Introduction
Triple-negative breast cancers (TNBCs), which lack expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), account for approximately 10 to 17% of all breast cancers [
1‐
3] and are associated with relatively poor clinical outcomes. About 70 to 80% of TNBCs comprise the basal-like breast cancer (BLBC) intrinsic subtype as defined by gene expression profiling [
4‐
6], although more recently, TNBCs have been further subclassified into six subtypes distinguished by gene ontologies and gene expression patterns [
7,
8]. The lack of targeted therapies for this aggressive breast cancer subtype is a key treatment issue and testing new therapeutic regimens is clinically important.
The mammalian target of rapamycin (mTOR) is a key downstream regulator of the phosphatidylinositide 3-kinase (PI3K) pathway, one of the most commonly activated signaling pathways in cancer [
9,
10]. mTOR exists in two complexes, mTORC1 and mTORC2. mTORC2 is less well understood but has been shown to regulate cell proliferation and cytoskeletal organization [
11,
12]. PI3K/mTORC1 is frequently activated in human cancers by gain-of-function mutations and amplifications of its upstream activators - such as epidermal growth factor receptor (EGFR), HER2 [
13], PI3K or protein kinase B (AKT) - and by the loss of its suppressors, such as phosphatase and tensin homologue (PTEN) [
14], inositol polyphosphate-4-phosphatase, type II (INPP4B) [
15], or the tuberous sclerosis complex (TSC), mediated by the tumor suppressor genes,
TSC1 and
TSC2[
16,
17]. Activated mTORC1, an evolutionarily conserved serine/threonine kinase, will phosphorylate downstream proteins, such as p70 ribosomal S6 kinase 1 (S6K1) [
18] and eukaryotic translation initiation factor 4E binding protein 1 (4EBP1) [
19], to regulate protein synthesis, ribosome biogenesis and autophagy that contribute to cell proliferation, differentiation and survival [
17,
20‐
22]. Activation of the AKT/mTOR pathway is a poor prognostic factor for many types of cancers, including breast cancer [
23‐
27].
Rapamycin (sirolimus) is a specific allosteric inhibitor of mTOR and is the active form of rapamycin analogs. The rapamycin analogs CCI-779 (temsirolimus) and RAD001 (everolimus) are approved for the clinical treatment of advanced renal cell carcinoma [
28], progressive neuroendocrine tumors of pancreatic origin [
29], subependymal giant cell astrocytoma associated with tuberous sclerosis [
30], and more recently for postmenopausal women with advanced hormone receptor-positive, HER2-negative breast cancer in combination with the aromatase inhibitor exemestane [
31]. Pertinent for other types of breast cancer, increasing lines of evidence indicate that the PI3K/mTOR pathway is activated in TNBCs and/or BLBCs at the genetic, gene expression and protein levels [
14,
32‐
37]. mTOR inhibitors show growth inhibition of TNBC cell lines in both
in vitro and
in vivo preclinical studies [
14,
26,
33,
38].
PIK3CA mutations have been shown to be associated with mTOR inhibitor sensitivity in both cell lines and clinical studies [
39‐
41]. mTOR inhibitors are among the therapeutic agents being actively investigated in clinical trials in patients with TNBC [
42‐
44], and recently, a phase II trial evaluating a combination of everolimus and carboplatin showed a clinical benefit rate of 36% in metastatic TNBC patients [
42].
In contrast to previous
in vivo preclinical drug testing studies using xenografts derived from established breast cancer cell lines, we were interested in determining preclinical drug efficacy in patient-derived TNBC orthotopic xenograft models generated from human tumors obtained fresh from the operating room. Personalized tumorgraft models, also called “avatars”, propagated using patient-derived tumors have shown some success when used to guide clinical treatment in patients with advanced cancer [
45,
46].
We generated a panel of seven patient-derived orthotopic xenograft models of primary and metastatic TNBC and showed that these models recapitulated histologic and molecular features of the patients’ tumors from which they were derived. We used the Connectivity Map, a compendium of genome-wide transcriptional data from cultured human cells treated with bioactive small molecules, to determine a rapamycin response signature. Applying this signature to large breast cancer datasets stratified into intrinsic breast cancer subtypes, we predicted that most BLBCs would show some sensitivity to rapamycin. We then proceeded with in vivo drug testing of two mTOR inhibitors, sirolimus and temsirolimus, in our patient-derived TNBC models, which demonstrated significant growth inhibition by both drugs. However, while growth inhibition was very impressive for all TNBC xenografts, none had complete tumor ablation. Our results strongly support the use of mTOR inhibitors as part of combined therapy for TNBC in preclinical and clinical trials and suggest the need for further investigations into appropriate drug combinations.
Materials and methods
Establishment of patient-derived orthotopic xenografts
Both the Stanford University Research Compliance Office’s Human Subjects Research and IRB Panel and Stanford’s Administrative Panel on Laboratory Animal Care (APLAC) approved this study. After obtaining informed written patient consent, breast cancer tissues were obtained fresh from operating rooms at Stanford Hospital and Clinics. In six cases of TNBC (SUTI097, SUTI103, SUTI110, SUTI151, SUTI319, SUTI368), fresh tumor tissue was sterilely obtained from primary breast cancer tissue that was undergoing surgical excision, and in one case (SUTI151M), the tumor tissue was taken fresh from a soft tissue TNBC metastasis to the quadriceps muscle in the thigh that was undergoing biopsy (SUTI151M is from the same patient who had months earlier donated a piece of her primary breast tumor SUTI151). Portions were frozen or placed in formalin and embedded in paraffin for later analyses. Fresh tumor tissue was kept on ice in RPMI 1640 medium supplemented with penicillin/streptomycin and 10% heat inactivated FBS (Invitrogen-Life Technologies, Carlsbad, CA, USA) for transport, minced into one to two millimeter fragments, then sterilely and orthotopically transplanted into the number two mammary fat pads of 5 to 10 female NOD SCID mice (NOD.CB17-Prkdc
scid
/J, Jackson Laboratory West, Sacramento, CA, USA). Briefly, the mice were anesthetized by inhalation of 1 to 3% isoflurane, their hair was clipped, and their skin sterilized with povidone-iodine and alcohol. A small skin incision was made in the lateral flank and minced tumor chunks were mixed with LDEV-free Matrigel (BD Biosciences, San Jose, CA, USA) and implanted into the mammary fat pad by trochar insertion. The incision site was closed with Vetbond tissue adhesive (3 M, St. Paul, MN, USA). Mice were maintained in pathogen-free animal housing. The established xenografts were subsequently passaged from mouse to mouse to expand xenograft numbers; xenograft tumors were also stored frozen in FBS containing 10% dimethyl sulfoxide (DMSO, EMD Chemicals Inc., Billerica, MA, USA) solution for future engraftment. Xenograft tumor tissue was frozen on dry ice for RNA isolation and microarray analysis and for subsequent protein analyses. Tumor fragments were also fixed in phosphate buffered saline with 10% formalin (Sigma-Aldrich, St. Louis, MO, USA) for histological studies. All animal care was performed in accordance with Stanford University and IACUC guidelines.
Immunohistochemistry
Formalin-fixed, paraffin-embedded tissue sections of patient or xenograft tumors were cut into 4 μm sections, deparaffinized in xylene, rinsed in ethanol and rehydrated. Staining was performed using the Ventana XT platform and internal antigen retrieval CC1 standard. The antibodies used were rabbit monoclonal antibodies for ERα (clone SP1, 1:25 dilution, Thermo Scientific, Fremont, CA, USA) and PR (clone 1E2, ready to use, Roche-Ventana Medical Systems, Inc., Tucson, AZ, USA). The universal secondary protocol and the DAB MAP kit (Ventana Medical Systems, Inc., Tucson, AZ, USA) were used to detect and amplify the signal. Both biomarkers were scored using a three-tier system: 0 = negative, 1 = weak, and 2 = strong, respectively defined as <1%, 1% to 50%, and ≥50% of tumor cell nuclei staining positively. HER2 protein expression was performed and interpreted using the Ventana PATHWAY HER2 antibody (rabbit monoclonal, clone 4B5; Ventana, Tucson, AZ, USA). The Food and Drug Administration-approved Ventana PATHWAY is scored from 0 to 3+. Staining in <10% of tumor cells is scored as showing no overexpression (0 or 1+). Strong, complete, circumferential membrane staining in >30% of tumor cells is considered overexpression and is designated as strong positive (3+). Strong circumferential membrane staining in <30% of tumor cells, or circumferential but less than strong staining in any proportion of tumor cells, is designated as equivocal (2+). All immunohistochemical assays were conducted in parallel with known positive and negative controls. The slides were observed using a Nikon Eclipse 80i microscope (Nikon Instruments Inc., Melville, NY, USA). Pictures were taken using a Nikon Digital Camera DXM1200F and images were obtained using Nikon ACT-1 software.
Array CGH and PIK3CAmutation analysis
Genomic DNA was extracted from patient or xenograft tumor samples using DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA, USA). Array CGH analyses were performed at SciGene (Sunnyvale, CA, USA) using Human Genome CGH Microarray 4x44K (Agilent Technologies, Santa Clara, CA, USA), and processed on SciGene's robotic aCGH workstations (ArrayPrep® Target Preparation System, Mai Tai® Hybridization System, and Little Dipper® Processor, SciGene, Sunnyvale, CA, USA).
Mutations were detected by sequencing PCR products derived from amplification primers in the introns flanking
PIK3CA exons 1, 2, 3, 5, 6, 7, 9, 18 and 20 using Ampli Taq Gold DNA polymerase (Applied Biosystems-Life Technologies, Carlsbad, CA, USA). The primer sets used in these reactions are listed in Table S1 in Additional file
1. Exons that had sequence homology with a known
PIK3CA pseudogene were not sequenced; however, the sequenced exons included all common mutation hotspots. The reaction was run using a touchdown PCR protocol where the annealing temperature was started at 63°C and decreased for 0.5°C per cycle for 12 cycles. Then the reaction was continued for another 25 cycles at 94°C, 30 sec; 58°C, 30 sec; and 72°C, 30 sec per cycle. PCR products were checked by 2% agarose gel against a GeneRuler 50 bp DNA Ladder (Frementas, Glen Burnie, MD, USA) and sequenced by BigDye Terminator v3.0 Cycle Sequencing Kits (Applied Biosystems-Life Technologies, Carlsbad, CA, USA). The sequencing results were analyzed with Sequencher 4.8 software (Gene Codes Corporation, Ann Arbor, MI, USA).
Microarray and TNBC subtype analysis
Frozen tumor tissues from xenografts were cut into small pieces on dry ice. RNA was extracted using RNeasy Plus Mini Kit (Qiagen) following the manufacturer’s instructions. The quantity and purity of the RNA sample was measured using the Agilent 2100 bioanalyzer (Agilent Technologies). RNA samples were submitted to Stanford Protein and Nucleic Acid core facility for microarray analysis using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays. All xenograft microarray datasets are posted on GEO under accession number GSE47079 [
47].
TNBC subtyping was done following the Pietenpol group’s methods [
7,
8]. The microarray data of the patient derived xenograft tumors were robust multi-array average (RMA) normalized and log transformed. For genes containing multiple probes, the probe with the largest interquartile range across the samples was chosen to represent the gene. The processed samples were uploaded to the website [
48], where the samples were subjected to an ER-filter scrutiny and then assigned TNBC subtypes.
Generation of an in silicorapamycin response signature
A rapamycin response signature was generated as described previously [
49] using Connectivity Map Build 02 gene expression data of 5 rapamycin treated and 18 control MCF7 cell samples [
50]. The Connectivity Map studies highlight the ability to use drug treatment on cell lines to identify a set of genes that reflect response to a drug; therefore, independent of the cell type profiled, a “signature” of drug response can be identified from the data that reflects the response to drug treatment [
50]. Multiple studies by other groups and our own have shown that such drug response signatures do also function as drug sensitivity/resistance signatures, with sensitive samples having gene expression patterns more like untreated cells and resistant samples having gene expression patterns more like treated cells [
49‐
52]. Specifically, tumors with dysregulation of genes that are modulated by treatment of rapamycin will be predicted as “sensitive” or “resistant” based on their correlation to gene dysregulation from rapamycin treatment in the Connectivity Map data. This approach, which uses expression data from cell lines before and after treatment with a drug, has the advantage over using expression data from cell lines classified as ‘resistant’ or ‘sensitive’ because cell line data have confounding factors, such as subtype that can affect prediction models. By using prediction models that include genes specific to a particular drug’s response, we are not limited by these confounding factors. Thus, treated cell lines of one subtype (for example, luminal MCF7 cells) may be used to predict drug response in samples of different subtypes (for example, basal-like or HER2-overexpressing cancer cells).
To generate the signature, Mas5 normalized gene expression data were quantile-normalized and log2 transformed and used as the training set. A Bayesian binary regression algorithm was then used on the training set to generate the signature. It was further optimized and internally validated in leave-one-out cross-validation (LOOCV) analysis, which tests each individual sample’s classification by leaving it out of the model and predicting if it is treated or untreated [
49,
51].
Validation and application of the rapamycin response signature
For signature external validation, CEL files were downloaded from GEO GSE18571, which contains gene expression data for both
in vitro and
in vivo rapamycin treatment samples on TNBC cell line MDA-MB-468 [
26], and from Connectivity Map batches 2, 35, 44, 56, 63, 70, 626, 757 and 767, and analyzed as described by Cohen
et al. [
49]. We then confirmed the ability of the rapamycin response signature to predict sensitivity to rapamycin
in vitro by comparing the EC50 (see below) for a diverse panel of cell lines to the predicted sensitivity, that is, similarity to untreated cells, based on gene expression.
As previously described [
49], 18 breast cancer cell lines were obtained from ATCC (HCC38, HCC1806, HCC1428, HCC1143, BT483, BT549, BT474, MDA-MB-361, MDA-MB-157, MDA-MB-435S (now considered to be of melanoma origin), MDA-MB-231, MDA-MB-453, SKBR3, ZR75, CAMA I, MCF7, Hs578t, T47D) and used for dose-response assays. Cells were seeded in 384-well plates (Nunc, Rochester, NY, USA) in MEBM media (Lonza, Walkersville, MD, USA) containing 5% fetal bovine serum (Gibco, Grand Island, NY, USA), at a density to yield 80% confluency in control-treated wells at 96 h post-treatment (as determined by growth curves). After 24 h, rapamycin was added at 10 doses of 0, 0.1 pM, 0.3 pM, 1 pM, 3 pM, 10 pM, 30 pM, 100 pM, 300 pM and 1 nM. A BIOMEK 3000 (Beckman Coulter, Indianapolis, IN, USA) robot was used to seed the cells and dispense the drug. After 96 h, CellTiter-Blue Reagent (Promega, Madison, WI, USA) was added to test cell viability. After 2 h of incubation at 37°C, the fluorescence was recorded (560(20)
Ex/590(10)
Em) using a Victor3V 1420 Multilabel Counter (Perkin-Elmer, Waltham, MA, USA) plate reader. After subtracting background fluorescence, EC50 was calculated using GraphPad Prism v5 (GraphPad Software, La Jolla, CA, USA) to fit a constrained sigmoidal dose-response curve. Predicted sensitivity for these cell lines was computed using gene expression data from Cohen
et al. [
49] and applying the Bayesian binary regression model. Detailed methods for running the regression model are given in [
53]. Predicted sensitivity was compared to actual EC50 using linear regression.
To examine the relationship between intrinsic subtype and rapamycin sensitivity, 1,401 breast cancer samples from eight microarray studies were then analyzed (Table S2 in Additional file
2, duplicate samples were removed from GEO datasets GSE6532, GSE7390 and GSE3494). Intrinsic breast cancer subtypes were assigned as previously described [
49,
54,
55]. Rapamycin sensitivity of the patient breast cancer sample was calculated as described by Cohen
et al. [
49]. A detailed method, the input files, output files and the logistic regression program used in this study are available in [
53].
In vivoTNBC xenograft drug treatment experiments
Rapamycin and CCI-779 (LC Laboratories, Woburn, MA, USA) were stored as 50 mg/ml solutions in 100% ethanol at -80°C. The stored solutions were diluted in PBS containing 4% ethanol, 5% polyethylene glycol 400 and 5% Tween 80 for treatment. When tumor xenografts grew to an average between 50 to 100 mm3 in tumor volume, mice were stratified and randomized by tumor volume into treatment groups of 5 to 10 mice each. The treatment groups included: 1) rapamycin (sirolimus) group, receiving intraperitoneal (IP) administration of 7.5 mg/kg rapamycin every other day for up to six weeks; 2) CCI-779 (temsirolimus) group, receiving IP administration of 7.5 mg/kg of CCI-779 every other day for up to four weeks; 3) control group, receiving IP administration of control vehicle for up to four weeks; and, in some sets of experiments, 4) a doxorubicin group, receiving IP administration of 2 mg/kg doxorubicin (Sigma-Aldrich, St. Louis, MO, USA) diluted in PBS once every week for three weeks. Tumors were measured twice a week with a caliper in two dimensions. Tumor volume was calculated by the following formula: tumor volume = (l x w2)/2, where l is the longest diameter of the tumor, w is the shortest diameter of the tumor. Mean tumor volumes were calculated, and growth curves were established as a function of time. The error bars indicated the value of the standard error of the mean. The Student’s t-test was used for statistical analysis. We considered P <0.05 as statistically significant.
Protein extraction and Western blot analysis
For protein extraction, frozen xenograft tumors were gently thawed and washed in ice-cold PBS. They were then homogenized using a glass homogenizer and lysed in radio-immunoprecipitation assay buffer containing protease and complete phosphatase inhibitors (Roche, West Sussex, UK). After quantification using Pierce’s BCA protein assay (Thermo Scientific Pierce, Leicestershire, UK), 5 to 10 μg total proteins were run on 4 to 12% SDS-PAGE gels (NuPAGE Bis-Tris Gels, Invitrogen-Life Technologies, Paisley, UK), then immunoblotted with the antibodies for PTEN, mTOR, p-mTOR (Ser 2448), 4EBP1, p-4EBP1 (Ser 65), S6K1, p-S6K1 (Thr 389), eIF4E and p-eIF4E (Ser 209) at 1:1,000 dilutions. Secondary horseradish peroxidase (HRP)-labeled antibodies were used at 1:5,000 dilutions. Tubulin was used as a loading control. All antibodies were obtained from Cell Signaling Technology (Hitchin, Hertfordshire, UK). Membranes were visualized by the ECL developer system (GE Healthcare Life Sciences, Piscataway, NJ, USA). Protein expression was quantified by analyzing a representative autoradiograph with Image Image J software (public domain software developed at the Research Services Branch, National Institute of Mental Health, Bethesda, MD, USA) [
56].
Discussion
Developing more effective therapies would be of significant benefit to patients with TNBC. We describe here multiple patient-derived orthotopic xenograft models that molecularly mimic patients’ original tumors and represent diverse TNBC subtypes. We use these to demonstrate the promising potency of mTOR inhibitors as suggested by in silico testing of a rapamycin response signature generated by our group.
We demonstrated that our models closely recapitulated original patient tumors morphologically, by molecular biomarkers, global copy number variation and
PIK3CA sequencing. Such patient-derived models have also been demonstrated by others to faithfully maintain histology [
57‐
63], gene expression patterns [
60‐
63] and genomic features [
57,
58,
61,
63,
64] in diverse human breast cancers, including triple-negative, ER positive and HER2-overexpressing tumors. These models have also been shown to be effective for preclinical therapeutic studies [
45,
46,
57,
58,
62,
63,
65,
66].
By sequencing, we observed PIK3CA mutations in two of our seven (29%) patient and xenograft pairs of TNBC tumors, with mutations in exon 6 (I391M, n = 1), and exon 20 (G1049R, n = 1). We also noted complete conservation of multiple SNPs in the flanking introns adjacent to the sequenced exons for the primary tumors, even on subsequent xenograft passages. The soft tissue metastasis of one of the primary tumors contained an intron SNP, which was not observed in the primary tumor or in the xenografts of the primary or metastatic tumor. The reason for this is unclear but may reflect lack of depth of our sequencing or increased heterogeneity in metastases.
Whole exome sequencing of 93 basal-like breast cancers by the Cancer Genome Atlas Network [
34] identified
PIK3CA mutations in 9 (10%). These were present in exon 1 (R88Q, n = 1; R108H, n = 1), exon 4 (N345K, n = 1), exon 9 (E542K, n = 1), exon 12 (F614I, n = 1) and, most commonly, exon 20 (H1047R, n = 4), none of which were detected in our panel. Another sequencing series has reported a 10%
PIK3CA mutation rate in 65 TNBCs [
35], with one mutation in exon 9 (E545K) and most in exon 20 (H1047R). For technical reasons, whole exome sequencing may not always identify mutations when the mutant cells make up less than 10% of the sample or because of lack of adequate sequencing coverage or depth. Thus, when a mass spectroscopy approach evaluated SNPs for 23 known site-specific mutations in
PIK3CA, 8% of 240 TNBCs revealed mutations located in exon 4 (N345K), exon 7 (E418K), exon 9 (E545K, E542K, P539R) and exon 20 (H1047R, H1047L, H1047Y, G1049R) [
67]. In sum and including our tumors,
PIK3CA mutations in TNBC have now been identified in exons 1, 4, 6, 7, 9, 12 and 20. We also note that despite the known genomic instability of TNBCs [
68], we observed that all
PIK3CA sequence variations persisted between patient primary tumors and xenograft models, and between xenograft models assayed during different sequential passages.
Our seven patient-derived xenograft models spanned different TNBC subtypes as described by Pietenpol’s group [
7,
8], who analyzed gene expression profiles of 587 TNBCs from 21 datasets to determine different TNBC subtypes. They identified six stable subtypes and an unstable subtype (UNS). The stable subtypes included two basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR). Using their analytic tools, we found that five of our seven TNBC xenografts represented four stable subtypes (BL1, BL2, M and IM), and two were in the UNS group, confirming our panel’s subtype diversity. Chang’s group recently analyzed 15 patient-derived TNBC xenografts and found that 12 spanned three subtypes (BL1, n = 8; M, n = 3; BL2/IM, n = 1) with three xenografts unclassified [
69]. MSL and LAR subtypes were not identified in our or Chang’s series of patient-derived xenograft models.
Interestingly, we found that a xenograft generated from a primary tumor (SUTI151) was classified as basal-like 2 (BL2), whereas the xenograft generated from its soft tissue metastasis (SUTI151M) was classified as mesenchymal (M). The BL2 subtype expresses genes involved in growth factor signaling, glycolysis and gluconeogenesis, whereas the M subtype is enriched for genes involved in cell motility, extracellular matrix receptor interaction and cell differentiation pathways, including the Wnt pathway, anaplastic lymphoma kinase (ALK) pathway and TGF-β signaling [
7]. This adds support to the idea that distant metastases acquire different signaling programs than the primary tumor.
Here, we developed and validated a rapamycin response signature that predicts sensitivity and resistance to rapamycin. The signature predicted that the majority of BLBCs should be sensitive to rapamycin, suggesting activation of the mTOR pathway in this subtype. This is consistent with data from the Cancer Genome Atlas Network group [
34]. They analyzed PI3K pathway activation in 390 human breast tumors across five intrinsic subtypes using mRNA expression signatures from different sources. Signatures from both Saal
et al. (PTEN loss in human breast tumors) and Connectivity Map (PI3K/mTOR inhibitor treatment
in vitro) showed similar patterns: the basal-like subtype had the highest PI3K pathway activity and luminal A had the lowest pathway activity [
32,
34]. These results agree with our rapamycin response signature predictions. In addition, they show that BLBCs have the highest expression levels of PI3K/AKT pathway genes, as well as a high
PIK3CA gene amplification rate (49%) [
34]. Also consistent is that protein levels of the mTOR pathway suppressors, PTEN and INPP4B, are relatively low in BLBC or TNBC patient tumors compared with other breast cancer subtypes [
14,
32,
34,
36]; and mTOR pathway-related proteins, especially AKT and 4EBP1, show high phosphorylation levels in BLBCs [
33,
34]. Moreover, Moestue
et al. recently demonstrated that BEZ235, a dual PI3K/mTOR inhibitor, had potent
in vivo efficacy in a patient-derived BLBC xenograft model, but not in a luminal model [
70], also supporting our findings.
Clinically, breast cancers are more commonly classified by their biomarkers (ER, PR and HER2) rather than by microarray analysis. As described above, most TNBCs (about 70 to 80%) are basal-like subtypes by gene expression analysis. It is thus reasonable to expect high rapamycin sensitivity among TNBCs according to our prediction model. This was confirmed by a remarkable 77 to 99% growth inhibition of either drug (mean 94%), whereas the average inhibition by doxorubicin was only 36%.
Supporting our growth inhibition findings, we showed that the mTOR pathway was activated in all our TNBC patient-derived xenografts, as indicated by the phosphorylation of mTOR and downstream proteins 4EBP1 and S6K1. This is consistent with observations in human TNBCs [
33,
34]. After treatment of the xenografts generated from primary tumors, overall decreased phosphorylation of these proteins suggested decreased mTOR pathway activity, which may have contributed to observed tumor growth inhibition. We observed that mTOR inhibitor treatment exerted a greater decrease in 4EBP1 phosphorylation (62%) than in S6K1 phosphorylation (33%), although individual tumor responses varied.
In our study, mTOR inhibitors showed a cytostatic effect on tumor growth (growth inhibition) but did not reduce original tumor volume over time. To obtain tumor shrinkage or complete ablation, it is likely that additional drugs need to be added. Supporting this is a negative Phase II single drug study of another mTOR inhibitor, everolimus, which did not show partial or complete responses in any of five ER negative/HER2 negative patients with metastatic breast cancer [
71]. In contrast, a recent phase II clinical trial evaluating temsirolimus and carboplatin achieved a 36% clinical benefit rate of patients with metastatic triple-negative breast cancer [
42]. As well as investigating the addition of mTOR inhibitors to current therapies, new drug combinations are also under study, such as mTOR catalytic inhibitors, dual kinase inhibitors of mTOR and PI3K, and combined targeting of the selective allosteric pan-AKT inhibitor MK-2206 with mTOR inhibition [
70,
72‐
76]. We are optimistic that mTOR inhibitors will broadly affect the treatment of breast cancer, especially TNBCs.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
HZ, SSJ, AHB, ALC and GVG conceived the study and MLT and SHD helped with its design. ILW, FMD and SSJ provided patient tumor tissue and clinical data, and ILW, GVG and SSJ performed clinical data analyses. HZ generated tumor xenografts and prepared samples for microarray analysis. TAL analyzed all pathology data. GD and MH performed and analyzed the array CGH studies, with additional interpretation and compilation by DOF. HZ and SK performed and analyzed DNA sequencing. MAC performed tumor subtyping using microarray data. ALC and AHB developed and validated the drug response signature, applied the signature to breast tumor datasets, and analyzed results. HZ and CMP performed in vivo drug testing and data acquisition. HZ, SSJ and GVG interpreted drug response in xenografts. SV and BL performed and analyzed Western blots, with additional interpretation by ACM. HZ, SSJ, ALC, AHB, GVG, SK, ILW, SV, GD, MAC, TAL, DOF and BL wrote the manuscript, while CMP, MH, MLT, FMD, ACM and SHD provided further input to the manuscript and/or critical revisions. All authors read, commented on and approved the final manuscript.