tumor tissues, even if they are the same tumor type, respond differently to the same culture conditions. For organoids generated from 687 the selective estrogen degrader, Fulvestrant, resulted in a shift towards the left indicating an increase in the K14 + population with the JSD score decreasing from ~ 71 to ~ 58 (Table 5). Fulvestrant increased apoptosis by ~ three-fold but unexpectedly increased proliferation in the K14 + population by ~ five-fold (Fig. 6I). This change in the normalized JSD score provides a readout for the development of a K14 + resistant population, which may be important for therapy response despite overall reduced growth of the tumor. No effect on proliferation, apoptosis or organoid size was observed with 4-OHT. Palbociclib reduced both K8 + and K14 + cell proliferation and consistent with these observations organoid size was decreased (Figs. 6I and 6J) [50]. Importantly, Palbociclib treatment for this patient tissue did not substantially change the distribution or normalized JSD score, and therefore, might be an effective therapy for this patient. Taken together, these results demonstrate that the analysis of K8 and K14 distribution appears sufficient to identify differences between chemotherapeutic responses in individual patients and thus, may provide a basis for use in personalized medicine.

Human breast tissue from reduction mammoplasty or breast cancer surgery was collected as waste tissue with institutional review board approval. A list of age, race, and diagnosis for each patient used in this study is provided (Table S1). Organoids were prepared as previously described [13]. Briefly, tissue was minced and digested in Collagenase A medium (DMEM/F12 (Thermo Fisher Scientific), 1 mg/mL Collagenase A (Roche Diagnostics), 1 μg/mL insulin (Sigma-Aldrich), 600 U/μL Nystatin (Sigma-Aldrich), 100 U/mL penicillin–100 μg/mL streptomycin (Thermo Fisher Scientific)) for 18-21 h in a 37 °C 5% CO₂ incubator. Digested material was pelleted at 180 g for 5 min and the supernatant collected for fibroblast isolation. The remaining pellet was resuspended in DMEM/F12 with DNAse I (1000 U/ml) (Sigma-Aldrich) for 3–5 min in a 37°C 5% CO₂ incubator. Fetal bovine serum (FBS) (0.5 mL) was added, and the digested tissue was pelleted at 180 g for 10 min. The pellet was resuspended in 9 ml of DMEM/F12 and centrifuged at 350 g for 15 s. This wash was repeated 4–6 times. The pellet was resuspended in 1 ml of base medium (DMEM/F12, 1 μg/mL hydrocortisone (Sigma-Aldrich), 10 μg/mL insulin-5.5 μg/mL transferrin–6.7 ng/mL selenium-2 μg/mL ethanolamine (Thermo Fisher Scientific), 2.5 μg/mL Amphotericin B (Sigma-Aldrich), 50 μg/mL gentamicin (Thermo Fisher Scientific), 100 U/mL penicillin-100 μg/mL streptomycin). A volume of 60 μl of a 60% Matrigel in base media was added into the wells of an 8-well LabTek plate and solidified for 15 min in a 37°C 5% CO₂ incubator. Organoids were counted and resuspended in a 50% Matrigel in base media. A volume of 40 μl of Matrigel/organoid solution containing 30–40 medium sized organoids was plated onto the solidified Matrigel layer and allowed to solidify for 15 min at 37°C.

Drug treatments starting at day 0 were treated with 4-hydroxy tamoxifen (Sigma-Aldrich), fulvestrant (Santa Cruz Biotechnology Inc.), palbocicilib (Selleckchem), or paclitaxel (R&D systems). For all conditions, the medium was replaced every 24–48 h and drugs were added with fresh medium every 24–48 h as appropriate. Paclitaxel has a half-life of 27 h (https://​reference.​medscape.​com/​drug/​taxol-paclitaxel-342187#10); the active metabolites of tamoxifen have half-lives of ~ 17 h [60]; fulvestrant has a half-life of 40 d (https://​reference.​medscape.​com/​drug/​faslodex-fulvestrant-342224#10); and palbociclib has a half-life of ~ 29 h (https://​reference.​medscape.​com/​drug/​ibrance-palbociclib-999995#10). The concentrations of drugs chosen were based on the maximum serum level in patients. The organoids were cultured for 7–10 days.

Organoids were fixed and immunostained as previously described [13]. Detailed methods for immunostaining, imaging, and analysis are provided in the supplementary experimental procedures.

Organoids on average have smaller area than ST. Therefore, to correct for potential artifacts in K8/K14 ratio, we subdivided the images obtained from ST into smaller tiles to match an average organoid size. K8/K14 quantitation was carried out on the resulting tiles if the total tissue area per image was greater than 5% of image area. K8 and K14 area were measured and log2 of K8/K4 ratio calculated. Median and quartile values of log2(K8/K14) from ST of normal breast tissue were determined and set as bin boundaries: -inf:Q25, Q25-median, median-Q75, Q75-inf. Subsequently all images were classified in these bins, and bin distribution was calculated per condition (N of sections in the bin_(1–4)/Total N of sections per condition) to generate distribution table. These distributions were then used to calculate JSD values for comparison of any two given conditions.

Quantitation and data analysis was carried out in Python3.8.10 using the following packages: Pillow (image tilling and RGB quantitation); Pandas, NumPy, Matplotlib and SciPy (data management, data manipulation, and statistical analysis); Seaborn (data visualization).

Fibroblasts obtained during the isolation of epithelial clusters were plated in base medium with 10% FBS. After two passages, the fibroblasts were washed extensively and cultured in base medium without serum. Conditioned medium was collected after 48 h. For conditioned medium from organoids, epithelial clusters were cultured in base media and medium was collected every 48 h for 6 days. The conditioned medium from fibroblasts and organoids was analyzed using Human Cytokine Antibody Array C5 (RayBiotech, Inc.). Multianalyte profiling of fibroblast-conditioned media was performed by the Vanderbilt Hormone Assay and Analytical Services Core using the Luminex-100 system.

RNA samples were prepared using the TruSeq mRNA library method (poly-A selected). Sequencing was done using the Illumina HiSeq 3000 at 2X75 paired-end reads by Vanderbilt Technologies for Advanced Genomics with a mean of 30e6 reads per library. TopHat (v2.0.9) spliced aligner software was used to align reads to hg19, using refseq transcripts as a guide [61]. Transcripts were assembled and quantified using refseq transcripts as a guide with cufflinks, and normalized FPKMs generated using cuffnorm [62]. Normalized FPKM expression levels were analyzed in R/Bioconductor. Principle Component Analysis was performed using pcaMethods [63].

Statistical analyses were performed using GraphPad Prism 6. Statistical significance was determined using the Mann–Whitney test (two-sided) and all p-values < 0.05 are reported.